International Journal of Emerging Trends in Computer Science and Information Technology
Enhanced Retail Experience Powered by Private Wireless: Architectures, Applications, and Performance Analysis
The retail industry is at a critical turning point, shifting from traditional brick-and-mortar operations to "Retail 4.0" a model characterized by the seamless integration of physical and digital environments. This "physical" transformation demands a robust connectivity infrastructure capable of supporting mission-critical applications such as autonomous logistics and immersive Augmented Reality (AR) customer experiences. This research paper provides a comprehensive technical analysis of Private 5G (P5G) networks as the key enabler for this enhanced retail experience. We examine specific architectural deployments defined by 3GPP Release 16 and 17, specifically comparing Stand-Alone Non-Public Networks (SNPN) with Public Network Integrated NPNs (PNI-NPN) in terms of retail scalability and data sovereignty. Through an in-depth comparison with Wi-Fi 6, this report demonstrates that Private 5G offers better determinism, supporting ultra-low latency (<10ms) and high device density (10⁶ devices/km²) necessary for advanced inventory tracking and robotic automation. Additionally, we analyze how network slicing can help achieve Payment Card Industry Data Security Standard (PCI-DSS) compliance, ensuring secure, instantaneous transactions across the retail space. The paper concludes that Private 5G is more than just a connectivity upgrade it’s a strategic asset that unlocks trillions in economic value by enabling real-time, data-driven optimization of the entire retail supply chain.
Silo, Pool, and Bridge for Multi-Tenant RAG: Measuring Isolation, Noisy-Neighbor Effects, and Cost in SaaS Microservices
Multi-tenant Retrieval-Augmented Generation (RAG) enables enterprise SaaS platforms to ground large language model outputs in customer-specific data while sharing infrastructure across tenants. This deployment model introduces a hard requirement for strict tenant isolation across storage, embedding generation, vector indexing, retrieval orchestration, and response construction, without unacceptable cost or performance variance under mixed workloads. This paper formalizes three isolation patterns for multi-tenant RAG systems, Silo, Pool, and Bridge, and introduces an isolation taxonomy across four planes: data plane, vector plane, orchestration plane, and LLM plane. A threat model specific to multi-tenant RAG is presented, covering cross-tenant embedding leakage through similarity search, membership inference risk, retrieval contamination from incorrect scoping or poisoned content, and metadata inference. A Kubernetes-native reference architecture is specified to implement tenant-aware controls and explicit policy enforcement points across ingestion and retrieval. The paper also defines an evaluation approach for comparing isolation patterns using leakage testing under adversarial retrieval scenarios, mixed-tenant latency measurements (P50 and P95) to quantify noisy-neighbor effects, cost-per-query decomposition, and operational overhead.
Measuring ROI of AI Investments in Insurance Underwriting and Fraud Detection
The global insurance industry is navigating a structural transition from a reactive "repair and replace" model toward a proactive "predict and prevent" paradigm. This evolution is driven by the integration of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) into the core domains of underwriting and fraud detection. For executive stakeholders (CEOs and CFOs), the primary challenge lies in quantifying the Return on Investment (ROI) of these complex, data-intensive technologies. This report provides a comprehensive analysis of the economic and operational impacts of AI adoption, synthesizing empirical data from industry leaders. We examine the mitigation of operational friction where AI reduces policy processing times by up to 90%—and the improvement of the combined ratio through the identification of organized fraud rings and the elimination of "explainability debt." By mapping current implementations against the NIST AI Risk Management Framework (RMF) and the NAIC Model Bulletin, this white paper offers a rigorous methodology for evaluating AI's role in driving total shareholder return (TSR), which for AI leaders has historically outperformed laggards by a factor of 6.1. The findings underscore that while AI facilitates hyper-personalization and efficiency, long-term profitability is contingent upon robust governance, the management of technical debt, and the transition toward continuous, usage-based underwriting models.
Comparative Study of Helmet Designs on Cervical Spine Loading Patterns in Athletes Recovering From Post-Concussion Head Injury
Cervical spine biomechanics are increasingly recognized as a critical factor in the recovery trajectory of athletes following sport-related concussion. Post-concussion neuromuscular impairments including delayed muscle activation, reduced cervical stability, and proprioceptive dysfunction can elevate the risk of secondary injury, especially during early return-to-play (RTP) phases. While helmet technology has made significant strides in reducing cranial injury and managing rotational acceleration, there is a paucity of research exploring how helmet design influences cervical spine loading in concussed athletes. This study addresses that gap by employing a mixed-methods approach, combining laboratory-based biomechanical impact testing with qualitative interviews from clinicians and athletes with concussion experience. Three helmet designs were evaluated: Standard Polycarbonate (SP), Hybrid Foam Shell (HFS), and Dynamic Response (DR). Biomechanical assessments utilized instrumented anthropomorphic test devices (ATDs) configured to simulate post-concussion neuromuscular sensitivity. Across multiple impact scenarios, DR helmets demonstrated the most substantial reduction in peak cervical axial loads (mean reduction >25% vs. SP, p < 0.01) and shear forces (p < 0.05). Qualitative findings corroborated biomechanical results, with participants consistently reporting improved comfort, perceived stability, and reduced neck strain when wearing DR helmets. These converging results suggest that helmet selection should extend beyond cranial protection and account for cervical load mitigation, particularly in RTP planning. Findings support the incorporation of dynamic design features in future helmet standards and rehabilitation protocols.
Real-Time Data Analytics in AI-Driven IoT Ecosystems: Leveraging Edge AI for Processing Massive Data Streams from Smart Devices, Enabling Applications in Healthcare Monitoring and Industrial Automation
The Internet of Things (IoT) devices have been extensively spread, which has led to an enormous influx of real-time data streams that need to be processed instantly to facilitate actionable data. Conventional cloud-based data processing solutions are also not usually adequate because of lag issues, bandwidth, and confidentiality. The Edge Artificial Intelligence (Edge AI) offers a new paradigm with the ability of making computational work nearer to the data sources to minimize latency and improve the performance of real-time analytics. The present paper explores the implementation of Edge AI into the IoT ecosystems, with healthcare monitoring and industrial automation as the applications. There are techniques, like stream processing, lightweight machine learning models and distributed resource management, which are examined. The paper further discusses such challenges as device heterogeneity, privacy, and reliability and examines the existing solutions such as federated learning and model compression. The architectural frameworks and implementations application-specific are explained through comparative analyses with using tables and conceptual figures. The paper proves that the Edge AI-based IoT systems are much more efficient in terms of performance, responsiveness, and operational efficiency, which preconditions the future studies and applications in the critical fields.
Developing End-to-End Concourse CI/CD Pipelines with Automated Testing, Scanning, Canary Deployments, and Rollback Logic
The increasing demand for rapid software delivery has elevated Continuous Integration and Continuous Delivery/Deployment (CI/CD) pipelines into mission-critical systems. Modern pipelines must not only automate builds and deployments but also ensure software quality, security, reliability, and compliance. This paper presents a comprehensive end-to-end approach for designing and implementing CI/CD pipelines using Concourse CI, integrating automated testing, security scanning, progressive canary deployments, and automated rollback mechanisms. A reference architecture and reusable pipeline patterns are proposed, followed by three practical case studies across cloud-native microservices, regulated enterprise platforms, and data engineering pipelines. The paper further evaluates pipeline effectiveness using industry-standard metrics and explores future directions including policy-as-code, software supply chain security, SBOM-driven delivery, and AI-assisted continuous testing.
Ambient Queue Management: Real-Time Load Distribution across Parallel User Journeys in Android
Modern Android applications particularly large-scale e-commerce and transactional platforms—must support thousands of concurrent user journeys under unpredictable peak loads. Traditional concurrency and task-scheduling mechanisms, while effective in isolation, fail to provide holistic load fairness when multiple user journeys compete for shared system resources such as CPU, memory, network bandwidth, and UI rendering pipelines. This paper introduces Ambient Queue Management (AQM), a real-time, context-aware load distribution model de-signed specifically for Android applications operating under high concurrency. AQM dynamically observes runtime signals—including UI frame latency, coroutine saturation, network throughput, and user intent priority—and redistributes execution across multiple parallel queues without disrupting foreground responsiveness. Unlike static prioritization or server-side throttling approaches, AQM operates continuously in the background (“ambiently”), adapting execution strategies as device and application conditions evolve. This paper presents the architectural foundation of AQM, its Android-specific implementation using Kotlin coroutines and reactive streams, and an empirical evaluation conducted under simulated peak traffic conditions inspired by large-scale retail mobile applications. Results demonstrate measurable improve-ments in UI stability, network fairness, and system throughput, positioning AQM as a practical and scalable model for real-time mobile load orchestration.
Integrating Conversational AI into Healthcare CRMs for Patient Engagement Optimization
Healthcare organizations are increasingly adopting Customer Relationship Management (CRM) platforms to improve patient engagement, care coordination, and operational efficiency. However, traditional healthcare CRMs often rely on reactive, manual interactions that limit personalization and real-time responsiveness. This paper examines the integration of Conversational Artificial Intelligence (AI) into healthcare CRM systems to optimize patient engagement. By leveraging natural language processing (NLP), machine learning (ML), and intelligent virtual assistants, Conversational AI enables proactive, personalized, and scalable patient interactions across the care continuum. The study highlights architectural frameworks, key use cases, performance metrics, security considerations, and future trends, demonstrating that AI-driven conversational interfaces significantly enhance patient experience, adherence, and clinical outcomes.
Agentic AI with Cybersecurity: How to focus on Risk Analysis via the MCP (Model–Control–Policy) Model
Rapidly advancing Agentic Artificial Intelligence (AI) systems that are equipped to autonomously reason, call upon tools, and perform self-directed tasks have transformed enterprise productivity as well as the threat landscape. In contrast to static machine learning (ML) pipelines, agentic systems purposefully interpret goals in real-time and make decisions, thereby injecting contextual feedback loops that increase attack vectors and introduce new classes of cyber-physical as well as data-centric risks. The current cybersecurity and governance models, such as NIST SP 800-53, MITRE ATLAS, and the OWASP Top 10 for LLMs, cover parts of this spectrum but do not have an integrated model that can capture AI behaviors while also integrating organisational systemic control logic and governance obligations. The research presents an integrated Model–Control–Policy (MCP) risk-analysis model for agentic AI settings. The Model layer characterizes the technical sources of risk arising from model design, data provenance, and adversarial vulnerability. The Control layer includes runtime safety checks, access controls, and automatic containment mechanisms that ensure safe operation within defined limits. The latter means they can map these controls to the organizational governance and compliance regimes (EU AI Act or NIST AI RMF, for example) and cross-border regulatory requirements they may need. Combined with the MCP model, such a multi-locus common approach enriches an analytical framework for businesses to assess, monitor, and mitigate AI risks in a traceable, accountable manner. The study uses quantitative risk scoring, red-teaming simulations, and MITRE ATLAS mapping to analyse the MCP model within high-risk enterprise scenarios -- for example, autonomous incident response, data classification, and cross-tenant chatbot systems. We find a 4.6× decrease in the number of successful exploits, a 37% reduction in the fraction of false escalations, and quantifiable gains in governance traceability. The MCP model integrates technical and policy aspects, providing a reproducible basis for controllable autonomy in AI systems. By integrating multilevel controls, continuous risk quantification, and compliance-aware governance, the MCP framework enables a structured approach to cybersecurity risk assessment for agentic AI systems. It provides a practical pathway toward AI architectures that are adaptive, transparent, and ethically aligned, while remaining responsive to regulatory and organizational policy requirements. In doing so, MCP supports the development of resilient AI systems with demonstrable accountability and regulatory conformance.
Active Ethernet in Residential Broadband Access Networks
The most popular technology used by a residential and small business broadband provider is Passive Optical Network (PON). In a PON architecture, the available bandwidth is shared among multiple subscribers via passive optical splitters. During peak usage times, heavy consumption by a few users can lead to network congestion, resulting in significantly slower speeds and performance degradation for all other users on the same shared segment. For a broadband Service Provider (BSP) it is a challenge when there are critical businesses to serve and they expect dedicated bandwidth downstream as well as upstream to carry heavy duty uploads for cloud native applications. The architecture of a network defines the performance a subscriber can expect when they opt for a high throughput service. Active Ethernet (AE) which is also sometimes known as point-to-point Active Fiber, is an access technology where each subscriber gets a dedicated fiber connection to the BSP’s network.
Modernizing Point-of-Sale (POS) Systems with Cloud and AI
The modernization of Point-of-Sale (POS) systems is critical to meet the evolving demands of the retail and hospitality industries. Traditional POS systems, often reliant on on-premises hardware and limited software capabilities, face challenges such as scalability issues, high maintenance costs, and inflexibility. Recent advancements in cloud computing and Artificial Intelligence (AI) have facilitated significant transformations in POS technologies, offering scalable, secure, and efficient alternatives. Cloud computing provides benefits such as centralized data management, real-time access, and enhanced security, while AI enables smarter, more personalized customer experiences through predictive analytics, automation, and fraud detection. This paper explores the integration of cloud and AI in modern POS systems, evaluating their impact on operational efficiency, cost-effectiveness, and customer satisfaction. Case studies highlight how businesses across various sectors are leveraging these technologies to improve transaction processes, enhance customer insights, and optimize inventory management. Furthermore, the paper discusses the challenges associated with this modernization, including technical barriers, security risks, and the costs of implementation. The research concludes by projecting future trends in POS technology, emphasizing the synergy between cloud and AI in shaping the future of retail and service-based industries.
A Modular Software Architecture for Safe and Scalable Mobile Manipulation Systems
Robotic manipulation systems are increasingly deployed in real-world environments where reliability, safety, and scalability are as critical as task performance. As these systems grow in complexity, software architecture has emerged as a primary determinant of operational robustness and long-term maintainability. This paper presents a modular software architecture for mobile manipulation robots that emphasizes separation of concerns, explicit task lifecycle management, and event-driven coordination under real-time constraints. The proposed architecture decomposes robotic functionality into layered subsystems spanning perception, task reasoning, motion and skill generation, and execution and control. Design choices are motivated by the need to manage heterogeneous time scales, partial failures, and safety-critical behaviors. The architecture is evaluated through multiple case studies, including a holonomic mobile base with a specialized cleaning end effector, warehouse automation systems, and assistive robotics platforms. The results demonstrate that disciplined architectural design improves fault containment, system observability, and deployment reliability, supporting scalable robotics development and safe operation in dynamic environments.
Google Cloud Platform Services in Build Out of a Digital Contact Center
Contact center architectures are becoming increasingly important in response to customer journey complexities and digital communication channels. Traditional contact center on-premise solutions experience problems with channel management fragmentation, scalability issues, and insufficient analytics; as a result they cannot meet the demand for modern customer interaction needs for real time, personalized interactions. This paper will explore the design, deployment, and operation of a cloud native digital contact center using Google Cloud Platform (GCP) to determine how the GCP platform and Google Contact Center AI (CCAI) can provide customer service operations with AI driven automation, microservice based scalability, and data focused decision support. This study will examine the capabilities of the GCP ecosystem including CCAI to transform the way customers interact with companies and the processes used to support those interactions. A qualitative architecture centered research method will be employed to evaluate critical components of the solution such as Dialogflow CX for conversational agents, intelligent routing of customer interactions and agent support, event based orchestration with Cloud Pub/Sub, containerized services on Google Kubernetes Engine, and advanced analytics provided by BigQuery and CCAI Insights. Additionally, the study will investigate best practices for securely integrating digital contact center solutions with enterprise CRM systems and the importance of end-to-end observability. Research results indicate that digital contact centers deployed on the GCP platform will produce significant improvements in key contact center metrics including decreased average handling times, higher use of virtual agents, increased customer satisfaction ratings, and higher levels of operational visibility. However, the study also identifies several key challenges related to data governance, regulatory compliance, and continuous improvement of AI models. Combining the architectural design aspects of digital contact centers with observed operational results provides an actionable reference model for organizations seeking to modernize their contact center operations and outlines potential areas for future research, specifically regarding the use of generative AI to enhance personalization and predictively drive customer engagement.
Practical Deployment Strategies for Reliable Production Releases a Decision Framework, Readiness Gates, and Schema-Safe Rollout Patterns
Production deployment is a risk-management problem: delivering change while controlling blast radius, downtime, and rollback complexity. This paper consolidates five deployment strategies - Big Bang, Rolling, Blue-Green, Canary, and Feature Toggles and adds an operational layer as an original contribution: (i) a Deployment Risk Index (DRI) to select a rollout pattern; (ii) a Compatibility Envelope (CE) model to reason about multi-version coexistence in stateful systems; and (iii) Release Readiness Gates (RRGs) that operationalize canary evaluation and automatic pause/rollback. The guidance is aligned with established SRE and cloud-provider recommendations.
Migrating Enterprise Applications from On-Premises to AWS in a Multi-Cloud Environment: A Framework for Scalability, Security, and Cost Optimization
A number of companies have begun to migrate away from their traditional monolithic on-site infrastructure to more flexible, cost-effective multi-cloud environments to avoid "lock-in" with their vendors, while maximizing the performance-to-cost ratio. In spite of the benefits that this provides, such migrations typically face numerous barriers due to issues of "data gravity," interoperability between different types of cloud services, and security compliance. As an example of how to create a comprehensive framework to help support the transition of a large portfolio of over 200 applications into a hybrid multi-cloud environment, which is based on Amazon Web Services (AWS), this paper will provide an overview of a migration strategy for migrating a large portfolio of applications into a hybrid multi-cloud architecture. We will utilize the AWS Cloud Adoption Framework (CAF) to develop a centralized Hub-and-Spoke network structure utilizing AWS Transit Gateway, as a means to address inter-cloud latency challenges, while we will also introduce a new governance layer that enforces a NIST-compliant zero trust security model, to ensure the protection of all cross-cloud traffic. A combination of targeted Rehost, Re-platform, and Refactor methodologies will be used to execute the migration, through the use of AWS Application Migration Service (MGN), and AWS Database Migration Service (DMS). Post-migration performance metrics will demonstrate the success of our migration strategy, which has resulted in 99.99% system availability; a 40% decrease in operational costs; and a 60% increase in deployment velocity
Dual-Model Machine Learning for Predictive Leak Forecasting and Detection in Liquid-Cooled AI Data Centers
Modern GPU data centers supporting AI training workloads have increas- ingly adopted direct-to-chip liquid cooling systems to manage thermal loads exceeding 50 kW per rack, far beyond air cooling capabilities. However, coolant leaks in these high-density facilities result in substantial energy waste through unplanned shutdowns, extended repair periods, and preventive isolation of adjacent racks. We present a novel smart IoT monitoring system combining LSTM neural networks for probabilistic time- to-leak forecasting with Random Forest classifiers for real-time binary detection. The dual-model architecture provides both advance warning (2-4 hours) for planned mainte- nance and immediate alerts (sub-minute latency) for sudden failures. Validation using simulation-based data generation following ASHRAE 2021 specifications demonstrates strong performance: 96.5% F1-score for binary detection and 87% forecasting accu- racy at 90% probability within ±30-minute windows. The dataset comprises 72 hours of minute-resolution monitoring with realistic leak scenarios incorporating documented industry patterns. Statistical analysis reveals strong predictive signals from humidity (r = 0.70, p ¡ 0.001), pressure (r = -0.50), and flow rate, while temperature shows minimal immediate response (p = 0.236) due to thermal inertia, guiding optimal sen- sor deployment. The integrated system achieves 98.4% coverage with 850ms end-to-end latency. Energy analysis shows this approach could prevent approximately 1,500 kWh annual waste for a 47-rack facility, supporting sustainable operations. The complete implementation is provided to facilitate validation in operational environments, estab- lishing a foundation for intelligent leak management as liquid cooling becomes standard in AI infrastructure
Zero Trust Identity Management with Azure Entra and Conditional Access
The rapid expansion of hybrid and multi-cloud environments has rendered traditional perimeter-based security models obsolete. Identity has emerged as the new security boundary. Microsoft’s Azure Entra ID, combined with Conditional Access and Zero Trust principles, establishes an adaptive, context-aware identity management framework that secures access across users, devices, and applications. This paper explores the architecture, automation strategies, and policy frameworks of Zero Trust identity management in Azure. It highlights how AI-driven risk detection, continuous evaluation, and identity governance together enable secure, scalable, and compliant cloud operations. Through analysis of case scenarios, it demonstrates measurable improvements in security posture, compliance, and operational efficiency
Cloud-Native PRM Systems for Next-Generation Telecom - Networking Infrastructure
The rapid evolution of next-generation telecom Networking infrastructure—driven by 5G, edge computing, cloud-native networks, and AI-enabled services—has fundamentally transformed how telecom operators engage with partners. Traditional Partner Relationship Management (PRM) platforms, built on monolithic architectures and manual workflows, are no longer sufficient to meet the scale, agility, and intelligence required in modern telecom ecosystems. This paper presents a cloud-native PRM framework tailored to next-generation telecom infrastructure, emphasizing microservices-based design, API-first integration, AI-driven partner intelligence, and real-time analytics. The proposed approach enables telecom enterprises to orchestrate complex partner networks, accelerate go-to-market strategies, and deliver secure, scalable, and data-driven partner experiences
Using Quantum Cryptography in Network Security Management
Quantum cryptography is an emerging methodology of classical cryptography. It secures communications with strong encryption and uplifts the principles of quantum mechanics. The encryption in quantum cryptography is stronger than that in classical cryptographic methods, making it unbreakable. Quantum Key Distribution (QKD) is a specific feature in quantum cryptography that checks for eavesdropping between two parties. Therefore, the present white paper aims to present the outline of quantum cryptography and its role in network security. The layout discusses limitations of present quantum cryptographic systems, like hardware requirements, distance restrictions, and challenges in integration. It also adds information on quantum computing threats, real-life influences with QKD deployment in industrial and government initiatives. It also covers the original case studies of QKD deployment, addressing adoption issues and alleviation procedures that use complementary strategies and technological advances. Above all, it explores hybrid methods to change quantum repeaters to global quantum, and provides insights for long-term protection in network security
Generalist Vision Models for Any-to-Any Image-to-Video Understanding
Current developments in multimodal foundation models are driving computer vision beyond sets of task-specific systems to generalist vision models that can be used to do a large number of tasks and modalities in the same architecture. Simultaneously, video understanding has transitioned off of specialized backbones and to large models that collectively reason over images, videos and language. Any-to-any vision models strive to bring such a trend together: they take in heterogeneous visual and textual inputs (e.g. image to caption, video to action labels, of image+text to edited image) and deliver heterogeneous outputs via a common interface. The article examines the new space of generalist vision models of any-to-any image-to-video understanding. We introduce the concept of any-to-any modeling first and place it in the context of the rest of the literature on multitask and multimodal learning. We next outline typical models of such as Unified-IO 2, UnIVAL, PaLi-3, and 4M-21 that handle a broad input/output model of images, videos, audio, dense labels and free-form language [1] . We draw attention to general building blocks (unified tokenization, transformer backbones, diffusion or autoregressive heads) and training strategies (large-scale pretraining, instruction tuning, and multi-task curricula). We comment on the performance of these models on image and video benchmark tasks, including question answering, captioning, and spatiotemporal reasoning by using public benchmark results. Lastly, we provide practical and ethical advice in the deployment of generalist vision models in practice and provide open issues, such as unified evaluation in any-to-any context, efficient management of long videos sequences, and safety of open-ended visual interaction. We would like to offer a systematic and human-readable introduction that can assist the researcher and practitioner that is interested in constructing or using generalist image-to-video understanding systems
Real-Time Instance Segmentation Using Lightweight CNN-Transformer Hybrids
The problem of instance segmentation is a basic computer vision problem in which localization, classification, and pixel-level localization of individual instances of objects should occur simultaneously. The trade-off between computational performance and segmentation quality is a big challenge in achieving real-time performance in instance segmentation especially in resource-constrained platforms like edge devices and embedded systems. Hybrid architecture Lightweight CNN Transformer hybrid networks have become an exciting solution, which combines the ability to extract local features efficiently of convolutional networks with the ability to model the global context of Transformers. This literature review entails an in-depth examination of the instant segmentation methods in real-time, with the focus on CNN-based pipelines, Transformer models, and their hybrid versions. Lightweight design strategies, such as model compression, efficient attention mechanisms, and backbone optimization, and standard datasets and benchmarking protocols to ensure consistent evaluation are discussed. Lastly, we look at real-life implementations in autonomous driving, robotics, and in industry vision, and determine current challenges and future research opportunities in accuracy, efficiency and robustness in real-time implementation
Zero-Trust in Connected Physical Systems: A Security Blueprint for Smart Homes and Industrial IoT
The rapid expansion of connected physical systems (CPS) from smart homes and connected water heaters to industrial HVAC ecosystems has intensified the need for security frameworks that operate under the assumption that no entity is inherently trustworthy. Traditional perimeter models fail in distributed IoT topologies characterized by heterogeneous devices, multi-protocol radios, global supply chains, and cloud-driven control surfaces. This paper proposes a Zero-Trust Security Blueprint for connected Physical Systems, integrating identity-first device authentication, robust PKI and mTLS frameworks, secure boot and runtime attestation, BLE/Wi-Fi/Thread/Matter-based commissioning, and end-to-end software supply chain integrity. The blueprint outlines architectural principles, implementation pathways, and governance models required to operationalize Zero-Trust across smart homes and industrial IoT ecosystems
ETL and TR-069 integrated into SaaS enabled Broadband Market Expansion
A major Telecom Cooperative in alliance with a Local service provider, are together a Broadband Service Provider (BSP) located in the Midwest. This BSP provides one of the best communications services at a fair price; employing modern technology and up-to-date services; offering people choices to be successful and enhance their quality of life. They believe online job opportunities and the expansion of local businesses allow their economies to thrive. A reliable internet is the bridge tying their current success to future growth. The schools in these towns rely on a range of online resources to create vibrant classrooms. Older students can connect to remote college classes, and those already in the workforce can use online resources to advance their careers. For all the reasons and more, the BSP had a mission to market advanced network access and managed services to their subscriber base, as well as expand and engage to prospective subscribers in current and neighboring towns. The mission statement was bringing powerful internet services to as many communities as possible at a very fast pace. This BSP spanned about 3,300 square miles, serving several thousand subscribers
From Manual to Smart Manufacturing: Advancements in Assembly for Future Factories
Manufacturing is experiencing a historic transformation from labor-intensive manual assembly toward intelligent, interconnected, and autonomous production. The increasing deployment of industrial robots, multifunctional machines (MFMs), and humanoid systems, combined with artificial intelligence (AI), is enabling the concept of “lights-off” factories that can operate continuously with minimal human intervention. This paper traces the evolution of assembly automation, from Henry Ford’s moving line and Toyota’s lean principles to Tesla’s high-automation journey, Apple’s precision robotics ecosystem, and FANUC’s fully autonomous “lights-off” plant. Lessons learned from these pioneers inform the design of future factories facilities that combine human intelligence with robotic precision, flexibility, and resilience
ML-Driven Anomaly Detection for EPICS PV Streams at the Edge: Implementation and Evaluation on Raspberry PI IOCs
Distributed control systems based on the Experimental Physics and Industrial Control System (EPICS) framework generate high-rate process variable (PV) streams that require robust, low-latency monitoring to ensure safe and reliable operation [1], [2]. Traditional centralized monitoring architectures can suffer from bandwidth limitations, latency, and single points of failure. This paper presents a comprehensive design, implementation, and evaluation of an edge-deployed, machine-learning-driven anomaly detection system for EPICS PV streams running on low-cost Raspberry Pi hardware. The proposed architecture integrates EPICS IOCs with lightweight Autoencoder-based and Isolation Forest models quantized for TensorFlow Lite [3], enabling continuous inference on PV windows and local generation of anomaly PVs. We present engineering details for EPICS integration, data preprocessing, model training, quantization, and deployment, and we evaluate system performance on a laboratory testbed with synthetic and recorded PV traces. Results show sub-20 ms inference latency, modest CPU footprint, and a significant reduction in central network traffic when deploying anomaly filtering at the edge. The paper also discusses operational considerations, fault modes, and directions for adaptive, federated learning across distributed IOCs [12]
Data Lake Governance – Establishing a Single Source of Truth in Healthcare Enterprises
Healthcare organizations generate enormous volumes of multi-modal data electronic health records (EHR), pharmacy claims, medical claims, imaging, genomics, IoT sensor streams, and administrative data. However, fragmented systems prevent efficient data sharing, analytics, and decision-making. A well-governed healthcare data lake provides a scalable architecture to integrate structured and unstructured data while maintaining quality, security, and compliance. This paper proposes a comprehensive governance framework enabling a unified Single Source of Truth (SSOT) for healthcare enterprises. The framework integrates metadata management, data lineage, interoperability standards, AI-driven quality checks, and federated access controls. The proposed model ensures trustworthy, timely, and regulated data access for clinical, operational, and financial use cases including population health, pharmacy benefit optimization, risk scoring, and value-based care. The framework further incorporates ethical safeguards to mitigate AI bias, enforce algorithmic fairness, and ensure transparency and accountability in all automated governance decisions
Building Resilient Supply Chains through Integrated Quality Engineering
Global supply chains are under unprecedented pressure from geopolitical disruptions, regulatory demands, and sustainability imperatives. In this context, resilience has become a strategic necessity for organizations seeking continuity and competitive advantage. This paper examines how Integrated Quality Engineering (IQE) acts as a foundational pillar for resilient supply chains. By uniting quality systems, automation, and predictive analytics, IQE empowers organizations to anticipate risks, maintain compliance, and ensure operational agility. Key strategies, enabling technologies, and governance frameworks are discussed, with case studies and future trends illustrating how IQE transforms supply chains into adaptive, risk-mitigated ecosystems
Biometric Authentication UX: Best Practices for Face ID, Fingerprint & Iris Scans
Biometric authentication methods such as Face ID, fingerprint, and iris scans are now common in digital banking, mobile apps, and high-security systems. This paper analyzes best practices in designing user-friendly biometric authentication, focusing on UX patterns, accessibility, privacy, and security. Based on current research, industry guidelines, and application in finance, the goal is to help designers build secure and trustworthy interfaces that are easy for all users while complying with regulations. Design principles, usability outcomes, technical barriers, and future trends are discussed, with IEEE-style references and clear formatting for direct practical use
Understanding Spring State Machine Using Proper Use Cases
Spring State machine turns temporal business logic into explicit, testable models. This paper shows how to design and operate real workflows with hierarchical states, guards, and actions, using a fuel‑retail case where reliability, safety, and latency is paramount. We present an architecture that isolates domain logic behind ports/adapters (EMV/acquirer, dispenser, receipt), persists context for recovery, and exposes rich observability through metrics, structured logs, and traces. The case study follows the full card‑present journey, card read, authorization, pump enablement, fueling, EOT computation, and receipt, highlighting idempotence, compensations, and controlled concurrency. Performance measurements quantify transition latency and memory footprint under Redis and JPA persistence. We close with guidance on testing, model versioning, and safe evolution so teams can replace brittle conditional flows with diagrams that act as living documentation and an executable contract
Full-Duplex Communication – Simultaneous Transmit/Receive Capability
Full-duplex (FD) wireless communication defined as the ability of a transceiver to transmit and receive simultaneously on the same frequency band has emerged as a promising technique for doubling spectral efficiency and reducing latency in next-generation networks (Sabharwal et al., 2014). Despite its theoretical advantages, practical implementation remains constrained by the challenge of suppressing self-interference (SI), which can exceed the power of the desired signal by over 100 dB (Duarte & Sabharwal, 2010). Recent prototype demonstrations have achieved over 110 dB of SI cancellation through a combination of passive suppression, analog cancellation, and digital signal processing (Bharadia et al., 2013), yet wideband, mobile, and power-efficient FD systems still face limitations caused by nonlinearities and hardware impairments (Riihonen et al., 2020). Advances in adaptive filtering, machine learning-based cancellation, and MIMO-assisted spatial suppression have further improved FD performance, positioning the technology as a key enabler for 5G/6G small cells, intelligent IoT devices, and ultra-dense networks (Ahmed et al., 2024). This paper evaluates the architectural components, cancellation techniques, and emerging research directions of full-duplex communication, highlighting the remaining gaps and the potential for achieving robust real-world FD deployment