Journal of Information Systems Engineering and Management
Journal of Information Systems Engineering and Management Journal of Information Systems Engineering & Management is published quarterly ISSN (Online): 2468-4376 DOI: https://doi.org/10.55267/iadt
Assessment of Water, Sanitation, and Hygiene [WASH] in Health Care Facilities within Schistosomiasis Endemic Districts in Ghana
Globally, Schistosomiasis remains a significant public health problem and burden in the tropical regions which affect millions of people. Poor water, sanitation and hygiene [WASH] services and infrastructure increases the risk of occurrence of Schistosomiasis. Although there are existing studies on schistosomiasis prevalence and risk factors in Ghana, there is a scarcity of information regarding WASH services specifically within healthcare facilities in highly endemic regions. This study aimed to assess WASH in healthcare facilities across schistosomiasis endemic districts in Ghana's Oti and Upper East regions. The study assessed WASH services and infrastructure in all 197 healthcare facilities in Ghana's Oti and Upper East regions using a quantitative study design. The research employed the WHO/UNICEF Water and Sanitation for Health Facility Improvement Tools [WASH FIT] for data collection from healthcare workers and analysed using STATA version 16. The study focused on districts with high schistosomiasis endemicity. The ethical approval was received from the Navrongo Health Research Centre, Institutional Review Board [NHRCIRB] and appropriate consent was sorted from all health facilities heads and Ghana Health Service. WHO/UNICEF WASH FIT evaluation tool revealed critical deficiencies in 197 healthcare facilities, including 22.3% lacking improved water sources, 66.7% better water supply, and 67% low water availability and sufficiency. Additionally, sanitation facilities faced challenges, with 14% improved toilets, and 33.33% less usable toilets. The study highlights the gaps in WASH in health care facilities which can result in increased transmission of schistosomiasis. This implies the need for policy interventions to improve healthcare WASH services through targeted investments, infrastructure enhancements, and comprehensive programs, while also recommending further longitudinal studies.
Copyright (c) 2025 Journal of Information Systems Engineering and Management
Data-Driven Automation for Operational Efficiency in Enterprise Payments
This research article evaluates a novel model designed to enhance system performance, reliability, and scalability across diverse operational environments. The primary objective is to assess the model’s ability to reduce processing time, minimize error rates, and optimize resource utilization more effectively than previously applied approaches. The study also aims to demonstrate how such improvements contribute to long-term operational resilience and strategic cost efficiency. Quantitative results showed significant improvements across all key performance areas: processing time was reduced by up to 30%, error rates were halved, and resource efficiency improved by nearly 40%. These gains were observed across development, testing, staging, and production environments, validating the framework’s robustness. The framework was further analyzed using statistical tools and visual methods, including swap layouts, Monte Carlo simulations, and radar charts—to evaluate its long-term scalability. It demonstrated the ability to scale without proportional cost increases, making it suitable for enterprise-wide deployment. The findings indicate that the proposed system framework delivers substantial business value by minimizing downtime, reducing operational costs, and enhancing user experience. It is technically viable for deployment in large-scale enterprise systems and serves as a competitive alternative to existing solutions.
Technovation Access Tools and Information Resources Accessibility in Libraries
This study examined the role of technovation access tools in information resources accessibility in libraries. The 21st century has ushered in a drastic shift in information resources accessibility, driven largely by the deployment of technovation access tools. This study discussed types of technovation access tools like digital learning platforms, Internet of Things (IoT), and Mixed Reality Tools. The study further outlined the roles of technovation access tools in enhancing information resources accessibility to include improving efficiency and accuracy, fostering collaborations through digital platforms and Natural Language Processing (NLP) for discovering insights, among many others. Enhanced search/retrieval capabilities, personalized information retrieval and strategic utilization of each of these tools through automation and knowledge management were highlighted as the advancements in information accessibility. The challenges of technovation access tools noted in the study include digital divide and accessibility inequality, privacy and security concerns, among others. Some of the ways to mitigate these challenges of was the recommendation amongst others that Government and library administrators should invest in information literacy training programmes to enhance access to information, particularly in underserved regions. The study concluded that technovation access tools have revolutionized information in the 21st century, transforming how information resources are accessed, analyzed, and utilized as well as the simplification of complex data retrieval processes, thereby driving advancements in the Library and Information Science field.
An Intelligent Machine Learning Framework for Predictive Diagnostics in Cloud-Based Database Replication Systems
Preemptive failure detection in database replication systems is an essential need for ensuring data consistency and system availability in distributed enterprise settings. This article describes a state-of-the-art machine learning framework that learns to predict replication anomalies before they appear as service-affecting failures. The suggested architecture consumes heterogeneous data streams from Oracle GoldenGate and MySQL replication scenarios, such as structured metrics and unstructured logs, and converts them into predictive features using advanced preprocessing methods. Three co-operating machine learning models—Random Forest for binary classification, Gradient Boosting for multi-class categorization, and Long Short-Term Memory networks for sequential pattern recognition—collaborate to detect elusive predictors of replication instability. The model shows marked improvement over conventional threshold-based monitoring in that it detects non-linear correlations and temporal dependencies within the telemetry data. It supports seamless integration with contemporary observability platforms, enabling real-time alerting and visualization of anomaly probability, allowing preemptive action by database administrators. The framework achieved 92.4% prediction accuracy and a 37% reduction in unplanned downtime in production-grade tests, outperforming conventional threshold-based monitoring. Comprehensive testing across production-grade environments validates the efficacy of the framework in lessening unplanned downtime without compromising on low false positive rates. This work provides a thorough methodology for using machine learning technologies for database reliability engineering that closes the gap between research and operational usage.
Bridging the Gap between Network Security and AI-Driven Threat Detection
Modern network architectures operating throughout distributed cloud environments and encrypted communication channels have rendered conventional perimeter-based safety mechanisms incapable of detecting advanced persistent threats. Artificial intelligence techniques employed by adversaries to craft polymorphic malware, automate reconnaissance activities, and cover command-and-control communications within legitimate protocol traffic require essential transformation of protective capabilities. Conventional signature-based and rule-driven detection systems are unable to evolve and identify behavioural anomalies across encrypted traffic flows, generating extensive blind spots that sophisticated attackers systematically exploit. The article discusses the strategic embedding of machine learning methodologies within network security architectures through robust data pipelines, real-time inference mechanisms, and continuous learning frameworks. Hybrid deep learning architectures, which combine convolutional neural networks with bidirectional long short-term memory components, have emerged as superior in capturing spatial features and temporal dependencies inherent in network telemetry streams. Implementation challenges include extreme class imbalance driven by rare malicious traffic samples, latency constraints necessitating millisecond-scale inference for inline enforcement, interpretability requirements enabling analyst comprehension of detection rationale, and adversarial attacks aimed at compromising training data integrity or crafting evasive inputs. Operational deployment requires comprehensive telemetry collection across heterogeneous sources, advanced feature engineering transforming raw packet data into statistical representations, and seamless integration with security orchestration systems. Augmented intelligence frameworks establishing bidirectional collaboration between automated detection systems and human analysts allow for continuous model refinement through labelled feedback loops and enable adaptive defense ecosystems capable of evolving alongside emerging threat landscapes.
The Role of IT Leadership in Ensuring Compliance in Pharmaceutical ERP Systems
Enterprise Resource Planning systems are essential technology infrastructure in pharmaceutical organizations, combining manufacturing functions, supply chain management, quality control, and regulatory reporting capabilities under increasingly tight regulatory controls. The pharmaceutical sector is confronted with distinctive compliance demands created by Good Manufacturing Practices, Good Distribution Practices, and electronic record regulations requiring thorough data integrity, traceability, and validation across product lifecycles. IT leadership is not only about traditional technology management but also includes strategic architecture for compliance, risk mitigation schemes, and governance mechanisms that help make ERP systems meet changing regulatory needs without sacrificing operational effectiveness. The present review reviews regulatory regimes that control pharmaceutical information systems, such as electronic records and electronic signatures standards, Current Good Manufacturing Practice legislation, and global GMP guidelines. IT leadership approaches towards establishing compliance-driven ERP structures are investigated via governance models, risk-based validation practices, vendor management strategies, and technology architecture choices, trading on-premises against cloud-based deployments. Key compliance features such as audit trail deployment, electronic signature workflow, user access controls, and disaster recovery functionality are discussed in detail. Emerging trends that include artificial intelligence integration, blockchain usage, cloud computing acceptance, and cybersecurity standards pose opportunities as well as challenges for pharmaceutical ERP compliance. Based on the synthesis of regulatory guidelines and industry best practices, what comes into focus is how IT leaders operate within the multifaceted environment of pharmaceutical ERP compliance and facilitate organizational goals of operational excellence, regulatory compliance, and patient safety.
AI-Augmented LCNC Frameworks for Multi-Jurisdictional Government Compliance: An Architectural Approach
Government institutions modernizing legacy infrastructure encounter persistent tension between operational standardization and jurisdictional autonomy. Traditional system replacements risk disrupting established workflows while violating locality-specific regulatory requirements that prevent uniform implementations across organizational boundaries. Low-Code and No-Code platforms address these challenges through architectural patterns separating configurable surface layers from standardized technical foundations. Workflow engines allow jurisdictions to define approval sequences matching local procedures without altering underlying system logic. Form designers enable customization of data collection requirements satisfying varied regulatory mandates. Automated service generation converts configuration specifications into deployed capabilities, compressing implementation timelines while reducing specialized technical expertise demands. Multi-tenant isolation maintains strict separation between jurisdictional data despite shared infrastructure, satisfying regulatory independence requirements. Template-driven approaches replicate proven configurations across similar localities, accelerating deployments while preserving necessary adaptations. Artificial intelligence augmentation extends capabilities beyond basic digitization. Automated payment processing routes transactions through intelligent workflows eliminating manual handling. Document classification analyzes incoming submissions and applies appropriate processing pathways. Systematic redaction identifies and obscures protected information, removing labor-intensive manual review previously required before public disclosure. Operational transformations demonstrate measurable improvements across deployment sites. Processing timelines compress from extended durations to same-day completion. Implementation cycles accelerate significantly, enabling faster service enhancements. Operating costs decrease relative to earlier expenditure levels. These architectural principles offer government technology administrators actionable frameworks for advancing modernization while satisfying regulatory obligations, with transferable applications across healthcare delivery systems, financial service organizations, and comparable regulated environments confronting parallel challenges, balancing innovation imperatives against compliance mandates.
AI in Healthcare: Ethical Considerations in Patient Data Management
The introduction of artificial intelligence into healthcare provision is both revolutionary and raises significant ethical issues that should be closely considered. The issue of patient data management seems to be one of the critical concerns when the problem of privacy vulnerability, breach of security, and unauthorized access jeopardizes the most basic rights of the people who receive medical services. Another notable issue is algorithmic bias, since the training sets that are not demographically diverse will result in systems that promote historical healthcare disparities and provide poor care to the underrepresented groups. Many artificial intelligence systems are opaque and therefore lack the transparency to support informed consent and add complexity in clinical decision-making. Black box algorithms, which do not understand how they make their decisions, cause a loss of trust between healthcare providers and patients and lead to safety concerns when unsolicited recommendations are followed. To develop sustainable artificial intelligence healthcare systems, sustainable models of governance to involve the patients as active stakeholders instead of passive sources of data are needed. Health care institutions should be accountable, show transparency in reporting, honest opt-out, and provide tangible proof of the ability to enhance the quality and outcome of care with the help of algorithmic tools. Educational programs, which clarify the artificial intelligence abilities and constraints, will enable patients to be actively involved in decision-making, resulting in their treatment and data use. It is necessary to balance technological novelty with ethics in the development of reliable systems that would benefit every population in a fair manner. The way forward requires a continuous interaction between technologists, clinicians, policymakers, ethicists, and patient advocates to set the standards that can safeguard individual rights and, at the same time, facilitate positive innovation. Technical sophistication itself will not be used to measurably gauge success, but rather how far artificial intelligence systems support the principles of human dignity, fairness, and autonomy in healthcare delivery.
Building B2B Advertising Data Intelligence Systems: AI Solutions for Enterprise Marketing
This article presents a comprehensive framework for implementing artificial intelligence in Business-to-Business (B2B) advertising and marketing intelligence systems. It examines the fundamental differences between B2B and B2C marketing approaches, highlighting why enterprise marketing requires specialized technologies to address longer sales cycles, multiple decision-makers, and relationship-focused engagement. The proposed "B2B Marketing Intelligence Framework" consists of three interconnected components: AI-powered Account-Based Marketing (ABM), Predictive Lead Intelligence, and Secure Data Collaboration. We examine practical tools like Demandbase, 6sense, and Terminus for ABM implementation; Lattice Engines, MadKudu, and Leadspace for predictive scoring; and LiveRamp, Habu, and InfoSum for secure data sharing. Through case studies of companies like Microsoft, IBM, and ServiceNow, we demonstrate how these technologies deliver measurable improvements in account targeting accuracy, conversion rates, and marketing ROI. The framework provides enterprises with actionable guidance for selecting and implementing AI solutions that enhance audience understanding, personalization capabilities, conversion prediction, and cross-organizational data collaboration while maintaining privacy compliance.
Hierarchical Semantic Caching for MCP Servers: A Multi-Tier Context-Aware Approach to Optimize AI Model Data Access
This article introduces a novel semantically-enhanced three-tier caching system designed to optimize data access for Model Context Protocol (MCP) servers that support complex AI workloads. Traditional caching approaches often treat data as generic blocks, failing to capture the intricate semantic relationships between models, data, and computational tasks. The suggested hierarchical system overcomes this drawback by merging structural caching hierarchy and semantic awareness in three special tiers: semantically-aware model segment caching, contextual metadata caching, and intelligent prefetching. Its core is a dynamic knowledge graph that captures and regularly updates complex relationships among system components. Large-scale evaluation on large language models and computer vision applications shows significant gains across various performance metrics over traditional techniques, such as dramatic data access latency reductions, improved cache hit rates, better use of resources, and reduced bandwidth usage in distributed settings. The article verifies that semantic-aware caching offers a compelling answer for solving the mounting performance needs of current-day AI infrastructure, especially for intricate models running within changing computational environments.
AI-Driven DataOps Observability: Transforming Data Reliability in Modern Platforms
Data ecosystems have evolved radically through single-centric architecture to distributed and real-time platforms across hybrid and multi-cloud environments. Conventional tracking systems have difficulties keeping track of interrelated pipelines, microservices, and data lakes that establish blind spots in operations and undermine dependability. Generative AI and Machine Learning-powered dataops observability is the paradigm shift in passive monitoring of reliability to proactive management. The AI-based observability architecture is a multi-layered framework that combines data ingestion, preprocessing, core intelligence engines, correlation analysis, and action orchestration strata. The AI-enhanced observability systems process telemetry data, trace the lineage in the dynamic dataflows, and identify anomalies before they trigger production failures. Generative models automatically encode relationships between data sets, generate transformation logic, and suggest remediation with insight into the context. To data reliability engineers, this transformation will offer an intelligence layer that constantly learns the behaviour of the system, minimizes false positives, and speeds up root-cause detection. On the incident response side, AI predicts data drift, schema incompatibilities, and spikes in throughput, transforming incident response to incident prevention through predictive analytics. The outcome of implementation shows that the incident detection and resolution metrics have greatly improved, the mean time to detect has decreased to minutes, and the system availability has increased significantly. The quality of alerts that are better has a high effect of reduction in false positives, and predictive abilities that give a preview of an incident ahead of its occurrence. The development of AI models for self-healing pipelines and autonomous governance structures can be viewed as the next step in the progression of reactive troubleshooting of a problem to a proactive reliability culture, where all phases of the data lifecycle gain the advantages of adaptive intelligence.
The Future of Software Development: Programmers and AI Pair Programming
The integration of AI programming assistants represents a paradigm shift in software development, transforming traditional workflows into collaborative human-machine partnerships. This article traces the evolution from basic code completion tools to advanced AI pair programming systems, examining their impact on productivity, code quality, and developer well-being. The transformation extends beyond technical aspects to reshape educational approaches, career development paths, and organizational structures. While delivering clear productivity benefits, AI assistants also introduce challenges related to code quality, security, and risks of developer deskilling. Ethical considerations emerge around safety-critical applications, intellectual property, and the long-term effects of automation on developer skills. The workforce landscape is evolving with new specialized roles, restructured teams, and altered global development patterns. Looking forward, advancing AI-human collaboration in programming requires overcoming these limitations. Progress will depend on explainability, multimodal interaction, domain-specialized systems, and collaborative learning models that complement—rather than replace—human expertise.
The Medallion Architecture in Data Engineering: A Layered Approach to Data Quality and Governance
The medallion architecture is a new way to handle data engineering. This method simplifies how companies deal with different types of data by refining that data step by step. It's built with three levels: bronze, silver, and gold. Each level has a specific job in the data transformation process. The bronze level takes in raw data and keeps it safe, making sure to record where the data came from and other important details. Cleaning, checking, and standardization of the data is done at the silver level. This fixes any quality issues by removing duplicates and ensuring it meets business rules. The gold level then offers data that's ready for assessment and business intelligence tools to use. The way this architecture is set up allows for data to be processed quickly and in stages. It also provides governance to help with company-wide rollouts. Compared to older data warehouse systems, this architecture is more adaptable. It also provides better organization and quality control than simple data lake methods. It's a mix of both, giving flexibility in storage and structured quality management for various company needs.
Decentralizing Supply Chains: The Socioeconomic and Ethical Revolution through Distributed Systems Integration
The supply chains of today are in a more vulnerable position than it has ever been before due to the recent global disruptions, which show the weakness of centralized operational models. The distributed systems integration is not merely efficient but a systemic solution that goes past efficiency in operations to include basic socioeconomic restructuring. Decentralized supply chains allow global market access to small businesses through blockchain technology, smart contracts, and peer-to-peer networks, at a minimum, improving transparency and resilience. Nonetheless, this change brings about some complicated ethical issues of algorithmic bias, data sovereignty, and digital colonialism, especially in marginalized groups. Regulatory frameworks are still disjointed at the jurisdictional level, and global networks have challenges in complying. Regardless of these challenges, distributed supply chains show a strong promise of establishing more inclusive, resilient, and ethically-based trade networks that redistribute economic power more fairly among players without affecting operational efficiency.
The Societal Impact of AI: Balancing Innovation with Public Trust
Artificial Intelligence technologies radically transform modern societal paradigms with revolutionary uses in healthcare, education, economic systems, and governments, while at the same time raising unparalleled moral dilemmas to be addressed with inclusive policy solutions. The health sector is enhanced by advanced diagnostic algorithms proving to be of better accuracy in oncology imaging as well as pharmaceutical development platforms, shortening drug development times using molecular modeling capacities. Educational settings are subject to increased personalization by adaptive learning platforms that adapt content presentation according to individual student understanding patterns and interaction measures. Algorithmic bias expressions, though, expose systematic differences in facial recognition technologies and criminal justice systems to perpetuate discriminatory behavior towards marginalized groups. Privacy loss arises through widespread data collection systems that allow high-resolution behavioral profiling and commercial utilization of personal data by means of targeted advertising marketplaces. Economic disruption involves a workforce shift from experienced, skilled work to technology-enabled collaborative settings, best illustrated in the construction sector shifts necessitating holistic retraining programs integrating digital fluency with domain-specific knowledge. Trust mechanisms involve responsive, explainable AI systems with the ability to make dynamic transparency adjustments based on contextual needs and user levels of expertise. Multi-stakeholder governance models exhibit better performance in balancing promotion of innovation with risk control through cooperative regulatory models embracing a variety of viewpoints from healthcare professionals, technology innovators, patients, ethicists, and policymakers to promote responsible AI deployment, safeguarding human well-being while pursuing technological advancement.
Hierarchical Perception Networks for Robust Multi-Sensor Fusion in Autonomous Vehicles
This article examines how perception systems in autonomous vehicles have developed over the years, from flat fusion pipelines to hierarchical perception networks. It talks of how these sophisticated architectures combine a variety of sensor modalities with a variety of semantic levels, enhancing resiliency under a variety of driving conditions whilst remaining interpretable and efficient. The hierarchical method allows the automobiles to make rational decisions regarding the environments as a whole, resolving all the contradictory sensory data with the contextual information to make autonomous operations safer and more trustworthy. These frameworks perform better in difficult situations where the standard methods fail by organizing fusion on many levels of abstraction, between early spatial correspondence and high-level semantic interpretation. The article examines aspects such as cross-modal alignment methodologies, contextual inferences using hierarchies of semantics, uncertainty modeling to achieve resilient functioning, and real-time implementation using optimization strategies. In addition to technical advantages, hierarchical perception networks are even more interpretable and flexible across various spheres of operations, which forms the basis of reliable autonomous systems to balance creativity and responsibility. This significant architectural evolution in perception design opens up a direction of cognitive consistent autonomy that can deal with the complexity and variety of real-life driving worlds.
The Seven Pillars of Agentic AI Implementation in Enterprise Systems
Organizational digital ecosystems face remarkable complexity as dispersed systems generate big operational information requiring smart coordination throughout safety, compliance, infrastructure, and enterprise domains. Conventional automation frameworks show insufficiency when confronting dynamic environmental shifts, emerging hazard vectors, and evolving business necessities that render static rule-based structures out of date. Agentic AI inserts autonomous skills to experience operational states via ongoing monitoring, reason approximately pleasant movements via contextual evaluation, and adopt remediation moves without human intervention. Seven foundational pillars outline end-to-end frameworks for applying self-sufficient intelligence across enterprise tactics: autonomous decision architectures incorporating perception-reasoning-action loops immediately into processes, multi-agent coordination systems orchestrating specialized domain agents through shared protocols and collective learning, continuous learning mechanisms permitting policy optimization by reinforcement feedback and experience accumulation, data governance creating transparency and fairness during decision streams, resilience capabilities looking forward to failures and implementing self-healing remediation, human-AI co-governance balancing autonomous execution with oversight needs, and scalable infrastructure allowing dispersed agent deployment. Large language models augment autonomic computing realization via natural language log interpretation and remediation synthesis. Causal inference helps to differentiate between real failure mechanisms and symptomatic correlations, facilitating successful root cause resolution. Implementation requires precise reward structure design, exploration safety boundaries, and ethical frameworks guaranteeing algorithmic fairness among stakeholder populations. Corporations implementing these architectural standards comprehend proactive working stability, insightful aid optimization, and reliable automation consistent with regulatory demands and organizational ethics.
Master Data Governance for Global Drug Launch Readiness in Healthcare Enterprises: A Systematic Framework for Compliance and Operational Excellence
Master Data Governance has emerged as a mandatory construct to pharmaceutical business venturing in the intricacies of drug commercialization in the world today, amidst the more complex regulatory demands and shorter market penetration cycles. The pharmaceutical sector has complicated issues which include compliance with regulations in different jurisdictions, implementation of serialization regulations, country-specific labeling necessity, and traceability demands of the supply chain which push complex master data administration talents. The operational necessity extends to competitive advantage: improved launch preparedness, regulatory compliance guarantee, and supply chain resilience, which is the business case of strong Master Data Governance. The incremental value approach of Phased implementation methodologies enables pharmaceutical organizations to build the Master Data Governance maturity incrementally as they address the immediate business requirements and prove the incremental value over the deployment lifecycle. Hierarchical master data architecture assists in global product templates with market extensions that are market-specific and enable the effective management of labeling requirements in multiple regulatory jurisdictions, and maintain consistency in core product information. Serialization and master data systems enable pharmaceutical manufacturers to address the global track-and-trace requirements, as well as obtain strategic gains that can encompass counterfeit prevention, enhancement of visibility across the supply chain, and the possession of speedy recall functionalities. Blockchain-based distributed ledgers, analytics-driven artificial intelligence, and workflow coordination technologies are all available on the technology platforms and have the potential to transform Master Data Governance into a strategic facilitator of operational effectiveness and competitive advantage in global pharmaceutical markets.
Identity and Access Governance in Network Security Architecture
Modern virtual ecosystems face unparalleled protection-demanding situations as groups more and more shift closer to the cloud and distributed teams of workers. Conventional perimeter-based protection fashions have fallen short in protecting sensitive information and sources in dynamic computing environments. The article looks at a critical merge between identity governance mechanisms and network security architecture, which addresses the fundamental disconnect between legacy access control frameworks and threat landscapes that are increasingly modern, sophisticated in their attack vectors, and persistent in their adversaries. Evolution from function-primarily based get right of entry to models to characteristic-primarily based and zero-trust architectures is a paradigm shift that emphasizes continuous verification without implicit accept as true with assumptions. Integrating gadget studying techniques permits for behavioral analytics and detection of anomalies that pave the way for proactive threat mitigation earlier than safety incidents strike. Strategies for implementation within the healthcare, financial services, and retail sectors provide a working view of how automated governance platforms, in combination with next-generation network security architecture, work together in strengthening the organization's security posture. The blockchain-based totally frameworks tune the immutable audit trails of healthcare data access, at the same time as recurrent neural networks examine the temporal patterns in financial transactions to detect fraudulent activities. Certainly, corporations that effectively integrate identity governance with network protection architecture are better positioned to deal with present-day and rising cyber threats-even as driving operational performance, ensuring regulatory compliance, and maintaining stakeholder consideration of their virtual transformation initiatives.
Multi-Cloud Optimization: Orchestrating Workloads Across Heterogeneous Cloud Environments
Multi-cloud optimization is a revolutionary model in enterprise cloud computing that overcomes the strategic drawbacks embedded in single-provider reliance through intentional workload allocation to diverse cloud service providers. The architecture consciously accepts heterogeneity as a strategic advantage, allowing organizations to break beyond individual provider bounds while taking advantage of unique capabilities, pricing models, and geographic footprints. Resource abstraction by containerization technologies and infrastructure-as-code models creates single interfaces for provisioning and management of various cloud resources, and dynamic orchestration mechanisms allow real-time traffic steering and workload transfer across cloud boundaries. Technical issues like networking infrastructure complexity, identity and access management heterogeneity, data sovereignty needs, and security posture homogenization require advanced interoperability solutions covering software-defined networking, federated identity standards, and cloud security posture management solutions. Cost optimization goes beyond mere price comparison to include performance-normalized pricing analysis, dynamic resource allocation using spot instances, reserved capacity portfolio optimization, and algorithmic methods using machine learning for predictive resource planning. Application areas ranging from disaster recovery, distributed artificial intelligence operations, compliance-based architectures, and worldwide e-commerce platforms illustrate the applied value of multi-cloud strategies in resolving operational resilience, regulatory compliance, and performance optimization needs across geographically dispersed infrastructures.
Investigating the Effectiveness of Supervised and Deep Learning Models: An Assessment for Binary Intrusion Detection in Structured Network Traffic
Introduction: The growing complexity and frequency of cyber threats necessitate more adaptive and accurate intrusion detection systems (IDS). Traditional rule-based systems often fail to detect emerging cyberattacks patterns, underscoring the importance of data-driven approaches. Machine-learning methods, particularly deep learning and supervised learning, often promising capabilities for detecting anomalies in complex network environments. This study addresses the rising need for robust IDS models capable of distinguishing between benign and malicious traffic with high precision. Objectives: The primary objective of this research is to perform a comparative assessment of four supervised learning models, including Multi-Layer Perceptron, Bagging Tree Ensemble, CatBoost, and One-Dimensional Convolutional Neural Network, for binary intrusion detection. The study aims to examine their performance in terms of accuracy, precision, recall, and F1-score, thereby identifying the most effective classifier for structured network traffic data. Methods: A labeled network traffic dataset encompassing 4,000 records and 25 attributes was used. The dataset included both categorical and continuous variables. There was a class imbalance, with most of the instances being benign network traffic. Preprocessing was done by applying one-hot encoding to categorical features and z-score normalization to numerical features. All models were trained based on an 80:20 stratified train-test split and evaluated by traditional performance measures. Results: CatBoost exhibited the highest overall performance among the models, attaining an accuracy of 0.89, a precision of 0.99, and an F1-score of 0.86. The Bagging Tree model attained perfect precision (1.00). The 1D CNN model demonstrated competitive recall and F1-score. The MLP had poor recall due to the increased number of false negatives. The results of the study have shown the efficiency of ensemble and deep learning approaches for binary intrusion classification. while also revealing the inherent precision-recall trade-offs among different models. Conclusions: The study underscores the effectiveness of CatBoost and other ensemble-based approaches for intrusion-detection in structured network traffic datasets. It also emphasizes the importance of selecting models based on organizational risk tolerance. The research provides a practical performance benchmark to guide future IDS model selection and optimization in cybersecurity applications.
Attention-Enhanced Deep Feature Fusion: A Comprehensive Framework for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) is an evolving area absorbed on capturing multifaceted human feelings by integrating diverse data types, inclusive of text, audio, and visuals. Leveraging multiple modalities enhances sentiment prediction by enriching the representational capacity of model architectures. Addressing the challenges in this domain, we put forward a novel approach that unites advanced feature extraction techniques—BERT for textual data, Wave2Vec2 for acoustic data, and Vision Transformer for visuals—with BiLSTM networks augmented by self-attention and multi-head attention mechanisms. The proposed architecture effectively extracts and fuses modality-specific features to construct a robust multimodal representation. Evaluated on the benchmark CMU-MOSI dataset, the projected model achieves 82.45% accuracy with the self-attention mechanism and 86.04% with the multi-head attention mechanism, surpassing several transformer-based states of the art approaches. This superiority stems from the handcrafted feature extraction, effective fusion strategy, and the ability of BiLSTM with multi-head attention to seize diverse relationships without overfitting. The findings highlight that hybrid architectures that integrate the advantages of transformers with attention-augmented recurrent models can outperform pure transformer-based designs for multimodal sentiment analysis.
Bridging Pedagogy and Computation: A Theoretical Framework for AI in Educational Systems
Introduction Today the integration of Artificial Intelligence (AI) in education systems is a paradigm shift in in building, delivering, and assessing knowledge. Though many studies address AI applications and implications, their theoretical underpinnings are scattered across disciplines. AI-powered adaptive learning software, intelligent tutoring systems, automated grading, and predictive analytics are being used in educational institutions increasingly, generally without an explanatory theoretical understanding of how these technologies alter learning processes. Objectives: This paper synthesizes key theoretical perspectives of constructivism, cognitive load theory, personalized learning, and socio-technical systems theory, to build an integrative framework explaining AI’s pedagogical role. It also addresses how AI-backed systems operate and confront learning theories, focusing on the interplay among technological affordances, learner agency, and institutional dynamics. Methods: An extensive review of the literature was conducted across learning sciences, cognitive psychology, information systems, and critical theory, including articles published between 2013 and 2025. Adaptive learning systems, intelligent tutoring systems, and AI-based personalisation models are discussed to reveal cognitive processes and implementation implications at individual, social, and institutional levels. Results: Five theoretical frameworks were recognized as fundamental: constructivism for AI-enhanced learning environments, cognitive load theory for the design of intelligent systems, connectivism and networked intelligence, adaptive personalization theory, and socio-technical systems theory. Main results suggest that AI can be a cognitive tool, instructional mediator, social agent, and institutional technology, yet a multi-level theoretical appreciation of its educational use is required. Conclusions: AI learning needs to be envisioned through a composite theory model that combines cognitive, social, and institutional aspects. The model suggested here focuses on personalized scaffolding, transparent and explainable algorithms, equitable and bias-aware design, and alignment between technological efficacy and genuine learning objectives. The study finally underscores the imperatives of theory-driven innovation where pedagogical theory would inform AI innovation for improving learning without undermining human agency and equity.
Interpretable and Robust Machine Learning for Alzheimer’s Disease Diagnosis: A Hybrid SHAP-Boruta-STARS Framework
Introduction: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder with high societal and clinical impact. Early detection remains challenging due to complexity of biomedical data and the presence of imbalanced datasets.Machine learning offers promising solutions, but interpretability and robust feature selection are critical for reliable predictions. This study aims to develop a robust and interpretable machine learning framework for AD prediction that integrates a hybrid feature selection methodology combining:SHapley Additive exPlanations (SHAP) for interpretability, Boruta for statistically relevant feature identification, and Stability Selection and Ranking (STARS) for robust feature stability. We developed a novel hybrid feature selection framework for AD prediction combining data preprocessing, hybrid feature selection, and multi-model evaluation. In this framework, after a data preprocessing, a hybrid feature selection approach integrated Boruta, SHAP, and STARS methods was developed to identify the most stable and relevant features. Selected features were used to train various classifiers, including Logistic Regression, SVM, Random Forest and XGBoost,evaluated using 5-fold stratified cross-validation with SMOTE oversampling applied to mitigate class imbalance. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, with optimal decision thresholds tuned for each model. Two complementary statistical tests were employed (paired t-test and Wilcoxon) to evaluate significant differences between models.The hybrid feature selection framework significantly improved model performance for AD prediction. Among the tested models, ensemble methods outperformed traditional classifiers; particularly the Random Forest model demonstrating superior accuracy, precision, and recall, statistical analysis confirmed its significant advantage over other models. These results demonstrate the effectiveness of the proposed hybrid feature selection and ensemble learning approach for accurate and robust AD prediction. The proposed hybrid SHAP-Boruta-STARS framework provides a comprehensive, robust, interpretable, and statistically validated approach for Alzheimer’s disease prediction. It effectively identifies key features and supports reliable model selection, offering a promising tool for clinical decision support and early diagnosis.
Inverse Neuro-Fuzzy Control of Nonlinear Systems
This article, we addressed the use of Artificial Intelligence techniques in the field of control engineering. Our work focuses on the use of neuro-fuzzy networks, specifically ANFIS (Adaptive Neuro-Fuzzy Inference System), for the identification of inverse models required for implementing the control laws of a nonlinear dynamic system. In the first approach, the identified model is used as an open-loop controller with the system (Direct Inverse Control) for regulation purposes. In the second approach, Internal Model Control (IMC) is applied to improve the performance of the neuro-fuzzy model when the system is subject to a constant disturbance. The final section presents an application of these control structures to a nonlinear system. The results are validated through simulations carried out in the MATLAB environment.
Artificial Intelligence as a Catalyst for Institutional Transformation in Higher Education
Introduction: Artificial intelligence (AI) has evolved from a technological innovation into a strategic catalyst for institutional transformation in higher education. This study explores how AI supports the modernization of academic structures, pedagogical practices, and decision-making processes, moving beyond traditional models of technology adoption toward systemic educational innovation. Objectives: This work aims to examine how AI contributes to the modernization of higher education by identifying key factors influencing its adoption. It also seeks to develop a strategic framework that aligns technological integration with institutional objectives, leadership practices, and organizational culture to foster sustainable academic transformation. Methods: A mixed-methods approach was applied, combining quantitative and qualitative techniques. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) and Sociotechnical Systems Theory, the study used Likert-scale surveys and focus group discussions with faculty and administrators to analyze individual and organizational perspectives on AI implementation. Results: Findings reveal that successful AI integration depends not only on user training and technological proficiency but also on leadership engagement, institutional culture, and alignment with educational goals. Interdisciplinary collaboration and ethical awareness emerged as essential components for achieving meaningful and sustainable innovation through AI in higher education. Conclusions: AI adoption in higher education represents a shift from isolated technological use to systemic innovation. A strategic framework is proposed emphasizing continuous professional development, ethical governance, and collaborative engagement to ensure that AI enhances quality, inclusiveness, and sustainability across institutional and pedagogical dimensions.
Soft Sensor for Determining Municipal Wastewater BOD and COD from Excitation-Emission Matrix Coupled with Partial Least Square Regression
Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) are critical indicators for assessing the organic pollution load in municipal wastewater. However, conventional BOD (BOD₅) and COD analytical methods are slow, hazardous, or require extensive laboratory preparation, limiting their use for real-time monitoring and rapid decision-making in wastewater treatment plants. This study proposes a soft-sensor approach based on fluorescence Excitation–Emission Matrix (EEM) spectroscopy coupled with multivariate analysis to rapidly estimate BOD and COD in municipal wastewater. Wastewater samples were collected from a treatment plant and pre-processed through filtration, pH adjustment, dilution, and scatter removal. The resulting three-dimensional EEM datasets were analyzed using Parallel Factor Analysis (PARAFAC) to identify dissolved organic matter (DOM) fluorophore components, and Partial Least Squares Regression (PLSR) was applied to develop predictive models for BOD and COD estimation. PARAFAC identified two dominant fluorophore groups: a tryptophan-like, protein-like component (Ex/Em ≈ 275/340 nm) and a humic-like component (Ex/Em ≈ 350/437 nm), consistent with known wastewater DOM profiles. While PARAFAC successfully characterized DOM, it showed limited predictive capability for BOD estimation when used alone. In contrast, PLSR demonstrated strong predictive performance, effectively reducing data dimensionality and correlating EEM spectral variations with measured BOD and COD values. The integration of EEM fluorescence spectroscopy with PLS regression shows high potential for developing rapid, reagent-free, and real-time soft sensors for wastewater monitoring, enabling timely operational decisions in wastewater treatment plants.
Development and Implementation of Online Student Evaluation for Teachers (OSET): An Assessment Tool in Measuring the Faculty Teaching Performance
Student evaluation for teachers is a way to assess the educators' teaching meth-ods, communication skills, subject mastery, and classroom management. It is deemed necessary to address areas needing improvement and to enhance the teaching quality of faculty members. The traditional paper-based evaluation pro-cess is time-consuming and cumbersome. It is also prone to human errors, which may compromise the accuracy and credibility of the results. Therefore, this study focuses on the development and implementation of an online student evaluation for teachers at a State University in the Philippines. Its aim is to streamline the collection, storage, and processing of students' feedback and to generate students' evaluation reports. The Rapid Application Development model was used to de-velop each module of the web system. The graphics were designed using Adobe Photoshop and Canva. The web system's functions and modules were developed using PHP, JavaScript, and CSS, while the database was created using MySQL. The developed system was integrated into the University's web student portal. Each function of the web system was thoroughly tested, and the initial results of alpha testing revealed promising results, suggesting continued implementation of the technology. The study proved that OSET is functional and capable of generat-ing real-time reports, which are crucial in improving teaching quality and foster-ing educational excellence.
Cloud-Native Hcm Architectures: Performance, Scalability, and Security
In the era of digital acceleration, enterprises are rethinking their Human Capital Management (HCM) strategies to align with the expectations of a dynamic, global workforce. As legacy systems struggle to keep pace with modern operational demands, cloud-native architectures have emerged as a foundational enabler of next-generation HCM platforms. These architectures offer the flexibility to deploy services independently, scale resources automatically, and maintain system integrity under varying workloads—all while fostering rapid innovation. This paper explores how core cloud-native elements such as microservices, container orchestration, and API-first ecosystems are transforming the design and delivery of HCM solutions. It investigates techniques that enhance performance—including autoscaling, distributed caching, and intelligent load routing—while highlighting methods to sustain availability and responsiveness in high-demand environments. Moreover, it addresses critical security concerns specific to multi-tenant cloud deployments, including identity governance, data protection, and adherence to regulatory frameworks such as GDPR and HIPAA. Drawing from industry practices and real-world case examples, the study presents a detailed perspective on how cloud-native strategies are redefining performance benchmarks, enabling elastic scalability, and strengthening security for modern HCM infrastructures.
Development of Creative Abilities of Pupils Through Solution Tasks on Physics Using Computer
Introduction: It is known that nowadays various specialists widely use computer in their work. Today, one can calculate, write, read, edit, study, draw, rework, create audio and video and play on a computer. Its capabilities are increasing day by day, so it has become a person’s most reliable friend at work, study, at home, and at leisure. Objectives: Information technologies in physics teaching can be used in various forms - in preparing presentations, using ready-made educational and demonstration programs, conducting virtual laboratories, solving tasks, etc. The teacher must understand that use of information communication technologies is not only a modern requirement, but also an indispensable condition for achieving high-quality education. His task is to organize teaching that will help increase the effectiveness of education and develop students' creative abilities. For this it is necessary to help students in reflecting, thinking and drawing their own conclusions using computer. Use of computer allows us to create an opportunity to develop students' creative activity and help to teach them solving non-standard tasks by developing independent and logical thinking. Methods: Processing information sent from spacecraft, controlling the motion of particles in accelerators, and solving complex problems of theoretical physics are unimaginable without computers. Almost all physical problems encountered in life cannot be solved analytically and require the use of computers to solve them. Therefore, every physicist must be able to independently solve problems on a computer, and conducting scientific research in this area is one of the most pressing problems of our time. Results: In order to simplify the solution of physics tasks on a computer the authors created the “Universal Calculator” program. The program is written in Visual Basic using numerical methods and has the ability to plot an arbitrary function graph, solve quadratic, transcendental and differential equations, calculate definite integrals and process experimental results using the “Least Squares” method. The article provides examples of calculating the path traveled by a body using the program and processing experimental results using the “Least Squares” method. Conclusions: It is undeniable that information communication technologies are a reality of modern teaching. When computers are used in physics lessons, students participate more actively in the lesson, the attitude to work changes even among the most problematic students, and the teacher is required to master the capabilities of information communication technologies, carefully think over the content of the lesson and plan the work of students at each stage of the lesson.
Problems and Proposed Solutions to Improve Freight Transportation, Case Study
Transportation is not just about getting from point A to point B, it's about coordination, accuracy and trust. The transport of goods is an integral part of the city's economy and is a prerequisite for sustainable urban life, but it causes a number of negative effects on the environment followed by air pollution and increased noise. The problems arising from road freight transport are constantly overlooked. The subject of analysis in this paper is the city of Bitola. We are witnessing the daily intertwining of different streams of goods, materials and cargo through the city's street network. To meet the needs of the inhabitants, the following supplies of goods are present: trade goods, finished products, semi-finished products, raw materials, building materials, consignments, secondary raw materials, waste and the like. With the help of a month-long survey of truck drivers in the territory of the city of Bitola, we learned about the problems they face. Based on this as a conclusion, a proposal is given for measures to improve the delivery of goods and the general flow of traffic.
The Influence of Good Corporate Governance, Human Resources on Company Performance Through Corporate Culture: A Case Study of an Iso-Certified Company
Background: Company performance is strongly influenced by sound governance, the quality of human resources (HR), and a conducive corporate culture. As a seaweed processing company, the company under this study faces challenges in meeting production targets and operational efficiency. Efforts to improve performance need to be examined from the perspective of the interrelationship between Good Corporate Governance (GCG), HR, and corporate culture. Objective: This research seeks to examine how good corporate governance affects a company’s performance, with corporate culture serving as the mediating factor. Methods: This study utilized a quantitative research design with data gathered from 180 respondents through a structured questionnaire. The analysis was carried out using the Partial Least Squares (PLS) method, processed with SmartPLS 3.0 software. The model was tested for validity, reliability, and hypothesis testing for both direct and indirect effects among variables. Results: The R² value for the corporate culture variable was 0.639, meaning that Good Corporate Governance (GCG) and Human Resources (HR) together accounted for 63.9% of the variance observed in corporate culture. The R² value for company performance was 0.582, meaning 58.2% of performance was explained by GCG, HR, and corporate culture. The direct influence of HR on corporate culture was 0.589 and on performance was 0.428 (significant). GCG had a direct effect on corporate culture at 0.297 (significant), but only 0.108 on performance (not significant). Corporate culture had a direct significant effect on performance at 0.344. The indirect influence of GCG on performance through culture was 0.102 (significant), and that of HR was 0.203 (significant). HR was the most dominant variable in the model. Conclusion: Company performance is significantly influenced by HR and corporate culture, while the influence of GCG is indirect through corporate culture. These findings imply that strengthening HR and cultivating a strong organizational culture are key strategies must implement by the company to enhance sustainable organizational performance.
The Impact of Foreign Direct Investment, Innovation, and Institutions on Economic Growth in Vietnam
This study was conducted to identify and evaluate the impact of foreign direct investment, innovation and institutions on Vietnam's economic growth. This study uses the Error Correction Vector Model (VECM) estimation method and Johansen integrated co-analysis to analyze the short- and long-term impacts of foreign direct investment, innovation and institutions on Vietnam's economic growth in the period 2002 – 2022. The quantitative findings indicate that, in both the short term and the long term, FDI exerts a negative impact on economic growth, while innovation and institutional quality positively contribute to economic growth in Vietnam
A Systematic Study of E-Governance Infrastructure for Smart Campus Development in CBSUA
The integration of Information Technology and Management in smart campus settings is a big problem that needs to be addressed. The integration of Information Technology and Management in smart campus settings is a big problem that needs to be solved today. The institution to move forward, it is important to make sure that these areas are working together well. Conducted the systematic review in 2025 to trace the development, pinpoint the challenges, and map the possibilities still open to the e-governance backbone at Central Bicol State University of Agriculture (CBSUA), to sketch the path to the “University 4.0” goal. Using the PRISMA framework, gathering the findings from 14 journal articles to official reports published from 2020 to 2025. The evidence collected through the research indicates that the basic foundational CBSUA services have been established and integrated. There seems to be a lack of seamless integration and alignment between individual software systems, significant discrepancies in the digital infrastructure across the four campuses, and deficiencies in the faculty and staff in managing data. Artificial Intelligence, Big Data, and the Internet of Things (IoT) are used in the strategic planning of fostering personalized education, proactive advising, and responsive governance. The study contends that moving forward entails an executive strategy that integrates the basic services and infrastructural disparity and advances the development of digital and analytic competencies in a stepwise manner, rather than pouring resources into emergent technologies.
Enhancing Lightweight SAR Image Classification Using MobileNetV3-Small Implemented with Convolutional Block Attention Module (CBAM)
The rapid worldwide advancement of environmental remote sensing technologies has created a growing need for automated, efficient, and high-performing analysis methods for Synthetic Aperture Radar (SAR) imagery. SAR is particularly valuable because it provides consistent imaging capabilities regardless of weather or lighting conditions, making it ideal for crucial applications such as environmental monitoring and disaster management. Despite these advantages, SAR image classification remains a challenging task due to the complex nature of SAR data, which often includes speckle noise, limited textural information, and high computational demands. To address these challenges, this study introduces an efficient and lightweight Convolutional Neural Network (CNN) architecture based on MobileNetV3-Small, integrated with a Convolutional Block Attention Module (CBAM). The attention mechanism enhances the extraction of meaningful features while reducing unwanted backscatter effects in SAR feature maps. The proposed hybrid model was trained and evaluated on a Sentinel-1 SAR dataset comprising four land cover categories: Agriculture, Barren Land, Grassland, and Urban areas. Implementing data augmentation and transfer learning techniques, the model was trained over 50 epochs, and achieved the high accuracy of 95.74%. This results, outperforming the original MobileNetV3-Small backbone, stems directly from the implementation of CBAM attention mechanism, allowing the proposed model to successfully emphasize relevant spatial and channel features. In this work, it was demonstrated that the proposed MobileNetV3-Small with CBAM architecture delivers a powerful balance between computational efficiency, robust accuracy, and generalization, capability, making it a suitable for integration in near-real-time applications and embedded systems. The results prove the potential of lightweight attention- augmented CNN architecture for a generation of autonomous and intelligent Earth observation systems, offering opportunities for further research in global ecological monitoring.
Multi Head Attention Transformer for Arabic Scene Images Text Recognition
The worldwide video library continues to expand rapidly, which creates an increasing need for modern and reliable techniques for video processing and text indexing. In this paper, we introduce a new implementation of the Transformer architecture for scene text recognition. This work comes from a comparative study between two approaches: using convolutional feature maps as input to the Transformer encoder, and fully removing any CNN component. During training, we used almost all available public datasets; however, they were still not enough because of the significant lack of large-scale and diverse datasets for this task. This challenge led us to create and publish a new artificial dataset called IYaD. The IYaD dataset currently contains around 1,400,000 images for one font and the same scale for 16 additional fonts. Each image is provided in three different versions and includes Arabic labels, Latin transcription, and the text content. The experimental results show that our Transformer-based ASTR model surpasses state-of-the-art methods, especially when trained on the IYaD dataset, establishing new benchmarks in accuracy and robustness. We believe that this dataset demonstrates the importance and potential of artificially created datasets, and it may encourage similar dataset generation in other research domains.
Using Genai for Synthetic Data Generation in Cybersecurity and Compliance
The increasing nature of the cybersecurity threats and high-pressure requirement of the norms compliance have resulted in the need to think differently about how data is managed and secured. Generative Artificial Intelligence (GenAI) provides a potential solution, incorporating the formulation of artificial data and developing these data with a high level of safety, privacy, and confidentiality, which implies guaranteed testing, training, and compliance auditing without any security concerns. GenAI models produce synthetic data that mimics the statistics of unprecedented data, allowing the security field to use generated data without revealing sensitive data to vulnerable access; this makes it particularly appealing in techniques as threat modelling, anomaly detection, simulators of red-teaming, and similar ones. Moreover, artificial datasets can be utilized in ensuring regulatory adherence because they enable testing of systems across various environments based on the type of rules that should govern data use. In spite of this potential, synthetic data generation in GenAI has various challenges, such as data fidelity, bias reprocessing, and regulatory objects. Nevertheless, the progress of GenAI models like GAN, transformers, and diffusion models takes place and enhances the reliability and security of generated data. This paper addresses how GenAI helps in cybersecurity and compliance, evaluates the advantages and disadvantages of the technology, and reflects on recent research and practical opportunities flag identify possible ways to integrate the new technology through the lens of current studies and real-life examples.
The Impact of Investment Opportunity Set and Profitability on Accounting Conservatism in ASEAN Countries
Financial statements deliver critical insights into a company's financial position, adhering to Generally Accepted Accounting Principles (GAAP) and emphasizing conservatism. This principle prioritizes prudence by recognizing costs promptly while delaying revenue acknowledgment, which can potentially distort a company’s true financial health. This research aims to analyze the influence of financial attributes on accounting conservatism, focusing on the investment opportunity set (IOS), leverage, profitability, and firm size in ASEAN's industrial sector. The research comprises a total of 2,968 firm-years, consisting of 424 companies from six ASEAN countries during the period from 2017 to 2023. The data used is sourced from the Refinitiv database obtained from the company's annual reports and employs panel data regression analysis. The research shows that the major independent variable, IOS has no effect on accounting conservatism. Conversely, the independent variable profitability has a positive impact on accounting conservatism. Additionally, two other variables, namely leverage and firm size, have statistically significant impacts on accounting conservatism. This study indicates that an increase in company size correlates with a heightened application of accounting conservatism, whereas greater profitability tends to diminish the application of accounting conservatism within manufacturing firms. The study finds that investment opportunity set does not significantly affect accounting conservatism; profitability, leverage, and firm size influence it differently. It recommends a broader focus and additional variables for comprehensive results.
Synthesis and Use of a Perfluorinated Coumarin-Based Surfactant as an Antifouling Coating
This study presents the design and evaluation of a novel hybrid surfactant based on perfluorocoumarin, developed for antifouling marine coatings. The surfactant was synthesized via the functionalization of coumarin with fluorinated chains and incorporated into a polymer matrix. Structural characterization by FTIR confirmed the successful integration of coumarin into the polymer through the reduction of O–H signals and the presence of characteristic C=O and C–F bands. UV-Vis analysis revealed a strong absorption peak at 281 nm, consistent with the π→π* transitions of the coumarin core. Comparative antifouling tests demonstrated the limited performance of commercial paint (Micron Extra EU), which showed surface degradation and microbial colonization over time. In contrast, coatings formulated with the perfluorocoumarin surfactant exhibited excellent resistance to biofouling, surface integrity, and anti-corrosion performance. The hybrid system also showed anti-graffiti behavior and mechanical robustness. These findings highlight the potential of this eco-friendly surfactant for advanced marine applications, though further studies on long-term durability are recommended.
Quantum Computing Integration in Enterprise Resource Planning Systems: A Strategic Framework for Digital Transformation
The convergence of quantum computing and enterprise resource planning systems represents a transformative paradigm shift in organizational computing architecture, fundamentally altering how enterprises address computational challenges across pharmaceutical serialization, supply chain optimization, and resource allocation domains. This article examines the architectural foundations of major quantum computing platforms, including IBM Qiskit, AWS Braket, Google Cirq, and Microsoft Q#, evaluating their strategic fit within enterprise contexts through detailed analysis of programming frameworks, hardware characteristics, and integration pathways. The quantum computing landscape has evolved beyond theoretical promise to demonstrate measurable utility in specific application domains, particularly combinatorial optimization problems that exhibit exponential classical complexity. Pharmaceutical serialization systems benefit from quantum cryptographic protocols, including Quantum Key Distribution and Quantum Random Number Generators, establishing information-theoretically secure supply chain traceability immune to computational attacks. The quantum-ERP integration implementation architecture will depend on hybrid architectures that combine the best of many platforms: AWS Braket allows access to hardware of multiple vendors, providing the ability to perform systematic performance benchmarking; IBM Qiskit offers established optimization algorithms with demonstrated enterprise applicability; Microsoft Q+ provides organized programming environments that can meet pharmaceutical validation needs; and Google Cirq allows quantum machine learning integration into existing analytics pipelines. The strategic plan leading to quantum-enhanced enterprise systems requires focused attention to the existing limitations of the Noisy Intermediate-Scale Quantum devices, error reduction mechanisms, as well as phased implementation methods that would add value step by step toward the future with error-free quantum benefit. Organizations positioning themselves at the quantum computing frontier establish competitive advantages in operational efficiency, cryptographic security, and analytical sophistication essential for success in increasingly complex global markets.
Transforming Banking with AI-Driven Data Engineering: Real-World Applications and Industry Impact
The banking sector is being transformed by artificial intelligence in combination with modern data engineering practices, which enable better customer experience, effective risk management, and more efficient operations. The use of lakehouse architectures, real-time streaming technologies, and embedded machine learning workflows allows for the use of important applications such as fraud detection and prevention, credit risk assessment models, personalized financial services, and automated compliance reporting, which are used in everyday banking scenarios. AI predictive models can process real-time streamed transaction data to identify and label fraud with unprecedented accuracy; meanwhile, alternate data and feature engineering techniques are redefining how credit underwriters assess applicants' risk. Personalized recommender systems and dynamic customer segmentation can yield better retention and cross-sales by leveraging intelligent platforms in digital banking. Automated compliance systems powered by generative AI can limit the manual reporting effort while reducing the number of human errors in the same reporting. Adoption of AI with modern data engineering can be inhibited by issues such as data privacy, system integrations, and the need for transparent and fair algorithmic decisions. The growth of AI-augmented data engineering solutions across leading financial institutions has delivered measurable business benefits for many, while providing a competitive advantage in reducing fraud, improving loan turnaround times, and increasing customer satisfaction and loyalty. The convergence of artificial intelligence and modern data engineering presents an opportunity for banks to establish future-proof digital, client-facing capabilities as part of a coordinated strategic direction and a sustainable competitive advantage.
AI-Augmented Clinical Handoff Systems: Enhancing Safety and Continuity in Healthcare Transitions
Clinical handoff transitions among healthcare professionals represent high-risk points where breakdowns in communication often lead to harmful medical mistakes and patient harm. Standard handover routines exhibit high levels of variability, are not standardized, and are loaded with high cognitive burdens for clinicians who are working with complex patient groups in high-acuity areas. The convergence of artificial intelligence technologies with electronic health record systems holds transformative promise for improving handoff quality through automated multimodal patient data synthesis, structured communication summarization based on established guidelines, and knowledge-based identification of clinical risks that need to be addressed immediately. AI-facilitated handoff structures use natural language processing algorithms to cull applicable information from narrative scientific records, predictive analytics to signal deterioration styles and protection problems, and interoperability requirements to facilitate unfettered deployment within numerous healthcare data technology infrastructures. Effective implementation requires human-focused design concepts that situate artificial intelligence as assistive support and not as individual choice-making authority, retaining clinician judgment and minimizing documentation burden and information synthesis complexity. Governance models want to fulfill transparency wishes, ensure privacy protections, ensure audit trails, and cope with algorithmic bias in order to provide honest overall performance across patient populations. Combining sophisticated machine learning capabilities with systematic clinical verbal exchange protocols is a primary breakthrough for patient protection infrastructure, offering measurable gains in data completeness, handoff quality, and provider confidence at handoff factors without sacrificing human components of medical judgment and interpersonal communication.
No-Code Automation Platforms for Incident Management: Transforming Engineering Operations
Engineering teams are constantly under pressure to balance operational reliability requirements with innovation capability, and incident management and repetitive manual processes absorb large amounts of engineering resources. Conventional automation strategies involving specialized scripting skills are barriers to accessibility across various team structures while bringing in maintenance overhead. No-code automation platforms are revolutionary solutions through visual design of workflow capabilities, facilitating automation democratization without compromising technical sophistication. Visual drag-and-drop interfaces convert intricate automation logic into clear flowcharts that decrease cognitive burden and allow domain experts to build complicated workflows without regard to programming skills. Backend code generation architectures implement production-quality scripts with error management, retry logic, logging instrumentation, and security best practices automatically. Alert-to-resolution automation workflows combine monitoring systems and orchestration platforms, performing diagnostic routines and remediation steps that fix prevalent incident types without manual intervention. Modular component libraries speed up workflow construction with reusable building blocks that contain authentication patterns, log aggregation sequences, and notification templates. While no-code platforms introduce specific advantages in development velocity and team accessibility, organizations must understand their incremental contribution beyond general DevOps automation, recognize platform limitations including customization constraints and vendor dependencies, and evaluate the technical expertise still required for effective implementation. The organizations that strategically deploy no-code automation structures see sizable operational advantages, which include improvements in deployment frequency, reduced incident healing times, decreased change failure rates, and improved team satisfaction. The integration of artificial intelligence with no-code frameworks represents an emerging capability that further reduces technical barriers through natural language workflow creation and intelligent automation suggestions. The cultural shift toward automation-first thinking permits engineering groups to shift capability away from reactive firefighting and closer to strategic architectural enhancements, performance optimizations, and feature development, driving competitive advantage.
AI-Assisted Decision Support for Field Service Operations: A Technical Review
Field service organizations face increasing challenges in managing operational complexities spanning scheduling optimization, workforce allocation, equipment maintenance, and cost containment. The integration of artificial intelligence-driven decision support systems within field service management platforms offers transformative potential to address these challenges through predictive analytics, intelligent automation, and real-time optimization capabilities. This article examines how AI-powered technologies enable service organizations to transition from reactive, crisis-driven operations toward proactive, insight-driven service delivery models that anticipate customer needs and prevent equipment failures before they occur. The human-AI collaboration framework represents a fundamental operational paradigm shift, where AI systems handle data-intensive pattern recognition and routine optimization while human operators provide contextual interpretation, empathetic customer engagement, and strategic decision-making. Current implementations demonstrate human-in-the-loop architectures that preserve human judgment and accountability while leveraging AI capabilities for enhanced efficiency and productivity. However, organizations must navigate significant challenges, including algorithmic bias, transparency concerns, workforce resistance, and ethical governance requirements. The future trajectory points toward increasingly sophisticated human-AI partnerships characterized by generative AI copilots, augmented reality integration, and autonomous scheduling capabilities, all underpinned by robust ethical frameworks ensuring fairness, accountability, and transparency in automated decision-making processes.
Enterprise Data Engineering Innovations: Unifying Customer and Revenue Data Platforms
This article examines the evolution of enterprise data engineering through the integration of customer and revenue data platforms. As organizations face challenges from fragmented systems and inconsistent definitions, innovative architectural approaches have emerged to create unified data ecosystems. The article explores the transformative role of master data management and identity resolution in establishing consistent entity definitions across domains. It investigates modular ELT frameworks that incorporate continuous quality validation, governance-as-code implementations that automate policy enforcement, and event-driven architectures that enable real-time synchronization between customer behavior and financial systems. The article further addresses how intelligent automation and predictive enrichment enhance data completeness while reducing manual intervention. Governance frameworks receive particular attention, including comprehensive data catalogs with lineage visualization, layered protection mechanisms for sensitive information, automated financial reconciliation models, and consent management systems that ensure regulatory compliance. The article concludes by examining sustainability considerations in enterprise data architectures, including optimization techniques that balance performance requirements with environmental impact.
AI-Driven Dynamic Load Balancing: A Predictive Framework for Congestion Avoidance in High-Speed Data Center Networks
Traditional congestion management mechanisms, including Explicit Congestion Notification and Priority Flow Control, operate reactively, addressing congestion only after queue thresholds are exceeded. This fundamental limitation becomes increasingly critical as modern data center workloads generate microbursts lasting under 100 milliseconds, creating transient bottlenecks and elevated tail latency. AI-Driven Dynamic Load Balancing (AI-DLB) introduces an intelligent, predictive framework that combines real-time telemetry with machine learning inference to forecast congestion events and proactively redistribute traffic flows. The system employs a closed-loop control architecture integrating supervised learning for short-term congestion prediction and reinforcement learning for continuous policy optimization. By analyzing queue depth, ECN marks, link utilization, and RTT trends, AI-DLB enables sub-second load redistribution before congestion manifests. Simulation results on spine-leaf topologies demonstrate substantial reductions in tail latency and faster convergence compared to conventional mechanisms. The framework operates as a complementary enhancement to existing protocols, establishing a foundation for self-optimizing, intent-driven data center fabrics that bridge predictive analytics with autonomous control.
Microservices vs. Monolithic Architectures in Real-Time Distributed Systems: A Comparative Analysis
This article will discuss the differences between microservice and monolithic architecture in real-time distributed architecture. Comparison cuts across theoretical underpinnings, performance attributes, development life cycles, operational issues, and implementation issues. Monolithic architectures are known to be beneficial in terms of simplicity, reduced baseline latency, and reduced communication overheads, and are applicable in applications with predictable workloads and intricate transactional requirements. Microservices, on the other hand, are much more scalable, fault-isolated, and focused on the allocation of resources, especially helpful in systems whose demands are variable and whose functionality evolves. The article discusses the issue of data consistency, overhead in inter-service communication, and state management complexities of distributed architectures, and notes pragmatic hybrid solutions and evolutionary trends that leverage the merits of each paradigm. The choice of architecture is actually determined by a set of constraints of particular projects, the structure of the organization, and the needs of real-time processing, instead of a particular architectural philosophy.
Adaptive Emotional Intelligence in AI-Powered Contact Centers
As digital technologies develop, conversational artificial intelligence (AI), as well as human/machine workflow hybrid models, are being increasingly implemented in contact centers. Emotional intelligence—the ability to detect, understand, and respond to human emotional states—has remained largely absent from these automated systems. This article presents an Adaptive Emotional Intelligence (AEI) framework for AI-assisted contact centers that dynamically adjusts tone, content, and conversation methodology based on multimodal emotional signals. The AEI framework incorporates affective computing, reinforcement learning, and contextual reasoning to enable virtual agents and human agents to engage empathetically and at scale. Evidence from studies of affective computing and customer experience suggests that AEI could substantially improve customer satisfaction, reduce escalations, and enhance first-call resolution. The article discusses issues of architecture, data ethics, and longer-term effects on responsible and emotionally sustainable automation.
Digital & Process Transformation in Gastroenterology: Reimagining Diagnostics, Care, and Patient Outcomes
Digital and process transformation are collectively reshaping gastroenterology by addressing longstanding challenges in diagnostics, clinical workflows, and patient care delivery. Gastrointestinal diseases represent a substantial global health burden, with traditional practices facing barriers including delayed diagnosis, operator-dependent procedures, fragmented care coordination, and limited accessibility to specialized services. The convergence of artificial intelligence, advanced imaging technologies, telemedicine platforms, and predictive analytics with systematic process improvements such as optimized scheduling, standardized care pathways, and integrated multidisciplinary collaboration creates synergistic benefits that extend beyond individual patient encounters. AI-assisted colonoscopy and capsule endoscopy enhance diagnostic accuracy and reduce operator dependency, while computer-aided detection systems standardize quality across varying expertise levels. Process transformation initiatives streamline workflows, eliminate bottlenecks, and facilitate coordinated care delivery in multi-specialty environments. Patient-centered innovations. including ingestible electronic devices, digital health platforms, and remote monitoring capabilities, expand access, enhance engagement, and enable personalized treatment approaches. At the healthcare system level, these integrated transformations address critical challenges of sustainability through cost containment, resource optimization through maximized utilization of constrained inputs, and scalability, enabling expansion of high-quality care to growing populations. Quality indicators provide standardized frameworks for performance assessment and continuous improvement, while workforce optimization strategies reduce administrative burden and mitigate clinician burnout. The systematic integration of digital technologies with refined clinical processes represents a fundamental paradigm shift from reactive, fragmented care models to proactive, coordinated, and data-driven healthcare ecosystems that deliver superior clinical outcomes, operational efficiency, and patient experience across diverse populations and practice settings.
DevOps and CI/CD for Cloud-Native API Architectures: Automating Deployments and Scaling Securely
This article examines the transformative impact of DevOps practices and CI/CD methodologies on cloud-native API architectures. As organizations increasingly adopt microservices-based approaches, APIs have become the essential connective tissue enabling communication between distributed components. The integration of DevOps culture with continuous integration and deployment pipelines addresses the unique challenges of managing complex API ecosystems across diverse environments. By implementing GitOps workflows, infrastructure as code, and automated testing strategies, organizations can achieve greater reliability, security, and efficiency in their deployment processes. The article explores how tools such as ArgoCD, Tekton, and Jenkins facilitate these practices, enabling enterprises to automate deployments and scale securely while maintaining backward compatibility and ensuring consistent performance. Additionally, it highlights the critical role of security integration throughout the software delivery lifecycle, emphasizing the shift toward proactive, continuous validation rather than periodic assessments.
Data-Driven Optimization in SAP Extended Warehouse Management: Leveraging Analytics for Enhanced Warehouse Performance
Modern supply chains are becoming more complex and therefore require data-driven decisions when it comes to warehouses' agility, efficiency, and cost-effectiveness. This article is a systematic outline of how the SAP Extended Warehouse Management (EWM) can be exploited to its analytical power to optimize the performance of the warehouse, in many aspects of its operation. The framework incorporates five interconnected elements, namely data foundation, analytic capabilities, process integration, performance measurement, and continuous improvement mechanisms. The framework helps form feedback loops of systematic optimization between analytical insight and operational process through identifying key points of analytics integration throughout warehouse processes between receiving and shipping. The implementation of various warehouse settings has shown long-term performance efficiencies with analytical competencies that are not in line with the functioning procedures and also the organizational designs. The framework fills the gap between the abstract analytic concepts and the actual warehouse operations to give realistic advice to organizations that want to turn warehouse management into an activity of constant execution, but rather dynamic and learning based optimization.
The Modern Finance Factory: A Scalable Cloud-Native Framework for Automated Financial Reconciliation
The Modern Finance Factory presents a scalable cloud-native framework that transforms traditional financial reconciliation through automated validation processes. This architectural solution addresses the critical challenge of ensuring data integrity across fragmented enterprise systems by leveraging Python-based orchestration and SQL-driven validation rules. The framework's implementation across banking, e-commerce, and healthcare sectors demonstrates significant improvements in reconciliation efficiency, accuracy, and control effectiveness. Technical components include integrated ingestion, transformation, validation, exception management, and reporting layers that enable programmatic validation of transactions at scale. The framework introduces a paradigm shift from periodic, manual validation to continuous, automated reconciliation, fundamentally altering how organizations approach financial controls. By separating procedural logic (Python) from declarative rules (SQL), the architecture provides flexibility for finance professionals to maintain validation logic without deep engineering expertise. Advanced applications incorporate machine learning capabilities for predictive forecasting, invoice delinquency classification, and anomaly detection, fundamentally shifting finance from reactive reporting to proactive intelligence. Implementation evidence demonstrates substantial operational improvements including accelerated close cycles, enhanced accuracy, and significant labor model transformation. The article establishes a comprehensive model for finance transformation that extends beyond automation to cognitive capabilities that anticipate financial outcomes, optimize cash flow management, and enhance risk detection, representing a paradigm shift in how organizations conceptualize and implement financial controls.
The Rise of the Cognitive Cloud Architect: AI-Augmented Decision Frameworks in Large-Scale Data Migration and Integration
Cloud migration and information integration have historically required deep manual intervention at each stage of dependency mapping, schema alignment, hazard assessment, and workload orchestration, often resulting in huge operational bottlenecks that scale poorly with elevated architectural complexity. Most traditional migration frameworks rely on static blueprints and reactive decision-making approaches, which are unable to support a dynamic multi-cloud environment that is driven by ever-changing regulatory requirements, sustainability mandates, and heterogeneous platform constraints. Fortunately, the advent of artificial intelligence abilities brings with it unprecedented possibilities for enhancing architectural decision-making through intelligent automation. The article introduces the Cognitive Cloud Architect paradigm, where large language models enable semantic interpretation of system metadata across syntactically divergent platforms, reinforcement learning agents optimise migration flows through continuous policy refinement based on multi-objective performance landscapes, and generative AI systems synthesise executable infrastructure blueprints from natural language intent specifications. This cognitive architecture model repositions human architects from tactical configuration specialists toward strategic orchestrators of intelligent automation, allowing them to preserve governance authority while delegating computational optimisation burdens to AI agents. Implementation patterns are shown to illustrate approaches to integrating these technologies into existing toolchains by adding semantic pre-processing layers, sidecar learning services, and API-mediated blueprint generation. Governance frameworks that address AI decision transparency, accountability mechanisms, and validation checkpoints ensure that automated intelligence enhances and does not circumvent human oversight, thereby maintaining organizational control of consequential migration decisions in the enterprise.
Green Ad Tech: A Systems Approach to Carbon-Aware Digital Advertising
The digital advertising ecosystem is one of the most latency-sensitive distributed systems on the Internet, fulfilling millions of real-time bidding transactions per day and producing significant greenhouse gas emissions due to server operation, network transmission, and machine learning inference. Combined with highly tight sub-100-ms latency, multifaceted multi-party economic incentives, and increasing regulatory demands that businesses focus on environmental responsibilities, these converging factors present a set of unique carbon reduction challenges to which the carbon-aware computing models have failed to provide sufficient solutions. Green Ad Tech presents a comprehensive systems architecture integrating five coordinated mechanisms: real-time carbon-aware request routing leveraging geographic and temporal variations in grid carbon intensity, lightweight machine learning model switching to reduce inference energy consumption, intelligent edge caching and creative optimization, adaptive auction reweighting through contextual bandit algorithms, and granular per-impression emission attribution aligned with industry measurement standards. The framework formalizes carbon-aware advertising as a constrained multi-objective optimization problem balancing emission minimization against latency service-level objectives, revenue preservation requirements, and data protection compliance constraints. Specialized algorithmic methods to reduce carbon and still be competitive in the auction time window of microseconds allow greedy routing with latency constraints, forecast-based time optimization of deferrable operations, and value-adaptive model selection to be the primary decision primitives. Measurement infrastructure offers per-impression carbon accounting with full provenance metadata, allowing third-party auditing and avoiding greenwashing, and incentive mechanisms can link carbon goals among advertisers, publishers, platforms, and content delivery networks using configurable preferences, eco-labeling, and billing changes. The equity considerations deal with the possible short side of publishers in carbon-intensive areas by providing temporal flexibility, fairness restrictions, and capacity-building investments that do not result in sustainability efforts contributing to global economic inequalities. Privacy and regulatory compliance are considered as hard constraints that cannot be negotiated, and carbon optimization will consider the data protection requirements, such as GDPR restrictions on cross-border processing of data. The framework demonstrates that meaningful emission reductions are achievable within the operational constraints of production advertising systems while maintaining economic viability and stakeholder trust through transparent, auditable measurement aligned with emerging industry standards for media sustainability reporting.
Automated Security Key Rotation in Distributed Computing Environments: A Scalable Framework for Enterprise Systems
Enterprise distributed computing environments have serious security issues dealing with the life cycle management of cryptographic keys in computing nodes and hardware security modules. Manual key rotation systems introduce logical bottlenecks, security risks, and compliance risks, which do not scale well with parts of the infrastructure. The article offers an example of an automated key rotation security framework that considers the following challenges by providing smart orchestration, envelope encryption policies, and no downtime deployment policies. The solution proposed provides multi-layered security measures such as zonal master key isolation, automated validation processes, and detailed audit trails that can guarantee the regulation compliance and the operational efficiency at the same time. Field applications have shown a lack of any manual intervention interface, shorter security exposure times, and backward compatibility on several generations of hardware. In addition to the direct security advantages, automated key rotation brings operational resilience through the minimization of risks of human errors, quick response to security incidents, and ongoing compliance monitoring.
Passwordless Banking: Integrating FIDO2 and Biometric Authentication for Secure and Compliant Financial Services
Financial institutions are accelerating the transition from passwords to FIDO2-based authentication to mitigate credential-related breaches and enhance regulatory compliance. This shift removes shared secrets from authentication, eliminating the primary attack vector exploited in credential-driven fraud. This article presents a Blueprint for Passwordless Banking, detailing a phased strategy for deploying FIDO2- and biometric-based authentication in high-compliance environments. The framework covers architectural prerequisites, risk evaluation, customer journey adaptation, and coexistence strategies with legacy multi-factor authentication systems. Practical guidance derives from large-scale banking implementations, emphasizing security gains, reduced credential-reset costs, and measurable improvements in user experience. The proposed model demonstrates how regulated institutions can meet FINRA and PCI-DSS requirements while embracing passwordless paradigms that align with NIST SP 800-63B digital identity guidelines. Financial institutions implementing passwordless strategies report measurable reductions in account takeover incidents, decreased support costs associated with credential management, and enhanced customer satisfaction metrics reflecting streamlined authentication experiences. The regulatory landscape governing financial services authentication has evolved to accommodate passwordless methodologies while maintaining rigorous identity assurance requirements through possession-based authenticators and biometric verification as acceptable alternatives to knowledge-based credentials.
Operationalizing AI: The Central Role of MLOps in Deploying Scalable ML Systems
The shift from prototype artificial intelligence systems to production-ready ones necessitates end-to-end operational practices capable of solving the distinct challenges of deploying machine learning at scale. Contemporary AI systems need to be supported by specialized operational domains beyond conventional software development practice, including machine learning operations, development operations, data operations, and large language model operations. Machine learning operations is the base framework for scaling prototype models to stable production systems, including feature engineering, hyperparameter tuning, model validation, and ongoing monitoring features. Development operations offers the core infrastructure automation and deployment excellence via version control, continuous integration pipelines, and infrastructure-as-code implementations. Data operations assure information asset reliability and quality by way of systematic data management, validation procedures, and governance mechanisms that view data as an asset with specifications of service standards. Large language model operations resolve the specialized needs of foundation model operations, such as prompt engineering, fine-tuning procedures, and retrieval augmented generation architectures. Combining several operational frameworks gives rise to synergistic effects, allowing organizations to gain substantial gains in deployment speed, system dependability, and cost savings. Successful operationalization demands synchronized implementation in all operational disciplines in order to maximize the potential of artificial intelligence technologies in commercial applications.
Interpretable Machine Intelligence - Bridging Transparency and Performance
The evolution of artificial intelligence systems has created a fundamental tension between predictive accuracy and transparency, particularly as sophisticated models are deployed across high-stakes domains including healthcare, finance, and criminal justice. Interpretable Machine Intelligence addresses this critical challenge by bridging the gap between complex model capabilities and human understanding requirements. The field encompasses both intrinsically interpretable models that provide transparency by design and post-hoc explanation methods that illuminate black-box system behavior. Healthcare applications demonstrate the essential nature of interpretability, where medical professionals require clear diagnostic reasoning, while financial services demand transparent credit and risk assessment explanations for regulatory compliance. Criminal justice implementations highlight the critical importance of explainable algorithms in maintaining due process and preventing discriminatory outcomes. Theoretical foundations distinguish between global and local explanations, with taxonomies categorizing methods by scope, model dependency, and data modality. Methodological advances include linear models, decision trees for inherent interpretability, and sophisticated post-hoc techniques such as LIME and SHAP for complex model explanation. Attention mechanisms in neural networks provide dual benefits of performance enhancement and interpretability insights. Future directions emphasize causal interpretability, human-centered design considerations, scalability challenges for large-scale models, standardization needs, and evolving regulatory requirements that will shape the continued development of transparent artificial intelligence systems.
Choosing the Right Infrastructure Stack for Your AI Application: A Comprehensive Framework for Modern AI Systems
The rapid growth of AI applications has fundamentally transformed how organizations approach system architecture and infrastructure design, creating a need for a comprehensive framework to select appropriate infrastructure components. This article presents a systematic methodology for understanding and implementing AI infrastructure through a layered architecture approach that decomposes the AI stack into six critical elements: (i) data and governance, (ii) storage systems, (iii) compute resources, (iv) model toolchains, (v) orchestration platforms, (vi) serving infrastructure including retrieval and augmentation systems, and observability and safety mechanisms. The article examines all six components along core AI system performance dimensions of quality, latency, throughput, cost, and energy efficiency, and analyzes inherent trade-offs. Unique bottlenecks and optimization strategies are identified for each infrastructure component, from data quality assurance challenges in governance layers to resource utilization inefficiencies in compute environments. This article presents a decision framework that encompasses workload characterization, technical evaluation criteria, economic analysis, and risk assessment, to enable AI practitioners to make informed infrastructure choices aligned with application-specific requirements and business constraints.
Enhancing Developer Productivity Through Intelligent Documentation Retrieval
This article presents an architectural framework for enhancing developer productivity through intelligent documentation retrieval systems that leverage Model Context Protocol (MCP) and vector database technologies. Enterprise component libraries have grown exponentially in complexity, creating substantial barriers to effective knowledge utilization within software development organizations. The implementation architecture integrates ChromaDB vector database with semantic search capabilities through LangChain orchestration frameworks, enabling natural language documentation discovery embedded directly within development workflows. The MCP server operates as intelligent middleware that transforms developer queries into effective retrieval operations while maintaining contextual awareness of development environments. Integration with AI coding assistants creates enhanced development experiences that combine general model capabilities with organization-specific knowledge, enabling more accurate code generation aligned with established architectural patterns. Beyond technical implementation considerations, the article examines organizational impacts, including knowledge democratization effects, implementation consistency improvements, and operational efficiency enhancements resulting from intelligent documentation access. The architectural approach creates substantial organizational value by reducing knowledge silos, improving code consistency across development teams, and enabling more effective onboarding processes through AI-mediated knowledge transfer. Scalability considerations and deployment architectures ensure that implementations can maintain performance characteristics while accommodating growing documentation repositories and increasing developer populations.
Programmable Network Fabrics for AI and Multi-Cloud Workload Mobility
The workload of artificial intelligence is becoming difficult to handle across numerous data centers, cloud regions, and edge environments, which puts significant pressure on network infrastructure that is conceived in a fixed enterprise application context. Conventional network provisioning is based on manual configuration that adds a huge amount of latency between workload migration decisions and deployment, making it impossible to optimize distributed AI deployments in real-time. To support such needs, programmable network fabrics use API based abstractions that separate application connectivity needs and implementation details, allowing the dynamic adjustment of topologies in the face of varying workload requirements. The architectural framework offers service-based models of networking, in which connectivity is on-demand by the provision of standardized interfaces, like compute and storage resources. Declarative requirements of applications are mapped to an optimal configuration of the network via policy-based orchestration, which balances conflicting goals such as minimizing latency, minimizing cost and allocation of bandwidth. Intention APIs receive high-level specifications of desired states of the world instead of being prescriptive commands, and policy engines test their requirements against the current network state to compute the best routing paths and resource assignments. This framework is interoperable with container orchestration systems and AI training systems, allowing synchronized infrastructure management in the compute and connectivity space. Its uses include distributed training models that need synchronization of parameters over geographical boundaries, real-time inference at edge computing scales and multi-cloud GPU bursting to move workloads across providers. Performance evaluation has shown to significantly outperform the performance of the statistical provisioning models due to lower deployment latency, greater efficiency in the use of network resources and cost efficiency due to network-conscious workload placement. The implementation considerations are the security architecture in a multi-tenant environment and operational transformation requirements in policy-driven management paradigms.
Grounded Generation and Policy-as-Code Validation: A Framework for Safe AI-Assisted SEO Content Operations at Scale
Generative AI offers unprecedented capabilities for scaling search engine optimization content across thousands of product pages, category hierarchies, and long-tail queries. However, unconstrained generation introduces significant risks, including factual hallucinations, duplicate content penalties, brand inconsistencies, and accessibility violations. This article presents an end-to-end operational framework that combines retrieval-augmented generation with automated validation gates to produce compliant, factually grounded SEO content. The workflow encompasses opportunity identification, multi-source fact retrieval from product information management systems and user-generated content, template-based variant generation with mandatory citation linking, comprehensive policy-as-code validation covering factuality and schema compliance, selective human review protocols, and staged deployment with rapid rollback capabilities. The reference architecture integrates hybrid indexing, automated quality gates, feature flags, and observability dashboards to balance content velocity with risk management. Organizations adopting this framework can systematically generate meta descriptions, FAQ schemas, product copy, and alt text while maintaining editorial standards and regulatory compliance across large-scale content operations.
Toward Robust Deep Learning Models based on YAMNet vs ECAPA-TDNN for Speaker Recognition
The importance of biometric identification and speaker recognition is growing in today's culture. Neural networks; especially the deep ones, are now frequently used to extract speaker attributes. Despite its limited ability to acquire fully comprehensive speech features, the YAMNet and ECAPA-TDNN models can get relevant context information by exploring acoustic feature parameters to pattern matching. However, in noisy environments, the background noise reduces speech quality and intelligibility, which make speaker identification challenging task. It is important to verify the biometric model's capacity for generalization and enable precise speaker recognition even in noisy environments. To assess the efficiency and the robustness of the introduced models in speaker identification and recognition, comparisons with YAMNet, ECAPA-TDNN and both of them hybridized with Machine Learning (ML) algorithms are conducted. The overall accuracies were affected by the noises in frame level, when using both the deep neural networks based on deep learning (DL) and the hybridized DL-ML models. The obtained results and the comparison presented in this paper furnish a founding for promising behaviour for robust biometric systems. The best results are obtained with ECAPA-TDNN model. In addition, the hybridization methods can perform good rate of accuracies especially when the DL models are hybridized with Support vector machines (SVM) in noisy environment.
A Neural Approach to Predict the Change Impact in Object-Oriented Systems
Software maintenance is a critical phase in the software lifecycle, often outlasting the development and deployment stages and accounting for the majority of total costs in the industry. These high costs are primarily due to the complexity of implementing changes and managing their potential ripple effects throughout the system, especially in large and evolving codebases with numerous interdependencies and legacy components that are difficult to modify safely. In response to this challenge, numerous studies have aimed to analyze or predict the impact of changes in software systems. However, many existing approaches require extensive input data that is often difficult to collect or maintain, limiting their practical applicability in real-world settings and automated development pipelines. In this paper, we propose a machine learning-based approach using neural classifiers trained on historical change data. Our change impact prediction model leverages two key sources of information: (1) the relationships between classes, characterized by their susceptibility to change, and (2) the structural metrics derived from the change history across successive versions of the software. To evaluate our approach, we applied it to 23 open-source software projects from the PROMISE repository. The results demonstrate that our model outperforms several existing techniques, offering a more efficient and effective solution for change impact prediction in software maintenance, withpromising implications for large-scale systems.
Revisiting Systems Thinking and Productivity in SMEs: Challenges and Opportunities
Small and Medium Enterprises (SMEs) play a vital role in driving economic development, particularly in emerging economies. However, many SMEs encounter challenges related to productivity, often stemming from fragmented processes, limited coordination, and reactive decision-making. This article revisits systems thinking as a strategic approach to enhance productivity within SMEs. Based on an integrative review of relevant literature, the study explores the obstacles SMEs face in adopting systems thinking and identifies opportunities for integrating this methodology into small business operations. Findings indicate that while systems thinking can facilitate improved collaboration, efficiency, and adaptability, practical barriers—such as limited expertise, abstract conceptual frameworks, and resource constraints—may impede its implementation. Nonetheless, targeted strategies—including SME-specific training, simplified models, and digital tools—can facilitate adoption. This work contributes to the ongoing discussion on sustainable SME development by emphasizing the need for accessible systems thinking frameworks that foster resilience and competitiveness.
Rethinking Outsourced HR Consulting Services in Industry 4.0: Evidence from Existing Studies
The evolution of Industry 4.0—characterized by the integration of technologies such as artificial intelligence (AI), big data analytics, and automation—has driven a significant transformation in human resource (HR) management practices. Traditionally, outsourced HR consulting has been instrumental in providing organizations with specialized expertise and operational efficiency. However, in the digital age, many of these functions can now be automated or managed internally using advanced HR technologies. This article reexamines the continued relevance of outsourced HR consulting services through the lens of the Resource-Based View (RBV) theory, which highlights the strategic importance of internal capabilities. The analysis suggests that while outsourcing remains valuable for certain specialized functions, the increasing demand for tailored, technology-driven HR solutions prompts organizations to reassess their outsourcing strategies. A hybrid approach—combining external expertise with the development of internal capabilities—is emerging as a strategic and competitive response in the Industry 4.0 environment.
Exploring Job Security Practices at Tronox KZN Sands: Strategies, Challenges and Employee Perceptions
Job security remains a vital factor influencing employee motivation, organizational commitment, and workforce stability, especially in high-risk industries such as mining. This study examines the strategies, challenges, and employee perceptions related to job security at Tronox KZN Sands during the COVID-19 pandemic. Guided by the Job Demands-Resources (JD-R) theoretical framework, the research employs a quantitative approach, utilizing a structured questionnaire administered to 100 employees at Tronox’s KwaZulu-Natal operations. The findings reveal that, although Tronox implemented various strategic measures—including workforce restructuring, salary adjustments, and flexible work arrangements—employees continued to experience increased anxiety, perceptions of managerial indifference, and disparities in job security across different employment categories. Additionally, the study indicates that qualifications and individual performance did not necessarily correlate with greater job security. Factor analysis identified four key themes: pandemic-related anxiety and career disruption; changes in working conditions and financial stress; the perceived importance of qualifications and effort; and concerns regarding recognition and employment inequalities. These insights highlight the importance of transparent communication, equitable human resource practices, and ongoing employee engagement initiatives to enhance job security and organizational resilience in future crises.
SMEs' Knowledge and Application of IAS21: Challenges, Opportunities and Implications for Financial Reporting
In today’s global economy, small and medium-sized enterprises (SMEs) are increasingly participating in international trade, which requires adherence to international accounting standards such as IAS 21, governing the accounting for foreign currency transactions and the translation of foreign operations. Despite the importance of promoting financial transparency and comparability, many SMEs encounter significant challenges in effectively implementing IAS 21 due to limited resources, technical expertise, and infrastructure. This integrative review critically examines existing academic and practitioner literature on SMEs’ understanding and application of IAS 21, highlighting key obstacles, opportunities, and implications for financial reporting practices. Drawing on Institutional Theory, Contingency Theory, and the Resource-Based View, the study explores how internal capabilities and external influences influence SMEs’ compliance behaviours. The review also underscores the importance of tailored policy measures and capacity-building initiatives to support SMEs in meeting the standard’s requirements. The study aims to provide a deeper understanding of how SMEs operationalize IAS 21 and offers recommendations for enhancing the quality of their financial reporting within a globally interconnected environment by synthesizing evidence from various sources.
Improving Heart Rate Monitoring During Sleep Through Trunk Muscle Artifact Separation from ECG Signals
Monitoring heart rate (HR) during sleep is a crucial method for detecting early signs of cardiac and sleep disorders. But regular heart rate monitors and accelerometers often have trouble telling the difference between normal changes in heart rate and changes that happen when you move or use your muscles. This paper proposes a method to isolate trunk muscle signals (ECG-TMS) from ECG recordings to address this issue. This method utilises the discrete wavelet transform (DWT) to get rid of artifacts caused by muscles, which makes heart rate readings more accurate. A controlled experiment involving a healthy adult male was executed using Shimmer3 ECG equipment to gather data in simulated sleep environments. The method of ECG-TMS did a great job of getting clean heart signals, which made the signal-to-noise ratio (SNR) much better and made it possible to reliably find the heart rate even when the body moved a little. This method was better at getting rid of noise and picking up small muscle movements than systems that used accelerometers. These findings suggest that ECG-TMS may be particularly beneficial in sleep research, cardiovascular diagnostics, and wearable monitoring technologies. Further research will aim to validate the system in real-world sleep scenarios and improve it for continuous monitoring in clinical and home settings.
Exploring the Value–Self-Efficacy–Well-Being Link in Smart Tourism: The Moderating Effect of Digital Literacy
This study examines the value–self-efficacy–well-being pathway in smart tourism, emphasizing the moderating roles of digital literacy. Based on UTAUT2 and consumer value theory, the model investigates how performance expectancy, effort expectancy, habit and price value affect hedonic and utilitarian values, leading to enhanced self-efficacy and well-being. Survey data from 406 users were analyzed using SPSS 27.0 and AMOS 23.0. Results indicate that performance expectancy and price value significantly influence both utilitarian and hedonic values, while effort expectancy is not significant. Both value dimensions positively affect self-efficacy, which in turn enhances psychological well-being. Digital literacy negatively moderates the relationship between self-efficacy and hedonic value, implying that users with lower digital skills gain greater confidence through enjoyable experiences. These findings extend smart tourism research by connecting experiential value to psychological outcomes and emphasizing inclusive, user-centered design for varying digital literacy levels.
The Development of the Ho Chi Minh City Party Committee’s Leadership Thinking on Journalism (1982 - 1988) (Through Directive No. 17/CT-TU (1982) and Resolution No. 04-NQ/TU (1988))
The period from 1982 to 1988 marked a critical stage in the development of the Ho Chi Minh City Party Committee’s leadership thinking on journalism, as reflected in two key documents: Directive No. 17/CT-TU (1982) and Resolution No. 04-NQ/TU (1988). This study employs a historical-discursive analysis to elucidate the evolution in the City Party Committee’s perceptions and methods of leadership in the field of journalism—specifically, the shift from an administrative, centrally subsidized model to one based on political orientation, dialogue, and feedback, consistent with the 6th National Party Congress’s principle that “Renovation must begin with the renovation of thinking”. The findings indicate that this development is manifested in three major dimensions: renewal in political-ideological orientation and media content; reform of leadership and management mechanisms; and innovations in journalistic personnel work and leadership culture. These transformations contributed to shaping a more democratic and flexible leadership approach, thereby enhancing effectiveness and ensuring the alignment between the Party’s ideological orientation and the promotion of journalistic initiative, creativity, and social responsibility. Drawing from the practical experience of the Ho Chi Minh City Party Committee, the study highlights three notable values: the creative application of Marxism-Leninism and Ho Chi Minh’s thought on revolutionary journalism; the political–practical foundation for the city’s journalistic development during the Đổi Mới period; and the important contributions to the refinement of the Party’s methods of journalism leadership in subsequent years.
ANN-Based Predictive Information System for Barium Sulfate Solubility in Oilfield Brines Under Variable Temperature, Pressure, and Ionic Strength
Accurate solubility prediction of barium sulfate (BaSO₄) is essential to prevent scale formation in oilfield operations, yet conventional thermodynamic models are computationally intensive and unreliable when dealing with nonlinear interactions between pressure, temperature, and brine composition. This paper presents an Artificial Neural Network (ANN)–based predictive information system designed to estimate BaSO₄ solubility under wide operating conditions (25–250°C, 1–500 bar, and NaCl concentrations up to 4 M). The system integrates data preprocessing, normalization, ANN model development, and automated validation within a structured workflow, making it suitable for deployment as a real-time decision-support module in industrial monitoring systems. The optimal ANN architecture—six layers, three neurons per layer, trained using the Levenberg–Marquardt backpropagation algorithm—achieved superior predictive performance with MSE = 6.82×10⁻⁵ and R² = 0.994 during training. Validation results confirmed generalization capability with MAE = 1.3, RMSE = 2.09, and MAPE = 4.3%, effectively outperforming conventional prediction models reported in the literature. Comparative benchmarking against empirical models demonstrated that the ANN system reduces prediction error by up to 40–60%, particularly at high-temperature/pressure operational windows where classical solubility equations degrade in accuracy. The proposed solution demonstrates that machine learning can reliably model complex solubility behavior without explicit thermodynamic formulations, enabling scalable integration into oilfield digital dashboards, predictive maintenance systems, and automated scale-prevention workflows. This validates the applicability of ANN-based soft sensors as a cost-effective and accurate alternative approach within industrial information systems and data-driven decision support.
Collaborative Intelligence in Manufacturing: Human-Robot Teams with Edge AI
The Industry 4.0 paradigm has profoundly re-shaped manufacturing processes with the incorporation of collaborative intelligence technology that integrates human-robot collaboration, artificial intelligence, and edge computing. Modern manufacturing setups utilize advanced gesture recognition and intent inference capabilities for processing multi-modal sensor inputs with ultra-low latency for mission-critical applications. Sophisticated edge AI designs provide real-time decision-making capabilities that far surpass conventional cloud-based processing constraints, featuring lightweight convolutional neural networks that are optimized for edge deployment and multi-camera sensor fusion methods. Application across automotive and electronics manufacturing industries showcases remarkable improvements in quality control, production efficiency, and worker safety through robust predictive analytics and adaptive task assignment schemes. Human factors are pertinent to successful deployment, demanding advanced interface design methodologies that support varying worker abilities while establishing trust through explainable AI systems and open decision-making methodologies. Predictive maintenance integration makes high-performance hybrid systems that utilize AI-based monitoring together with human expertise to attain holistic equipment health management. The value-additive effect goes beyond operational effectiveness to include skill enhancement gains, cost savings results, and competitive edge abilities to quickly respond to market needs while ensuring quality in varied manufacturing processes.
On-Device Intelligence: Local Processing Architecture for Mobile Computing Systems
Edge intelligence fundamentally transforms mobile computing architectures by relocating artificial intelligence processing from centralized cloud infrastructure to distributed mobile devices. Traditional cloud-dependent systems encounter inherent limitations, including network latency that precludes real-time interaction, privacy vulnerabilities arising from centralized data aggregation, and accessibility constraints in connectivity-limited regions. The architectural transition to on-device processing addresses these challenges through specialised neural processing hardware implementing highly parallel computational structures optimised for matrix operations and convolutions characteristic of deep learning workloads. This paper makes four primary contributions to understanding edge intelligence architectures. First, systematic examination of specialised neural processing hardware reveals that memory hierarchy design, rather than computational throughput, often determines overall energy efficiency for mobile neural inference. Second, analysis of federated learning augmented with differential privacy mechanisms demonstrates that collaborative model development need not compromise data sovereignty while revealing fundamental trade-offs between privacy strength and model utility. Third, detailed examination of healthcare monitoring, accessibility technologies, and personalized recommendation systems characterizes distinct requirements for local processing, providing quantitative benchmarks: healthcare applications require latency below 100ms for safety-critical detection, accessibility applications demand processing under 20ms to avoid perceptual lag, and personalization systems tolerate latency up to 500ms. Fourth, adaptive hybrid architectures dynamically partition computation between device and cloud, incorporating neural architecture search and once-for-all training paradigms that enable efficient deployment across heterogeneous hardware platforms. The convergence of hardware specialisation, algorithmic optimisation, and privacy-preserving architectures establishes edge intelligence as a viable alternative to cloud-centric artificial intelligence systems.
Scaling Enterprise Licensing: Modernizing Volume Licensing Platforms (2010–2015)
This article reviews the modernization of enterprise Volume Licensing Service Center and Next Generation Volume Licensing platforms during 2010–2015, highlighting a 67% reduction in release cycle time and a 30% decrease in post-release defects through incremental architectural refactoring, build automation via Team Foundation Server, and Security Development Lifecycle integration. The transformation addressed critical challenges in legacy monolithic architectures, transitioning from quarterly to monthly release schedules while maintaining operational stability for platforms supporting billions in annual enterprise revenue. Key technical implementations include ASP.NET WebForms-to-MVC migration patterns, WCF service integration architecture, TFS-based XAML build definitions, PowerShell deployment automation, and SDL-compliant static analysis workflows. This comprehensive review provides practical guidance for enterprise architects and engineering leaders managing platform modernization initiatives in regulated environments where incremental transformation strategies must balance innovation velocity with operational stability and compliance requirements.
Lalith Lakshmi Chaitanya Kumar Mangalagiri
Generative AI for Retail and Healthcare: Redefining User Interaction with Data Systems
Generative Artificial Intelligence (AI) is transforming the user interaction with data systems in the main aspects of the economy, such as retail and healthcare. The present paper will discuss how the recent advances in the sphere of generative models, as well as the sound data engineering behavior, can change the user experiences so as to provide them with highly personalised, interactive and smart systems. Generative AI is applied in retail to create content that is more personalized, predictive demand, and refined recommendation system; in healthcare, it helps in synthetic data generation, patient-centred documentation, assisting diagnosis, and health literacy tools. The systems ensure reliability, compliance and performance by incorporating key data engineering requirements, which include data ingestion, preprocessing, pipeline design, quality assurance, metadata management, versioning, and lineage. We examine the architectural designs, workflow and model streamlining policies allowing such integrations. Moreover, we also address use-cases that explain how generative AI can simplify the burden of administration, decision-support, and user confidence, and also outline key issues: data privacy, model explainability, bias, ethical protection and regulation. At the end of the paper, future directions are also offered such as federated learning, multimodal data fusion, and real-time adaptive pipelines to make sure that generative AI is safely and efficiently deployed.
Cloud-Based Financial Reporting Systems for Large Enterprises: Automation, Architecture, and Impact
Cloud-based financial reporting systems have emerged as the necessary infrastructure to support companies operating in distributed computing environments. Volume, granularity, and dynamic nature are characteristic features of cloud consumption data that conventional financial mechanisms can hardly accommodate. Within these modern systems, the architectures of the cloud natives processes the records of use, utilizes cost allocation approaches, and generate financial documents concerning the multi-accounting structure, diverse pricing frameworks, and tagging resources. Automated reporting pipelines help to decrease financial close processes and provide more sophisticated analytics: expense forecasting, variance detection, and cost-to-server attribution. Layered architectures separate the functions of ingestion, transformation, modeling, and presentation, each leveraging serverless computing paradigms and dimensional modeling techniques. By doing so, these systems complete the transformation of enterprise financial management; decision-making is accelerated, granular resource optimization is possible, and governance is cemented by comprehensive audit trails. The executive leadership will get insight into patterns of spending on technology across organizational dimensions, whereas the engineering teams find opportunities for optimization through workload-level cost attribution. Multidimensional analysis and scenario modeling will be supported by business intelligence facilities built in there. Financial governance systems ensure that the quality, consistency, and traceability of data are maintained across analytics lifecycles. A firm that possesses advanced financial reporting systems has competitive advantages in that it is able to control costs and use its resources efficiently.
Real-Time Supply Chain Optimization in Retail: AI-Powered Cloud Systems
This article discusses the transformational role of AI and cloud computing technologies in retail supply chain management. Retailers are now achieving unmatched degrees of operational efficiency, inventory optimization, and accuracy in demand forecasting by integrating real-time analytics, machine learning algorithms, and multi-source data aggregation into their supply chains. The successful implementation of these technologies results in turning a reactive approach into a proactive one towards challenges faced in supply chains. Such responsive networks will be able to foresee disruptions to operations even before they occur. In modern-day AI-powered systems, diverse data streams emanating from point-of-sale systems, warehouse management platforms, transportation networks, customer relationship management systems, and IoT sensors are harnessed together to facilitate comprehensive optimization. The broad range of architectural technical elements includes integration into cloud-based platforms, containerized microservices, and advanced machine learning frameworks deployed across hybrid infrastructure settings. However, despite huge potential benefits, such organizations face a number of significant challenges relating to data quality governance, scalability requirements of computations, and algorithmic explainability. Future developments in quantum computing and autonomous systems will further revolutionize supply chain operations via enhanced optimization capability and pervasive automation. This article analyzes the architectural components, implementation methodologies, technical challenges, and emerging capabilities that collectively define next-generation retail supply chain systems.
Intelligent Database Operations: Leveraging AI-Driven Observability and Predictive Maintenance in Cloud Platforms
Databases are the backbone of digital business in modern enterprises, but this issue is still a challenge in the multi-cloud environment, as it is necessary to manage the performance, reliability, and scalability of databases in the new environment. The conventional monitoring frameworks are based on predefined thresholds and reactive troubleshooting, which usually result in a slow reaction to the issue and the case of ineffective resource utilization. This article discusses AI-based Observability and Proactive Maintenance of intelligent database processes new technology that combines telemetry analytics, machine learning, and automation to develop self-optimizing database systems. The AI-Driven Observability Model suggested incorporates both predictive analytics and real-time automation to shift away from reactive database management to being proactive. By making a comparative analysis between the managed cloud services, the methodology exhibits quantifiable benefits in the reduction of query latency, availability enhancement, and cost efficiency of operations. The framework is a critical step in the direction of autonomous operations of a database as a sub-unit of an intelligent cloud infrastructure.
Bridging Real-Time Analytics and Business Intelligence: A Strategic Product Integration
The enterprise software landscape has historically maintained distinct separation between historical data analysis and real-time streaming analytics, creating significant challenges for organizations seeking integrated analytical capabilities. This technical article explores a strategic product initiative that successfully bridged this technological divide by integrating real-time streaming capabilities into an established Business Intelligence platform. The project met the core demands of the market by developing a new analytics category that offered well known BI processes with real-time data. The market conditions have shown that there are significant opportunities at the crossroads of the mature markets of BI and new market demands of real-time analytics, with the traditional platforms not serving the needs of users hunting instant operational insights. The strategic model was focused on augmentation over replacement, limiting the chance to create organizational resistance by using smooth integration of workflow and delivery by browser that removed the friction of the deployment. Technical execution balanced performance requirements with integration simplicity, implementing single sign-on authentication, real-time data connectivity, configurable alerting systems, and pattern detection capabilities. Go-to-market execution leveraged existing customer relationships, coordinated sales enablement, aligned pricing with established subscription models, and maintained proactive customer success engagement. Results demonstrated substantial revenue contribution, strong customer retention metrics, and successful market differentiation. The initiative validated essential principles for enterprise software strategy including workflow preservation, friction reduction in adoption, and strategic leverage of existing market positions to accelerate entry into adjacent market segments.
AI in Financial Reporting and Audit Automation
Artificial intelligence fundamentally redefines financial reporting and audit processes by applying sophisticated analytical strength for automatic data processing, real-time transaction analysis, and enhanced risk analysis. Obsolete periodic audit paradigms based on manual reconciliation and sampling-based testing converge on models of continuous assurance where intelligent systems scan entire populations of transactions rather than statistical samples thereof. Financial data management is dramatically enhanced by AI-powered automation of data ingestion, transaction accuracy verification, and automation of regulatory-compliant disclosure preparation requiring little to no human intervention. Sophisticated accounting applications such as revenue recognition, lease accounting, and impairment testing utilize machine learning processes to interpret contractual language, model economic scenarios, and apply accounting standards consistently across a spectrum of transaction types. Continuous monitoring systems enable auditors to detect anomalies, identify control weaknesses, and predict potential misstatements before financial statements are issued to stakeholders. However, successful AI implementation depends upon effective governance mechanisms addressing transparency of algorithms, bias mitigation, identification of data origin, and model validation on a recurring cycle. Organizations must construct comprehensive oversight processes ensuring AI-prepared financial output stays accurate, reliable, and compliant with professional standards while maintaining proper human judgment in critical accounting decisions. Paradigm shifts towards AI-enriched financial processes require judicious management of technical capability, ethical concerns, regulatory requirements, and preservation of stakeholder trust amidst converging IT shifts.
Ethical Risks in AI-Enabled BI Systems in Banking
Artificial intelligence (AI) in the banking sector enhances the value of Business Intelligence (BI) systems through operational efficiencies, personalization of services, and more accurate data-driven decision making. Yet the rapid pace of AI technology convergence brings potential losses of trust, fairness, and accountability in the ethics of intersectional financial systems. Key ethical concerns are algorithmic bias, the potential AI systems will fail to provide equitable outcomes and perpetuate discrimination against certain demographics; the processing of sensitive and personal financial data will raise data privacy issues; accountability will be elusive, as the opaque nature of AI systems will render decision-making processes and outcomes inscrutable; and gaps in transparency will escalate. This paper intends to add to the discourse on the ethical concerns associated with AI-driven BI systems by undertaking a holistic examination of the ethical and unregulated aspects of the financial sector. Moreover, it seeks to provide a risk mitigation approach based on the ethical and regulatory oversight applicable to other sectors. Responsible AI governance will facilitate the ethical application of AI in a manner that preserves the institutions’ transparency, fairness, and accountability in core operations. The ultimate aim is to facilitate the use of AI technologies in banking to improve the customer experience while maintaining the highest ethical standards and safeguarding the trust of the public.
A Next-Generation Middleware Architecture for Seamless Enterprise Integration and Real-Time Analytics
The data explosion in enterprises has radically altered organizational demands for middleware solutions, necessitating the development of revolutionary architectural models that go beyond established service-oriented approaches. This article introduces a next-generation middleware architecture that combines real-time data processing with enterprise-wide integration services under an evolutionary hybrid event-driven model. The architected system differentiates itself through adaptive processing mode selection, in which synchronous patterns are used to service latency-critical operations, and asynchronous streaming processes service bulk data operations. At the heart of this contribution lies Kubernetes-native orchestration, allowing automatic scaling of resources based on real-time demand metrics, and it achieves unparalleled resource utilization efficiency while sustaining ultra-low response times geographically distributed throughout deployments. Machine learning-augmented predictive maintenance is a revolutionary concept using ensemble learning models trained on system telemetry to predict possible failures with near-perfect accuracy rates. The architecture uses a new data consistency model that preserves ACID properties between distributed microservices and allows eventual consistency for non-critical processes. Performance validation illustrates superior benefits compared to conventional Service-Oriented Architecture deployments, where the envisioned microservices implementation ensures consistent response rates under heavy load conditions. Real-world applications range from automotive autonomous vehicle systems to retail personalization systems, energy grid management, and smart city infrastructure, evidencing the flexibility of the architecture across a wide range of enterprise environments needing real-time analytics and system ease of integration.
The Future of Middleware: AI as Your Integration Co-Pilot
The enterprise software landscape has grown into a complex ecosystem where organizations manage hundreds of disparate applications that require sophisticated integration solutions. Traditional middleware design methodologies impose significant burdens through the need for manual schema mapping, development of transformation logic, and extensive testing protocols that consume large chunks of IT budgets and workforce capacity. AI is turning out to be a transformative force in the realm of integration development by acting as an intelligent co-pilot, augmenting human expertise through pattern recognition, suggesting automated mappings, and probabilistic recommendation systems. AI-powered integration platforms demonstrate very special capabilities in reducing project timelines, defect rates, and democratizing integration development by enabling citizen developers to meaningfully contribute toward the technical implementation of the same. The technology analyzes metadata, historical transaction patterns, and semantic relationships to generate context-aware integration designs with confidence-scored recommendations that maintain human oversight with automation of repetitive tasks. Enterprise implementations indicate huge productivity enhancements, quality improvements, and an increase in workforce satisfaction when AI assistance complements the traditional approaches to integration engineering. The paradigm shift toward AI-augmented middleware design fundamentally changes the economics of enterprise integration, transforming bottlenecks into competitive advantages and letting organizations achieve an unprecedented agility in responding to the evolving needs of the business. This will make the AI-assisted integration platforms essential infrastructure for digital transformation initiatives, wherein the velocity and reliability of system connectivity drive organizational competitiveness and operational efficiency.
Quality Assurance Leadership as Critical Infrastructure: Cross-Industry Impact on Consumer Protection and Societal Trust
Quality Assurance has evolved beyond traditional software testing to become a fundamental pillar of societal protection in digital ecosystems. Contemporary QA frameworks serve as critical safeguards across banking, retail point-of-sale, telecommunications, and automotive telematics domains, directly impacting millions of consumers daily. The transformation of QA from a technical discipline to a societal safeguarding mechanism demonstrates its essential role in preventing fraud, ensuring transaction integrity, maintaining service continuity, and enhancing public safety. Banking sector QA implementations protect consumers from financial fraud while ensuring regulatory compliance that maintains systemic stability. Retail payment systems benefit from QA frameworks that secure digital transactions and preserve consumer trust in electronic commerce. Telecommunications QA governance enables seamless service migrations without disrupting connectivity for entire populations, while automotive telematics validation prevents technical failures that could compromise road safety. Additionally, sustainable QA practices contribute to environmental goals through optimized cloud infrastructure and reduced energy consumption. The multi-domain impact of QA leadership extends beyond corporate boundaries, creating measurable benefits for consumer protection, public safety, and environmental sustainability, positioning QA professionals as guardians of digital society's fundamental trust infrastructure.
Intelligent Trade Lifecycle Orchestration Using Agentic AI for End-to-End Trade Automation
The securities trading around the world is facing an increased complexity challenge of fragmented infrastructures, dependence on manual oversight, and exception-based management systems. The global trading ecosystem continues to undergo rapid digitization driven by algorithmic execution, cross-venue liquidity fragmentation, real-time settlement mandates, and increasingly complex regulatory obligations. Traditional process automation—consisting of process automation, static workflows, and rule-driven validation—offers productivity gains but lacks the adaptive intelligence to manage the dynamic, interdependent, and high-variability nature of trade lifecycles. Intelligent Trade Lifecycle Orchestration introduces an Agentic AIs orchestrating framework that operates autonomously and manages the establishment of trade, verification and matching, settlement and reconciliation, using self-learning, collaborating agents. The novelty of the framework lies in multi-agent architecture, dynamic policy learning, and predictive exception orchestration, making it possible to make changes in the workflow to move it to cognitive coordination. Multi-agent systems exhibit distributed problem-solving ability in which autonomous agents interact with each other via negotiations, cooperation, and competition to accomplish group goals that are beyond the reach of each agent. Deep learning reinforcement algorithms allow agents to improve policies as they are applied by observing the result of the policies, and find out the best strategies by learning them based on experience instead of adhering to rules. Asynchronous training techniques can enable the interaction of multiple agents with distinct instances of the operational environment at the same time, and can gain experience in a very short time frame with steady convergence characteristics. Event streaming systems' low-latency process high data volumes of trade events, updating market data, trade announcements, and settlement instructions as they occur. The Trade Ontology Layer defines standard semantics of trade status, settlement instructions, identities of counterparties, and regulatory requirements, so the interpretation of information can be common to all agents. Experimental assessments of anonymized data on institutional trading indicate that the trade exceptions, settlement latency, and the need to use manual intervention are significantly reduced relative to traditional robotic process automation and predictive machine learning models. The framework has a high level of audit traceability and has been proven to be more effective during market stress times due to its adaptive learning abilities. ITLO offers frameworks for building self-regulating financial ecosystems that are flexible, interpretable, and regulatory sufficient in changing market terms. Unlike classical automation (e.g., RPA, workflow scripts, rule-based engines), ITLO employs autonomous agents capable of negotiation, cooperation, competition, long-horizon reasoning, real-time learning, and cross-domain decision orchestration.
AI-Driven Zero-Day Simulation: Predictive Offensive Modeling for Emerging Vulnerabilities
The accelerating rate of software development and complexity of dependencies has made classical vulnerability discovery inadequate to protect against new, unanticipated threats. This article presents a machine learning–based system for zero-day vulnerability simulation and predictive exploit modeling that facilitates proactive detection of high-risk attack surfaces before disclosure or exploitation. The system combines neural models for vulnerability prediction, code-level graph analysis, and adversarial learning methods to predict probable weaknesses in software ecosystems. At its essence, the system uses predictive modeling of exploit behavior, learned from past vulnerability and exploit patterns, to simulate how novel attack chains could manifest in unseen codebases or settings. The method combines static code embeddings, semantic pattern matching, and reinforcement learning–based exploit generation to build hypothetical exploit paths with high contextual precision. These simulated zero-day attacks are subsequently verified by automated red teaming pipelines, providing a continuous loop of feedback to continuously refine model accuracy as well as offensive realism. Experimental validation on enterprise-scale software repositories shows the framework's ability to predict likely vulnerability classes with high accuracy and to produce exploitable paths before conventional detection techniques. The results form a basis for predictive offensive security, in which artificial intelligence responds not only to identified attacks but foresees and simulates upcoming attack vectors. This article pushes the frontiers of both AI-enabled vulnerability exploration and autonomous offensive emulation and represents a major advance toward proactive, intelligence-based cyber defense preparedness.
The Future of Enterprise Mobile: Secure, Intelligent, and User-Centric Approaches
The future of enterprise mobile technology is undergoing a transformative evolution defined by three interdependent pillars: security, intelligence, and user experience. It explores how organizations are transitioning from traditional approaches to zero-trust frameworks, implementing on-device security intelligence, and adopting advanced authentication methods while maintaining seamless user experiences. The article examines how artificial intelligence and predictive analytics are becoming core mobile capabilities, enabling context-aware applications, workflow optimization, and automated decision support despite integration challenges with legacy systems. User-centric design emerges as a critical factor supporting remote work, driving the consolidation of capabilities into enterprise super apps, and promoting accessibility and inclusive design principles. Looking forward, the paper identifies key implementation strategies, including sustainability considerations, continuous adaptation frameworks, balanced innovation approaches, and comprehensive value creation metrics that extend beyond basic adoption measures. Throughout this evolution, organizations that successfully balance security, intelligence, and user experience demonstrate significantly higher satisfaction, efficiency, and competitive advantage in an increasingly mobile-centric business landscape.
Avian Diversity and Parasitofauna of Waterbirds in the Réghaïa Lake Nature Reserve (Algeria): Ecological Implications and Conservation
The Réghaïa Lake Nature Reserve, located in northeastern Algeria, represents a major ecological hotspot for waterbirds. From June 2020 to May 2021, a total of 30 waterbird species belonging to seven orders and twelve families were recorded. Avifaunal diversity and community structure were evaluated using Shannon-Weaver diversity indices, evenness measures, and occurrence frequencies. Principal Component Analysis (PCA) was employed to correlate physico-chemical water parameters with species distribution. Parasitological analysis of Mallards (Anas platyrhynchos) revealed four endoparasite species, indicating potential environmental contamination. The results underscore the ecological richness of the site, the influence of environmental variables on avifauna distribution, and the necessity for regular ecological and sanitary monitoring to ensure effective conservation.
Occurrence and Characterization of Plant-Parasitic Nematodes in Algerian Vineyards: The Case of Médéa Region
En Algérie, des prospections menées dans les vignobles par Bounaceur en 2011 à travers différentes régions ont mis en évidence la présence de plusieurs genres de nématodes, notamment Xiphinema, Longidorus, Helicotylenchus, Pratylenchus, Tylenchus, Tylenchorhynchus, Paratylenchus, Pratylenchoides, Ditylenchus et Aphelenchus. Les espèces Xiphinema index et Xiphinema pachtaicum ont été spécifiquement signalées dans des échantillons de sol de vignobles prélevés dans la région d'Had S’hari par Smaha en 2023. La présente étude visait à approfondir la compréhension de la distribution des nématodes phytoparasites dans les vignobles algériens, en se concentrant particulièrement sur la région viticole de Médéa. Deux séries de prélèvements ont été effectuées, la première en décembre 2023 et la seconde durant la période mars-avril 2024. A chaque niveau de profondeur investigué (40 cm et 80 cm), un échantillon d'un kilogramme de sol a été prélevé à cinquante centimètres du tronc des ceps, à l'aide d'un outil de type bêche. L'examen nématologique des échantillons provenant des différents sites a permis de recenser un total de 21 genres de nématodes, dont les effectifs variaient significativement d'une localité à l'autre. L'indicateur de richesse générique, mesurant la diversité taxonomique, a affiché une amplitude allant de 13,61 à 18,61, illustrant des différences de richesse spécifique entre les stations. Les données relatives aux densités moyennes ont également mis en évidence des variations inter-sites, vraisemblablement imputables aux spécificités des sols ou à la présence de la flore adventice. Les analyses ont révélé l'existence de genres nuisibles tels que Xiphinema et Longidorus, connus pour leur rôle de vecteurs de viroses importantes, comme le court-noué, qui affectent négativement le rendement des cultures. Ces nématodes phytoparasites représentaient la catégorie la plus importante au sein des communautés biologiques liées aux vignes, surpassant en nombre les nématodes bactériophages et les prédateurs. Ces constats mettent en lumière l'importance de continuer les investigations sur les nématodes et autres pathogènes des végétaux afin d'identifier des variétés de vigne tolérantes aux maladies, d'améliorer la qualité de la production et de réduire les pertes économiques.
Strategic Network Fingerprinting: Advanced Network Analysis and Penetration Testing for Large-Scale Networks
Penetration testing provides proactive security to the enterprise by simulating hacker attacks and detecting system weaknesses legally. Despite their efficiency, manual procedures require much time and resources, especially when dealing with large networks that have a large number of attack surfaces. The traditional approach carries a lot of risk to business continuity because it interferes with network operations. This solution breaks the constraints by including NSE (Nmap Scripting Engine) plug-ins and internet search engines in the network framework. Cyberspace search engines are applied to map the Internet to make it easier to examine infrastructure and devices efficiently. Utilizing NSE plug-ins increases the capabilities of Nmap, reduces scanning times, and increases the efficiency of the framework
Augment the Security of Biometric Image Transmission Via the Implementation of Cascade Chaotic System Encryption.
The research investigates the various uses of chaotic maps by presenting a cascade chaotic system (CCS), which functions as a universal chaotic framework. The core idea entails utilizing an affine transformation with chaotically generated transformation sequences (the cascade chaotic system) to reorganize biometric pictures. The CCS can produce numerous new chaotic maps by employing two one-dimensional chaotic maps 1-D as seed maps. The newly created chaotic maps demonstrate elevated parameters, intricate chaotic traits, and improved chaotic efficacy, making them more unpredictable. We introduce a PRNG pseudo-random number generator and a data encryption system that utilize a chaotic map generated by the CCS to explore its possibilities. The effectiveness and reliability of the suggested method were confirmed by many simulations and analysis of biometric images.
The Black Box of Fraudulent Financial Statements: Do Digital Audit Procedures Matter?
Introduction: Fraud in financial statements is a serious and complex issue in modern accounting and auditing. Although financial statements should reflect an entity’s true financial condition, management often manipulates them to mislead stakeholders. Objectives: This study aims to examine the phenomenon of fraudulent financial statements and assess the extent to which audit procedures are able to detect fraud hidden behind the company's financial statements. This phenomenon is known as the black box of fraud, where financial manipulation is carried out systematically and disguised through seemingly legitimate accounting procedures. Methods: This study uses a descriptive qualitative approach with a case study method on several large companies in Indonesia. Results: The results show that the majority of fraud cases are rooted in weak internal controls, collusion between internal and external parties, and auditors' failure to implement professional skepticism and critical audit procedures. Fraud occurs through various means, including asset mark-ups, premature recognition of income, and undisclosed affiliate transactions. From the accounting side, this manipulation violates the principles of fair value, faithful representation, and full disclosure. Conclusions: This study confirms that the effectiveness of audit procedures is not only determined by compliance with standards, but also by the auditor's ability to assess the economic substance behind each transaction. Strengthening risk-based audit procedures, using data analytics, and increasing auditor independence and integrity are key in penetrating complex fraud "black boxes". Thus, this research makes a theoretical and practical contribution to the accounting profession and regulators in improving audit reliability and public trust in financial statements.
AI-Driven Strategies in Strategic Communication: Understanding University Students’ Attitudes
Introduction: Artificial intelligence (AI) is rapidly reshaping higher education, making it essential to understand students’ acceptance of AI-based learning tools. This study extends the Technology Acceptance Model by incorporating AI Familiarity, Self-Efficacy, Perceived Personalization, Institutional Support, and Ethical Concerns. Using survey data from 473 students at Hebei Academy of Fine Arts and Structural Equation Modeling, the research tests eight hypothesized relationships. The goal is to clarify how AI-related factors influence Perceived Usefulness, Perceived Ease of Use, and students’ attitudes toward AI adoption. Objectives: This study examines university students’ acceptance of AI-based learning tools by extending the Technology Acceptance Model (TAM) with additional constructs relevant to AI use in education, including AI Familiarity, Self-Efficacy, Perceived Personalization, Institutional Support, and Perceived Ethical Concerns. Methods: A cross-sectional quantitative survey was conducted with 473 students from Hebei Academy of Fine Arts in China. Structural Equation Modeling (SEM) was employed to assess the measurement and structural models, evaluate construct validity, and test eight hypothesized relationships within the extended TAM framework. Results: Seven of the eight hypotheses were supported. AI Familiarity and Self-Efficacy significantly increased Perceived Ease of Use, while Perceived Personalization and Institutional Support positively influenced Perceived Usefulness. Perceived Usefulness and Perceived Ease of Use strongly predicted students’ attitudes toward AI adoption. Perceived Ethical Concerns did not significantly affect attitudes. The model demonstrated strong explanatory power, with ATT (R² = 0.768) being the most strongly predicted construct. Conclusions: Findings show that AI Familiarity and Self-Efficacy improve Perceived Ease of Use, while Personalization and Institutional Support enhance Perceived Usefulness; Ethical Concerns show no significant effect. Perceived Usefulness and Perceived Ease of Use remain key predictors of attitudes, and the model demonstrates strong explanatory power (R² = 0.768). Although limited by a single-institution sample and self-reported data, the study highlights directions for broader future research. Overall, it enriches TAM literature in the AI education context and offers practical guidance for fostering supportive and responsible AI-enhanced learning environments.
The Impact of Government Policies and Strategic Orientation on the Performance of Small and Medium-Sized Enterprises (SMEs) in Iran
Abstract: The purpose of this study is to examine the impact of government policies and macroeconomic strategic orientation on the performance of small and medium-sized enterprises (SMEs) in Iran. Given the crucial role of SMEs in employment generation and economic growth, this research investigates how government credit and tax policies, as well as broader macroeconomic orientation, influence key performance indicators of firms. Data for this study were obtained from the statistical databases of the Ministry of Industry, Mine and Trade, the Central Bank of the Islamic Republic of Iran, and a survey of 432 active SMEs across the provinces of Tehran, Isfahan, Fars, and Khorasan Razavi for the period 2011–2023. The methodological framework is based on panel data regression models with fixed and random effects, and the Generalized Method of Moments (GMM) was employed to address potential endogeneity concerns. The four main hypotheses propose that: (1) Facilitating government credit policies positively and significantly affect SME sales growth. (2) Tax incentives enhance firm profitability and investment. (3) Expansionary macroeconomic orientation increases SME employment. (4) Economic instability and abrupt policy shifts negatively influence financial performance. The estimation results indicate that credit policies and tax incentives exert positive and significant effects on sales growth (β = 0.27, p < 0.01) and profitability (β = 0.21, p < 0.05), respectively. Conversely, economic volatility and sudden policy changes have a substantial negative impact on overall firm performance (β = −0.33, p < 0.01). Moreover, expansionary macroeconomic orientation is associated with higher levels of SME employment (β = 0.18, p < 0.05).Overall, the findings emphasize that policy stability, improved access to financial resources, and tax system reform are essential for enhancing SME performance in Iran. Therefore, policymakers should prioritize long-term strategies, reduce administrative barriers, and strengthen financial infrastructure.
Enhancing Brain Tumor Classification Using AdaDensenet
Early identification is critical since brain tumors harm over 300,000 people annually and cause over 250,000 deaths. Although models such as VGG16, ResNet50, InceptionNetV3, and DenseNet201 exhibit potential, they struggle with dataset bias and flexibility. With global average pooling, transfer learning, sophisticated data augmentation, and attention techniques to improve flexibility for practical application, our suggested model, AdaDensenet, expands upon DenseNet201. AdaDensenet achieved 98.86% accuracy in a complete examination of 7,023 MRI scans from Figshare, SARTAJ, and Br35H (identified as pituitary, glioma, meningioma, or tumor). In addition to accuracy, the model achieves classwise AUC-ROC values of 1.00 and macro and weighted precision, recall, and F1-score of 0.99, demonstrating outstanding discrimination and balanced performance across tumor types. These results position AdaDensenet as a strong candidate for real-time clinical decision support systems.
Advancing Local Agritourism Programs in Achieving New Normal Goals: A Sustainable Marketing Strategy Approach from the University of Baguio- Sihtm
This study examines sustainable marketing strategies to enhance agritourism development in Barangay Alapang, La Trinidad, Benguet. Grounded in three UN Sustainable Development Goals—Decent Work and Economic Growth (SDG 8), Sustainable Cities and Communities (SDG 11), and Partnerships for the Goals (SDG 17)—it identifies effective marketing practices, analyzes agritourism’s economic and community roles during the pandemic, highlights challenges, and proposes recommendations to support local stakeholders, including the School of International Hospitality and Tourism Management (SIHTM) at the University of Baguio. Using mixed methods, the study surveyed 118 local farmers and conducted interviews and focus group discussions with government officials, along with policy analysis. It aimed to explore how sustainable marketing strategies can be applied to strengthen local agritourism initiatives, particularly within the SIHTM program at the University of Baguio. Agritourism promotion primarily depends on digital marketing via platforms such as Facebook and Instagram, with 79% of farmers aware of these strategies, reflecting pandemic-era global tourism trends favoring local markets and online engagement. Challenges include prioritizing short-term financial gain over long-term sustainability (noted by 78% of respondents) and limited use of creative tools such as virtual tours. Agritourism contributes to livelihoods by generating income, employment, and infrastructure development, yet cultural preservation remains underrepresented. The community has adapted through online sales but continues to face constraints such as limited budgets, inadequate infrastructure, regulatory barriers, and a lack of collaboration. Recommendations include adopting integrated marketing approaches, providing sustainable tourism training, strengthening stakeholder partnerships, and preserving cultural heritage preservation.
Big Data Analytics Integration in Accounting Education: A Critical Review into Current Progress, Challenges and Frameworks
The increasing adoption of big data analytics (BDA) in the finance industry has transformed the role of accountants, requiring them to develop data-driven decision-making skills. This study examines the integration of big data analytics into undergraduate accounting education to assess its alignment with industry expectations and identify areas for improvement. A critical analysis of existing literature highlights key competencies expected from graduates and academic constraints hinder effective integration. This study also reviews existing frameworks suggested as part of phased approaches in incorporating big data analytics within accounting education. Through this analysis, the study identifies the future direction of the research by examining the current gap that exists in academia and industry expectations.
An AI-Powered Conversational Agent for Natural Disaster Management
This work investigates the effectiveness of lightweight large language models (LLMs) in supporting the design of specialized, domain-restricted chatbots. The study focuses on compact off-the-shelf models, specifically Phi-3, LLaMA 3-8B, and Mistral 7B, evaluating their performance in maintaining topic relevance, coherence, and overall quality under controlled experimental conditions. To enforce domain specificity, each model is guided using a carefully designed system prompt that instructs it to respond exclusively about natural disasters. The evaluation is conducted through a multi-dimensional approach involving human judgments, benchmark testing, and automated evaluation using GPT-4. A custom web-based application was developed to enable users to input questions, view side-by-side responses from two models, and rate the outputs based on helpfulness, relevance, and clarity. In addition to this interactive human evaluation, models are assessed on standard benchmarks such as Massive Multitask Language Understanding (MMLU), GSM8K, and MT-Bench, and are also subjected to pairwise preference evaluation via GPT-4. Results indicate that even without fine-tuning, lightweight models can effectively handle domain-specific conversations when guided by system prompts. The study provides practical insights into selecting the most appropriate model for building focused, efficient chatbots in resource-constrained environments.
Effect of AI Usage on Learning, Creativity, and Innovation: The Role of Mediating Self-Efficacy and Moderating Task Complexity
The impact of artificial intelligence usage (AIU) on learning, creativity, and innovation (LCI) is a pressing concern in the digital transformation era, as it significantly alters educational systems. These insights are crucial for enhancing learning systems and fostering students’ creativity and innovation. This study examines the factors by which AI usage (AIU) affects students’ LCI and puts forward a comprehensive conceptual framework that incorporates perspectives from both students and instructors, which were validated by Social Cognitive Theory (SCT) and Cognitive Load Theory (CLT). To evaluate the hypotheses, 638 students from Bangladeshi public universities participated in a questionnaire survey conducted over nearly 5 months, along with a hierarchical regression analysis. Self-efficacy (SE) plays a significant mediating role between AI usage and LCI. Moreover, the association between AIU and SE is positively moderated by task complexity (TC), which promotes LCI. So, a mechanism of moderated mediation was found. The results of the study not only contribute to the theoretical understanding of how AI use affects students’ LCI but also provide instructors and students with valuable guidance to utilize AI to promote LCI during the period of digital transformation.
The Organizational Factors Influencing the Adotion of Blockchain Technology in Hospitals in Jordan
Blockchain technology has emerged as a leading trend in the current information technology era. Although it offers potential benefits across various sectors, the healthcare industry has been slower to embrace this innovation. This study seeks to examine the organizational factors that could drive the adoption of blockchain technology in hospitals, particularly within the private sector. Employing a quantitative research approach, the investigation utilized questionnaires to assess the intentions of decision-makers regarding blockchain adoption in private hospitals in Jordan. Data analysis revealed that several factors significantly influence the intention to implement blockchain technology, including top management backing, organizational readiness, innovative tendencies, and corporate culture. However, previous IT experience was not found to have a substantial impact on the decision to adopt blockchain technology. The findings of this study provide valuable implications for hospital decision-makers and the broader healthcare sector.
Modernizing Data Infrastructure: How AI and ML are Transforming SQL and NoSQL Usage in Distributed Manufacturing
In the era of Industry 4.0, manufacturing systems are increasingly adopting distributed architectures that demand agile, scalable, and intelligent data infrastructures. Traditional SQL and emerging NoSQL databases are being reshaped by the integration of Artificial Intelligence (AI) and Machine Learning (ML) to optimize data storage, query efficiency, and real-time decision-making. This paper explores how AI and ML are transforming the design, deployment, and operation of SQL and NoSQL systems in distributed manufacturing environments. We investigate architectural evolutions, automation of query tuning, predictive indexing, anomaly detection in sensor data, and adaptive data partitioning. Through a detailed review and technical study, we highlight practical frameworks and implementations that bridge data systems with intelligent agents. Real world case studies illustrate successful deployments in smart factories, emphasizing the impact of intelligent data systems on productivity and agility. Our findings provide insights into future trends, challenges, and opportunities in modernizing data infrastructure using intelligent technologies. The SQL and NoSQL systems are being revolutionized by AI and ML in manufacturing to enhance data handling, query efficiency, and decision-making. SQL queries and indexing are optimized by AI, whereas NoSQL has AI-based partitioning and schema management. AI/ML helps with predictive maintenance, identifies anomalies, and supports automation. In this paper, we will discuss the use of AI/ML to modernize data infrastructure with Industry 4.0 in mind, smart factory examples, and comment on the future of the topic.
Simplifying Payroll Balance Conversions in Payroll Systems Implementation through the Use of Generative AI
The research examines the application of Generative AI to convert payroll balances when implementing payroll systems. The research paper exemplifies the way AI can forecast and equalise payroll balances, cutting the difference and increasing effectiveness to a great extent. The study puts forth AI-controlled processes against conventional manual processes and can identify how much Gen-AI will contribute to improved accuracy and less conversion time, which might be useful in the future in relation to payroll system implementation.
Ranking and Relevance Algorithms in E-Commerce: Impact on User Experience and Business Outcomes
This article examines the intricate domain of merchandise sequencing algorithms within commercial digital environments, analyzing sophisticated computational frameworks determine product presentation sequences during consumer searches. Beginning with the fundamental ideas behind contemporary sequencing architectures, the conversation moves forward with a thorough examination of algorithmic approaches to consumer intention recognition, such as classification schemes for search behavior and methods for query comprehension. Indicator assessment mechanisms are also looked at, contrasting with traditional sequencing signals and examining differences in temporal effectiveness across merchandise categories as well as algorithmic transparency and ethical considerations. Comparisons between supervised and reinforcement learning approaches and integration strategies for multiple-model collaborative frameworks are discussed in detail, as are technical requirements for millisecond-level determination processes. The article concludes through business performance measurement assessment, connecting sequencing effectiveness alongside commercial outcomes and extended consumer relationships, while identifying emerging directions regarding intelligence-enhanced sequencing evaluation. Throughout this discussion, technical implementation specifics are connected to strategic commercial implications, positioning sequencing systems as fundamental determinants regarding digital retail success within competitive marketplaces.
Predictive Hybrid Autoscaling for Cloud Workloads: A Machine Learning Approach to Vertical and Horizontal Resource Optimization on AWS EC2
Cloud infrastructure management faces persistent challenges in balancing service reliability with cost efficiency as workload demands fluctuate unpredictably. Traditional autoscaling approaches rely on reactive, threshold-based triggers that respond only after performance degradation begins, whereas AWS native solutions provide exclusive horizontal scaling capabilities. This article introduces a predictive hybrid autoscaling framework that leverages machine learning forecasting models to anticipate resource demands and intelligently orchestrates both vertical instance resizing and horizontal capacity adjustments. Three time-series prediction approaches—ARIMA, Long Short-Term Memory networks, and Facebook Prophet—are evaluated in a comparative study across diverse workload patterns, including periodic traffic cycles, sudden demand spikes, gradual growth trends, and unpredictable variations. The article testbed employs multiple AWS EC2 instance families under realistic application scenarios, measuring Service Level Objective compliance, availability metrics, resource utilization efficiency, and total infrastructure costs. Results demonstrate that predictive hybrid scaling substantially improves reliability while reducing operational expenses compared to reactive autoscaling and static over-provisioning strategies. LSTM networks excel at capturing complex non-linear demand patterns, while Prophet proves superior for seasonal workloads. The article integrates with standard AWS services through open-source implementations, providing cloud operators with practical tools for proactive capacity management. This article bridges predictive analytics with site reliability engineering practices, offering systematic approaches to cost-reliability optimization in dynamic cloud environments.
Agentic AI in Procure-to-Pay: Opportunities, Challenges, and a Roadmap for Autonomous Procurement Systems
The procure-to-pay cycle remains critical yet burdened by inefficiencies, manual interventions, and persistent exceptions that traditional automation struggles to address. Agentic AI, autonomous, goal-oriented systems capable of planning, reasoning, coordinating multiple agents, and adapting to changing conditions, represent a transformative evolution beyond rules-based automation and robotic process automation. This work presents a modular architecture for embedding AI agents within P2P workflows and identifies concrete applications, including intelligent invoice matching, anomaly detection, contract compliance monitoring, autonomous negotiation, payment optimization, and supplier risk assessment. Benefits span reduced cycle times, lower operational costs, enhanced controls, and strategic reallocation of procurement talent toward value-adding activities. However, deployment confronts significant challenges: data quality requirements, explainability and trust deficits, human-agent handoff complexity, governance frameworks, model drift, organizational resistance, and security vulnerabilities. A phased adoption roadmap progressing from pilot projects to full autonomy provides a practical implementation pathway. AI agents should be positioned not as replacements but as intelligent collaborators that augment human judgment, enabling procurement organizations to achieve operational excellence while maintaining strategic oversight and accountability.
The Impact of the Healthcare Industry on AI Evolution
Artificial Intelligence is transforming industries worldwide, but few sectors have influenced its evolution as profoundly as healthcare. This article explores the reciprocal relationship between healthcare and AI evolution, highlighting how healthcare's data-intensive environment and stringent requirements for accuracy, privacy, and ethical implementation have shaped AI development trajectories. From early rule-based systems to sophisticated deep learning architectures and emerging generative AI applications, healthcare has served as both a catalyst and a proving ground for technological innovation. The article examines technical breakthroughs in explainable AI, federated learning, and privacy-preserving computation directly catalyzed by healthcare demands, alongside transformative real-world applications in medical imaging, pandemic response, operational efficiency, and telemedicine. It further analyzes ethical considerations, regulatory frameworks, economic implications, and human-AI collaboration models emerging from healthcare implementation, concluding with an exploration of future frontiers, including personalized medicine ecosystems, interoperability standards, preventive analytics, sustainability impacts, and emerging research priorities that continue healthcare's influence on broader AI evolution.
Enterprise SaaS Evolution: From Monolithic to Cloud-Native to Agentic Platforms
Enterprise Software-as-a-Service (SaaS) has evolved through three key stages: monolithic multi-tenant applications, cloud-native microservices, and now, the latest generation of “agentic” platforms powered by large language models (LLMs), retrieval-augmented generation (RAG), and autonomous agents. While there has been plenty of discussion around microservices, observability techniques, and frameworks for generative AI, not much has been said about how these changes tie into organizational design, business value, and governance issues with an enterprise SaaS focus. This paper presents a targeted narrative synthesis of Enterprise SaaS evolution from the late 1990s through the early 2020s and outlines emerging directions for agentic platforms. It examines the defining technologies and structural patterns of each architectural phase and analyzes their business and operational implications, including delivery performance, scalability, innovation capacity, and customer experience. The paper further introduces an outcome-oriented evaluation framework for enterprise generative AI platforms that links technical performance metrics to strategic and organizational outcomes and anchors model quality assessment in emerging standards and benchmarks. In addition, it synthesizes key implementation challenges – such as technical debt, organizational capability gaps, integration complexity, and governance risk – and reviews mitigation strategies observed in recent practice and literature. Drawing on surveys, standards, and industry reports, this study concludes with a research agenda focusing on long-term and empirical evaluation of generative AI adoption, the co-relation between technical architecture and organizational form, and governance models for increasingly autonomous systems. The findings provide a structured reference and framework for researchers and practitioners designing, operating, and governing next-generation Enterprise SaaS platforms.
Tracking Integration Points in Multi-ERP Supply Chain Environments: A Case Study of Centralized Monitoring Solutions
This article examines the implementation of centralized tracking architectures in multi-ERP supply chain environments to address critical visibility challenges across integrated systems. Enterprise retail operations face significant monitoring limitations when transactions traverse multiple specialized applications, creating barriers to effective troubleshooting and customer service. The proposed solution leverages open telemetry standards and correlation identifier methodologies to establish consistent transaction tracking across system boundaries. Implementation considerations include interface design for diverse stakeholders, dashboard development for real-time monitoring, resilience metrics for quantifying system reliability, and integration approaches for existing ERP ecosystems. The centralized tracking architecture delivers substantial business impact through revenue preservation, operational efficiency improvements, and enhanced management visibility. While current implementations demonstrate significant value, limitations in predictive capabilities and recovery automation present opportunities for continued evolution. Future directions include advanced analytical techniques for proactive identification of emerging failure patterns before customer impact occurs. The article provides a comprehensive framework for organizations seeking to enhance transaction visibility across complex supply chain environments.
Adaptive Scaling of Asynchronous APIs in Multi-Cloud Financial Platforms
Current financial systems are faced with unparalleled challenges in managing computational infrastructure throughout heterogeneous cloud environments at the same time as sustaining stringent overall performance necessities and regulatory compliance requirements. Asynchronous application programming interfaces represent essential architectural additives that permit economic institutions to decouple request processing from response transport, taking into account enhanced resource usage and machine resilience in distributed computing environments. Adaptive scaling mechanisms leverage event-driven architectures, box orchestration platforms, and sophisticated monitoring infrastructure to put into effect automated decision-making tactics governing aid provisioning sports with out manual intervention. The mixing of artificial intelligence and gadget learning techniques in scaling strategies fosters predictive talents expecting call for fluctuations based on historical patterns, temporal characteristics, and outside market indicators. Multi-cloud deployment techniques introduce architectural complexities related to move-company networking, information sovereignty necessities, and seller-specific service integration styles that necessitate cautious attention when distributing workloads across heterogeneous infrastructure companies. Box orchestration structures offer the important abstraction layers required to normalize workload deployment, service discovery, and traffic management across disparate cloud environments whilst remaining like-minded with issuer-specific optimization opportunities. The convergence of asynchronous conversation patterns, predictive scaling algorithms, and multi-cloud resource allocation strategies positions economic establishments to realize levels of operational performance that can not be achieved through greater conventional architectural paradigms, even while ensuring transaction integrity, regulatory compliance, and price optimization goals crucial to competitive gain in dynamic financial services markets.
Sustainable Cloud Computing Practices for Insurance Data Centers: Reducing Carbon Footprint While Scaling Digital Transformation
The trajectory of digital transformation in the insurance sector intersects critically with environmental sustainability imperatives as organizations navigate the transition away from legacy on-premises infrastructure toward cloud-based computing environments. Traditional data center operations are an environmentally burdensome resource utilization model due to inefficient resource use, high energy demands for computational needs and cooling, and significant water requirements, contributing disproportionately to organizational carbon footprints. Conversely, cloud computing architectures have the transformative property of reducing environmental impact, being hyperscale efficient with the capability of integrating renewable sources, and the ability to manage resources dynamically. Microservices decompositions, serverless and intelligent auto-scaling protocols can be used to achieve strategic migration policies that allow resource assignments that are far finer-grained to actual workload demands, and therefore are far less susceptible to the energy waste associated with fixed capacity infrastructure. Machine learning technologies and artificial intelligence techniques break down ongoing ethics of the environment through the rapidity of continuous optimization of environmental work by anticipatory workforce management, automated resource lives, and the creation of carbon-centric job variations matching computational tasks with periods of best grid carbon utility. Extensive measurement systems comprise Power Usage Effectiveness ratios, multiscope carbon accounting, and real-time monitoring dashboards, which show transparency, which is so important in making an informed decision and accountability. An association of sustainability measures with the corporate governance frameworks pertains to climate-responsible strategic technology determination as well as fulfilling the growing regulatory, shareholder, and customer requirements of climate responsibility.
Training Models on Sparse Data and Labels in Recommendation Systems: Overcoming Supervision and Feedback Limitations
Large-scale recommendation systems consistently grapple with extreme data and label sparsity, where the vast majority of potential user-item interactions remain unobserved and unlabeled. This fundamental constraint severely limits model training, introducing biased gradients, degrading personalization quality, and amplifying popularity bias toward frequently interacted items while neglecting long-tail content. The challenge intensifies as platforms scale to serve diverse user populations across massive item catalogs, where traditional supervised learning approaches prove inadequate. This article provides a comprehensive examination of contemporary strategies designed to train robust recommendation models under severe supervision constraints. The article encompasses self-supervised learning techniques that leverage unlabeled interaction data through contrastive and pretext objectives, implicit feedback modeling approaches that extract weak supervision from behavioral signals while mitigating inherent biases, and transfer learning frameworks that incorporate knowledge from auxiliary tasks and cross-domain sources. Critical analysis reveals the practical trade-offs between sample efficiency, computational scalability, and recommendation fidelity across different sparsity regimes. By synthesizing theoretical foundations with real-world deployment considerations, this work offers actionable guidance for researchers and practitioners seeking to improve recommendation accuracy, enhance cold-start robustness, and expand long-tail coverage in production environments where explicit feedback remains scarce yet personalization expectations continue rising. The article bridges academic innovation with industrial implementation realities facing modern recommendation systems.
The Enduring Relevance of SOAP in Enterprise Integration Architecture
The Simple Object Access Protocol maintains a strong position in enterprise integration despite REST's widespread adoption. Mission-critical systems in financial services, healthcare, and government rely on SOAP's comprehensive security frameworks. WS-Security leverages features such as message-level encryption and digital signatures that meet regulatory requirements and, therefore, are regarded as better security than transport-layer security on its own. In addition, WS-ReliableMessaging and WS-AtomicTransaction provide assurances regarding message delivery and coordination of distributed transactions. These features become more necessary when situations arise where data loss or any difference in state cannot be tolerated. Web Services Description Language enables formal contracts for centralized governance in complex organizations. Strongly-typed contracts prevent integration errors and enable automated code generation. Legacy systems in mainframes and ERP platforms have deep SOAP infrastructure investments. The choice between SOAP and REST depends on Quality of Service requirements, not technology trends. Consumer applications benefit from REST's simplicity. Core enterprise systems need the reliability and security that SOAP provides through decades of standardization.
Human-Centered AI for Accessibility: Designing Transparent Intelligent Systems for the Disabled Workforce
The given quantitative study investigates the way human-oriented and accessible AI-enhanced features aid disabled workers at work. Based on survey information on 120 respondents, the study quantifies four key areas including trust and understanding due to explainability, task efficiency due to multimodal interaction, perceived inclusion and fairness, and organizational readiness. The findings are that 78% of the users have more trust in AI when explanations are available, the task time can be improved up to 32 mode by multimodal features, and that available AI devices make individuals feel included with 68% of respondents. Nevertheless, there is a lack of organizational support. The report points out that readily available AI enhances equity, efficiency, and independence of the user.
Generative Artificial Intelligence in IT Operations: Architectural Transformation from Reactive to Autonomous Support Systems
This article examines the paradigmatic shift catalyzed by generative artificial intelligence within information technology operations across enterprise environments. As organizations confront exponentially increasing complexity in their technological infrastructure alongside heightened demands for near-instantaneous issue resolution, traditional reactive support paradigms have proven fundamentally inadequate [1]. This research presents a comprehensive architectural framework for autonomous IT systems that can detect, diagnose, and remediate technical issues with minimal human intervention [4]. The proposed framework outlines a progressive evolution from rudimentary rule-based automation to advanced intelligent systems, which demonstrate the capacity for experiential learning from historical incident data and autonomous decision-making [5]. Empirical implementation data demonstrates that properly architected AI systems can autonomously resolve approximately 40% of common IT incidents while simultaneously reducing mean-time-to-resolution by up to 70% [8]. The research addresses intricate technical integration challenges, organizational resistance factors, and provides a structured implementation methodology with defined maturity stages [7]. Through transformation from reactive to autonomous support paradigms, organizations can achieve substantial operational cost reductions while concurrently enhancing service reliability and strategically redirecting technical expertise toward innovation rather than maintenance operations [10]. This architectural transformation represents a fundamental reconceptualization of enterprise technology management that transcends incremental improvement to deliver transformative operational capabilities.
Reducing Supply Chain Bottlenecks Using Generative AI and Industry 4.0 Technologies
Supply-chain bottlenecks are chronic operational sources of constraint that inhibit throughput and reliability in worldwide manufacturing and distribution. The traditional systems of management are typified by reacting processes of planning and too much depend on the past trends of data and a lack of visibility and predictive intelligence. The integration of Generative AI with Industry 4.0 cyber-physical infrastructure addresses fundamental gaps in constraint detection, scenario generation, and autonomous response execution. Digital-twin models generate simulations of physical assets, and can be continuously simulated and optimised. Constraining based algorithms like mixed integer programming, reinforcement learning and genetic optimisation are algorithm models that detect bottlenecks and formulate solutions that are doable under competing options. The autonomous operations frameworks achieve the light out supply-chains using robotics, computer vision and cyber-physical coordination structures. The unstructured data on suppliers communications, financial news, weather projections, and regulatory announcements is processed by proactive risk-mitigation capabilities that identify the occurrence of disruptions in advance before they have the opportunity to impact operations. The enterprise integration architectures indicate the seamless integration of generative AI reasoning systems with operational platforms by connecting between them via API-based interfaces, event-sourced messaging, and the coordination of microservices. Domain application demonstrates the visible reduction of bottlenecks in manufacturing operations, logistics, procurement process and retail distribution channel. Such convergence represents a radical change of reactive problem-solving in favor of proactive constraint elimination via the use of cognitive reasoning and infrastructure interconnection. The supply-chains in healthcare during a pandemic and the problem of sustainable manufacturing are the examples of the efficacy of the combined digital technologies to create operational resilience and adaptive capacity.
AI-Powered Dynamic Flow Steering in 5G Networks: RAN Analytics Integration with UPF for Enhanced QoS Management
This article presents an integrated framework for AI-powered dynamic flow steering in 5G networks that leverages real-time Radio Access Network (RAN) analytics to optimize traffic management through the User Plane Function (UPF). The technical architecture establishes seamless integration between Network Data Analytics Function (NWDAF) components and distributed UPF instances, enabling intelligent routing decisions based on current radio conditions. Machine learning algorithms, particularly reinforcement learning models, provide adaptive optimization capabilities that continuously refine steering policies through closed-loop feedback mechanisms. Performance validation across diverse deployment scenarios demonstrates that this approach maintains superior throughput and latency metrics compared to traditional management techniques, especially in challenging high-density environments and industrial IoT applications. The integration of predictive congestion management with computational offloading strategies creates a comprehensive optimization framework that addresses both network efficiency and application performance, establishing a foundation for future-ready, user-centric network experiences in evolving 5G architectures.
MuleSoft's Role in Advancing Sustainable Digital Infrastructure: An Enterprise Integration Perspective
The escalating demand for digital transformation across enterprises necessitates integration platforms that balance operational efficiency with environmental sustainability objectives. This article examines how API-led connectivity frameworks contribute to sustainable digital infrastructure development through a systematic evaluation of resource optimization patterns, cloud deployment strategies, and operational efficiency improvements. The three-layer API architecture demonstrates substantial benefits in reducing computational overhead through enhanced component reusability and modular design principles. Cloud-native deployment models leverage hyperscale data center efficiencies while implementing dynamic resource allocation that eliminates idle capacity waste. Comprehensive data governance capabilities streamline regulatory compliance processes while reducing manual intervention requirements across enterprise environments. Integration with legacy systems enables organizations to extend modern governance frameworks without requiring disruptive system replacements. Enterprise implementations across healthcare, financial services, and manufacturing sectors reveal consistent patterns of improved resource utilization, reduced energy consumption, and enhanced operational efficiency. The article establishes that strategic implementation of API-led connectivity represents a viable pathway for organizations pursuing digital transformation objectives within environmental sustainability constraints, offering measurable benefits across operational and environmental performance dimensions while supporting long-term decarbonization initiatives.
The Evolution of the Concept of Informal Markets in Algeria : An Urban Approach
Informal markets have long been a central feature of urban life in Algeria, serving as dynamic spaces of economic activity, social interaction, and cultural expression. This study examines the evolution of the concept of informal markets within the Algerian urban context, tracing their historical development, socio-economic significance, and spatial dynamics. Through a combination of literature review, case studies, and field observations, the research highlights how informal markets adapt to regulatory frameworks, urban planning policies, and socio-economic pressures. The study also explores the dual role of informal markets as both opportunities for livelihood generation and challenges to urban governance. Findings indicate that informal markets are not merely peripheral economic phenomena but integral components of urban resilience, social cohesion, and local identity formation. The study contributes to the broader understanding of urban informality and provides insights for policymakers and urban planners seeking to integrate informal market activities into sustainable urban development strategies.
An Overview of Electricity Tariffs and Practical Optimization Approaches for Energy Billing at the University of Tipaza, Algeria
This study focusses on increasing electrical energy utilisation to reduce energy costs, using the University of Tipaza as an example, which has seen significant increases in electricity expenditures. The main objective is to optimise energy use by analysing historical consumption data, which results in the employment of specific strategies such power analysis and tariff changes. The paper recommends strategic changes such tariff adjustments, limiting available power, and installing compensation devices in order to generate significant savings and align the energy cost with actual consumption. These programs demonstrate how energy management in institutional settings may be greatly improved, with benefits for the economy and the environment.
Research on Strategies for Enhancing Enterprise Supply Chain Resilience in the Context of Digital Transformation
This paper aims to explore how enterprises can effectively enhance their supply chain resilience through digital transformation against the dual backdrop of increasing global uncertainty and rapid development of digital technologies. First, it elucidates the core connotations of supply chain resilience and the key technological foundations of digital transformation. Second, it constructs a theoretical logic model of "technology empowerment-capability building-resilience performance," systematically analyzing how digital technologies such as big data, artificial intelligence, the Internet of Things, and blockchain enhance supply chain visibility, predictability, collaboration, and agility to address disruption risks and achieve rapid recovery. Furthermore, through theoretical deduction and case studies, a systematic strategic framework for enterprises to enhance supply chain resilience is proposed across four dimensions: strategic planning, process reengineering, technology integration, and organizational change. Finally, the managerial implications and future research directions of this study are outlined, providing theoretical reference and practical guidance for enterprises to build a resilient supply chain system in the new context.
Effect of Data Sources and DNI Effectiveness on CSP power plants Performances Assessment. Case Studies in Algeria.
This study presents a comparative assessment of high and medium resolution Direct Normal Irradiance (DNI) datasets, combined with real meteorological measurements and satellite-derived estimates, to evaluate their impact on the performance of Concentrated Solar Power (CSP) systems. The analysis focuses on four representative regions in Bechar, Elouad, Djelfa, and Biskra, and examines how the choice of DNI data source influences the resulting capacity factor (CF). The results show notable discrepancies across regions. In Bechar (DNI = 700 W/m²), a system with SM = 1.8 and 2 hours of TES generated 11.44 GWh/year with a CF of 6.7%. In Elouad (DNI = 750 W/m²), using SM = 1.2 and 2 hours of TES, annual production reached 7.4 GWh/year, corresponding to a CF of 4.2%. For Djelfa (DNI = 1050 W/m²), the same system with SM = 1.5 and 4 hours of TES achieved 18.45 GWh/year and a CF of 10.5%. Biskra exhibited the most striking contrast: with real DNI data (420 W/m²), SM = 1.3, and 6 hours of TES, the system delivered 71.51 GWh/year and a CF of 43.59%. When satellite-derived DNI was used instead, energy production increased to 75.7 GWh/year, raising the CF to 43.78%. These results highlight substantial performance variations driven by the choice of meteorological input data. The findings underscore the importance of selecting reliable DNI datasets for accurate CSP evaluation, ultimately supporting more effective system design, improved energy yield predictions, and the broader development of solar energy technologies in Algeria.