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 as Code (IaC) Maturity Model: A Framework for Secure and Scalable Automation
With the development of modern businesses toward cloud-native, automated infrastructure, Infrastructure as Code has become a fundamental part of scalable systems engineering. However, there is often no easy way to assess the level of adoption maturity beyond superficial metrics, such as tool use. This article presents a five-step Infrastructure as Code Maturity Model, which is a systematic mapping of the progression of IaC Implementations between ad hoc scripting and policy-based automation. The maturity level is characterized by quantifiable attributes in four key dimensions, namely level of automation, level of governance, level of observability, and level of security integration. This framework is based on empirical validation in the finance, telecommunications and government sectors, and it shows quantifiable improvements over the life cycles of organizations as maturity is achieved. Validation studies demonstrate that organizations that progress to the next level of governed automation, based on initial levels of automation attain reduced configuration drift incidents, compliance audit preparation time, and infrastructure provisioning time, and at the same time, an improvement in deployment frequency and automated compliance coverage. The model fits perfectly with the new security models such as Zero-Trust Architecture and Supply-chain Levels of Software Artifacts, which offer organizations the diagnostic feature and the roadmap of the infrastructure evolution. Through defining steps of progression with quantifiable results, this study will allow companies to undertake the task of infrastructure modernization in a systematic manner to avoid automation as a one-off script into verifiably secure, compliant, and resilient operations that uphold governance in a cloud scale.
AI-Augmented Dynamic Performance Engineering: A Hybrid Platform Architecture
Modern distributed systems encounter unprecedented challenges in maintaining performance stability amid volatile workload patterns and constrained resources. Traditional reactive monitoring methodologies prove insufficient for anticipating degradation and orchestrating preventive interventions. AI-Augmented Dynamic Performance Engineering (AIDPE) presents a hybrid platform architecture synthesizing predictive analytics, autonomous optimization, and intelligent root cause diagnosis. The architecture leverages temporal neural networks for resource demand forecasting, reinforcement learning agents for autonomous parameter adjustment, and transformer-based models for causal incident interpretation. Integration of these components through continuous feedback loops enables anticipation of anomalies, execution of preventive optimizations, and diagnosis of failures with minimal human intervention. Critical gaps in existing methodologies receive direct attention, particularly the inability to correlate predictive signals with optimization actions and fragmentation between detection and remediation systems. Contemporary cloud-native architectures generate massive telemetry volumes, creating an information abundance paradoxically complicating management tasks. The fundamental limitation stems from the absence of closed-loop systems integrating forecasting, optimization, and diagnostic capabilities within unified decision-making frameworks. When prediction models identify impending resource exhaustion, no automated pathway translates insights into concrete infrastructure adjustments. AIDPE addresses these deficiencies through cohesive architectural design, enabling self-adaptive performance management across heterogeneous infrastructure environments.
Evolving Architectural Paradigms: Transitioning from Monolithic to Event-Driven Microservices in Cloud Environments
The evolution from monolithic architectures to event-driven microservices represents a fundamental transformation in cloud-native system design. This article explores architectural evolution patterns, identifying key enablers and inhibitors influencing successful transitions while evaluating sustainability implications across operational, technical, and environmental dimensions. Through mixed-method evaluation combining theoretical frameworks, comparative analysis, and case implementation, the article identifies four predominant evolution patterns—incremental decomposition, domain-driven extraction, infrastructure-first transformation, and strangler-facade implementation—each offering distinct advantages in different organizational contexts. The sustainability assessment reveals complex trade-offs, with distributed architectures typically showing reduced sustainability during transition before achieving enhanced outcomes in mature states. Based on these observations, the article presents a migration framework balancing agility, resilience, and sustainability throughout the transformation process, addressing critical decision points from domain analysis through continuous evolution. The framework validation demonstrates effectiveness in guiding architectural transitions that maintain system viability while establishing foundations for long-term sustainability. The article contributes both theoretical advancement in sustainability as a multidimensional quality attribute and practical guidance for organizations implementing architectural transformations in cloud environments.
Insured Profiles Segmentation using Unsupervised Machine Learning
The National Social Insurance Fund (CNAS) in Algeria manages a crucial pillar of social security against risks such as illness or disability, covering salaried workers through various benefits. This study applies the K-means algorithm, an unsupervised machine learning method, to the Big Data from the General Directorate of CNAS, which is processed using the scikit-learn package in Python. to segment the insured into eight homogeneous profiles based on age, the frequency of prescribed medication care, and the amounts of reimbursements. The segments identified by the execution of the algorithm reveal a behavioral diversity in healthcare consumption, providing the CNAS with a basis for targeted actions in prevention, awareness, and optimization of reimbursements.
Sustainable Supply Chain Practices on Organizational Performance of Selected Fast-Moving Consumer Goods Companies in Nigeria
Introduction: The rising demand for sustainable business operations has made environmentally responsible supply chain strategies essential in the fast-moving consumer goods (FMCG) sector. In Nigeria, limited empirical studies assess how these practices affect firm performance. Objectives: This study investigates the influence of sustainable manufacturing, reverse logistics, green packaging, and supplier relationship management on organizational performance, with stakeholder satisfaction as the key measure. Methods: A cross-sectional survey was conducted with 357 supply chain personnel across seven listed FMCG firms. Data were analyzed using Ordinary Least Squares (OLS) regression to determine the effects of the selected practices. Results: Sustainable manufacturing (β = 0.192, p < 0.001), reverse logistics (β = 0.164, p < 0.001), supplier relationship management (β = 0.133, p = 0.001), and green packaging (β = 0.117, p = 0.001) all showed significant positive impacts on performance. Conclusions: Embedding sustainability in supply chain operations enhances stakeholder satisfaction. Firms should invest in cleaner production, structured reverse logistics, collaborative supplier partnerships, and scalable green packaging, supported by consistent policy frameworks
Modelling Innovation Diffusion Using Optimized Marketing Mix Strategies
In this paper, we studied the primary limitation of the original Bass Diffusion Model by developing an extension of the Generalized Bass Model (GBM) that introduces key marketing mix variables into the model. Since innovation and imitation are not the only drivers of product adoption given the competitive and technologically advancing nature of markets, the study presents a time-varying marketing influence function as a linear combination of advertising expenditure, customer service quality, product performance, innovation capability, and extension of distribution. Built multiplicatively into the equation of diffusion, this function is designed to reflect the accelerating influence of marketing activities on adoption rates. The study also utilizes Linear Programming (LPP) to maximize marketing impact function under practical constraints, allowing for the computation of optimal resource allocation between marketing variables. This upgraded model is a marketing planning support tool, performance measurement tool, and resource allocation tool, especially in sectors such as smartphones, electric vehicles, and consumer electronics, enabling firms to determine high-impact areas for investments and develop effective marketing campaigns for maximizing product diffusion effect.
Machine Learning in Precision Agriculture
Introduction: Agriculture has been a fundamental aspect of human existence for thousands of years, dating back to around 9000 BC, when humans began transitioning from a nomadic lifestyle to settled farming [1]. Although agriculture is usually considered an “old-fashioned occupation,” recent advancements in machine learning have paved the way for transforming agricultural forecasting through intelligent crop and yield prediction systems. Despite this, many farmers still use traditional crops and yield prediction methods that are often manual, data-scarce, and inaccurate. Objectives: This research investigates the potential benefits of integrating machine learning into agricultural forecasting, which can reduce the risk of crop failure and financial loss associated with traditional methods. The paper aims to make technology more accessible and actionable rather than just theoretical. Methods: Previous studies have applied multiple machine learning algorithms, such as Random Forest and Support Vector Machine (SVM), for crop prediction. Despite promising results, little work has been done on combining both crop type and yield predictions into an integrative machine learning framework. This paper explores the utilization of ensemble-based models, namely XGBoost, LightGBM, and Random Forest, trained on two datasets to accurately predict crop type. Additionally, it examines the use of regression-based algorithms to predict crop yield accurately, employing feature selection and 5-fold cross-validation. Results: The ensemble-based models returned an accuracy of 98% for both crop type prediction and yield forecasting, showing the effectiveness of multiple algorithms. The algorithms also achieved an accuracy of 99.8% on the less comprehensive dataset, while individual models such as CatBoost achieved varying accuracies highlighted in Table 1. Conclusions: The findings obtained in this study can help future farmers reduce effective costs, increase production rates, and enhance crop yield by minimizing waste. This approach contributes towards the realization of AI-driven, data-centric precision agriculture, optimizes resource utilization, and supports intelligent decision-making in modern farming
Integrating Bass Innovation Diffusion into Inventory Modeling: A Lifecycle Optimization Approach
Inventory management for innovative products presents unique challenges because demand is not constant but evolves as consumers adopt new technologies. Traditional inventory models fail to capture this dynamic adoption behavior. This research paper develops a mathematical inventory model that integrates the Bass innovation diffusion model into the inventory decision framework. By explicitly embedding the coefficients of innovation and imitation into demand functions, the model accounts for the time-dependent adoption process. The objective is to minimize the total cost consisting of ordering, holding, shortage, and obsolescence costs under diffusion-driven demand. Analytical derivations, supported by numerical illustrations, demonstrate how adoption dynamics significantly influence inventory decisions. Results show that firms ignoring diffusion effects either overstock in early periods or understock during growth phases. This research paper provides both a theoretical contribution by extending inventory models with diffusion theory and practical insights for firms dealing with high-tech or short lifecycle products.
Citizen Participation and Urban Governance: A Vital Partnership for Enhancing Quality of Life in Boumerdes city – algeria-
The purpose of this article is to expose the shortcomings of Algeria's housing policy and to highlight the significance of urban improvement as a new approach. This approach aims to restore the city's role as a structuring unit for urban coherence and social cohesion. From this perspective, this work attempts to embody the principle of citizen participation in enhancing their places of residence. The Frantz Fanon housing estate in Boumerdes clearly exemplifies the deterioration of collective housing in Algeria and other developing countries.To achieve our objective, we conducted a household survey. The collected data was analyzed using statistical methods with SPSS25 software, and the results were presented graphically and cartographically. This simplified the findings, emphasized the problems, and helped in extracting solutions, all supported by Geographic Information Systems.
AI-Driven Real-Time H₂S Monitoring and Risk Mitigation During Drilling in Southern Iraq Fields Using Fuzzy ART Unsupervised Learning
Drilling in formations with hydrogen sulfide (H₂S) in southern Iraq poses environmental and safety problems. To create more sophisticated, intelligent monitoring systems, this study implemented an AI-driven, real-time methodology to monitor and mitigate the risks associated with H₂S. The system was developed using a self-learning AI agent trained using field data from 21 wells with 41 H₂S incidents. The AI agent applies fuzzy adaptive resonance theory (ART)-based unsupervised learning to check the alert levels, predict H₂S spikes, and recommend the optimal adjustment for the mud pH to counterbalance the dissolved H₂S in the drilling fluids. Unlike static rule-based systems, this approach is based on adaptive thresholds that improve with learning and combines the alarm protocols adopted by Health, Safety, and Environment (HSE) with the proposed tresholds that reflect the formation data. This process enabled more rapid responses to fluctuations in gas levels due to earlier problem detection plus more accurate assessments of the crew’s cumulative exposure. This AI-based technology delivered a strong and adaptable approach, with several advantages. It provides an accurate estimate of the amount of H2S in each formation, reduces the operational risk, and allows the team access to safer, data-driven decisions when drilling in H₂S bearing formations.
Ethical Crossroads: Navigating the Intersection of AI Sales Automation and Privacy Compliance
This article explores the ethical dimensions and regulatory challenges of integrating artificial intelligence into sales automation systems. It examines the tension between operational efficiency and responsible data stewardship as organizations increasingly rely on algorithm-driven customer journeys. The article shows data architecture strategies that prioritize privacy, including data minimization principles and consent-driven frameworks. It addresses critical ethical challenges in automated sales intelligence, such as campaign saturation, behavioral data collection boundaries, algorithmic bias, and cross-platform governance. The regulatory landscape is analyzed through the lens of major privacy regulations and emerging industry standards, with particular attention to cross-border data complexities. Looking forward, the article identifies emerging models for transparent AI decision-making, organizational governance structures, and approaches for balancing competitive advantage with ethical considerations. Through empirical analysis and case studies, this article provides a comprehensive framework for responsible AI integration in revenue systems while highlighting a forward-looking research agenda for compliant sales technologies.
Mirror, Discipline, and Negotiation: A Qualitative Exploration of the Impact of "Viva La Romance" on the Gender Perception of Young Chinese Women in Contemporary China
General debates about media, gender, and intimacy in contemporary China highlight how romantic reality television can both reinforce and challenge traditional norms of wifehood and marriage. This paper will explore the effects of the reality show in influencing the gender perception of young Chinese ladies between the ages of 25 and 35. It tackles the wider issue of how most aestheticized images of couples in terms of their travel and emotional communication affect expectations of femininity, emotional labour and marital roles. The study aims at the four objectives of analysing gender roles encoding in the show; studying the ways viewers perceive these representations; studies perceived effects on gender identity and relationship expectation; and patterns of acceptance, negotiation, and resistance, guided by the Gender Theory and Acceptance Theory. The qualitative design in the study involved semi-structured interviews and focus groups, and purposely chosen participants were used. According to the thematic analysis, there was heightened awareness of women's emotional labour, reinforcement of traditional roles in part, encouragement of relationship self-reflection and reinforcement of egalitarian beliefs among a critical minority. The majority of the participants were involved in negotiated readings where they admired intimacy at the expense of unequal burdens imposed on wives. The paper comes up with the conclusion that Viva La Romance is an object of reflection, of discipline and of negotiation, of stabilisation and destabilisation of gender norms in modern China that is both relatively stable and more or less unsettled.
Managerial Assessment of the Quality of Outdoor Spaces on University Campuses
This bibliometric analysis explores the managerial assessment of outdoor space quality on university campuses by synthesizing findings from 93 publications indexed in Scopus. Using VOSviewer software, four types of network visualizations were generated: citation frequency distribution, keyword co-occurrence, international authorship patterns, and co-citation networks. Results reveal rapid research growth after 2018, with core contributions from authors such as Wickens (2021), Hall (2014), and Burgoon (2021). Keyword analysis identifies dominant themes in sustainability, thermal comfort, accessibility, wellbeing, and participatory design. Geographic mapping shows a concentration of research output in developed countries, especially the United States, the United Kingdom, China, Germany, and Australia. The study diagnoses methodological evolution from qualitative surveys to multidimensional assessments incorporating space syntax, GIS, and mixed methods, and highlights persistent gaps in equity, standardized indicators, and cross-regional applicability. The findings offer critical insights for managers and researchers seeking evidence-based strategies to enhance campus outdoor space quality and guide future studies in this multidisciplinary field.
Investigation of Structural and Electronic Characteristics of TmS Using LSDA and LSDA+U Approximations
Este artigo investiga as propriedades estruturais, eletrônicas e magnéticas do TmS, um material de terras raras, utilizando os métodos LSDA e LSDA+U. Os resultados mostram que o TmS favorece um estado antiferromagnético e apresenta potencial para aplicações em spintrônica. As propriedades estruturais, incluindo parâmetros de rede e módulo de elasticidade, estão em boa concordância com os dados experimentais, validando o uso dos métodos LSDA e LSDA+U. A análise eletrônica revela um comportamento metálico, com a influência significativa do potencial de Coulomb sobre os orbitais 4f. Este estudo aprimora a compreensão das características magnéticas e eletrônicas do TmS e destaca o impacto dos diferentes métodos de aproximação.
Electrical Performance Analysis of Thermoelectric Generator HZ-20 with particle swarm optimization Algorithm
Introduction: The Thermoelectric Generator (TEG) represents a recent development renewable energy technologies, harnessing heat energy to generate electricity from various waste energy sources. the maximum power point (MPPT) is an essential approach to maximizing energy extraction from thermoelectric generators. the position of this point is not fixed but it moves according to the difference temperature and load. Many classic methods and controllers have been widely developed and implemented to track the maximum power point. The Perturb and Observe algorithm was chosen, due to its simplicity and convergent capacities. However, the operating point oscillates around the MPP that increase the loss of energy in the power of the TEM. Objectives: of the paper is a comparative analysis of an optimization technique Particle Swarm Optimization (PSO) along with Perturb & Observe (P&O) for the extraction of maximum power from the thermoelectric generator. Methods: Particle Swarm Optimization PSO is based on two fundamental principles: the retention of information on past performance, and communication between the agents forming the swarm. The chosen DC-DC converter approach, a one directional boost converter, is used in combination with the MPPT approach to construct the power conditioning unit. The TEG modeling was integrated with the Perturb and Observe (P&O) MPPT technique and the Particle Swarm Optimization (PSO) MPPT method. Results: In this paper we will evaluate the performance of PSO and P&O algorithms for maximum power point tracking with thermoelectric generator connected to load via Boost converter is verified on MATLAB/Simulink environment. The simulation results shows that the two techniques defeat the maximum power problems and the Particle Swarm Optimization (PSO) method has advantages compare to Perturb and Observe (P&O) method. From this comparison it is observed that faster convergence is achieved in PSO algorithm when compared to P&O algorithm.
Design of an Intelligent Framework for Risk Prediction and Alert Optimization in Smart University Environments
This paper presents the design of an intelligent and integrated framework for predicting and managing both academic and operational risks in smart university environments. The proposed system conceptually combines machine learning and soft computing techniques to enable proactive and interpretable risk governance. The framework integrates data from multiple sources including Learning Management Systems (LMS), Internet of Things (IoT) sensors, and external environmental APIs to generate predictive insights and optimize alert dissemination. The architecture leverages ensemble models such as Random Forests, Gradient Boosting, and Explainable Boosting Machines (EBMs), alongside metaheuristic algorithms including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to enhance decision accuracy and delivery efficiency. Although the implementation is ongoing, a simulation-based validation plan demonstrates the feasibility and scalability of the framework within a smart campus context. The proposed design emphasizes transparency, adaptability, and real-time awareness, providing a foundation for future deployment in intelligent university risk management systems.
Respiration Signal Monitoring of Multiple Individuals via BSS and UWB-SIMO Radar
In recent years, multi-person respiration detection has been more interest, especially in medical environments and emergency situations where several people need to be monitored at the same time. However, it is still challenging to get accurate vital signs from people who are very close together, either at the same distance or in the same angular cell, because the reflected signals overlap. To overcome this limitation, this study employs a Blind Source Separation (BSS) approach using the Fast Independent Component Analysis (FastICA) algorithm to separate multiple targets situated in close proximity or sharing similar angles. The Fast Fourier Transform (FFT) is then used to estimate the respiratory frequencies. Simulation results show that the proposed solution is capable and efficient. They also show that it can successfully separate individual respiratory signals in complex situations with more than one person.
The Impact of Artificial Intelligence on the Audit Process and Internal Audit Quality: A Study of Listed Companies on the Vietnamese Stock Market
In the context of the Fourth Industrial Revolution, adopting artificial intelligence (AI) in internal auditing has become an essential trend to enhance the efficiency and accuracy of audit processes. As corporate information systems grow increasingly complex, AI provides critical support in optimizing audit procedures by enabling automated data processing, advanced risk detection, and more comprehensive analyses. This study investigates the key factors influencing AI adoption in internal audit functions among companies listed on the Vietnamese stock market. Using structural equation modeling (SEM), the analysis reveals that internal auditors’ technological competency, managerial support, organizational culture, and information technology infrastructure all have a significant positive impact on AI adoption. Furthermore, the study examines the relationship between AI adoption, audit process effectiveness, and internal audit quality. The results indicate that higher levels of AI integration improve audit process efficiency by reducing manual workloads, minimizing errors, and enhancing the detection of risks and anomalies. Importantly, improvements in audit processes serve as a crucial mediating mechanism through which AI adoption contributes to overall internal audit quality. These findings underscore the strategic importance of integrating AI within well-structured audit procedures and highlight the need for organizations to invest in technology, training, and supportive organizational practices to fully realize the benefits of AI in internal auditing.
Exploring the Interplay of Digital Leadership, Digital Workplace and Employees’ Dynamic Digital Capabilities in Malaysian Public Universities
Digital transformation has become an essential expectation in Malaysian public universities, but its implementation is often shaped by the everyday realities of administrative officers who must interpret and navigate shifting technological demands. This study explores how digital leadership, the digital workplace, and dynamic digital capabilities interact to influence officers’ experiences as they manage systems that promise efficiency but often exhibit instability. Using a qualitative narrative approach, the study draws on reflective accounts that reveal how leadership clarity, system usability and peer support shape officers’ confidence and capacity to adapt. The findings show that digitalisation is not experienced as a straightforward transition but as a relational and emotional process influenced by workload pressures, uneven system alignment and the degree of guidance provided by leaders. Officers build capability gradually, drawing on trial-and-error and informal learning networks as they respond to ongoing changes in their work environment. The study suggests that meaningful digital transformation requires coherent alignment between technological tools, leadership practices and opportunities for experiential learning. It highlights the need for institutions to closely attend to the human conditions that sustain digital work and to support the development of environments where capability can grow in practical, sustainable ways.
The Literature Review in the Study Design in the Quantitative Research Methodology
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Adaptive Neural Embedded Systems for Real-Time Driver State Monitoring in Advanced Driver Assistance Systems
As Advanced Driver Assistance Systems continue to evolve rapidly, more sophisticated methods for continuous assessment of driver cognitive and physical states are essential to address safety risks during the transition toward vehicle autonomy. Adaptive neural embedded systems represent a transformative approach to real-time driver monitoring, combining multiple sensor types with edge-deployed artificial intelligence to create personalized safety models. The architecture integrates Convolutional Neural Networks for extracting spatial features and Long Short-Term Memory networks for identifying temporal patterns, processing facial images, physiological signals, and behavioral indicators to detect fatigue and distraction states. Implementation on embedded FPGA-System-on-Chip platforms achieves sub-second inference latency while maintaining high classification accuracy across diverse operational environments. Baseline calibration procedures establish driver-specific detection thresholds that account for individual physiological differences, while ongoing model refinement through incremental learning adapts to changing behavioral patterns over extended operation periods. Direct integration with Electronic Control Units enables graduated intervention strategies ranging from gentle sensory alerts to active vehicle control inputs, including lane-centering assistance and controlled braking. Transfer learning techniques accelerate model development by leveraging pre-trained representations, achieving exceptional precision and recall metrics through fine-tuning on domain-specific drowsiness datasets. The embedded architecture addresses fundamental limitations of cloud-dependent systems, including latency constraints, connectivity dependence, and privacy concerns, delivering deterministic real-time performance essential for safety-critical automotive applications in increasingly autonomous vehicular environments.
MindMate: An NLP-Based Emotional Support and Motivational Growth Agent for Mental Wellness
The global mental health crisis affects millions of individuals while creating substantial barriers to accessing traditional therapeutic services, including geographic limitations, economic constraints, and social stigma. This article presents MindMate, an innovative Natural Language Processing-driven emotional support system that combines transformer-based emotion detection with evidence-based motivational psychology interventions to provide accessible mental wellness support. The system employs DistilBERT and RoBERTa architectures for sophisticated emotion recognition while implementing Self-Determination Theory and PERMA model principles for therapeutic response generation. The implementation utilizes a modern technology stack including FastAPI backend, MongoDB database, and React.js frontend with a comprehensive security infrastructure ensuring privacy protection and HIPAA compliance. The evaluation involved diverse participants across multiple demographics during an extended intervention period, demonstrating significant improvements in depression, anxiety, and stress measures alongside enhanced self-efficacy scores. Technical performance validation confirmed robust emotion detection accuracy while professional assessment by licensed mental health practitioners verified response quality across empathy, appropriateness, and safety dimensions. Behavioral impact assessment revealed strong goal achievement rates and sustained skill retention at follow-up periods, indicating successful facilitation of long-term behavior change. Safety validation protocols confirmed zero adverse events while maintaining appropriate crisis detection and professional referral capabilities. The article establishes that AI-driven mental health support systems can provide effective, accessible therapeutic benefits while complementing rather than replacing human mental health care, offering scalable solutions to address mental wellness challenges across diverse populations.
End-to-End Supply Chain Integration: John Galt Demand Planning, Oracle Cloud ERP, and Critical Manufacturing MES
Demand planning and manufacturing execution integration is a strategic need of the present-day manufacturing organizations in an environment of unprecedented supply chain volatility and market complexity. Integration of the high-level platform of demand planning created by John Galt, the supply chain execution platform of Oracle Cloud ERP, and a real-time production controller implemented by Critical Manufacturing MES creates a connected, closed-loop ecosystem, which aligns the forecasting, planning, and execution of the whole value chain. This architectural framework eliminates traditional data silos, reduces information latency, and aligns operational decisions with real-world production performance through continuous feedback mechanisms. Organizations implementing this integrated model across medical device, electronics, automotive, industrial equipment, and consumer goods manufacturing sectors achieve substantial improvements in forecast accuracy, significant reductions in inventory investment, enhanced schedule adherence, and notable decreases in production disruptions. The integration architecture establishes Oracle Cloud ERP as the master data governance platform, leverages John Galt's machine learning-driven forecasting capabilities for demand prediction, and utilizes Critical Manufacturing MES for granular shop floor control with complete material traceability. Event-driven architectures in real time allow the production real to stream back through the planning systems and formulate self-optimizing engines of intelligence that keep on optimizing based on measured performance forecasting parameters, safety stock policy, and lead time assumptions. Exception management systems are automated to detect and get rid of material shortages and capacity constraints, quality holds, and yield variances, and can dramatically decrease the decision latency and allow mitigation to be taken in advance before customer impact. This technical exposition provides a detailed discussion of the digital architecture, integration patterns, data movement, closed-loop feedback mechanisms, and industry-specific implementation results that can deliver highly responsive and intelligent end-to-end supply chain processes that can address modern manufacturing demands.
Experimentation at Scale: Deep Statistical Concepts for Trustworthy A/B Testing in E-Commerce
To stay competitive in the e-commerce space, retailers must always introduce new features to their website to enhance the user experience and improve operational performance. A/B Testing can help identify if these features have a positive impact by allowing retailers to conduct a controlled experimental design using statistical methods to experimentally compare a group of users who experience the new features against a group of users who do not experience the new features. When they are doing a test, a retailer must come up with a hypothesis that they will test, then measure and analyze by statistical methods a sample of users (for example, by setting up a sample size, choosing metrics and determining randomization procedures) to find statistically significant evidence that points to whether the test result is a real improvement or just caused by random chance. Besides this, if you fail to consider common pitfalls of the experimental design, like the novelty effect, selection bias, or problems related to multiple testing under the same conditions, the results of your experiments may be incorrect. Traditional A/B Testing is a reasonable tool for use in most experimental needs; however, advanced experimentation techniques provide additional capabilities when conducting experiments under more complex scenarios. Multi-Armed Bandit Algorithms can dynamically optimize the allocation of web traffic to different versions of a webpage while minimizing the cost associated with directing users to suboptimal experiences. Lastly, causal inference methods can enable businesses to measure the impact of changes on their websites without using randomization methodology, thereby allowing them to evaluate the effectiveness of changes across the entire platform. "Trustworthy Experimentation" combines a solid foundation of statistical methodology with experience gained from actual business decisions to help businesses learn and iterate more rapidly while reducing risk. The principles of valid experimentation provide a framework for businesses to implement valid experiments to support evidence-based decision-making as it relates to e-commerce.
Real-Time Embedded AI Framework for Autonomous Robotic Decision-Making
Autonomous robotic systems require sophisticated artificial intelligence capabilities while operating under stringent embedded computing constraints, including limited processing power, memory, and energy resources. This article introduces the Real-Time Embedded AI Framework (RE-AIF), a novel hybrid architecture that successfully integrates deterministic real-time control with adaptive AI inference within resource-constrained embedded platforms. The framework implements a three-layer hierarchical design separating perception, cognition, and execution functions while maintaining tight integration and temporal coordination across all system components. RE-AIF utilizes a strategic hybrid programming methodology combining C++ for deterministic real-time control with Python for flexible AI inference, achieving performance characteristics unattainable through single-language implementations. The architecture incorporates comprehensive sustainability features including dynamic voltage scaling, energy hibernation strategies, and memory optimization techniques that address the complete spectrum of embedded AI deployment challenges. Performance optimization strategies encompass lightweight AI model deployment through quantized convolutional neural networks, hardware acceleration utilization, and compile-time optimization techniques that maximize embedded system capabilities. Real-time orchestration mechanisms implement priority-based task scheduling, dynamic workload reassignment protocols, and predictive task mapping for power management, enabling sustained autonomous operation. Industrial applications demonstrate the framework effectiveness in precision assembly systems, continuous inspection operations, and cycle time enhancement while defense applications validate capabilities in autonomous navigation, swarm coordination, and sovereign autonomy scenarios. The implementation framework provides practical integration examples, including hybrid execution flows, neural network deployment, and real-time control loop implementations that demonstrate the viability of embedded AI deployment in mission-critical autonomous systems.
Modernizing Legacy Enterprise Systems: A Framework for Scalable Insurance Administration Platforms
Legacy enterprise systems create significant roadblocks for organizations trying to modernize. Insurance and financial companies still run applications built decades ago. These old systems can't keep up with what customers expect today. The shift from monolithic architectures to cloud-native platforms isn't straightforward. This article looks at how companies move from Apache Tapestry and Java EE systems to modern React.js frontends with Spring REST APIs. The transformation involves several stages: assessment, breaking things into modules, re-engineering code, moving data, and testing everything. Business logic needs to be separated out. APIs become the main way different parts communicate. Containers running on cloud infrastructure make deployment easier. What matters most are real improvements in how well systems scale, how easy they are to maintain, and how fast they perform. Insurance workflows like contribution management and billing show how these changes work in practice. Better performance, fewer bugs, and easier configuration are the main benefits. Modernization isn't just about technology. It's about giving organizations the ability to adapt quickly and stay competitive over the long term.
Predictive Monitoring for Distributed and Relational Database Management Systems (RDBMS): A Comprehensive Analysis
Predictive monitoring represents a paradigm shift in database management that transforms reactive maintenance practices into proactive optimization strategies. Traditional database monitoring systems detect problems only after they manifest, resulting in costly downtime and performance degradation. Predictive monitoring leverages machine learning algorithms, statistical analysis, and time-series forecasting to anticipate system failures, performance bottlenecks, and resource limitations before they impact end users. Both distributed databases and relational database management systems benefit from predictive capabilities that enable early detection of node failures, query performance degradation, and hardware malfunctions. Implementation strategies encompass automated scaling mechanisms, resource optimization algorithms, and capacity planning procedures that respond to predicted workload changes. Real-world applications demonstrate significant improvements in system availability, cost reduction through proactive maintenance, and enhanced customer experiences across e-commerce platforms, financial transaction systems, and healthcare data management environments. Advanced monitoring perspectives integrate with automation tools to create self-healing systems that perform root cause analysis and continually improve models. The operational benefits include substantial cost savings, minimized downtime, and optimized performance metrics. Strategic advantages encompass enhanced decision-making capabilities, competitive positioning through system reliability, and improved customer satisfaction. The evolution toward predictive database monitoring enables organizations to maintain high-performance, reliable systems that support critical business operations while reducing administrative overhead and infrastructure costs.
A Framework for Modernizing Legacy Core Banking Systems into Cloud-Native Microservices
Monolithic designs and rigid frameworks of the legacy core banking systems make the growth of the financial services industry difficult, along with the flexibility and the generation of new technologies. This article presents a comprehensive strategy for the migration of legacy banking systems to their cloud-native microservices architecture counterparts. The architecture design patterns, data migration methods, and models for systematic transition are part of the framework that assists banks and other financial organizations in making their systems resilient, their operations effective, and their ability to innovate at an increased speed. The strangler pattern provides step-by-step replacement options that create the least amount of turmoil while still permitting ongoing validation. Event-driven architectures are targeted at real-time data analytics, required for financial services today. Domain-driven design ensures that the boundaries of the services align with business concepts. Replication mechanisms and incremental migration procedures are two ways in which solutions to data transition address consistency problems. The phased migration models must compromise between goals of innovation and requirements for risk management by carrying out thorough assessments, pilot implementations, core functionality migration, and ongoing optimization. Only those financial institutions that will successfully undergo this change will have technology platforms capable of driving innovation while continuing to meet reliability and compliance standards that form the premise of regulated financial services.