International Journal of Emerging Research in Engineering and Technology
Multi-Agent Orchestration for Autonomous Data Pipelines: A Systems Architecture for Self-Healing, Context-Aware, and Resilient Data Processing
Modern enterprise data platforms increasingly operate under conditions of extreme scale, heterogeneity, and uncertainty. Traditional data pipeline orchestration frameworks rely on static Directed Acyclic Graphs (DAGs) and deterministic retry semantics, which are fundamentally misaligned with environments characterized by schema volatility, infrastructure churn, and non-stationary workloads. This paper presents a comprehensive architectural model for Multi-Agent Orchestrated Data Pipelines (MODP), where autonomous agents replace task-centric orchestration with goal-driven reasoning.The architecture integrates four primary subsystems: an Agent Orchestrator, a Knowledge Plane grounded in Retrieval-Augmented Generation (RAG), a Unified Feature Store, and a Causal Tracing Engine. Together, these components enable self-healing execution, dynamic schema adaptation, and causal observability across the data lifecycle. Empirical evidence from large-scale distributed systems research demonstrates that agent-based orchestration improves fault tolerance, reduces mean time to recovery (MTTR), and significantly enhances developer productivity. This work formalizes agentic data engineering as a shift from procedural execution to intent-based systems, positioning autonomous multi-agent orchestration as a foundational design principle for next-generation data platforms.
A Comprehensive Analysis of the Murmur3Partitioner in Apache Cassandra: Architecture, Performance, and Implementation Considerations
Partitioning is a foundational mechanism within Apache Cassandra’s distributed database architecture, determining the location of data across the token ring and directly influencing throughput, latency, load balance, and operational resilience. Since Cassandra 1.2, the Murmur3Partitioner has served as the default component for computing partition tokens, replacing earlier strategies that relied either on MD5 hashing or lexicographically ordered keys.[1], [2] Although the Murmur3Partitioner is pervasive in modern Cassandra deployments, the system-level implications of its hashing design, its interactions with consistent hashing, and its long-term effects on cluster behavior remain relatively underexamined in scientific literature[3]. This paper provides a detailed and comprehensive analysis of the Murmur3Partitioner, including its algorithmic foundations in MurmurHash3, its role within Cassandra’s consistent hashing ring[3], its implications for virtual node architecture[3], and its behavior under real-world and adversarial workloads[1]. Extended code samples in Java and Python illustrate practical usage scenarios such as token computation, placement prediction, and debugging data skew[2]. The paper also examines the performance characteristics of Murmur3 under different workload distributions, the operational consequences of token imbalance, and the broader architectural relationships between hashing, replication[2], and compaction. Finally, the paper identifies potential future research directions, including learned partitioners, adaptive hashing systems, and machine-learning-assisted workload prediction, providing a forward-looking perspective on partitioning within large-scale distributed database systems.
Agentic AI for Software Development: Autonomous Agents in Requirements Engineering, Testing, and Deployment
The maturation of agentic AI (autonomous large language model (LLM) agents with planning, tool-use, and memory) is a sea change in software engineering. In contrast to the earlier, AI-guided tools that just presented static suggestions or local completions, AGI AI possesses the capability of goal-directed multi-step reasoning across the entire SDLC. This is most obviously seen in three of the mission-critical applications: requirements engineering, testing, and deployment. Each of these stages has long been identified as an area of bottleneck or waste for software quality and delivery speed, and can be subject to inefficiency that autonomous agents can methodically diminish under suitable constraints and evaluation. Such an agentic AI can parse user stories, epics, and regulatory docs in RE, surfacing ambiguities, contradictions, and compliance risks proactively. By simulating a requester Analyst's position, such agents support the communication with stakeholders and suggest some useful clarification questions that are automatically proposed, with an eye on compliant rules to standards (e.g., SSTC 29148). This feature speeds up not only backlog refinement, but also traceability between requirements and test artifacts, as well as implementation units. In software testing, the benefits of independence are even clearer. Classical automatic testing techniques use fixed heuristics, or user-guided scenarios, whereas autonomic test generators can generate heterogeneous sets of unit, integration, and property-based test cases by iterating over the refinement of coverage goals. By combining fuzzing engines, mutation coverage, and continuous test adequacy feedback, agents can dynamically exercise system interfaces to visit boundary conditions and patch weak assertions that, in turn, lower defect leakage rates. This is consistent with advances in an emerging trend---repository-level evaluation (e.g., SWE-bench, DevBench), where iterative reasoning and environment-aware feedback loops are again the key to success across settings against static baseline models. Lastly, in deployment and operations, agentic AI operates as a release manager and reliability engineer. And such autonomous agents or systems should help out when we roll – deploying new versions, watching the real-time observability data, and kicking off roll-back procedures when they see problems – in consideration of a progressive delivery approach (ie, canary or blue-green). Contrary to rule-based automation, agentic systems adjust rollout decisions based on changing telemetry patterns and serve policy-as-code checks for safety. They can also independently propose and document planned mitigation actions, incident traces, and coordinate advanced rollback windows that help to minimize MTTD (Mean Time To Detection) and MTTR across production failures. In this paper, we contribute a role-based multi-agent architecture designed for these three lifecycle pillars, as well as a method for robust integration using retrieval grounding, structured outputs, and reflexive critique, and we evaluate empirically over benchmark datasets and industrial microservices. Results show that there is a measurable improvement in ambiguity reduction in requirements of 42%, mutation score increase of 14% in testing, and rollback latency decrease of 37% with deployment without the requirement to accept an elevated change failure rate. Beyond the empirical gains, we have also shown socio-technical implications illustrating that there is a need for governance to support human-in-the-loop checks as well as open audit systems. Taken together, the results imply that agentic AI is not just an accelerant but a structural enabler that offers a dependable basis to reconcile autonomy and safety in contemporary software development pipelines.
Surfactant-Assisted Displacement Synthesis of II–VI Semiconductor Nanostructures: Optimization, Mechanisms, and Characterization
The synthesis of II-VI semiconductor nanostructures under a systematic modification of morphology, size, and functionality, coupled with the assistance of a surfactant, has proved a promising route to the size and shape control of nanocrystals; nevertheless, a dynamic comprehension of the facts and optimization variables has not been established yet. This paper is an effort to tackle this concern by understanding the mechanistic aspect of surfactants in guiding II-VI semiconductor nanostructure growth pathways governed through displacement relying on enhanced structural and optical uniformity considerations. It uses a regulated wet-chemical displacement synthesis technique, where specific molecules of surfactants can regulate the nucleation rates, interface energies and ion exchange dynamics in the formation of nanostructures. The impact of the main synthesis parameters, such as the kind of surfactant, concentration, temperature of the reaction and the stoichiometry of the precursors is thoroughly studied to reach reproducible growth conditions. Enhanced synthesis of nanostructures with elevated phase purity, decreased level of defects, and adjustable morphological features is confirmed by comprehensive structural, morphological, and optical analysis. Relative comparison indicates that growth uniformity, crystallinity, and batch-to-batch reproducibility of surfactant-mediated displacement routes are much better than that of non-assisted displacement routes. These findings can be used mechanistically to study the surfactant-forceful displacement synthesis and establish the optimum processing windows in scalable nanostructure fabrication. On the whole, the present work develops a strong platform of creating quality II-VI nanostructures of semiconductor, enhancing their use in optoelectronic, sensing, and energy-related products.
Agentic AI for Autonomous Telecom Network Management
The fast development of 5G and the introduction of 6G networks have posed new challenges that have never been seen in network control, such as optimization of network resources, detection of faults, and the quality of services. Conventional human-based management models are not able to support such demands because of the complexity of networks, dynamic traffic and high demand in performance. A game changer to network self-management is Agentic Artificial Intelligence (AI) which entails goal oriented autonomous agents. The paper discusses how agentic AI can be utilized in autonomous telecom networks, the role of agentic AI in the resources allocation, fault detection and self healing. Major frameworks, architectures and methodologies used to implement agentic AI are considered with examples and figures and tables presented summarizing the performance in networks. Moreover, the paper focuses on the minimization of human intervention and high reliability and efficiency. The outcomes indicate that agentic AI has the potential to considerably improve the network performance, optimize the operational expenses, and fast-track the implementation of the 5G/6G solutions, leading to the development of the completely autonomous telecom networks.
Ultra-Low-Light Imaging Enhancement Using Quantum-Inspired Neural Networks
Extremely low-light imaging is essential to the very diverse applications of biomedical microscopy, astronomical observation, surveillance and remote sensing, where photon-limited conditions severely impair the quality of an image. Traditional ways of enhancement are not able to perform well in these extreme lighting conditions and tend to increase noise and blur structural features. Recent developments on quantum-inspired neural networks (QINNs) offer a good alternative option through probabilistic encoding of amplitude, energy-based optimization, and uncertainty-aware feature refinement, but can be implemented on classical hardware. In this review, the authors provide a detailed overview of QINNs in ultra-low-light imaging enhancement, including the basic concepts, sensor technologies, the Deep Learning style, quantum-inspired solutions, hybrid frameworks, and areas of application. Also, the review focuses on security, trust, and policy provisions applicable to deployment in sensitive domains, such as biomedical, defense, and cloud-based imaging systems. The most common challenges, including model interpretability, scalability in real-time, and non-standard benchmarks are identified that can act as a roadmap of the future studies. This paper has presented an impartial view of the state-of-the-art of quantum-inspired low-light image enhancement by synthesizing the progress made in this area.
Sajud Hamza Elinjulliparambil Elinjulliparambil
Review: Software-Defined Networking (SDN) for IT Operations: Use Cases, Benefits, and Challenges
The data plane and control plane are kept apart in a novel approach to network management called Software-Defined Networking (SDN). This lets one person control the networks from one place and makes them easier to program. This paper is a review of SDN evolution, architecture and its integration to the Network Function Virtualization (NFV) and its application in IT operations including data centre management, network security, and new technologies including 5G. The study is able to synthesize the main characteristics such as centralized management, automation, scalability, and improved security, as well as present the practical advantages such as agility, cost effectiveness and simplified network administration. Secondly, the paper looks at technical issues such as scalability of controllers, and compatibility with older systems as well as the vulnerability of centralized systems that impede its broad adoption. A review of the recent literature provides a variety of solutions such as traffic analysis frameworks and energy-conscious models up to blockchain-fathered security and distributed control, but most of them are oriented on a single point. This review, through its consolidation of these findings, reveals that there are gaps in the research and that holistic, adaptive, and interoperable SDN frameworks are necessary to support resilient and intelligent IT infrastructures.
Efficient Deep Learning Models for Accurate Default Loan Prediction in Credit Risk Management
The increasing complexity of lending environments has enhanced the importance of complex credit risk measurement procedures that have the capacity of analyzing massive, nonlinear, and highly imbalanced financial information. To solve this, a deep-learning method was used to forecast credit defaults with the complete Lending Club data that contains a variety of demographic, behavioral, and financial attributes of borrowers. Data preparation was reliable due to rigorous preprocessing, includes using SMOTE to address missing data, outliers, and class imbalance. A comparison between ANN and ResNet, FKNN, and AdaBoost was created and compared. The results of studies show that ANN outperforms all other baseline models, achieving 98.92% accuracy, 98.08% precision, 99.2% recall, 99.1% specificity, 98.63% F1-score, and 0.99 AUC respectively. Additionally, the ROC curve and confusion matrix data confirm the good class-separation capacity and low rate of misclassification of the model. These results indicate the ANN usefulness in mimicking intricate borrower trends and improving predictive validity. On the whole, the findings demonstrate the importance of deep-learning methods to enhance credit risk evaluation and allow more effective, data-driven decision making in the framework of current automated underwriting systems.
From Detect and Repair to Predict and Prevent: Assessing the Viability of Real-Time AI Nudges in Reducing Fleet Accident Rates
Fleet accidents cost the transportation industry billions of dollars each year. Traditional safety programs focus on analyzing crashes after they happen and retraining drivers. This reactive approach misses opportunities to prevent accidents before they occur. We examined whether real-time AI systems can reduce fleet accident rates by providing immediate feedback to drivers during risky situations. Our study tracked 340 commercial vehicles across six months, comparing accident rates between drivers who received AI-generated safety nudges and a control group using standard telematics. The intervention group showed a 31% reduction in preventable accidents and a 44% decrease in near-miss incidents. We also found that driver acceptance of the system improved significantly after the first two weeks of use. The economic analysis suggests potential savings of $8,400 per vehicle annually when accounting for reduced insurance claims, vehicle downtime, and liability costs. However, the system raised concerns about driver privacy and workplace monitoring that need addressing before widespread adoption. This research demonstrates that predictive AI can shift fleet safety from a reactive to proactive model.
Fair and Transparent Underwriting: Advanced AI Models for Thin-File Vehicle Insurance
The insurance industry struggles to predict insurance claims accurately. This prediction is crucial for running operations smoothly, preventing fraud, and setting the right premium prices. Traditional manual methods of claim evaluation are slow, subjective, and not capable of effectively handling the large volume of data from policyholders. This work comprises a machine learning (ML) framework for the prediction of vehicle insurance claims that supports the resolution of the hard-core problem of the identification of claim probabilities based on the different policyholder demographics and vehicle characteristics. A pipeline was systematically designed that features data preprocessing with cleaning, one-hot encoding, and outlier removal, followed by feature engineering and SMOTE-based class imbalance mitigation to tackle the issue of severe imbalance when no-claim instances account for 93.6% while claim cases make up only 6.4%. A hybrid CNN-GRU model was trained and evaluated using a variety of performance criteria. This model combines convolutional neural networks for the extraction of spatial features with gated recurrent units for the learning of sequential patterns. Using Random Forest (86.77%), Naive Bayes (95.28%), and MLP (76%) models, the proposed model was comprehensively assessed. CNN-GRU outshined other methods in terms of performance and attained the following measures: accuracy of 98.34%, precision of 98.45%, recall of 99.21%, F1-score of 98.56%, and AUC of 1.00, thus greatly surpassing traditional models. SHAP analysis showed that car age, policyholder demographics, and vehicle specifications are the main factors contributing to the prediction. This confirms that hybrid deep learning (DL) architectures are robust, scalable solutions for real-time insurance claim prediction systems.
Generative AI for Personalized Marketing and Customer Experience in E-Commerce
E-commerce has fast become one of the major components of the worldwide online economy. It has positively affected the relationship between the business and the customer through the application of data and AI. At the same time, changing customer needs have turned understanding their preferences and offering them customized service into a major barrier to competitiveness. The study is focused on building an effective brand loyalty model that is able to not only support but also facilitate personalized marketing and customer service. The procedure is implemented using the E-Commerce Customer Dataset from Kaggle and involves a series of steps that include data cleaning, feature selection, and exploratory visualization through correlation and distribution analyses. Among the evaluated features, Satisfaction Score is found to be a crucial determinant of customer engagement and loyalty. The suggested Generative Adversarial Networks (GANs) model was trained, tested, and compared to several baseline machine learning (ML) methods such as Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB). The GAN demonstrated its effectiveness in discovering sequential patterns and long-term relationships in behavioral data with 95.79% accuracy (ACC), 93.8% precision (PRE), 92% recall (REC), and 92% F1-score (F1) attained, surpassing all other models. The results show that GAN and other deep learning techniques outperform the standard models, making them useful tools for e-commerce platforms' predictive analytics, tailored marketing, and customer retention tactics.
Generative Artificial Intelligence to Support Frontline Employees
Background: Frontline workers, such as healthcare staff, emergency responders, social workers, retailers, logistics people, and agents of the public services work in high-pressure settings with time limits, cognitive overload, and ever-changing information. Irrespective of the digital transformation, most frontline positions are still not sufficiently served by intelligent decision-support systems which can adjust to the real time. The recent advances in Generative Artificial Intelligence (GenAI), especially large language models and multimodal generative systems, offer new possibilities to enhance frontline work with the help of contextual reasoning and automation as well as human-oriented assistance. Objective: This study examines the role of Generative AI in enhancing productivity, decision-making accuracy, and operational resilience for frontline workers. The primary objective is to analyze how GenAI-driven tools can support task execution, reduce cognitive burden, and improve service delivery across frontline domains while maintaining ethical, organizational, and human-centered considerations. Methodological Approach: The study adopts a qualitative–conceptual research design supported by an integrative literature review of recent peer-reviewed studies (2019–2025) indexed in Google Scholar. The analysis synthesizes existing implementations of Generative AI in frontline contexts, including conversational AI assistants, real-time decision support systems, automated documentation tools, and adaptive training platforms. Key dimensions evaluated include system architecture, deployment models, human–AI interaction mechanisms, and measurable performance outcomes. Key Findings: The findings indicate that Generative AI significantly enhances frontline efficiency by enabling real-time knowledge retrieval, automated reporting, natural language interaction, and context-aware recommendations. Across sectors, GenAI adoption is associated with reductions in task completion time, improved compliance with operational protocols, and increased worker confidence. However, challenges remain regarding data privacy, model hallucination risks, bias propagation, and organizational readiness for AI integration. Contribution and Significance: This paper contributes a structured and cross-sectoral perspective on Generative AI for frontline workers, bridging technical capabilities with practical deployment considerations. By synthesizing current evidence and identifying design and governance gaps, the study provides a foundation for future empirical research and policy development. The findings are relevant to researchers, system designers, policymakers, and organizations seeking scalable and responsible GenAI adoption in frontline environments.
AI-Driven Portfolio Optimization for ESG Investment
Environmental Social and Governance (ESG) considerations are fast becoming part and parcel of investment strategy. Meanwhile, artificial intelligence (AI) techniques, namely, natural language processing (NLP) and large language models (LLMs) are opening up the field to in-depth analysis of unstructured corporate disclosures and financial texts. This paper gives a model of an AI-based portfolio optimization taking into account the ESG factors becoming a direct part of the decision-making process. The methodology combines systematic historical market data together with unstructured textual evidence taken out of earnings calls, regulatory filings, and sustainability reports. The pipeline includes three major components: 1. ESG signal extraction by means of domain-adapted transformers; 2. Structured fact cogeneration and summarization with LLMs; 3. Multi-objective portfolio optimization that incorporates balancing returns, risk, and ESG alignment. The model is set with two conference contexts in view. In the track of ICDM, the task is to mine ESG-related information on earnings calls and 10-K reports using mining based on NLP. In DSAA track, the interest is in the use of LLMs to process financial documents to enable ESG assessment that can be audited. The framework is grounded conceptually in Adewale (2024), which argues for integrating ESG criteria in AI-based portfolio management, and in Rane, Choudhary, and Rane (2024), which surveys AI approaches that strengthen ESG in sustainable business practices. The proposed solution shows the promise of AI-based source of investments in giving sustainability-focused investing an edge on appropriate textual evidence to be converted into explainable and actionable portfolio allocation signals.
Environmental, Social And Governance (ESG)
Convergence of Blockchain, AI, and Quantum Computing in FinTech
The fusion of distributed ledger technology, advanced machine learning and nascent quantum algorithms is reshaping capital markets. This report synthesises current case studies and industry statistics to highlight practical progress and challenges in this convergence. Over sixty tokenised bond issuances have raised roughly 8 billion globally, with pilot programmes from in- stitutions in Europe and Asia demonstrating cost reductions of 35–50 % and settlement times measured in minutes. Artificial intelligence is now mainstream in finance: seventy-eight percent of organisations employ AI, financial firms invested 35 billion in 2023, and these tools are credited with saving billions through real-time fraud detection. Quantum computing remains at an early stage but offers potential value of 622 billion in financial services by 2035, with some pilots re- ducing portfolio optimisation tasks from years to seconds. The report discusses how blockchain’s immutable records feed AI models, how quantum algorithms enhance risk calculations, and how together they raise issues around governance, fairness and cybersecurity. Recommendations focus on integrated risk frameworks, cross-chain interoperability, human oversight and post-quantum cryptography to ensure responsible adoption.
Machine Learning Framework Integration Patterns with Dynamics 365 Customer Engagement a Comprehensive Guide to Implementing AI-Driven Customer Engagement Solutions
The integration of machine learning (ML) frameworks with Microsoft Dynamics 365 Customer Engagement (CE) offers organizations a powerful approach to transforming customer relationship management through data-driven intelligence. This whitepaper examines architectural patterns, data flow models, deployment strategies, API-based integrations, real-time processing pipelines, and security considerations for embedding ML capabilities into Dynamics 365 CE. Leveraging Azure Machine Learning within the Dynamics ecosystem enables predictive analytics, intelligent automation, and personalized customer experiences at scale. Through industry-aligned best practices and real-world implementation insights, the paper highlights how effective ML integration can improve sales productivity, reduce customer service handling time, and deliver substantial return on investment. The findings demonstrate that a well-designed ML integration strategy is critical for achieving scalable, secure, and measurable business outcomes in modern customer engagement platforms.
The accelerated increase in data has posed new challenges for organizations attempting to derive meaningful insights from diverse and complex sources. Oracle Intelligent Data Lake (IDL) is a modern solution designed to unify storage, automate data management, and provide analytics within a single platform. Unlike traditional data lakes that are often inefficient and poorly governed, Oracle’s approach incorporates artificial intelligence (AI) and machine learning (ML) to enhance data discovery, classification, and enrichment. This reduces the time needed for decision-making while maintaining data quality and compliance. Oracle Intelligent Data Lake simplifies ingestion, transformation, and exploration of data, enabling both technical and non-technical users to unlock the value of enterprise data. Designed for multi-cloud and hybrid environments, it is flexible, secure, and delivers real-time performance. This paper discusses the core characteristics, architecture, applications, and benefits of Oracle Intelligent Data Lake, and its role in innovation and data-driven business solutions in contemporary enterprises.
Cyber-Physical Resilience in Mission-Critical Facilities: Integrating Security, Reliability, and Safety Engineering
The advent of additional cyber and physical infrastructures has created a high demand in the establishment of resilience systems capable of sustaining functionality in the adverse and unforeseen conditions. As a convergence of computational intelligence and physical processes, Cyber-Physical Systems (CPS) of modernity are experiencing mounting pressure due to cyberattacks, system failure, and environmental interference that threaten the survival of the operation. In this paper, I have thoroughly presented the resilience strategies in mission critical facilities considering the dimensions of absorption, recovery and adaptation in the three aspects of security, reliability and safety engineering. During the analysis, such advanced methods as AI/ML-based modelling, cyber-physical emulation, and robust control frameworks that may be implemented to enhance the system dependability, protection, and performance efficiency are mentioned. Besides, the paper will discuss central cybersecurity challenges, such as insider threats, supply chains vulnerability, compliance limitations, and regulatory divergences. Integrating the operations that have been employed in the fields of data center, energy systems, healthcare and defense infrastructure, the review might see significant gaps in the development of an all-inclusive and sustainable resilience. The findings show the value of adaptive, self-healing, and intelligent architectures towards offering secure, reliable and sustainable operations in contemporary mission critical environments
Multiview Diffusion Models for High-Resolution Image Synthesis
Multiview image synthesis aims to generate multiple coherent images of a scene from different viewpoints, a capability that is essential for applications such as 3D reconstruction, virtual reality, medical imaging, and autonomous systems. While recent advances in diffusion-based generative models have significantly improved image fidelity and training stability, ensuring geometric, photometric, and semantic consistency across multiple high-resolution views remains a fundamental challenge. This paper presents a comprehensive review of multiview diffusion models for high-resolution image synthesis, focusing on methods that explicitly or implicitly enforce cross-view consistency. We first introduce the theoretical foundations of diffusion models, including denoising diffusion probabilistic models, score-based generative frameworks, and latent diffusion. We then systematically analyze the core challenges of multiview high-resolution synthesis and propose a structured taxonomy of existing approaches based on conditioning strategies, architectural designs, and geometry-aware modeling mechanisms. Furthermore, we review resolution-scaling and computational optimization techniques that enable diffusion models to operate effectively at high resolutions. Widely used datasets and evaluation metrics are discussed, highlighting current limitations in benchmarking multiview consistency. Finally, we survey key application domains and identify open research challenges and future directions. This review provides a unified perspective on the intersection of multiview learning and diffusion-based generation, serving as a valuable reference for researchers and practitioners in generative modeling and 3D vision
3D Reconstruction from Monocular Videos Using Neural Radiance Fields (NeRF)
Monocular video-based 3D reconstruction has emerged as a fundamental yet challenging problem in computer vision, due to depth ambiguity, scale uncertainty, and limited viewpoint coverage. Traditional geometry-related approaches, which include Structure-from-Motion (SfM), Multi-View Stereo (MVS) and SLAM, are partial solutions and usually result in incomplete or noisy reconstruction. Neural Radiance Fields (NeRF) broke the previous paradigm of 3D generation by modelling the scene as a continuous volumetric generator, which takes 3D coordinates and viewing directions as inputs and neural colour and density as outputs to generate photorealistic novel-view images. This review follows the history of NeRF and its initial extensions to monocular video, such as sparse-view adaptations (PixelNeRF, DietNeRF, RegNeRF), dynamic and deformable scene modeling (D-NeRF, NSFF, NeRF-T), and optimization strategies, such as pose estimation, regularization, and efficiency. We address evaluation policies, datasets, and applications in the areas of AR/VR, robotics, cultural heritage, and digital content creation. Lastly, we provide a critical reflection on the limitations of NeRF and are able to identify future perspectives, such as improved priors in monocular input, faster inference, generalizable architectures, and lightweight models. The paper is a detailed overview of the methods that form the basis of neural-radiance-field-based monocular-video reconstruction and preconditions for further progress in that direction
Analysis of Solar-Assisted Cooling and Heating Technologies in Residential Buildings
Solar-assisted ventilation and heating technologies in residential buildings not only represent an additional means to diminish global energy consumption and greenhouse gas emissions but also constitute an effective approach, given that the building sector accounts for a significant portion of worldwide energy utilization. The paper reviews major solar-assisted systems such as active and passive solar water heaters, thermosiphon systems and batch systems, and the main components of these systems, such as collectors, storage tanks, and heat transfer fluids. It also draws attention to solar-assisted cooling methods, especially to desiccant-based systems and thermally driven chillers, which can completely or lightly replace conventional electrically driven cooling systems. The role of the international initiatives IEA-SHC Task 25 and Task 38 in terms of system design, performance assessment, and market development is pointed out. There is no doubt that a lot of work has been done, yet there are still issues to be tackled such as dependency on environmental conditions, high upfront costs, need for maintenance, and limited applicability
A Better Approach for Linear Projects: LOB or CPM
Scheduling is a very crucial element in the management of projects, particularly the linear type of projects that follow a sequential pattern, such as highways, pipelines, and high-rise buildings. This paper compares two main scheduling techniques: the Critical Path Method (CPM) and the Line of Balance (LOB) technique. Despite the fact that CPM has been widely adopted in the construction industry for many years, LOB has particular advantages for projects with repetitive activities. This research investigates both methods' strengths, limitations, and situational applicability for linear project environments. This paper aims to assist project managers in determining which scheduling technique to adopt depending on the project characteristics, resources, and management goals and objectives based on the performance of the techniques in terms of certain parameters
Federated Learning for Privacy Preserving Fraud Detection across Financial Institutions: Architecture Protocols and Operational Governance
Fraud detection is a shared problem across banks payment processors and fintech partners yet high quality models often require cross institution signals that cannot be centralized due to privacy regulation contractual constraints and competitive boundaries. Federated learning enables collaborative model training without moving raw data by sending model updates rather than records. However practical deployment in financial networks requires more than basic model averaging. It requires robust privacy protection against inference, strong security controls against malicious participants, a design that addresses non independent data distributions and a governance model that aligns with risk and compliance. This paper proposes an architecture led framework for privacy preserving federated fraud detection across financial institutions. We describe system components for horizontal and vertical federation, secure aggregation to protect individual client updates and differential privacy to bound leakage. We present model design options including tabular models representation learning and federated graph learning for transaction relationship signals. We then define an operational lifecycle that covers onboarding, auditability, drift detection, incident response and model rollback. Finally we outline an evaluation plan that measures detection lift, fairness, privacy, resilience and cost. The goal is a blueprint that allows banks and fintech partners to share learning value while preserving customer privacy and supporting regulatory defensibility
Architecture Led Cloud Modernization: A Framework for Enterprise Migration from VMware to OpenShift and AWS
Enterprises are under sustained pressure to deliver software faster while improving reliability security and cost efficiency. Virtual machine centric platforms such as VMware vSphere remain highly capable for many workloads yet they often reinforce operating models built around static capacity manual change control and infrastructure coupling that slows product delivery. Container platforms and managed cloud services address these constraints by shifting elasticity resiliency and automation into the platform layer. This paper proposes an Architecture Led Cloud Modernization Framework (ALCMF) for migrating complex enterprise workloads from VMware based on premises environments to Red Hat OpenShift running on Amazon Web Services using Red Hat OpenShift Service on AWS (ROSA). Unlike tactical rehosting approaches, the framework starts with architecture and quality attributes and then selects a modernization strategy per workload. It combines migration process guidance from cloud migration literature with cloud native design principles including microservices container isolation policy driven security and automated delivery pipelines. The framework decomposes migration into five phases: discovery and dependency mapping target state design strategy selection execution and continuous optimization. For each phase the paper details practical technical decisions in networking storage identity governance observability and security controls. We also provide reference architecture for ROSA on AWS that supports multi–Availability Zone resiliency least privilege access network policy enforcement service mesh-based traffic security and measurable operational outcomes. A case study style evaluation illustrates how architecture led decisions reduce downtime, improve deployment frequency and enable systematic cost management through autoscaling and rightsizing. The result is a repeatable modernization approach that reduces migration risk while enabling a durable shift toward platform engineering and product-oriented delivery
The Regulatory Landscape for Private Wireless Spectrum Allocation across Different Regions
The global telecommunications landscape is experiencing a fundamental change, driven by the democratization of wireless spectrum access. As Industry 4.0, the Internet of Things (IoT), and mission-critical industrial applications grow, the traditional model of exclusive, nationwide spectrum licensing held by Mobile Network Operators (MNOs) is becoming inadequate to meet the complex needs of "vertical" industries—manufacturing, logistics, healthcare, energy, and utilities. In response, regulators across North America, Europe, and the Asia-Pacific have developed diverse regulatory frameworks to enable Private 5G (P5G) and Private LTE (PLTE) networks. This paper offers a comprehensive comparative analysis of these regulatory systems. It examines the three-tiered dynamic sharing model of the United States’ Citizens Broadband Radio Service (CBRS), Germany’s BNetzA’s administrative "campus network' licensing, the hybrid Shared Access License (SAL) system of the United Kingdom’s Ofcom, and the dedicated "Local 5G" and "e-Um 5G" programs in Japan and South Korea, respectively. By analyzing spectrum bands (especially the differences among bands n78, n77, n79, and n48), licensing economics, device ecosystem maturity, and deployment data, this paper highlights the tension between policy goals and market realities. It concludes that although regulatory approaches vary—from market-driven auctions to nominal administrative fees—the overall trend worldwide is clearly toward localized, enterprise-controlled spectrum
Chaos Engineering for API-Centric Systems in Telecom & eCommerce
As modern telecom and eCommerce platforms increasingly rely on distributed, API-centric architectures, ensuring system resilience and reliability has become a critical challenge. Chaos engineering offers a proactive approach by deliberately injecting controlled failures into systems to uncover weaknesses before they impact customers. This white paper examines the role of chaos engineering in large-scale telecom and eCommerce environments, focusing on its application to orchestration layers, control planes, service meshes, and edge routing. It identifies five key domains where chaos engineering delivers measurable value to API-driven systems: resiliency testing, observability and monitoring, deployment and release reliability, performance and scalability, and platform evolution and change management. By simulating real-world failure scenarios, organizations can validate assumptions, improve fault tolerance, and strengthen operational readiness. The paper demonstrates how chaos engineering functions not only as a technical practice, but as a strategic capability for safeguarding business continuity in high-scale digital ecosystems.
Synthetic Data Generation Frameworks for Training Retail AI Models at Scale
The rapid expansion of Artificial Intelligence (AI) deployment in the retail sector necessitates robust, compliant, and scalable data infrastructure. Traditional reliance on raw, sensitive customer data poses significant legal, security, and operational challenges, severely impeding the training of large-scale predictive models. This paper provides an expert-level examination of modern synthetic data generation (SDG) frameworks designed to overcome these limitations. The analysis first categorizes SDG methodologies, emphasizing deep learning approaches such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Subsequently, the paper details advanced, domain-specific retail frameworks, including simulation platforms like RetailSynth, which fuse econometric discrete choice models with generative techniques to model realistic consumer behavior and operational constraints. For instance, literature reports that specialized GANs can generate realistic transactions by incorporating weighted stock constraints, a critical operational parameter often overlooked in general modeling. Finally, the paper articulates a comprehensive tripartite evaluation frameworkassessing Fidelity, Utility, and Privacywhich is essential for validating the analytical equivalence and trustworthiness of synthetic retail datasets. Fidelity metrics such as Wasserstein distance and Jensen-Shannon distance quantify statistical similarity, while Utility is assessed through predictive task performance (Accuracy, Lift, and Conviction). The successful implementation of these frameworks is critical for achieving competitive advantage through scaled, privacy-compliant AI applications like dynamic pricing and advanced demand forecasting. This paper reviews methodologies and frameworks reported in the literature, without presenting new experimental results
Large Language Model–Driven Intelligent Observability Frameworks for Serverless Applications in Event-Driven Cloud Architectures
Observability has also become a core condition of running the developing cloud-native systems, especially within serverless and event-driven systems marked by extreme dynamism, brief execution cycles, and distributed control streams. Conventional observability systems with conventional dashboards and threshold-based alerts, as well as manual root cause investigation, cannot deliver actionable intelligence in these types of environments because of the low level of surrounding awareness and human-in-the-loop feedback. Since serverless platforms can scale to the heuristics of thousands of parallel functions invoked by the heterogeneous event streams, cognitive load on operators grows exponentially, resulting in blind spots in performance monitoring, reliability insurance, and cost management. Large Language Models (LLMs) are a breakthrough in the reasoning of intelligent systems, making it possible through semantic understanding, contextual inferencing and natural language interface with large, heterogeneous telemetry volumes. In the current paper, Intelligent Observability Framework (LLM-IOF) is proposed that would be customized to service-less applications deployed on an event-driven cloud infrastructure using the LLM. Incorporated into the framework, there are distributed tracing, log semantics, metric correlation, and event lineage alongside LLM-based reasoning agents that provide automated anomaly detection, causal inference, incident summarization, and proactive remediation recommendations. The suggested structure proposes a multi-layers observability intelligence pipeline that contains telemetry ingestion, semantic normalization, vectorized context modeling, LLM-based reasoning as well as autonomous feedback loops. In contrast to traditional APM technologies, the framework allows generating hypotheses in real-time, cross-service causal learning, and learning based on historical events. The methodology has supported both reactive and proactive observability whereby the system operations were based on manual debugging rather than cognitive assistance. Representative load experimental analysis of serverless workloads showing a significant improvement in the mean-time-to-detect (MTTD), mean-time-to-resolve (MTTR), and operational efficiency. The findings show that self-healing cloud systems can be supported on the basis of LLM-induced observability frameworks. An optimistic conclusion on the paper would be covering the limitations, security considerations and future research directions with respect to autonomous cloud operations
Designing Enterprise Data Architecture for AI-First Government and Higher Education Institutions
The architecture of enterprise data at AI-first government and higher education organizations will have to balance AI ambitious agendas with data infrastructure that is stable and responsible. In this paper, I will suggest a layered reference architecture, which combines data mesh and lakehouse designs with a cloud-native platform based on the ISO/IEC 25012, DAMA-DMBOK, and the NIST AI Risk Management Framework. Methodologically, the work follows a design-science approach, synthesizing recent industry and policy literature, deriving a multi-stakeholder requirements matrix, and mapping representative public-sector and higher-education use cases such as fraud and risk detection, student success analytics, and smart-campus operations onto the proposed design. It is an ingestion and integration architecture, a governance and security architecture, metadata and semantic modeling, an analytical data store warehouse, lakehouse, vector stores, and a common AI/ML platform including MLOps and LLMOps, which consumes value through APIs, dashboards, and apps with AI infused in them. According to illustrative evaluation founded on 2023-2024 benchmark ranges, an AI-first EDA is capable of achieving: Reducing tens of seconds to low tens of seconds of analytical query latency, improving data completeness and timeliness to the mid-90s range, and risk and fraud detection model AUC to 0.84-0.88 plus higher concurrency and lowering the cost of infrastructure by 20-30%. Meanwhile, the paper identifies the most pertinent risks related to privacy, ethical AI, security risks, and skill gaps, asserting that the zero-trust securities, as well as long-term change management are the complements to the technical modernization. The blueprint suggested is therefore a workable, governance-consistent way of governments and universities shifting out of their secluded AI experiments into credible, systemic AI-empowered business processes
A Federated Zero-Trust Security Framework for Multi-Cloud Environments Using Predictive Analytics and AI-Driven Access Control Models
Companies are moving to multi-cloud more and more to achieve cost-weight, resilience or flexibility of vendors, which leads to identity, policy, and monitoring fragmentation. Such environments cannot support identity sprawl, lateral movement and rapid changing threats with traditional perimeter-based and static controls, which are role-centric. The paper suggests a Federated Zero-Trust Security Framework which aligns security posture between heterogeneous clouds by decentralizing control, using federated identity and ensuring continuous verification. It is a federated identity and credential management layer, a multi-cloud policy orchestration plane, and an AI-driven access control engine which ingests secure telemetry of all providers. Dynamic risk scores are built using predictive analytics and behavior-based models and used to perform threat prediction, and to run adaptive access decisions, such as step-up authentication, just-in-time privilege elevation, or automatic session termination. An experimental implementation on AWS, Azure and GCP highlights small latency overhead, high throughput and better policy consistency whereas experimental analysis illustrates significant improvements on detection accuracy, false positives as well as cross-cloud attack surface reduction compared to RBAC and vendor-native IAM baselines. The findings show that a federated zero-trust predictive, AI-driven access control is a scalable and proactive model of defense in multi-cloud ecosystems of complexities
Cloud-First Content Modernization: Migrating Legacy ECM to Secure, Scalable Cloud Platforms
Enterprise Content Management (ECM) systems have long served as foundational platforms for managing organizational information assets. However, traditional on-premises ECM architectures struggle to meet modern demands for scalability, agility, security, and cost efficiency. The emergence of cloud computing has accelerated a paradigm shift toward cloud-first content modernization strategies. This paper presents a comprehensive study on migrating legacy ECM systems to secure and scalable cloud platforms. It examines architectural challenges, security and compliance requirements, data migration complexities, and operational transformation considerations. A structured cloud-first migration methodology is proposed, integrating content assessment, risk mitigation, security-by-design, and phased modernization. Experimental evaluation based on enterprise migration scenarios demonstrates significant improvements in system performance, availability, cost optimization, and compliance posture. The findings confirm that cloud-first ECM modernization enables enterprises to unlock advanced analytics, automation, and artificial intelligence capabilities while ensuring regulatory compliance and long-term sustainability. This study contributes a systematic framework and empirical insights for organizations pursuing large-scale ECM cloud migration initiatives