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
Infrastructure Architecture for Hybrid Legacy-Cloud Ecosystems: Balancing Tradition and Innovation
The problem that infrastructure architecture of hybrid legacy-cloud ecosystems overcomes is one of the most serious challenges that organizations within regulated industries face: how to both adopt cloud innovation and maintain the stability of the existing systems. This paper discusses architectural designs that facilitate smooth integration between the traditional and the modern platform with particular attention to the area of healthcare and financial services. The article provides a wide perspective of hybrid ecosystem design and implementation by focusing on interoperability frameworks, security, modernization routes, and new technologies. The tactical value of gradual change is realised when organizations aim at sustaining business continuity coupled with a gradual shift to cloud-based solutions that will promote flexibility and service provision. Hybrid models build bridges across technology generations through judicious architectural planning and governance, safeguarding precious and long-term investments and allowing innovation with the ability to adapt to the changing needs of customers.
AI-Driven Cybersecurity Engineering for Enterprise-Wide Cloud Asset Protection, Application Data Security, and Multi-Cloud Threat Intelligence Automation
The accelerated digital transformation of enterprise infrastructure has significantly altered the cybersecurity ecosystem, particularly as many organizations are realizing they have entered into multi-cloud ecosystems that leverage multiple cloud platforms and hybrid environments. This level of distributed architecture provides generally unmatched operational flexibility and scalability, but certainly creates serious security challenges that many legacy cybersecurity frameworks are unable to accommodate. The explosive growth of cloud-based assets and modern cyber threats requires a significant shift in cybersecurity frameworks to include intelligent, adaptive security engineering solutions. This article provides a holistic assessment of an AI-Enhanced Multi-Cloud Security Framework that addresses critical gaps in current enterprise cybersecurity maturity through integration of advanced application protection mechanisms, real-time threat intelligence and processing, and automated security orchestration into an adaptive defense architecture. Organizations can continuously discover assets and improve the efficiency of contextual risk assessment by applying artificial intelligence technologies to risk management methods. Organizations can also detect anomalies in real time while building automated compliance enforcement actions across the vast cloud environments. Automation of AI-based security methods into the DevSecOps process is immense, allowing organizations to conduct digital threat hunting, put in place automated incident responses, and leverage preemptive styles of data protection while continuing their controls. The solution sets the stage for autonomous, adaptable security architectures capable of protecting enterprise data and applications from existing threat vectors, as well as developing AI-influenced cyber attacks, representing a forward-thinking paradigm of the security engineering space within enterprise cybersecurity.
Agentic Cloud Infrastructure Management: The New Age of Development and Operations
Cloud infrastructure management is at a revolutionary crossroads where autonomous software agents are essentially redefining operational paradigms. Conventional infrastructure automation is based on procedural scripts necessitating explicit specification of all sequences of actions, involving heavy maintenance overhead and restraining flexibility to unexpected conditions. Agentic management systems provide goal-directed execution frameworks in which human engineers specify high-level goals while autonomous agents autonomously decide optimal implementation schemes. These smart systems utilize continuous learning features to monitor telemetry streams, identify patterns in performance, and create advanced behavior models for proactive intervention prior to insignificant deviations becoming service interruptions. Moving from reactive threshold-based monitoring to proactive self-healing mechanisms greatly decreases the number of incidents while improving recovery processes. Quality-of-service-based component choice, constraint-based resource placement, and adaptive autoscaling are key technological underpinnings for autonomous optimization across various operational axes. Human engineering jobs change proportionally, moving from tactical deployment towards strategic architecture definition, policy making, and governance framework design. Engineers have to develop skills in declarative specification languages, multi-objective optimization formulations, and mechanisms for validating agent behavior. Service-level agreement languages and service-oriented programming models offer crucial abstractions for describing operational intent to autonomous systems. The intersection of machine learning, multi-agent coordination protocols, and cloud-native architectures creates the technical foundation for infrastructure environments that are always evolving, self-optimizing, and ensuring resilience through smart autonomous decision-making in lieu of manual action.
Pathways to Becoming an OpenBMC Developer: Skills, Tools, and Community Integration
This discourse establishes methodical trajectories for engineers and technical specialists seeking competence in OpenBMC advancement. The text examines core technical abilities necessary for productive involvement, encompassing Linux kernel programming, Yocto construction framework proficiency, and interaction protocols fundamental for baseboard administration controllers. The manuscript elaborates configuration procedures for development settings, equipment simulation approaches, and troubleshooting methodologies, jointly facilitating productive firmware creation without necessitating tangible equipment access. The discussion investigates OpenBMC's component-based structure, including service arrangement, monitoring frameworks, and protocol compatibility, establishing an adaptable groundwork for platform-specific customization while preserving uniform administration interfaces. The exposition additionally considers community participation procedures, including contribution workflows, evaluation involvement, and guidance prospects supporting knowledge dissemination between veteran contributors and newcomers. Through organized explanation covering both technical prerequisites and community practices, the manuscript provides extensive progression planning supporting valuable contributions toward this essential open-source firmware environment.
Online Evaluation of Conversational Agents Using Machine-Learned Metrics
The article discusses the new paradigm of metrics learned by machines to be used in the assessment of conversational agents in a real-time setting. With voice assistants and chatbots playing an ever-larger role in human interactions with computers in many domains, the classical evaluation techniques are fatally limited in their range, precision, and dynamism. Manual feedback mechanisms are primarily retrospective, introducing significant delays between defect identification and corrective action. Machine-learned evaluation methods, in contrast, utilize computational models that have been trained over historical interactions to automatically evaluate the satisfaction of users solely based on the content of a dialogue, conversation metadata, and behavioral cues. Hierarchical systems process information at multiple temporal resolutions. This enables both detailed and summary-level evaluations of dialogue quality. Applied in real time, these measures allow conversational systems to adapt dynamically during interactions. Remediation strategies may include response adjustment or escalation to human operators. Empirical studies explain that such approaches have a stronger association with user satisfaction, spot deficiencies in quality earlier, extrapolate to other areas of application, and continue to enhance their performance through online education. However, these benefits are accompanied by major challenges such as ensuring explainability, adapting to cultural contexts, evaluating multimodal interactions, modeling long-term engagement, preserving user privacy, and establishing standardized evaluation frameworks.
Detecting Financial Signals from Alternative Data: A Hybrid GenAI and Machine Learning Approach
Financial markets increasingly rely on signals hidden within unstructured data sources. This article introduces a hybrid system combining Large Language Models with traditional machine learning to extract, interpret, and validate financial indicators from alternative data. The architecture uncovers nascent financial indicators embedded within corporate discourse, digital platforms, and unconventional resources via four interconnected frameworks: comprehensive normalization of heterogeneous data streams; contextual examination of narrative elements; mathematical validation of predictive correlations; and specialized agent-based analytical processing. Implementation domains encompass portfolio optimization, enterprise financial operations, and compliance oversight, facilitating anticipatory strategic positioning through the identification of market movements before conventional visibility. Performance assessments reveal extended predictive horizons, refined signal accuracy metrics, and substantially improved interpretative capabilities relative to traditional analytical approaches currently deployed across financial sectors.
The Pharmaceutical IT Ecosystem for Safe and Secure Drug Manufacturing and Distribution
The pharmaceutical manufacturing landscape has made significant changes with the integration of sophisticated information technology ecosystems, especially in the context of increasing dependence on contract manufacturing organizations (upstream partners) and distribution networks, including third-party logistics providers and distributors (downstream partners). This IT framework facilitates important tasks, including spontaneous information exchange, data integrity maintenance, regulatory compliance, and efficient supply chain management within organizational boundaries. CMOs showcase the digitally mature drug companies taking advantage of integrated platforms for cooperation, displaying marked improvements in quality metrics, batch release timelines, and regulatory results. The ecosystem consists of several interacting components, including Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems, Quality Management Systems, serialization technologies, and track-and-trace solutions, each contributing to increased operational performance and patient safety. The implementation of risk-based verification approaches within these environments improves adequate efficiency, ensuring regulatory compliance. As the manufacturing system is rapidly connected, it has become necessary to continuously monitor both the network partitions, the enhanced advanced authentication system, and to protect both construction operations and intellectual property. This interconnected pharmaceutical IT ecosystem serves as the foundation of safe, obedient, and efficient drug building within rapidly complex global supply chains.
Efficient Lightweight PCA-KNN-Based Model for Annual Dengue Risk Mapping in Urban Indonesia
Introduction: Dengue Hemorrhagic Fever (DHF) remains a critical public health concern in tropical urban environments, particularly those constrained by limited resources. As part of Southeast Asia’s endemic dengue belt, Indonesia experiences recurrent seasonal outbreaks requiring timely, scalable, and data-driven risk stratification strategies. Objectives: This study aims to develop an efficient and interpretable machine learning framework for annual dengue risk mapping in urban Indonesia. The model enables binary classification of outbreak severity, supporting early warning systems and guiding public health interventions. Methods: A hybrid approach was implemented, combining Principal Component Analysis (PCA) for dimensionality reduction and K-Nearest Neighbor (KNN) for binary classification. Semarang City, characterized by persistent transmission and pronounced interannual variability, was selected as the empirical case study. The dataset included morbidity and mortality records from 2020 to 2025, enriched with epidemiological, climatological, and demographic indicators. PCA was applied to extract the most informative components, followed by KNN to classify each year into high-risk or low-risk categories. Model performance was evaluated using Leave-One-Out Cross Validation (LOOCV). Results: The PCA-KNN model achieved an overall classification accuracy of 83.33%, 66.7% precision, 100% recall, and an F1-score of 80%, demonstrating robustness across temporal variations. Its lightweight architecture and minimal computational demands underscore its suitability for deployment in resource-constrained settings. Conclusions: This study presents a replicable and pragmatic annual dengue risk stratification framework. The model’s computational efficiency, interpretability, and operational relevance highlight its potential utility in epidemic preparedness, vector control planning, and public health surveillance, particularly in urban regions with limited infrastructure and high disease burden.
Efficiency of Kolmogorov-Arnold Networks for Nonlinear Systems Identification
The identification of nonlinear systems remains a critical challenge in control engineering, requiring models that balance accuracy. This study explores Kolmogorov-Arnold Networks (KAN) as a novel solution, leveraging their inherent ability to decompose multivariate functions into hierarchical univariate operations, a property aligned with the Kolmogorov-Arnold representation theorem. KANs excel in capturing complex nonlinearities through adaptive activation functions and sparse subnetwork compositions, offering superior interpretability and parameter efficiency compared to conventional architectures. We demonstrate KAN's efficacy by identifying three nonlinear systems spanning low to high complexity and nonlinear dynamics. The identified models are further validated in Model Reference Adaptive Control (MRAC), showcasing closed-loop stability and tracking performance. Experimental results reveal that KANs achieve lower identification error and lower parameters than MLP and LSTM, while maintaining comparable computational overhead.
Measuring Information Quality on E-Government Websites: Evidence from Saudi Arabia
This study investigated user perceptions of e-government information quality in the context of Saudi Arabia and examined the effect of information quality on user satisfaction. It adopted the Wang and Strong model of data quality to identify the most influential dimensions on e-government websites. The study also assessed the current status of Saudi e-government websites. Based on survey data collected from 208 respondents in Saudi Arabia, the findings indicate that the overall status of e-government websites is satisfactory and that the information provided is perceived as valuable. The results show that information quality has a positive impact on user satisfaction. Among the dimensions, intrinsic and accessibility data quality have a highly significant effect on overall information quality, whereas contextual and representational data quality dimensions do not exhibit a statistically significant impact.
Enhanced Micro-Expression Recognition Using MBCC-CNN Architecture
This study presents an innovative approach for micro-expression recognition based on a multi-branch cross-connected convolutional neural network (MBCC-CNN). Unlike conventional single-stream architectures, the proposed framework effectively captures and enhances image features through a multi-branch design integrated with residual connections, Network-in-Network modules, and hierarchical (tree-based) structures. A cross-linked shortcut mechanism is incorporated to merge outputs from different convolutional layers, facilitating smoother inter-branch information flow, expanding the receptive field, and strengthening feature extraction. This design enables efficient fusion of branch-level features, overcoming the limitations of inadequate learning in isolated branches and significantly improving recognition accuracy. Experimental evaluations conducted on benchmark datasets—SAMM, CASME I, CASME II, and CAS(ME)²—achieved recognition rates of 99.89%, 98.73%, 100.00%, and 99.58%, respectively. Comparative analysis with state-of-the-art approaches demonstrates that the proposed MBCC-CNN architecture achieves superior recognition performance and enhanced robustness in micro-expression analysis.
Building an ML Portfolio That Gets Noticed: Beyond GitHub Repositories
This article examines the evolution of machine learning portfolios beyond traditional code repositories, highlighting strategies for effectively showcasing problem-solving competencies that resonate with industry employers. The article looks at methods for recording decision intelligence throughout the development lifecycle, strategies for balancing portfolio breadth and depth across career stages, frameworks for choosing strategic projects that show business value, and multifaceted portfolio presentation techniques. By integrating visualization, storytelling, and communication elements alongside technical implementations, practitioners can create portfolios that demonstrate not just coding ability but the full spectrum of competencies required for successful ML implementation in organizational contexts. The article provides practical guidance for ML practitioners seeking to create portfolios that effectively communicate their capacity to deliver business value through thoughtful problem formulation, rigorous methodology, and effective communication—the true differentiators in today's competitive ML job market.
Development of a Portable Water Quality Monitoring System Integrating pH, TDS Sensors, and CNN-Based Visual Analysis
Access to safe, clean drinking water is still a global challenge. Currently, over 2.2 billion people are using water sources that are not safe for consumption. Traditional water quality testing is labor intensive and time-consuming, limiting access. This study focuses on the design and development of a smart, portable prototype to assess tap water quality in real-time and to recommend filters/filtration methods. Using pH and TDS sensors along with a Convolutional Neural Network (CNN) on a Raspberry Pi (RP), the prototype classifies water samples into clean, colored, oily, saline, and turbid categories. The water sample’s sensor readings and images are used to make suggestions about the suitable methods for filtration through an intuitive user interface. The classification accuracy of the CNN model is 75.53% on the test dataset and the sensors proved to be highly reliable against commercial meters. The entire inference pipeline takes less than three seconds, including image capture, classification, and recommendation. The proposed solution is a viable and cost-effective solution for monitoring water quality in both domestic and industrial settings; providing real-time support for decision-making regarding filtration methods while improving public health outcomes. Future work could include increasing CNN dataset diversity, incorporating additional sensors, adaptive learning mechanisms from user feedback, and adding cloud capabilities in the proposed system.
Top 10 Pitfalls in Cloud Migration and How to Avoid Them
Cloud migration offers organizations transformative potential for agility, scalability, and innovation, yet the journey is fraught with challenges that can derail even well-planned initiatives. Organizations frequently encounter pitfalls, including underestimated legacy system complexity, inadequate cost modeling, ineffective migration strategies, neglected security considerations, and skills gaps. Additional obstacles arise from poor data migration planning, insufficient governance frameworks, overlooked performance considerations, inadequate change management, and missed opportunities to leverage cloud-native capabilities. This article examines these common pitfalls based on empirical evidence and provides strategic approaches to navigate these obstacles. By understanding these challenges and implementing proactive strategies, organizations can accelerate their cloud transformation while minimizing disruption and maximizing business value.
Automating Malicious Code Detection Through Ml and Behavioral Analysis
Malicious programs that intentionally carry out damaging actions are known as malware. Over the past ten years, there has been an observed increase in the creation of malware. Malware's exponential development and sophistication pose a major threat to network and computer security. Malware is becoming a common tool used by hackers and attackers to carry out assaults on computer systems in order to achieve their harmful goals. The primary means of launching a malware assault on computer systems is the internet, which is used to send malicious emails, drive-by downloads of software, and malicious websites. Computer systems are penetrated for a variety of purposes, including financial gain, the theft of private or sensitive information, the creation of bots inside the system, the inaccessibility of services within the system, etc. The effectiveness of the analytic methodologies used to extract discriminative malware characteristics determines the malware detection system's efficacy. The primary goal of this research project was to identify discriminative characteristics that may be malware and utilise that knowledge to identify it with accuracy. Supervised machine learning techniques have been used to suggest a behavior-based malware detection method. The Cuckoo sandbox was used to execute both the malicious and safe samples in the dynamic analysis environment. When all four machine-learning algorithms were applied concurrently, the empirical data shows that the model identified malware in real-world apps with a higher detection rate. This method works quite well in identifying malware from unidentified families. All things considered, the web-based architecture offers a practical and fast way to identify malware on Android devices, making it a crucial tool in the battle against malware. The goal is to create a system that can automatically determine if a given application or file is malicious or not. This calls for the development of an algorithm or model that can analyse file attributes and differentiate between files that contain malicious code or activity and those that are clean.
Machine Learning-Based Framework for Drinking Water Quality Classification
Access to safe drinking water remains one of the most critical global public health issues. Contamination from chemical and microbial sources poses a serious threat to millions of people worldwide. Machine Learning (ML) has emerged as a promising approach to assess and predict water quality efficiently using complex physicochemical data. This study aims to investigate and compare the performance of various Machine Learning algorithms for classifying water potability and to identify the most accurate and reliable models for practical water quality assessment. Several ML algorithms—Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, Gradient Boosting, and Artificial Neural Networks—were applied to a benchmark water quality dataset. The study focused on preprocessing steps, handling missing data, and addressing class imbalance to improve model reliability. Experimental results showed that ensemble-based algorithms such as Random Forest and Gradient Boosting achieved the highest accuracy and F1-scores. Traditional and shallow models performed moderately, while deep learning models showed limited improvement due to the small size of the tabular dataset. Machine Learning provides a powerful tool for automated water potability classification. The findings emphasize the importance of preprocessing, data balancing, and model selection for reliable predictions. This work contributes to improving the interpretability and performance of ML systems for real-world water management applications.
Optimising the integration of phase change materials in construction in South Mediterranean regions: A case study from Algiers, Algeria
This study investigates the integration of phase change materials (PCMs) into conventional building envelopes in Mediterranean climates, with a focus on Algiers, Algeria a region characterised by hight diurnal temperature fluctuations and peak summer temperatures reaching 35 °C. The paraffin-based PCM RT35-HC (melting point ≈35 °C) was embedded at the core of wall assemblies composed of four locally prevalent construction materials: fired brick, aerated concrete, stabilised earth concrete, and slag concrete. Using COMSOL Multiphysics®, a two-dimensional transient heat transfer model was developed to evaluate the thermal performance of each wall configuration under representative climatic conditions. Simulations assessed the influence of material type and PCM placement on indoor thermal stability, particularly on time-lag, temperature attenuation, and heat flux reduction. Results indicate that aerated concrete combined with RT35-HC PCM provides the most effective thermal buffering, significantly delaying heat transfer and maintaining lower interior surface temperatures throughout the diurnal cycle. These findings support using PCM-enhanced lightweight materials to improve energy efficiency and thermal comfort in South Mediterranean regions.
Distributed Systems Architecture for Large-Scale Affiliate Retail Catalog and Inventory Management: Scalability, Consistency, and Performance Optimization
Modern affiliate retail ecosystems face unprecedented challenges managing massive product catalogs that span millions of items across multiple channels while maintaining real-time inventory accuracy. Distributed architectures employing sharded databases and microservices decomposition enable horizontal scaling of catalog operations, addressing the computational and storage demands of high-volume retail environments. Event-driven synchronization mechanisms, coupled with multi-tier caching strategies, facilitate immediate propagation of inventory changes across affiliate networks, ensuring data freshness and reducing query latency. Flash sales and sudden traffic spikes present significant challenges to system stability, necessitating robust fault-tolerance designs including circuit breaker patterns and eventual consistency models. The implementation of sophisticated message queuing systems and publish-subscribe architectures enables seamless handling of frequent product updates while mitigating data inconsistency risks. Cache invalidation protocols and conflict resolution strategies maintain catalog accuracy across distributed nodes, directly impacting customer experience and affiliate credibility. Performance optimization through strategic data partitioning and load balancing mechanisms ensures responsive catalog queries and inventory checks. These distributed system principles collectively create resilient, scalable infrastructures capable of supporting the dynamic requirements of contemporary affiliate retail operations while delivering consistent, accurate product information to end customers.
Leadership for Human–AI Collaboration in Management Education: Developing the LHAC Framework
Introduction: Artificial Intelligence (AI) is becoming an essential part of Management Information Systems (MIS) in higher education. Its integration has changed how institutions manage teaching, research, and administration. Yet, the real challenge is not the technology itself but how leaders guide people and systems to work together effectively. Objectives: This paper aims to develop a conceptual framework that explains leadership strategies for managing Human–AI collaboration in management education. Methods: The discussion draws on ideas from Transformational Leadership Theory, Socio-Technical Systems Theory, and Change Management literature. Through conceptual synthesis, these perspectives are combined to form the Leadership for Human–AI Collaboration (LHAC) Framework. Results: The LHAC Framework provides a starting point for understanding how leaders in higher education can manage Human–AI collaboration more strategically. It emphasizes that strong leadership is not only about technology adoption but also about sustaining trust, creativity, and shared purpose across the institution. Conclusions: This study presents the Leadership for Human–AI Collaboration (LHAC) Framework, highlighting three interconnected leadership dimensions—Strategic Digital Vision, Ethical and Collaborative Governance, and Continuous Learning and Adaptation—that enable effective Human–AI collaboration in higher education Management Information Systems (MIS). The framework demonstrates that leadership, rather than technology alone, is the key to fostering innovation, trust, and sustainable digital transformation. By guiding institutions to align AI adoption with organizational purpose, ensure ethical practices, and cultivate ongoing learning, the LHAC Framework offers both a conceptual foundation and practical guidance for responsible, human-centered AI integration in management education.
Comparison of PSO, AG and Krill Herd algorithms for the optimization of a PI regulator applied to photovoltaic MPPT
Optimizing PI controller parameters is crucial for improving the performance of photovoltaic systems, especially in the context of maximum power point tracking (MPPT). Meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Krill Herd Algorithm (KHA) are widely used for this type of optimization. PSO is valued for its fast convergence and ease of implementation, but it can sometimes get trapped in local optima. GA, while efficient in global search, has higher algorithmic complexity and longer computation time. In contrast, KHA, inspired by the behavior of krill schools, offers a good balance between exploration and exploitation, allowing for more stable convergence and better accuracy in finding the maximum power point. Comparative studies show that KHA provides better dynamic control, with faster transient response, reduced overshoot, and effective error minimization (ITAE), surpassing in several cases the performance achieved with PSO and GA. The choice of algorithm, therefore, depends on the desired compromise between speed, accuracy, and robustness
Development of Education in Uzbekistan with Progressive International Experiences
Introduction: At present the great attention for developing educational process of Uzbekistan using methods of developed countries educational systems. Objectives: Creation of the modern educational method requires development of the state educational standards through approaching competition, production and implementation of new generation of studying methodic complexes, provision of convenient of educational organizations, improvement and development of the teaching quality and also retraining and professional development of pedagogy workers Methods: As the effective education technology for creating the natural and scientific literacy on pupils we use the “STEAM” educational technology. This technology helps to construct the scientific, technologic, constructive, artistic-aesthetic and mathematical conceptions on pupils. The “STEAM” educational technology is the projecting activity of participants in educational process based on the contexts, electronic devices, distribution materials and natural equipment providing self-integration of the different modern method and shapes of education, collective, group and individual shapes experiences aided as acquisition of practical knowledges. Results: The developing process of inclusive education is difficult and broad activity, the task of which is creation of inclusive society defining each human as the personnel filling one a full fledged member of the society. The history of creation of inclusive education proceeds differently in each country when taken into account concrete political and social-economic reforms in concrete country. The intensive education requires not only intensive including pupils to school, but also active value-semantic restructuring total education system for providing educational requirements of each child. Conclusions: In order to providing the basis of the educational organizations the manuals, didactic games, toys, equipment based on the South Korea and other developed foreign countries experiences are organizing. In realization of these tasks we must take into account that maintain the health of children is required, which is important in increasing the educational quality. It follows that the education system end education program’s requirements on the best foreign experiences and approached them creatively level is being developed. All of them guarantee to progressive adulthood of youth generation of Uzbekistan which by world is confessing.
Investigation of Ferroresonance Phenomenon under Different Conditions in Power System
Introduction: Ferroresonance is a nonlinear oscillatory phenomenon that occurs in electrical power systems when a capacitance interacts with a nonlinear inductance such as a transformer’s magnetic core. This interaction can produce high overvoltages, distorted waveforms, and potential damage to equipment. Understanding ferroresonance behavior under various conditions is crucial for ensuring power system stability and protection, especially in networks containing single-phase transformers and capacitive components. Objectives: The main objective of this study is to investigate the ferroresonance phenomenon using a real single-phase transformer under different operating conditions. The work aims to analyze how changes in loading conditions (no-load, resistive load, and R-L load) and variations in series capacitance values influence the occurrence, magnitude, and waveform of ferroresonance overvoltages. The ultimate goal is to identify conditions that minimize or suppress ferroresonance effects in power systems. Methods: The investigation combines experimental measurements and numerical simulations. A 2 kVA single-phase transformer was modeled, taking into account nonlinear magnetic characteristics and core saturation effects. The simulations were performed using ATPDraw software to model the circuit comprising a voltage source, line impedance, transformer, and varying capacitors. Different scenarios were tested for steady-state and transient regimes, under both unloaded and loaded conditions, to observe the voltage behavior and harmonic distortions induced by ferroresonance. Results: Simulation results revealed that ferroresonance leads to significant overvoltages and distorted, non-sinusoidal voltage waveforms when the transformer operates in both steady-state and transient conditions. Increasing the series capacitance reduces the amplitude of these overvoltages and improves waveform stability. For loaded transformers, especially with resistive or R-L loads, the phenomenon is less pronounced. When the series capacitance reaches 100 µF, the voltage waveforms become nearly identical and sinusoidal, indicating a stabilized system response. Conclusions: The study confirms that ferroresonance is highly sensitive to system parameters such as load and capacitance. Small capacitance values intensify overvoltages, whereas larger capacitances mitigate them. Proper selection and control of capacitance can effectively limit ferroresonance and protect electrical equipment. These findings emphasize the importance of considering ferroresonance during the design, modeling, and operation of power transformers and distribution networks.
Ethical AI in Data Engineering: Mitigating Bias in Data-Driven Decision-Making
Bias in AI models can lead to unfair or discriminatory outcomes, creating significant challenges for organizations implementing data-driven decision-making systems across industries. This comprehensive review synthesizes current methodologies for integrating ethical AI principles into data engineering processes to detect, measure, and mitigate biases in data pipelines and machine learning models. The review focuses on three critical areas: bias detection algorithms that identify unfair patterns in data and models, fairness-aware data preprocessing techniques that remediate biased datasets before model training, and governance frameworks that provide organizational structures for implementing ethical AI practices at scale. As organizations increasingly rely on AI-driven decision-making systems, addressing algorithmic bias has become essential for ensuring equitable outcomes across diverse populations. The review demonstrates that preprocessing interventions can substantially reduce discriminatory outcomes while maintaining model accuracy within acceptable performance ranges. Contemporary implementations reveal that comprehensive bias detection frameworks require additional computational overhead above baseline model training costs, with processing times varying significantly based on dataset size and complexity. Organizations implementing dedicated ethical AI roles experience 67% fewer compliance violations compared to those relying on distributed responsibility models. IBM's AI Ethics Board across 147 projects achieved 89% reduction in post-deployment ethical issues with 78% project approval rate and average 12.4 days decision timeline. The integration of ethical AI principles represents both a moral imperative and a practical necessity for responsible artificial intelligence deployment.
Zero Trust Architecture in Mission-Critical Systems: From Perimeter Security to Continuous Verification
Higher-risk organizations in health care, finance, and law enforcement are experiencing increasing cybersecurity risk that threatens the traditional perimeter-based security model. Zero Trust Architecture (ZTA) involves a fundamental shift in the way we think about security. ZTA eliminates trust assumptions, fixes legacy trust-based security models, and shifts to a model that assumes every access request must be verified, and does so across every request regardless of where the request originates. The "never trust, always verify" model that ZTA operates in extends to all interactions with devices, applications, and machine-to-machine communications, expanding the security coverage to the overall ecosystem. The incorporation of artificial intelligence expands ZTA as well as behavior analytics, anomaly detection, and modifying policies in a real-time sense of risk. Organizations will be able to identify unusual behavior and mitigate access for the normal user of those systems, instead of static Non-PJudicial Acquisition access. Using ZTA in higher-risk organizations has settled operations since its inception and demonstrated effectiveness in breach prevention, regulatory compliance, and overall operational resiliency. ZTA is centered upon identity, micro-segmentation, and least-privilege principles that are rooted in identity, which greatly lowers the attack surface, and is transparent to normal operating system availability. ZTA is a critical piece of infrastructure for organizations that deal with sensitive data and critical operations. ZTA provides a tangible way forward in moving towards a sustainable model for establishing systemic trust and an institution's credibility, especially as organizational ecosystems become increasingly complex and threatened.
Artificial Intelligence in 3D Printing of Foods: From Ingredient Dispensing to Personalized Nutrition
Artificial intelligence is fundamentally reshaping food technology by integrating with three-dimensional printing systems to enable predictive modeling, precise ingredient control, and personalized nutrition at scales previously unattainable through conventional manufacturing. This convergence addresses critical global challenges, including diet-related chronic diseases, food waste reduction, and the growing demand for individualized dietary solutions that respect both health requirements and cultural preferences. The article explores how machine learning frameworks developed for industrial additive manufacturing translate to food applications, where success metrics shift from mechanical strength to nutritional density, sensory appeal, and cultural authenticity. Through detailed examination of ingredient vectorization and multi-objective optimization, the article demonstrates how computational representations of food enable dynamic recipe generation tailored to individual metabolic profiles, taste preferences, and dietary restrictions. A case study focusing on potato-based chips illustrates the practical application of spice microdosing and micronutrient fortification, transforming common snack foods into vehicles for functional nutrition while preserving cultural flavor traditions. The article presents a comprehensive architectural framework comprising consumer data ingestion, vector encoding, reinforcement learning-based recipe engines, precision microdosing control systems, and continuous feedback loops that improve personalization over time. Applications span healthcare settings where texture-modified therapeutic meals support patients with swallowing difficulties, disaster response scenarios requiring portable nutrition solutions, space exploration missions demanding sustainable closed-loop food systems, and everyday consumer contexts seeking healthier eating patterns without sacrificing taste or convenience. Future directions encompass autonomous smart kitchens, supply chain resilience through dynamic ingredient substitution, and cultural preservation through digitized culinary heritage, while acknowledging barriers including regulatory challenges, consumer acceptance hurdles, technical costs, and nutritional integrity concerns that require systematic resolution before widespread adoption becomes feasible.
AI-Driven Automation in Clinical Statistical Programming
The pressure on clinical statistical programming is increasing to align with timelines and preserve data integrity as the trial complexity increases. Technologies based on artificial intelligence have a transformative solution for automating data cleaning, transformation, and validation processes that previously required a lot of manual time. Machine learning algorithms are good at ranking quality issues according to analytical effect, and natural language processing can be used to extract and standardize information contained in unstructured clinical narratives automatically. It has been shown in real-world applications to achieve high efficiency improvements and error reduction in oncology trials, adverse event coding, and cardiovascular data validation. Nevertheless, implementing it successfully requires a close consideration of legacy system integration, privacy safeguarding with the use of federated learning, all-encompassing training programs with a focus on technical skills and critical thinking, strict regulatory validation to account for probabilistic system behavior, and continuous monitoring to identify performance deterioration. The phased rollout of strategies that start with specific pilots and human-AI collaboration models that ensure proper oversight by experts constitutes the best avenues towards automation in organizations. The change is necessitated by the need to harmonize the technological capabilities with the domain knowledge, situational awareness, and ethical governance to transform clinical programming processes without compromising on the scientific rigor.
From Content to Connection: How Social Media Features Drive PSI and e-WOM on TikTok
Tourism organizations increasingly rely on social media to shape destination impressions and influence consumer behavior. However, there is a challenge in how tourism organizations develop social media content to successfully attract consumers’ attention, induce their psychological interaction with the destination, and motivate their E-WOM behavior. This study investigates the direct and indirect effects of three social media content features on consumer behavioral responses, with parasocial interaction (PSI) as a key mediating mechanism. This study adopted the Elaboration Likelihood Model (ELM) to construct content features in social media, and collected research data from 396 Malaysian TikTok users and applied PLS-SEM for model testing. Findings show that information quality significantly predicts both PSI and E-WOM, while source credibility only directly affects E-WOM. Interactivity had no significant impact. These findings extend the ELM framework by highlighting the mediating role of PSI in tourism branding and offer practical guidance for destination brands to optimize content strategies in social media platform.
Systematic Literature Review of Continuous Validation and Improvement Methods for AI Systems
This systematic literature review aims to systematically analyze current methods of continuous validation and improvement in AI systems. It investigates techniques, application domains, scope of implementation, and real-world challenges to identify prevailing trends, limitations, and opportunities for enhancing adaptive, reliable, and ethically responsible AI deployment This systematic review employed a structured search across IEEE Xplore, Scopus, Web of Science, and ACM Digital Library (2018-2025), using defined keywords. Studies were screened through title, abstract, and full-text review, applying inclusion/exclusion criteria. Quality was assessed using context, design, validity, rigor, and relevance dimensions. A total of 51 studies were analyzed. Cross-validation (25.49%) and online retraining (23.53%) were the most used validation methods. Improvement efforts centered on iterative model refinement (21.57%) and integration with feedback and workflows (15.69% each). Most studies focused on smart manufacturing and robotics (each 27.45%), with healthcare (13.73%) and environmental systems (11.76%) trailing behind. Industry-focused deployments dominated (19.61%), while only 9.80% addressed cross-regional implementations. Key challenges included model drift (13.73%), generalizability issues (11.76%), and ethical concerns (11.76%). Real-time feedback mechanisms, regulatory alignment, and interpretability remained under-addressed, signaling critical gaps in sustainable and trustworthy AI development. While technical validation and improvement strategies are maturing, gaps persist in real-world adaptability, ethical integration, and socio-technical feedback loops. Bridging these gaps will require collaborative, context-aware, and regulatory-informed AI systems capable of maintaining performance and trust across diverse, evolving environments.
The Converging Impact of 5G and AI on Business Connectivity: A Paradigm Shift
This article examines the transformative impact of 5G and artificial intelligence convergence on modern business connectivity, addressing the fundamental shift from connectivity as an ancillary concern to a critical business prerequisite. The article explores how these technologies reshape customer engagement through cognitive networking and ultra-reliable low-latency communications, enable workforce evolution via unified communication platforms and distributed teams, and build business resilience through network slicing and edge computing infrastructure. Drawing upon systematic reviews and empirical evidence, the analysis demonstrates how small and medium businesses particularly benefit from democratized access to enterprise-grade connectivity solutions, while acknowledging implementation challenges, including security vulnerabilities across OSI layers, privacy concerns, and skill gap requirements. The article reveals that successful digital transformation depends on leadership-mediated cultural change and comprehensive frameworks that balance technological capabilities with ethical considerations, ultimately creating a paradigm shift in how businesses conceptualize operational strategies and navigate an increasingly interconnected global marketplace.
Enterprise Cloud Security: Implementing Defense-in-Depth Strategies in Multi-Cloud Environments
Enterprise cloud adoption has fundamentally transformed organizational IT infrastructure, necessitating sophisticated security frameworks that address the complexities of distributed computing environments. The shared responsibility model establishes a collaborative security paradigm where cloud service providers maintain physical infrastructure security while customers manage identity controls, data encryption, and compliance configurations. Defense-in-depth strategies provide comprehensive protection through seven integrated security layers: physical security, perimeter controls, network segmentation, endpoint protection, application security, data encryption, and identity management systems. Contemporary threat vectors combine multiple attack methodologies, including privilege escalation, lateral movement, and social engineering techniques that challenge traditional security boundaries. Continuous monitoring, threat detection, and adaptive security postures enable organizations to respond dynamically to evolving cyber threats while maintaining operational efficiency. The integration of automated security tools, compliance frameworks, and incident response protocols creates resilient security architectures capable of protecting enterprise assets across public, private, and hybrid cloud deployments. Organizations implementing comprehensive defense-in-depth strategies demonstrate enhanced security resilience, reduced vulnerability exposure, and improved regulatory compliance outcomes. The collaborative nature of cloud security governance requires ongoing partnership between providers and customers to maintain effective protection against sophisticated cyber threats targeting enterprise cloud infrastructure.
React's Architectural Limitations in Distributed UI Systems: A Critical Analysis
This article critically examines React's architectural limitations when applied to distributed user interface systems, particularly in micro-frontend implementations. It explores the fundamental tension between React's component model, which assumes a unified runtime with shared context and rendering cycles, and the distributed nature of modern frontend architectures that emphasize team autonomy and independent deployment. The article progresses through React's core design assumptions, identifies specific challenges in cross-boundary state management and server-side rendering coordination, evaluates current industry mitigation strategies, including isolation techniques and module federation, and explores emerging paradigms that reimagine frontend architectures for distributed contexts. By identifying the architectural mismatches between React's monolithic design and distributed UI requirements, the article provides insights for organizations navigating these competing paradigms while seeking to maintain both system cohesion and team independence.
Network Uptime Excellence: A Multi-Pillar Framework for High-Availability Service Delivery in Global Operations
This article presents a comprehensive framework for achieving network uptime excellence through the strategic integration of global leadership coordination, standardized operational processes, and advanced monitoring technologies. The article examines the implementation of a three-pillar approach that addresses the complex challenges of maintaining consistent network availability across distributed operations centers spanning multiple geographic regions and cultural contexts. The article emphasizes proactive incident management through the development of standardized troubleshooting playbooks, the establishment of clear accountability structures for distributed teams, and the deployment of predictive analytics capabilities that enable early detection of potential service disruptions. Key findings demonstrate that organizations achieving superior network reliability outcomes require coordinated attention to human leadership factors, systematic process engineering, and technology integration rather than isolated investments in individual operational domains. The article reveals that effective global network operations center management depends heavily on structured knowledge sharing mechanisms, cross-cultural team coordination protocols, and continuous improvement processes that adapt to evolving operational requirements. Implementation results show substantial improvements in incident response effectiveness, failure detection capabilities, and overall service level compliance, validating the synergistic effects of combining leadership excellence with standardized procedures and advanced monitoring systems. The article contributes valuable insights for practitioners seeking to enhance operational resilience while providing theoretical foundations for understanding the integration of human-centered management approaches—specifically through empowered leadership structures that prioritize people development, cultural intelligence, and distributed decision-making authority—with technology-driven process improvements in critical infrastructure environments. The framework's modular design enables adaptation across different organizational contexts and operational scales, suggesting broad applicability for organizations dependent on reliable network infrastructure for business continuity.
Understanding Enterprise Architecture in Cloud-Based HR Platforms for Aviation
This article explores the enterprise architecture of cloud-based human resources platforms tailored for the aviation industry. It investigates layered architectural components—spanning user interfaces, business logic, and data governance—that address unique operational and compliance challenges in airline HR systems. Emphasis is placed on modular design, multi-tenant security, and integration patterns linking HR systems with operational platforms such as crew scheduling and payroll. The paper also examines aviation-specific adaptations, including support for mobile workforces and complex labor agreements. Case studies illustrate how architectural strategies enable regulatory compliance and operational efficiency in complex, globally distributed aviation environments.
Trust, Transparency, and Ethics: A Framework for Sustainable LLM Integration in Enterprise Information Systems
Large Language Models are an evolutionary power in business information systems that fundamentally change the way companies process information, streamline workflows, and enable strategic decision-making. The deployment of these advanced transformer-based architectures into mission-critical business contexts brings unprecedented powers while also generating difficult-to-manage challenges around trust, transparency, and ethics. Trust deficits arise due to the probabilistic nature of model outputs, such as hallucinations, domain-specific constraints, and intrinsic biases against stakeholders' confidence. Transparency imperatives are triggered by the textualizing frameworks that demand explainability of automated decisions, which is contrary to the architecture of deep learning that manifests its transparency in environments marked by opaqueness. Fairness, accountability, data privacy, and workforce change are the ethical issues that require high-level governance structures to balance innovation and responsible deployment. This paradigm accommodates these interrelated aspects by structured observation of technical processes, organizational forces, and social implications. Adoption environment shows industry-specific trends of adoption in terms of regulatory restraints, organizational maturity, and environment. An end-to-end implementation would require explainable AI methods, mitigation, and bias-detection methods, human-in-the-loop topologies, and real-time monitoring networks. By understanding the sociotechnical nature of LLM integration, businesses can walk the complex line between technological capacity and ethical responsibility and eventually achieve sustainable use within organizations in line with both the business objectives and values of society.
Deliver 50% Coding Productivity with Cursor.AI
Generative AI is transforming the field of software development by reducing repetitive tasks and significantly enhancing productivity. This paper examines Cursor.AI, an AI-first coding assistant that delivers productivity gains of up to 50%. The study evaluates productivity metrics across developers with varying skill levels, programming languages, and prompt engineering quality, using a quantitative research design. Data were collected from multiple teams employing Cursor.AI in real-world development projects. Findings indicate that experienced developers benefited the most, achieving code acceptance rates between 45% and 54%, while junior developers showed moderate improvements. Languages such as Go and Python performed particularly well due to stronger AI model support. The literature further emphasizes that effective prompt design, model selection, and cost optimization are critical for sustainable outcomes. Results confirm that AI-assisted coding is highly effective when used as a collaborative tool to augment—rather than replace—human creativity. Combining Cursor.AI with appropriate workflows and governance enables organizations to accelerate software delivery while improving developer satisfaction.
Bridging Digital Gaps: ICT Utilization and Competency of Faculty Members
The digital age has revolutionized the teaching, learning, and research in the higher education by the use of information and communication technology (ICT). The faculty members are the major actors in the process of incorporating ICT in classrooms to promote student participation, better learning, and meet the international education standards. Nevertheless, use of ICT and competency is diverse in different institutions brought about by factors like digital literacy, resources availability, institutional support and personal preparedness to change. The article examines the degree of ICT competency among faculty members, the degree of ICT use in teaching and research, problems faced during the process of digital integration, and ways of improving faculty digital abilities. The findings suggest that enhancing ICT competency among faculty requires strengthening both their technical and research skills while also addressing challenges in pedagogical integration. To achieve this, institutions should invest in targeted professional development programs, digital pedagogy training, and ethical ICT practices, enabling faculty to fully leverage digital technologies for teaching, research, and scholarship.