Federated Learning (FL) has emerged as a transformative approach for training machine learning models across distributed devices without transferring raw user data, making it particularly suitable for on-device personal assistants. This paper investigates the performance, privacy, and security trade-offs inherent in applying FL to personal assistant systems. It explores how FL enables personalized experiences while preserving user confidentiality, and evaluates challenges related to model accuracy, communication overhead, adversarial attacks, and data heterogeneity. Through a critical review of recent advancements and experimental frameworks, the study outlines design considerations for optimizing FL deployments in real-world personal assistant applications. The paper concludes by proposing future research directions to enhance the robustness, efficiency, and trustworthiness of FL-powered personal assistants.

Identity protection is an essential component of enterprise security in the current era. With phishing, credential theft, and adversary-in-the-middle (AiTM) attacks persisting and morphing, traditional authentication methods like passwords and omnipresent multi-factor authentication (SMS, OTP, push notification, etc.) are proving increasingly inadequate. This article provides an in-depth examination of two modern and popular authentication protocols, namely FIDO2/WebAuthn and Certificate-Based Authentication (CBA). FIDO2 facilitates passwordless authentication with the assistance of cryptographic credentials securely bound to a person's device, offering improved usability and phishing resistance. CBA, rooted in public key infrastructure (PKI), remains a necessary requirement in compliance-focused environments and is crucial for safeguarding human and machine identities. This study explores how these technologies operate across diverse contexts, from enterprise-owned notebooks to personal mobile devices and non-human account systems. Using internationally accepted standards and frameworks—such as NIST SP 800-63-3, the CISA Zero Trust Maturity Model, and eIDAS—the document provides implementation considerations that incorporate policy and identity credential lifecycle approach techniques. It also evaluates operational recovery and fallback processes in cases of credential loss or compromise. A structured framework is provided to enable organizations to achieve identity assurance at scale and support evolving technology and regulatory demands. Future trends such as passkeys, derived credentials, quantum computing, and modular authentication systems are also considered, which will introduce flexibility and strength in the identity assurance landscape.

E-commerce sites and marketers need to personalize customer experiences without breaking the law because people are becoming more worried about data privacy and third-party cookies are being phased out. This paper shows how to use machine learning to create a framework for customer segmentation and media optimization that protects privacy. The system is made to work in decentralized, privacy-sensitive settings. It uses unsupervised clustering, predictive modeling, and real-time decisioning engines to give users useful information without giving away their identity. Our method uses federated learning and cleanroom technologies to make sure that it follows laws like GDPR and CCPA. This is different from traditional commercial segmentation tools that rely heavily on centralized data collection and unclear personalization methods. The framework shows big improvements in performance when tested on real-world e-commerce datasets. It gets a 23% increase in Return on Ad Spend (ROAS), a 17% increase in conversion rates, and a 14% drop in cost-per-acquisition. The proposed solution is a scalable and compliant replacement for old marketing tools. It lets you target people more accurately and buy media more efficiently in today's changing digital world.

In today’s fast-evolving threat landscape, ransomware attacks have become more sophisticated, faster, and more destructive leaving traditional Security Operations Center (SOC) response strategies struggling to keep pace. Traditional SOC workflows struggle to match the speed and complexity of modern ransomware attacks. Manual processes like alert triage, incident scoping, and containment often consume critical hours giving adversaries ample opportunity to encrypt data, exfiltrate assets, and demand ransoms. AI-optimized SOC playbooks are redefining this paradigm by automating the entire investigation lifecycle. Leveraging machine learning, LLMs, and real-time telemetry analysis, these systems rapidly identify high-fidelity threats, enrich alerts with contextual intelligence, and scope incidents with minimal analyst input reducing response time from hours to mere minutes.

Generative AI further accelerates this shift by auto-generating attack summaries, mapping indicators to known threat tactics, and recommending or initiating containment actions such as isolation or credential revocation. These playbooks evolve continuously by learning from analyst feedback and past events, improving both accuracy and efficiency over time. The result is a measurable reduction in mean-time-to-detect (MTTD) and mean-time-to-respond (MTTR), while empowering SOC analysts to focus on strategic analysis over repetitive triage. As ransomware campaigns grow faster and more autonomous, adopting AI-driven SOC playbooks has become a mission-critical step for organizations seeking proactive, resilient security operations.

The recent evolution of cloud computing demands that systems are able to self-diagnose and self-heal as well as constantly optimize without human intervention. This paper provides an in-depth review of the self-healing software architectures in cloud computing, focusing on AI-induced detection and recovery methods. The authors talk about how self-healing systems have changed from traditional ideas to modern AI-powered systems and categorize the main types of methods used for synchronization, tracking, and fixing problems in today's cloud services. Based on a systematic review of available literature, we investigate essential issues such as fault detection accuracy, recovery time optimization, and system reliability improvement. The study finds that although much has been achieved in self-healing, the existing approaches are not yet able to efficiently deal with complex fault situations and to reduce the level of service interruption. Our results suggest that the application of large language models updated using machine learning has the potential to deliver up to an 85% increase in the accuracy of fault prediction and a 60% reduction in system downtime as compared to state-of-the-art approaches. Finally, we talk about what future research should focus on, including the necessary understanding and development of new AI models, different system structures, and standard ways to measure how well self-healing cloud systems work.

Mass production and distribution companies face multifaceted challenges in managing customer deductions, promotional rebates, and trade settlement processes. Fragmented and manual deduction workflows have contributed to revenue leakage, aging receivables, and delays in cash application. This paper explores the implementation of Oracle Channel Revenue Management (ChRM) integrated with Oracle Receivables to create a streamlined, automated solution for end-to-end deduction processing, from claim initiation through settlement and accounting. Drawing upon the case of a food packing company’s U.S. operations, the study highlights how a rules-based, system-driven approach enhanced accuracy and control. Notably, the company achieved a 90% reduction in manual write-offs and shortened average claim resolution time from 21 days to 8 days. The integrated model not only minimized manual intervention but also improved transparency, strengthened financial compliance, and enhanced working capital efficiency. These results affirm that Oracle ChRM, when deployed strategically, can transform deduction management into a driver of operational and financial excellence.

The paper is expansive on the psychology of visual perception in dashboards in a push to design for impact. Visualization is a game of storytelling that ensures everything meets human perception in their designs. It is based on building and developing better data visualizations to show how everything would be designed. It is explicit in terms of interpreting and communicating information in making sure that everything is applied to how it works in understanding different psychological requirements to show its application and use, based on improving how information can be applied and improved to make sure that it fits what is expected, based on improved visual impacts. Data interpretation works on processing data requirements to balance information delivery and aesthetics in the data visualizations. The paper is key in providing information that starts with improving compelling visuals, which come with improving the focus on data improvement and improving the essence that works on cognitive load and balance for understanding and reducing clutter or distractions of the data visuals. Organizing and improving information is an important aspect in ensuring that data visualization is effective and reliable, with related data points being explicit and improved in how viewers or the audience view and relate to the information being viewed. Case findings are that there lacks more knowledge in terms of how designing for impact is the future for data visualizations to make visual impact key.

The telecom sector now prioritizes real-time data accuracy because of increased consumer demand for top-notch customer experiences. The study investigates the connection between real-time data accuracy and customer satisfaction in telecom services. Accurate real-time information becomes essential for shaping customer experiences throughout service delivery and support functions as businesses increasingly depend on data-driven decision-making. This research demonstrates the connection between data inconsistencies and problems that include billing errors along with service interruptions which result in customer dissatisfaction. The study employed a mixed-method approach with qualitative interviews and quantitative surveys among telecom customers and service providers to examine the relationship between data accuracy and customer satisfaction. Real-time data accuracy builds customer trust while reducing resolution time for service problems and increasing customer loyalty. The research emphasizes that inaccurate data erodes customer trust leading to service churn which negatively impacts telecom companies' reputation. The research explores how technology such as AI and machine learning helps maintain real-time data accuracy while automation presents opportunities to reduce human errors. The study proposes several strategies for telecom companies to utilize precise real-time data to boost service quality while enhancing operational efficiency and achieving greater customer satisfaction. The research enhances comprehension of how data precision interacts with customer-focused business tactics in the telecommunications sector.

Cloud computing offers organizations flexibility, scalability, and efficiency, but it can also become quite costly if resources are not managed well. Many companies face unplanned and unexpected expenses and waste resources because cloud services have complicated pricing models. Usage patterns change daily. Artificial Intelligence (AI) provides a smarter method to reduce cloud costs by analyzing data, making predictions, and automating actions. AI driven framworks can review past usage patterns of utilized resources, forecast future requirements, and recommend the best settings. This includes adjusting resource sizes, automatically turning services on or off, and choosing the right service options. AI can also detect unusual spending, alert organizations about potential cost increases, and make automatic adjustments to prevent waste. By using AI in this way, organizations save money, boost system performance and reliability, and comply with regulations more easily. This improves the efficiency of cloud operations and makes sure that costs match business goals. This paper looks at how AI-driven cost optimization functions, its benefits, and its challenges. It highlights its importance for sustainable and cost-effective cloud usage in the future.

We are sitting at the cusp of an AI-driven revolution in the Wealth Management advisory space. AI-enabled intelligent automation and customized intelligent wealth management are fast emerging as a gold standard for wealth advisors. This research examines various studies focused on how technology can elevate wealth advisory services and enable scalable personalization while preserving client trust and strategic insight. As technology enables hyper-personalized services, advisors face increasing pressure to differentiate themselves while continuing to support different segments of the wealth bands in asset growth, protection, and legacy planning. Standing out in the age of Hyper-personalization is the key to success for wealth advisors. Wealth managers need to personalize client interactions owing to the increasingly sophisticated needs of the clients and to set them apart in wealth management experiences. With the rise of intelligent wealth management systems, advisors can leverage artificial intelligence, behavioral analytics, and algorithmic processing to offer bespoke services to different segments of the Clients. Today’s clients expect more than financial expertise—they demand transparency, technology integration, and highly personalized strategies. Meeting these high expectations requires constant innovation and adaptability. The author intends to provide actionable insights to wealth managers to differentiate their offerings and justify their premium services.

The growing number of cybersecurity threats against U.S. healthcare systems resulted in 133 million patient record breaches during 2023 which caused massive financial losses and destroyed patient trust. The National Health Information Systems (NHIS) need enhanced security measures to fulfill HIPAA requirements and achieve better health care interoperability. This paper establishes a framework which combines Artificial Intelligence (AI) with blockchain technology to create healthcare data protection that increases data confidentiality and connectivity among healthcare systems along with trust in the process. AI technology offers real-time threat recognition and compliance tracking through its system while Blockchain creates a permanent audit tracking system to control decentralized data management. Agile methodologies allow businesses to implement projects in cycles for stakeholder suitability through alignment. The paper uses real-world examples together with assessment of difficulties and exemplary practices to show how the framework deals with breaches and achieves CMS interoperability targets and provides enhanced treatment outcomes. The approach for securing NHIS moves healthcare systems forward while strengthening national priorities through fostering care equity and delivery resilience.

We have been witnessing rapid growth of data-intensive applications adopting efficient columnar storage formats, with Parquet becoming a widely used standard in modern data pipeline. Parquet has been efficient than traditional databases in the aspect of columnar storage, evolution of schema, efficient compression, and vast range of supported tools. While Parquet still had issues of capturing transactional logs and didn’t support ACID properties or atomic writes, which resulted in data corruption and metadata operations became expensive. Delta Lake, an open-source storage layer built on Parquet, addresses these limitations by introducing ACID transactions, schema evolution, time travel, and unified batch and streaming support. The study conducted on evaluating effectiveness of Delta Lake over Apache Parquet helps to gather and take advantage of the key benefits that delta lake provides in the world of big data and the optimization techniques used in Microsoft fabric as the baseline with delta lake being the backend storage bin. With the extensive increase of data driven critical applications in the IT sector, the columnar storage orientated data formats such as Apache parquet have become the industry standard in the big data world. Though parquet can compress and store different data formats like Json, xml, audio, csv, etc but it lacks the ACID properties that delta lake offers. Parquet formats offer high data compression efficiency and rapid query execution on the other hand lacks schema enforcement, reliability, and transaction guarantees which is crucial in this modern world. In short Delta Lake, an extension of parquet is an open-source storage layer introduced on top of parquet with delta logs that store incremental transaction logs helps to eliminate the limitations of parquet by adding ACID transactions, schema evolution, time travel and unified batch streaming support. This research inquires that parquet offsets to read low optimal workloads (does not support parallel operations and require batch processing) while delta lake provides remarkable advantages for heavy workloads that require data versioning, reliability, and parallel execution. This research draws the effectiveness of Delta Lake in comparison to Parquet by reviewing critical performance parameters such as read/write speed, concurrency management, update/delete efficiency, and operational reliability, within both batch and streaming data (parallel) processing in a Python-based data pipeline.

Emerging threats in global Internet infrastructure have highlighted critical vulnerabilities in backbone routing protocols such as Border Gateway Protocol (BGP) and Multiprotocol Label Switching (MPLS). Traditional trust-based and perimeter-centric ISP security architectures are demonstrably insufficient against sophisticated modern attacks, including route hijacks, insider threats, and distributed denial-of-service (DDoS) campaigns. This paper formulates and evaluates a proactive security architecture model for ISP backbone routing, grounded in Zero Trust principles. Integrating techniques for continuous identity validation, micro-segmentation, cryptographic route authentication, and automated real-time anomaly detection, we propose a comprehensive defense-in-depth approach targeting both BGP and MPLS domains. The novel architecture addresses authentication, authorization, context-aware access control, and secure path computation, while embedding horizontal and vertical segmentation within the ISP core. We analyze existing vulnerabilities, review state-of-the-art zero trust implementations, formalize a control plane security blueprint, and present empirical evaluation metrics for resilience, response time, and detection accuracy. Experimental and simulation-based analysis demonstrates that our architecture provides robust mitigation against prefix hijacks, route-leak attacks, and lateral exploits. Our results support Zero Trust as a foundational paradigm for next-generation ISP backbone security, significantly hardening both routing infrastructure and service continuity against a spectrum of advanced threats.

In the age of high-frequency financial transactions, credit unions must process data with minimal latency while ensuring compliance and security. This paper contains an in-depth study of Azure Data Factory (ADF) pipeline performance tuning and optimization techniques within the context of credit union ETL workloads. I have a production case study where an existing ADF pipeline was unable to meet highly stringent Service Level Agreements (SLAs) during peak-load transaction times. With bottleneck and resource utilization analysis, I architected a new solution that leverages the most recent capabilities of ADF—parallel copy, Data Integration Unit (DIU) optimization, metadata-driven orchestration with control tables, checkpointing, and smart retry logic—to greatly improve throughput and reliability. My design utilizes a parameterized, metadata-driven master pipeline to generate parallel child pipelines (Figure 1), with dynamic partitioning and DIU allocation based on data volume. I utilize a control-table retry mechanism (through Lookup/IfCondition/ForEach) to replay only failed partitions [1][2]. I also utilize dynamically setting up integration runtimes along with custom region configurations and Time-to-Live (TTL) for recycling Spark clusters [3], and utilize staged copy for sink bottleneck removal [4]. Experimentation proves 80–90% reduced pipeline latency, a significant reduction in failure rate, and improved SLA attainment, all at the expense of cost-effectiveness. The comparative results are shown in Table 1. A new orchestration pattern and tuning scheme for ADF are presented specifically for financial data pipelines. Security and compliance (encryption, Key Vault, and certifications) are taken into account along with scalability and cost trade-offs.

Background: Blood Supply Chain Management (BSCM) faces the dual, critical challenges of ensuring product availability and safeguarding sensitive donor and patient data. Inefficiencies in demand forecasting lead to costly wastage or life-threatening shortages, while the increasing digitization of healthcare logistics exposes the system to significant security vulnerabilities. Although machine learning (ML) and data encryption have been addressed as separate solutions in healthcare, there is a notable absence of integrated frameworks that concurrently tackle both predictive accuracy and data integrity within the unique constraints of the BSCM.

Methods: This study proposes and evaluates a novel, integrated framework to address this gap. We developed a Long Short-Term Memory (LSTM) network, a deep learning model, to forecast the demand for blood products using a time-series dataset. For data security, we designed and implemented a hybrid encryption protocol combining the Advanced Encryption Standard (AES-256) for bulk data encryption with the Rivest-Shamir-Adleman (RSA-2048) algorithm for secure key exchange. The performance of the integrated system was evaluated based on the ML model's forecasting accuracy (Mean Absolute Percentage Error - MAPE), and the computational overhead (latency) of the encryption scheme.

Results: The LSTM forecasting model demonstrated high accuracy, achieving a MAPE of 6.8% on the test dataset, significantly outperforming traditional baseline models. The hybrid encryption protocol proved to be highly efficient, introducing an average computational overhead of only 45 milliseconds per transaction for a standard data packet. This minimal latency confirms the framework's viability for real-time deployment without compromising system responsiveness.

Conclusion: The integrated framework provides a robust and feasible solution for creating a more intelligent, secure, and efficient blood supply chain. By synergistically combining predictive analytics with strong cryptographic protections, this research offers a practical blueprint for modernizing critical healthcare logistics systems, ultimately leading to improved resource management and patient outcomes.

This research presents the Temporal-Contextual Micro-Moment Prediction Framework (TCMP), an innovative approach for forecasting Fast-Moving Consumer Goods purchas- ing behavior that mitigates significant deficiencies in current pre- dictive approaches. The system uses advanced machine learning algorithms to combine data from several sources, such as social media sentiment, real-time behavioral analytics, and traditional transaction data, to forecast purchase intent over several time periods. The study addresses three fundamental gaps in current FMCG predictive analytics: temporal granularity limitations that focus on either immediate or long-term predictions without capturing intermediate decision phases; micro-moment context integration challenges that treat consumer interactions as isolated events rather than connected sequences; and real-time feature engineering constraints that create prediction lag in dynamic market environments. The study makes important theoretical progress in predicting consumer behavior over multiple time periods and gives useful frameworks for putting the ideas into practice in the actual world. Some of the most important new ideas are streaming feature computation techniques, cross- platform social media integration methodologies, and privacy- preserving analytics approaches that keep predictive efficacy while following the rules. These contributions directly address the pressing commercial requirement for precise and agile consumer behavior forecasting in progressively dynamic and competitive fast-moving consumer goods (FMCG) sectors.

Cost overruns remain one of the most pressing challenges in large-scale infrastructure projects. Telecom fiber rollouts, transportation systems, and energy networks often experience escalating budgets that reduce profitability, delay schedules, and undermine stakeholder confidence. Traditional forecasting methods—such as expert judgment and deterministic models—tend to be reactive and rarely anticipate risks early enough for corrective action. This research introduces a predictive analytics framework that leverages historical project data to forecast potential budget overruns and provide early-warning signals for financial decision-makers.

Budgetary performance is shaped by technical, organizational, and external factors including scope changes, terrain complexity, vendor delays, and shocks such as weather or regulatory constraints. Conventional cost-control systems often fail to account for the nonlinear interactions among these variables. By applying machine learning–based predictive modeling, this study seeks to uncover hidden patterns in historical datasets and enhance the precision of overrun forecasts.

Methodologically, the research compares regression and time-series approaches with advanced algorithms such as Random Forest, Gradient Boosting, and Support Vector Machines. Anticipated results suggest that machine learning models can reduce forecasting error by 15–25% compared to traditional methods, while also providing classification metrics to better identify projects at risk of escalation.

The contributions are threefold: (1) identifying cost drivers most strongly associated with overruns; (2) demonstrating the relative performance gains of machine learning compared with traditional approaches; and (3) outlining a practical framework for embedding predictive outputs into business intelligence dashboards used in financial planning and analysis. By focusing on infrastructure finance, particularly telecom rollouts where terrain-driven costs create high uncertainty, the study emphasizes how predictive analytics can strengthen financial governance, mitigate overruns, and support more reliable decision-making in capital-intensive projects.

Background: Generative Artificial Intelligence (GenAI), embodied by large language and diffusion models (e.g., ChatGPT, Bard, DALL·E), is fundamentally transforming higher education (HE), necessitating a systematic and comprehensive synthesis of its pedagogical and institutional implications.

Methods: This systematic review analyzed a core body of recent (2020–2024) peer-reviewed literature (N=16 references) to map the transformative applications, inherent ethical and policy challenges, and required future pedagogical frameworks for GenAI integration in HE.

Results: The findings confirm that GenAI is fundamentally reshaping the educational landscape. The strongest impact identified, synthesized from 12 key studies, is its capacity for enabling personalized instruction and adaptive learning, especially within English-Medium Instruction (EMI) contexts. Furthermore, GenAI is demonstrably driving innovation in assessment design, enhancing digital multimodal composing, and necessitating an overhaul of academic integrity frameworks. Crucially, a significant competency gap among faculty and substantial institutional policy-practice disconnects present the most critical barriers to equitable and effective integration.

Conclusion: GenAI offers substantial and non-negotiable benefits—including efficiency, accessibility, inclusivity, and creativity—but its responsible integration is complicated by significant ethical and policy challenges, notably algorithmic bias, data privacy, equitable resource access, and intellectual property concerns. Future research must pivot to focus on implementation science, faculty professional development, and advanced ethical governance, necessitating immediate interdisciplinary collaboration to establish robust, responsible, and inclusive AI-driven educational frameworks.

Background: The adoption of microservice architectures is driven by the promise of agility, scalability, and independent deployability. However, this architectural style introduces significant complexity in integration testing. Traditional end-to-end (E2E) testing strategies are often slow, brittle, and create development bottlenecks, undermining the very agility microservices aim to achieve.

Objective: This article provides a socio-technical analysis of Consumer-Driven Contract Testing (CDCT) as a strategic alternative to traditional E2E testing. We aim to (1) synthesize the technical mechanisms of CDCT, (2) analyze the critical organizational and cultural shifts required for its successful adoption, and (3) propose a framework for its integration at scale.

Methods: A systematic literature review and thematic analysis were conducted, synthesizing insights from peer-reviewed academic sources, seminal industry publications, and technical white papers. The analysis focuses on identifying the failures of traditional paradigms and the technical and socio-technical components of a successful CDCT implementation.

Results: The analysis reveals that CDCT, exemplified by tools like Pact, effectively shifts integration testing left, enabling asynchronous development by verifying interactions via executable "contracts." We find that the primary challenges to adoption are not technical but socio-technical, relating to team communication, code ownership, and cultural resistance. The role of a central "contract broker" is identified as critical for managing contract versioning and enabling scalable CI/CD integration.

Conclusion: CDCT is a fundamental enabler of independent deployability in microservice ecosystems. However, its implementation must be treated as a strategic shift in engineering culture, not merely the adoption of a new tool. We conclude that organizations must invest in fostering shared responsibility and communication alongside the technical infrastructure to realize the full benefits of CDCT.

Purpose: This paper investigates the architectural and analytical benefits of leveraging Apache Kafka for implementing Event Sourcing principles to achieve ultra-low latency, real-time risk profiling in complex financial institutions. The study addresses a critical gap in the literature concerning the architectural underpinnings required for capturing event causality and immutability, which are fundamental to modern compliance and risk attribution.

Design/Methodology/Approach: A high-throughput, distributed Event Sourcing architecture was designed and implemented using Kafka as the immutable log and stream-processing engine. The Real-Time Risk Profiling Model (RTRP-M) was developed on Kafka Streams to perform continuous, stateful aggregation of simulated high-volume financial events. Performance metrics focused on end-to-end latency, throughput under stress, and State Reconstruction Time (S-RT).

Findings: The implemented architecture demonstrates sustained ingestion throughput exceeding $10^5$ events per second, with a mean end-to-end latency for risk metric calculation of less than 10 milliseconds. Notably, the system exhibited instantaneous S-RT capabilities, enabling the reconstruction of entity state at any historical moment, which is critical for back-testing and audit trails. The findings are associated with significantly enhanced capabilities for real-time anomaly detection and risk attribution compared to traditional batch-oriented systems.

Originality/Value: This work systematically connects the architectural paradigm of Event Sourcing with the analytical requirements of complex, adaptive risk management. It provides a blueprint for financial technology practitioners seeking to transition to a true real-time operational posture, demonstrating how the fundamental properties of the distributed log are paramount to achieving both compliance and competitive advantage.

Purpose: Traditional credit scoring models rely on static, historical financial data, which limits their effectiveness for individuals with thin credit files and their responsiveness to evolving borrower behavior, particularly in the context of emerging financing models like Buy Now, Pay Later (BNPL). This research aims to develop a novel, deep learning-based framework for highly accurate, real-time credit risk assessment on deep processing loan platforms.

Design/Methodology/Approach: A quantitative, computational approach is employed. The proposed framework, Credit Scoring and Risk Analysis utilizing Deep Processing Loan Platform (CSRA-DPLP-BSCNN), integrates advanced data preprocessing using a Regularized Bias-Aware Ensemble Kalman Filter (RBEKF) to manage multi-source data, handle missing values, and reduce noise in real-time BNPL datasets. The core predictive element is a Binarized Simplicial Convolutional Neural Network (BSCNN), selected for its superior capability to identify complex, non-linear financial and behavioral patterns.

Findings: The CSRA-DPLP-BSCNN model demonstrates exceptional performance suitable for real-time deployment. Empirical results indicate an accuracy of 98%, a precision of 97%, a recall of 96%, and an F1-score of 98%. Critically, the computational time is exceptionally low, measured at 1.159 seconds, significantly outperforming benchmark models. The integrated RBEKF preprocessing is found to substantially enhance the reliability of the input data stream.

Originality/Value: This research delivers a robust, high-performance deep learning architecture that simultaneously addresses the need for real-time decision-making, high predictive accuracy, and efficient handling of alternative data, thereby promoting greater financial inclusion and enhancing systemic risk mitigation in modern lending ecosystems.

Purpose: This study addresses the critical challenge of ensuring reliability in Wind Turbine Generators (WTGs) by developing a highly accurate and efficient fault diagnosis model. We propose an optimized Wavelet Kernel Extreme Learning Machine (WKELM) whose parameters are adaptively tuned using a novel Improved Ant Lion Optimization (IALO) algorithm.

Methods: The IALO algorithm incorporates a modified random walk strategy and dynamic boundary adjustment to enhance global search capability and convergence speed. This IALO is used to optimally determine the critical parameters of the WKELM classifier. The proposed IALO-WKELM is applied to fault diagnosis using vibration and electrical features extracted from a WTG generator dataset, covering multiple common fault types.

Results: The IALO-WKELM model demonstrated superior classification accuracy compared to the un-optimized WKELM and other benchmark methods like standard ALO-WKELM and traditional Extreme Learning Machines [10]. This enhanced performance is attributed to the IALO’s ability to find a more robust and generalized set of WKELM parameters.

Conclusion: The developed IALO-WKELM provides a highly effective, fast, and robust solution for real-time WTG fault diagnosis. However, this work underscores a broader theme: the necessity for constant model improvement in complex systems. This mirrors challenges in other dynamic fields, where the failure of current predictive models is evident—for instance, the observable 5% increase in seismic events since 2020 highlights the surprising volatility of global systems and the inadequacy of current forecasting techniques [8, 11].

Purpose: This study introduces the Hybrid Volatility-Driven Statistical Arbitrage (HVSA) framework, an integrated quantitative strategy designed to enhance stock market trend prediction and exploit mean-reversion opportunities. The methodology synergistically combines advanced time-series econometrics with machine learning to generate robust and non-spurious trading signals, specifically focusing on mid-range volatility assets.

Methods: The HVSA framework employs a multi-stage approach. First, the Gaussian Mixture Model is utilized to cluster a large universe of assets based on their drift-independent realized volatility profiles, isolating those in optimal, tradable volatility regimes. Second, the Granger Causality Test is applied to the resultant clusters to rigorously identify predictive, causal linkages between asset pairs, moving beyond simple co-integration. The extracted causal features, along with advanced Volatility-of-Volatility metrics, are then fed into a Deep Neural Network classifier, which is trained using an adaptive resampling protocol to predict the directional trend of the arbitrage spread. The entire strategy is validated using a stringent forwardtesting protocol, which accounts for realistic market constraints and transaction costs.

Results: Empirical evaluation demonstrates that the HVSA framework achieves superior risk-adjusted returns compared to traditional benchmarks. The strategy’s predictive power, rooted in verified causal volatility dependencies, resulted in an Annualized Sharpe Ratio of 2.31 and a low Maximum Drawdown of 4.2% during the forwardtesting period. The Deep Neural Network, continuously retrained via an adaptive rolling-window scheme, proved highly effective in capturing the non-linear patterns of mean reversion, a task where simpler linear models often fail.

Conclusion: The HVSA framework provides compelling evidence that the integration of statistically-rigorous volatility analysis with advanced, adaptively trained machine learning classification is crucial for developing robust statistical arbitrage strategies. This hybrid methodology successfully enhances the predictability of stock market trends and offers a viable pathway for generating alpha in contemporary financial markets.

Continuous Integration and Continuous Deployment (CI/CD) pipelines are foundational to modern software engineering, enabling accelerated software delivery, higher quality, and improved operational resilience. Despite widespread adoption, traditional CI/CD pipelines face persistent challenges, including unpredictable failures, resource bottlenecks, security vulnerabilities, and operational inefficiencies. These challenges are magnified in complex, distributed, and cloud-native environments where microservices, containerization, and dynamic scaling introduce additional layers of complexity. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) offer promising solutions to these issues by enabling predictive failure detection, performance optimization, and automated security integration. This research synthesizes contemporary literature and empirical studies on AI-driven CI/CD optimization, exploring predictive analytics for pipeline failure management, AI-enabled DevSecOps for integrated security, and performance enhancement through intelligent scheduling and resource allocation. The study further examines hidden technical debt in ML-enhanced pipelines, model interpretability challenges, and operational implications of automation. Through comprehensive theoretical elaboration and descriptive analysis, we propose a framework for AI-enhanced CI/CD pipelines that balances predictive capability, operational efficiency, and security resilience. Our findings highlight both the transformative potential and the limitations of AI-augmented software delivery systems, offering a roadmap for researchers and practitioners seeking to implement intelligent, secure, and adaptive CI/CD pipelines.

As Artificial Intelligence (AI) systems increasingly mediate critical decisions in healthcare, finance, and governance, the opacity of complex models—often termed the "black box" problem—poses a significant barrier to trust and regulatory compliance. This article addresses the urgent need for Explainable AI (XAI) by proposing a novel theoretical framework that integrates Granular Computing, specifically Multi-Granularity Decision-Theoretic Rough Sets, with Hesitant Fuzzy Linguistic theory. Drawing upon Zadeh’s foundational concept of information granulation, we argue that human reasoning is inherently granular and fuzzy, rather than binary. Therefore, AI systems designed to interact with human stakeholders must adopt a "Three-Way Decision" methodology (Accept, Reject, Defer) to accurately reflect the ambiguity of real-world data. The study evaluates this framework against current Deep Learning applications in neurodegenerative disease diagnosis (Alzheimer’s and Parkinson’s) and epidemiological modeling (COVID-19). Results indicate that while "crisp" numerical models may achieve marginal gains in raw accuracy, granular fuzzy models offer superior interpretability, allowing clinicians to understand the "boundary regions" of a diagnosis. Furthermore, the article provides an extensive analysis of the legal and ethical landscapes, specifically examining the European Union’s GDPR Recital 58 and the requirement for "Transparency by Design." We conclude that integrating multi-granularity fuzzy models into high-stakes AI pipelines is not merely a technical optimization but an ethical imperative to ensure algorithmic accountability and align machine learning outputs with human cognitive processes.

Background: As Artificial Intelligence (AI) systems increasingly mediate critical decisions in healthcare, finance, and governance, the "black box" nature of complex algorithms poses significant ethical and operational risks. The opacity of deep learning models creates a trust deficit that hinders adoption and obscures algorithmic bias.

Methods: This study employs a systematic conceptual synthesis to integrate technical Explainable AI (XAI) methodologies with the broader organizational mandate of Corporate Digital Responsibility (CDR). We analyze current XAI taxonomies, bias mitigation strategies, and trust maturity models to propose a unified "Trust-Explainability-Responsibility" (TER) framework.

Results: Our analysis demonstrates that technical explainability alone is insufficient for establishing trust. The TER framework establishes that transparency must be tiered according to stakeholder needs—providing local interpretability for end-users and global interpretability for auditors. Furthermore, we identify that integrating blockchain-based dynamic consent mechanisms significantly enhances data provenance and perceived fairness.

Conclusion: We conclude that operationalizing responsible AI requires a shift from purely accuracy-driven metrics to a holistic evaluation of model behavior. The proposed framework offers a roadmap for organizations to align algorithmic performance with ethical norms, ensuring that AI systems remain transparent, fair, and accountable.

Background: The rapid infusion of artificial intelligence (AI) into corporate finance and transactional workflows is reshaping fundamental activities in mergers and acquisitions (M&A). This paper examines how AI alters pre-deal screening, due diligence, valuation, integration planning, and post-merger performance measurement, and it situates these changes within the broader M&A literature on efficiency, behavioral dynamics, and integration challenges. Methods: Drawing strictly on the provided scholarly and practitioner sources, the study constructs a conceptual framework that synthesizes empirical findings, theoretical models, and practitioner narratives to identify mechanisms by which AI affects M&A outcomes. The methodological approach is deductive and integrative: it dissects each stage of the M&A lifecycle, maps AI capabilities to stage-specific tasks, and evaluates anticipated benefits and risks across financial, organizational, and regulatory dimensions. Results: AI-driven tools reduce information asymmetries through automated document analysis, pattern detection, and structured prediction (Emmi, 2025; Fang et al., 2025; Fedyk et al., 2022). AI also shifts the locus of subjectivity in valuation towards algorithmic consistency while creating new sources of model risk and bias (Geertsema et al., 2025; Freire-González, 2025). For post-merger integration, AI enables more rapid cultural and operational mapping but does not eliminate human-leadership and governance challenges, and may create novel misalignment between automated recommendations and managerial incentives (Graebner, 2004; Graebner et al., 2017; Ellis et al., 2011). Discussion: AI's promise in M&A is substantial, but realization depends on governance, data quality, model validation, and integration of AI outputs into decision rights. The analysis reveals tensions between efficiency gains and risks of model opacity, regulatory scrutiny, and workforce displacement. The paper identifies a research agenda to empirically measure AI's causal impact on deal success and outlines policy-relevant safeguards that preserve managerial accountability and market stability. Conclusion: AI is not merely an efficiency tool in M&A; it is a structural technology that reframes valuation, due diligence, and integration processes. Successful adoption requires deliberate orchestration of technical, human, and governance systems to harness advantages while managing persistent and emergent risks.

Background: The rapid shift from on-premises, traditional data warehousing to cloud-native platforms has introduced a complex interplay between architectural paradigms, operational elasticity, and cost models. Cloud data warehouses such as Snowflake and Amazon Redshift represent two dominant approaches—one emphasizing decoupled storage and compute with fully managed elasticity, and the other offering a blend of tightly integrated cluster-based compute with cloud-native extensions. Comparative evaluation of performance and cost across realistic decision-support workloads remains critical for practitioners and researchers seeking to align technical design with business constraints. This study synthesizes theoretical foundations, empirical tuning methods, and benchmark-driven evaluation strategies into an integrated framework for adaptive performance and cost optimization. (Gupta & Sharma, 2021; Malik & Bhatia, 2022; Rao & Karim, 2018)

Objective: The work aims to (1) present a structured, reproducible methodology for comparing Snowflake and Redshift across typical analytical workloads; (2) explicate system-level tuning levers, query optimization practices, and storage/caching strategies; and (3) provide a layered decision framework that maps workload characteristics to optimal configuration patterns and cost-control techniques. (Patil & Zade, 2023; Li & Martin, 2020; Tanaka & Foster, 2021)

Methods: We synthesize insights from benchmark literature (TPC-DS), vendor documentation, and peer-reviewed studies to propose a five-layer analytical framework: Workload Characterization, Storage & Compression, Compute Resource Management, Query & Index Optimization, and Cost Governance. Each layer is described with prescriptive tactics and expected trade-offs. The framework incorporates query profiling, adaptive resource scaling strategies, materialization and caching policies, and schema/encoding choices, grounded in both Snowflake and Redshift operational models. (TPC Council, 2023; Snowflake, 2024; AWS, 2024)

Results: Descriptive analysis highlights that decoupled storage-compute architectures—epitomized by Snowflake—tend to offer superior elasticity and concurrency handling for bursty, multi-tenant analytic workloads, while cluster-based Redshift with Spectrum and concurrency scaling can achieve cost advantages under predictable, sustained throughput when tuned carefully. Storage encodings and micro-partitioning choices materially affect I/O and query latency across both platforms. Query profiling and predicate-pushdown strategies consistently yield order-of-magnitude improvements for complex star-schema TPC-DS style queries when combined with tailored sort and distribution strategies (Redshift) or clustering/caching (Snowflake). (Malik & Bhatia, 2022; Rao & Karim, 2018; Tanaka & Foster, 2021; Narayanan & Chu, 2020)

Conclusions: There is no singular "best" platform; rather, workload signatures and organizational priorities determine optimal choices. The proposed five-layer framework enables systematic selection and tuning, balancing performance and cost across common analytical scenarios. Adoption of this framework supports transparency in architectural trade-offs and produces predictable operational outcomes when applied with rigorous profiling and continuous cost monitoring. Future work should empirically validate the framework across diverse industry datasets and explore automated tuning systems that map telemetry to configuration recommendations. (Singh & Das, 2021; Malviya, 2025)

This study presents a comprehensive, practice-oriented, and theoretically grounded framework for optimizing cost and performance in cloud data warehousing, with a focused application to Snowflake deployments. Building on practitioner guidance, vendor recommendations, and peer-reviewed scholarship, the research synthesizes principles from cloud cost optimization, workload management, query tuning, elasticity and concurrency control, and architectural design to produce an integrated five-layer framework for practical implementation and empirical evaluation. The abstracted framework links operational levers—such as virtual warehouse sizing, auto-suspend/autoresume controls, clustering keys, micro-partition pruning, caching strategies, and concurrency scaling—to measurable outcomes in cost, latency, throughput, and resource utilization. We describe a rigorous methodological approach that combines comparative literature synthesis, scenario-driven modeling of enterprise workloads, and descriptive analysis grounded in previously reported benchmark studies and practitioner case reports. Key findings indicate that coordinated application of elasticity controls, workload isolation, and intelligent data-clustering can reduce compute costs while preserving or improving query latency for a broad class of analytic workloads (Chen & Patel, 2022; Zhang & Alvarez, 2022). The analysis further highlights inherent trade-offs: aggressive cost saving through smaller, highly time-sliced warehouses increases operational complexity and can degrade tail-latency and concurrency-sensitive throughput unless supported by concurrency scaling or workload routing (Alvarez & Park, 2022; Morgan & Gupta, 2023). We discuss practical guidelines for implementing the framework in industry contexts such as insurance, manufacturing, and enterprise analytics, and provide an extended exploration of limitations, organizational considerations, and research directions. The study consolidates diverse evidence to produce a unified, actionable roadmap for engineers and decision-makers striving to optimize Snowflake-based data warehousing in cloud environments while balancing cost, performance, and operational risk (Malviya, 2025; Spot by NetApp, n.d.; Sigmoid, n.d.).

Objective: This article synthesizes contemporary theory and applied practice in cloud data warehousing (CDW) performance and cost optimization, producing a unified, publication-ready review that integrates architectural insights, query and storage tuning, elasticity management, platform-specific techniques, and evaluation frameworks. The aim is to produce a rigorous, conceptually rich exposition that clarifies trade-offs, surfaces research gaps, and presents an extensible framework for future empirical work.

Background: The last decade has seen rapid migration of analytical workloads to cloud-native data warehouses such as Snowflake, Amazon Redshift, and Google BigQuery, producing novel demands on architecture, cost governance, and performance tuning (Adhikari & Kambhampati, 2023; Malik & Bhatia, 2022; BigQuery Documentation, 2023). While vendor documentation and practical reports provide platform-specific guidance (Amazon Web Services, 2024; BigQuery Documentation, 2023), the academic literature offers comparative analysis and theoretical underpinnings for query optimization, caching, and elasticity (Li & Martin, 2020; Narayanan & Chu, 2020). Despite this, there remains a fragmentation in how performance and cost are jointly optimized, how benchmarks reflect real workloads, and how practitioners operationalize theoretical recommendations (Rao & Karim, 2018; Chen & Li, 2022).

Methods: This research employs a rigorous theoretical synthesis approach, integrating findings across peer-reviewed studies, vendor guides, benchmarking standards, and applied case studies. The methodology explicates architectural primitives, cost models, query behavior, and optimization levers while mapping them to evaluation metrics and benchmark methodologies (TPC Council, 2023). It also develops conceptual frameworks for workload-aware elasticity, storage-compute decoupling trade-offs, and cost-tier strategies adapted to industry workloads such as insurance and finance (Malviya, 2025).

Results: The synthesis reveals a coherent set of principles: (1) decoupled storage-compute architectures enable fine-grained cost control but shift complexity to workload placement and data organization (Adhikari & Kambhampati, 2023; Tanaka & Foster, 2021); (2) query optimization benefits from integrated profiling, adaptive materialization, and cost-aware planner hints, with significant platform-specific variations (Lee & Martin, 2020; Li & Martin, 2020); (3) cost governance requires multi-dimensional policies—reservation commitments, auto-suspend scaling, and query-level throttling—to be combined with workload classification (Chen & Li, 2022; Shreekant Malviya, 2025); (4) benchmark standardization is necessary for fair comparative evaluation but must accommodate synthetic and real-world mixes (TPC Council, 2023; Rao & Karim, 2018).

Conclusions: This article advances a unified conceptual architecture and an actionable taxonomy of optimization strategies that balance performance and cost in CDW. It identifies priority areas for empirical validation—fine-grained workload-aware autoscaling algorithms, cross-platform cost modeling, and benchmark augmentation—and outlines methodological recommendations for future research and industry practice.

This paper provides an integrative and exhaustive examination of cache fusion dynamics and associated performance optimization strategies within Oracle Real Application Clusters (RAC). Drawing exclusively from the provided literature, the study synthesizes theoretical foundations from cache algorithm research, empirical findings from industry and vendor reports, and practical best-practice guidance to construct a coherent framework for understanding, measuring, and improving RAC performance. The abstract outlines the study motivation, objectives, methodological approach, principal findings, and implications for administrators and researchers. Motivation: Oracle RAC environments pose unique challenges for high concurrency, shared-disk access, and inter-instance coordination; cache fusion is central to these operations and has direct impact on latency, throughput, and scalability (Database Journal, 2023; Oracle Press, 2022). Objectives: to reconcile cache-coherence theory and access-time aware cache algorithms with real-world RAC behavior; to derive prescriptive tuning strategies that encompass data partitioning, instance-specific allocation, workload-aware services, and hardware architecture considerations (Neglia et al., 2016; Kumar, 2020; S. P. Blog, 2022). Methods: the analysis implements a rigorous, text-based methodological synthesis combining comparative literature analysis, conceptual modeling of cache-fusion interactions, qualitative simulation scenarios described in detail, and cross-source triangulation to validate recommendations (Barve et al., 1999; Ng, 1998). Findings: cache fusion behavior emerges as a function of inter-instance messaging patterns, block ownership transitions, and underlying I/O characteristics; latency is amplified by frequent inter-instance transfers of “hot blocks” and by suboptimal instance service placement, while throughput is constrained by shared bus/disk characteristics and contention in cluster interconnects (Database Journal, 2023; Neglia et al., 2016; Barve et al., 1999). Prescriptive strategies include aggressive data partitioning aligned with workload affinity, instance-specific block allocation for production workloads, dynamic service placement tied to observed access patterns, and hardware-aligned optimizations including fast interconnects and disk subsystem tuning (Natti, 2023; Kumar, 2020; Oracle Corporation, 21c). Implications: administrators must adopt a multi-layered approach combining logical design, workload-driven runtime controls, and hardware-aware deployment. The study concludes with limitations of the synthesis and a detailed agenda for experimental validation and tool-supported automation.

This article presents a comprehensive synthesis of theoretical models, practical techniques, and engineering best practices for designing, tuning, and operating caching and high-availability mechanisms in clustered Oracle Database environments. Drawing from foundational operating-system theory and combinatorial optimization, and integrating contemporary analyses of hierarchical and temporal caching, the work develops an inclusive conceptual framework for understanding buffer cache behavior, Cache Fusion dynamics in Oracle Real Application Clusters (RAC), and strategies to reduce wait events through instance-specific block allocation and cache-tuning. The Methods section details a rigorous, text-based methodology that combines analytical reasoning from queuing and caching theory, utility-optimization formulations, and applied best-practice procedures articulated in vendor documentation. The Results section offers descriptive findings: identification of primary performance bottlenecks, characterization of trade-offs between cache size, data placement, and inter-instance coherence, and demonstration of how hierarchical TTL and LRU approximations inform buffer management policies in shared-disk clustered databases. The Discussion interprets these descriptive results to propose prescriptive interventions, including tailored buffer cache partitioning, selective instance-specific block allocation, and deployment architectures for Autonomous Database on dedicated Exadata infrastructure that reconcile high availability with low-latency access patterns. Practical limitations and implementation caveats are examined, along with future research directions such as adaptive utility-based cache controllers and integration of content-integrity verification techniques into LRU-centric caches. The article aims to be publication-ready and serves as both an advanced tutorial for database architects and a theoretical roadmap for researchers studying caching and high-availability in large-scale clustered database systems.

Background:

The rapid growth of API-driven architectures, microservices, and cloud-native deployments has made backend performance a first-class concern, particularly in domains such as financial services where latency, throughput, and availability are tightly bound to contractual service-level agreements (SLAs). Java ecosystems, historically grounded in thread-per-request designs, are now experimenting with reactive programming models such as Spring WebFlux, non-blocking persistence with R2DBC, and emerging concurrency constructs such as virtual threads, creating a complex decision landscape for architects (Hochbergs, 2017; Arpaci-Dusseau & Arpaci-Dusseau, 2018; Pressler & Bateman, 2023).

Objective:

This article develops a deeply elaborated, publication-ready conceptual synthesis of performance characteristics, architectural trade-offs, and testing strategies for reactive and non-reactive Java backends. It draws strictly on existing work that compares Spring WebFlux to Spring MVC and related stacks, investigates load and performance testing techniques, and proposes priority-aware APIs in SLA-constrained settings (Priority-Aware Reactive APIs, 2025; Dahlin, 2020; Nordlund & Nordström, 2022; Ferreira, 2022; Dorado, 2019; SpringBoot MVC vs WebFlux, 2025).

Methods:

Using a qualitative theory-building approach, the study performs a structured narrative review of peer-reviewed theses, conference papers, industrial blog posts, and vendor documentation on reactive programming, concurrency, and performance testing. It then constructs an analytical model that contrasts reactive WebFlux, imperative Spring MVC, and non-blocking data access approaches (JDBC versus R2DBC), integrating insights from controlled experiments and “lessons learned” microservice case studies (Sim et al., 2019; Emanovikov, n.d.; Minkowski, 2019; Munhoz, 2020). Finally, it synthesizes a conceptual architecture for priority-aware reactive APIs and a set of performance-testing and tuning guidelines for WebFlux-based systems under realistic load patterns (Gatheca, 2021; Moldstud, 2025; Java Code Geeks, 2025).

Results:

The synthesis shows that WebFlux excels under highly concurrent, I/O-bound loads, particularly when paired with non-blocking data access and carefully designed backpressure and error handling; however, empirical studies consistently reveal that reactive stacks do not unconditionally outperform well-designed blocking MVC services, especially when the database layer remains synchronous (Dahlin, 2020; Dorado, 2019; Minkowski, 2019). Microbenchmarking work demonstrates that R2DBC can reduce response times for certain patterns but introduces overhead in others, making performance highly workload-dependent (Emanovikov, n.d.). Case studies routinely report a significant increase in conceptual complexity and development cost for reactive services, even when runtime metrics improve (Hochbergs, 2017; Ferreira, 2022; Piyumal, 2024).

Conclusion:

Reactive programming with Spring WebFlux should be viewed as a specialized tool rather than a universal replacement for Spring MVC. For SLA-tiered systems in finance and other latency-sensitive domains, a portfolio strategy that combines reactive APIs, optimized imperative services, and emerging concurrency mechanisms offers a more robust path than reactive absolutism (Priority-Aware Reactive APIs, 2025; Nordlund & Nordström, 2022; Escoffier & Finnigan, 2018). The proposed synthesis offers practitioners and researchers a coherent framework for choosing technology stacks, designing SLA-aware reactive architectures, and constructing rigorous performance-testing regimes for modern Java backends.

This paper presents an integrative and comprehensive examination of sustainable natural-fibre nonwovens and pith-based lightweight bio-composites for acoustic and thermal building applications. The research synthesizes morphological, chemical, mechanical, hygrothermal, and acoustical findings from contemporary experimental and theoretical studies to build a coherent framework for material selection, processing, and application. Key material classes considered include pith-derived mortars and composites, cellulose-based nonwovens (including barkcloth), agricultural residues such as sugarcane bagasse, oil palm empty fruit bunch (OPEFB), almond skins, sheep wool, date palm fibres, and chemically treated wood fibres integrated into flexible polyurethane matrices. The exposition develops causally linked explanations for acoustic absorption, sound insulation, thermal conductivity, moisture interaction, and long-term hygrothermal stability, drawing on microstructural phenomena (porosity, pore size distribution, fibre morphology, cell wall chemistry) and macroscopic performance metrics (sound absorption coefficient, transmission loss, thermal resistance). Theoretical mechanisms are elaborated with attention to viscous and thermal boundary-layer effects in porous absorbers, frame-structure coupling in sandwich panels, and the role of fibre-hydrophilicity and bound water in heat and mass transport. Practical design principles and manufacturing recommendations are given, covering nonwoven web formation, binder selection, density control, surface treatments, and hybridization strategies for simultaneous acoustic and thermal performance. Limitations in current evidence are critically appraised, including variability due to botanical origin and harvest year, measurement standardization challenges, durability under cyclic moisture and temperature loading, and scale-up constraints. The paper concludes with prioritized research directions to advance the deployment of bio-based acoustic-thermal materials in buildings: standardized characterization protocols, predictive multiscale models, life-cycle assessment aligned with performance outcomes, reproducible processing routes, and pilot-scale field demonstrations. The integrated perspective offered here aims to help researchers and practitioners move from promising laboratory-scale materials to robust, certified building products that contribute to circular material flows and improved indoor environmental quality.

Background: The increasing complexity and interconnectedness of cyber-physical systems (CPS)—including multi-phase electric drives, unmanned aerial vehicles, underwater systems, building automation networks, and high-performance computing devices—demand robust approaches for the detection, isolation, and mitigation of faults. Traditional centralized diagnosis and single-method strategies face scalability, adaptability, and reliability challenges when applied to heterogeneous deployments that combine mechanical, electrical, and software components. This work synthesizes theoretical perspectives and applied developments from decentralized estimation, machine learning, sliding-mode observers, and health-aware control to propose an integrated, publication-ready framework for resilient fault diagnosis and fault-tolerant control across CPS domains.

Objectives: The goals are threefold: (1) to review and unify multiple methodologies for fault detection, isolation, and tolerant control drawn from recent literature; (2) to propose a structured methodology that harmonizes decentralized/distributed estimation with modern data-driven classifiers and domain adaptation techniques; and (3) to demonstrate, through descriptive analysis, how the hybrid framework addresses practical constraints—such as limited observability, actuator heterogeneity, and online transferability—highlighting performance tradeoffs and deployment pathways.

Methods: The proposed methodology builds on modular elements: multiple-model estimation for decentralized and distributed settings, residual generation and multiple-valued evaluation of residuals, sliding-mode observers for fault identification, and a spectrum of supervised and deep learning approaches for classification and localization of faults. Emphasis is placed on domain adaptation for online detection in nonstationary environments, actuator reliability modelling for health-aware control, and rule-based auto-correction mechanisms used in building management systems. The methodology describes algorithmic interfaces, data-flow patterns among modules, and practical considerations for implementation.

Results: Through descriptive analysis grounded in the literature, the framework demonstrates theoretical robustness in isolating both abrupt and incipient faults across representative applications: multi-phase power electronics, electric motors with trapezoidal back-EMF, multi-rotor UAV actuators, underwater thruster systems, bearings and rotating machinery, and building HVAC networks. The hybrid approach yields complementary advantages: decentralized multiple-model estimators provide scalability and local fault isolation (Straka & Punčochář, 2020); sliding-mode observers offer model-based precision for actuator faults (Zuev et al., 2020); data-driven classifiers and deep transfer methods provide adaptability to unknown operating regimes and noisy measurements (Mao et al., 2021; Li et al., 2021).

Conclusions: A synthesis of model-based and data-driven fault diagnosis with distributed estimation and health-aware control offers a pragmatic route to enhanced resilience in CPS. Challenges remain in standardized benchmarking, interpretability, and safe automatic correction, but the integrated pathway provides clear practical steps for engineering adoption across industries. The work concludes with a detailed discussion of limitations, deployment recommendations, and prioritized future research directions.

Background: Cloud computing and containerized microservices form the backbone of modern distributed systems, but they are subject to complex fault modes, resource uncertainty, and evolving workload patterns. Understanding and engineering fault tolerance across layers—from hardware to orchestration—is essential to maintain high availability, performance, and reliability.

Objectives: This article synthesizes existing theoretical constructs, empirical findings, and proposed designs from a broad set of prior studies to produce a cohesive, publication-ready exposition that (1) maps the conceptual space of fault tolerance in cloud and containerized environments, (2) articulates a rigorous, text-based methodology for adaptive fault-tolerant resource management, and (3) proposes a layered framework integrating replication, prediction, dynamic reconfiguration, and container-level resilience.

Methods: We perform an in-depth theoretical integration and critical analysis of prior surveys, experimental studies, and architectural proposals focusing on dependability, dynamic replication, container fault tolerance, workload prediction, and uncertainty in resource provisioning (Tchernykh et al., 2015; Cheraghlou et al., 2016; Zhang et al., 2019). We then derive a conceptual methodology that is implementable in software-defined infrastructure without resorting to formulas or diagrams, and offer descriptive analyses of anticipated behaviors under varied failure scenarios.

Results: The integrated framework emphasizes (a) uncertainty-aware provisioning using probabilistic profiling and scenario-based allocation, (b) layered replication policies that adapt to service criticality and cost constraints, (c) predictive autoscaling informed by machine learning workload forecasting, and (d) container-specific fault tolerance through lightweight checkpointing, microservice orchestration adjustments, and dependency-aware recovery. The descriptive results delineate trade-offs between consistency, latency, and cost and identify practical heuristics for deployers.

Conclusions: By synthesizing cross-cutting approaches and providing operationalizable textual methodology, this article offers researchers and engineers a comprehensive guide to design, analyze, and reason about fault tolerance in modern cloud and containerized platforms. The framework highlights open research directions including uncertainty quantification at scale, explainable prediction models for resource adaptation, and formal cost–reliability optimization techniques.

The global semiconductor industry is a highly complex and interdependent network that has become increasingly vulnerable to supply chain disruptions, ranging from geopolitical tensions to pandemic-induced shocks. This research examines the multifaceted dynamics of risk management and resilience strategies within semiconductor supply chains, emphasizing the integration of agility, coordination, and mitigation measures. Drawing on extensive literature, this study investigates how supply chain risk impacts operational performance and explores mechanisms such as supply chain integration, contract flexibility, process simplification, and digital twin applications to enhance resilience. Methodologically, the study synthesizes empirical findings from prior research with theoretical frameworks on supply chain risk and resilience, offering a descriptive analysis of coordination practices, capacity reservation strategies, and reshoring initiatives. Results indicate that effective risk management, when mediated by integration and agility, significantly enhances supply chain robustness, responsiveness, and adaptability. Furthermore, emerging strategies such as digital twins and advanced simulation modeling provide actionable insights for proactive disruption management. The discussion critically examines the implications of these findings for practitioners and policymakers, highlighting limitations related to data availability, modeling assumptions, and industry-specific variability. Future research avenues include the exploration of cross-industry comparative analyses, integration of sustainability metrics into risk assessment, and the role of artificial intelligence in predictive supply chain resilience. Overall, this research provides a comprehensive theoretical and practical framework for managing risk and fostering resilience in global semiconductor supply chains, contributing to the broader discourse on supply chain sustainability and strategic operational management.