International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management
The International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management (IJMRSETM) is a bimonthly, peer-reviewed, open-access academic journal dedicated to the advancement and dissemination of knowledge in science, engineering, technology, and management. The journal welcomes high-quality original research, review papers, case studies, and short communications. Scope of the Journal IJMRSETM covers a wide range of topics including but not limited to: Science: Physics, Chemistry, Biology, Environmental Science, Life Sciences Engineering: Mechanical, Civil, Electrical, Electronics, Computer Science, etc. Technology: AI, IoT, Data Science, Cybersecurity, Software Engineering, etc. Management: Business Administration, Operations, Finance, HR, Marketing, Strategic Management
Deep Transfer Learning for Secure Healthcare Modernization Quality-Assured Integrated Maintenance and Cyber-Threat Intelligence for Data-Scarce Regions
Healthcare systems in data-scarce regions face dual challenges: limited annotated clinical data and rising cyber threats that compromise safety and continuity of care. This paper proposes a Deep Transfer Learning (DTL) framework that integrates quality-assured predictive maintenance for healthcare infrastructure with an embedded cyber-threat intelligence (CTI) module to secure medical devices and data flows. Our approach leverages domain-adaptive transfer learning, synthetic data augmentation, and federated learning to overcome limited local datasets while preserving patient privacy. The maintenance component uses pre-trained deep predictive models fine-tuned to local sensor signatures and operational logs, enabling proactive fault detection and reducing downtime. The CTI module employs transfer-learned representations to detect anomalous network behavior, adversarial probing, and device tampering, integrating with local incident response workflows. We evaluate the framework in simulated and small-scale pilot deployments across three representative clinical settings, demonstrating improvements in fault detection accuracy (average +18%), reduction in unscheduled downtime (median −27%), and early cyber intrusion detection (false positive rate reduced by 22%) compared to baseline local models. The paper discusses practical implementation considerations, governance and privacy-preserving mechanisms, and a roadmap for scalable adoption in resource-constrained health systems.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
AI Horizons in Healthcare: Deep Transfer Learning–Enhanced Quality Assurance and Maintenance for Data-Scarce, Cyber-Threatened Environments
Healthcare systems in many regions face a dual challenge: sparse, heterogenous clinical data and an escalating cyber threat landscape that undermines availability and trust. This paper proposes a practical, deployable framework that combines deep transfer learning for quality assurance (QA) with an integrated maintenance system (IMS) to modernize healthcare delivery in such constrained environments. The deep transfer learning component leverages pre-trained models from related domains (medical imaging, physiological signal analysis, and electronic health record patterns) and adapts them via fine-tuning and domain adaptation techniques to perform QA tasks — including anomaly detection, data completeness checks, semantic validation, and provenance inference — under severe data scarcity. The IMS couples predictive maintenance for edge devices, models, and software stacks with continuous security posture assessment, automated patch orchestration, and resilient fallback strategies. We evaluate the framework conceptually using representative case studies (rural diagnostic centers, small clinics with legacy devices, and emergency response networks) and present a mixed-methods results-and-discussion that synthesizes expected improvements in diagnostic consistency, model robustness, and operational uptime. The approach emphasizes privacy-preserving transfer, lightweight model footprints, and security-by-design, enabling accelerated modernization with minimal disruption and risk.
AI-Driven Healthcare Modernization in Data-Scarce Regions: A Quality-Assured Integrated Maintenance Management System with Deep Transfer Learning and Cybersecurity Threat Intelligence
Low-resource and data-scarce regions face persistent challenges: aged/poorly maintained biomedical equipment, scarce labeled clinical data, weak cybersecurity posture, and limited local engineering capacity. This paper proposes an integrated, quality-assured maintenance management ecosystem for healthcare facilities in such regions that combines (1) a lightweight Computerized Maintenance Management System (CMMS) augmented with IoT telemetry and prioritized workflows, (2) deep transfer learning pipelines to enable high-performance predictive maintenance and clinical decision support under extreme data scarcity, and (3) an embedded cyber threat intelligence (CTI) layer to protect patient data and operational continuity. The architecture emphasizes modularity, human-centered design, and continuous quality assurance (QA) cycles to match local constraints (intermittent connectivity, low compute, limited staff). We evaluate expected benefits qualitatively and via simulated experiments on publicly available, small-sample datasets and maintenance logs (transfer learning fine-tuning experiments and RUL estimation simulations) and show improved predictive accuracy versus training-from-scratch approaches and measurable reductions in downtime risk when maintenance is prioritized with ML-driven scheduling. The system also reduces common cyber risks by integrating CTI feeds, anomaly detection, and operational playbooks. We conclude with deployment guidance, limitations, and a research roadmap for field trials and policy alignment.
A Risk-Aware AI Governance Framework for Rural Clinics and Cloud Financial Systems: Cybersecurity Strengthening and Autonomous Threat Detection in DevOps Environments
This paper proposes a unified, risk-aware AI governance framework tailored for two critical yet contrasting domains—rural healthcare clinics and cloud-based financial systems. As both sectors increasingly adopt AI-driven workflows, they face heightened exposure to cyber threats, data integrity risks, and operational vulnerabilities. The framework integrates principles of responsible AI, domain-specific regulatory requirements, and continuous assurance practices into a DevOps and MLOps pipeline. It emphasizes adaptive risk scoring, explainability standards, privacy preservation, and compliance-aligned monitoring to safeguard patient and financial data. A multilayer cybersecurity model is outlined, incorporating autonomous threat detection, anomaly-aware observability, and automated incident-response orchestration. The proposed approach demonstrates how resource-constrained rural clinics and high-availability financial cloud systems can leverage shared governance controls while implementing context-appropriate hardening measures. By embedding risk-aware AI governance into the full software and model lifecycle, the framework aims to improve resilience, transparency, and trust across diverse operational environments.
Next-Gen Intelligent AI Cloud for Fraud Detection and Cybersecurity Defense: Time-Optimized ML Architectures with Deep RiskPredict Intelligence
The rapid rise of digital transactions, multi-tenant cloud platforms, and evolving cyber threats has intensified the need for intelligent, scalable, and real-time security frameworks. This paper introduces a Next-Generation Intelligent AI Cloud Framework that unifies fraud detection and cybersecurity defense through time-optimized machine learning architectures and Deep RiskPredict Intelligence. The framework leverages cloud-native data pipelines to integrate heterogeneous streaming data—including transactional logs, behavioral signals, network telemetry, and contextual metadata—enabling continuous monitoring and rapid incident response in large-scale environments. Central to the design is a suite of time-optimized ML models and deep learning–based RiskPredict engines that balance computational efficiency with predictive accuracy, making the framework suitable for latency-sensitive and resource-constrained operational settings. The RiskPredict module incorporates deep neural networks, multivariate feature interactions, and adaptive risk scoring mechanisms to identify emerging fraud patterns and cybersecurity threats in real time. Empirical evaluation demonstrates significant reductions in detection latency, improvements in fraud and threat classification accuracy, and enhanced system efficiency compared to conventional ML and rule-based approaches. The proposed framework establishes an advanced, cloud-ready security model capable of evolving with complex threat landscapes while supporting proactive defense and financial risk intelligence. It contributes a robust foundation for next-generation AI-driven security systems deployed across diverse and high-demand cloud ecosystems.
Secure DevOps in Cloud-Native Systems: Integrating Cyber Intelligence, Blockchain, and AI for Zero-Trust Enterprise Applications
In the era of cloud‑native architectures, microservices, containers, and continuous deployment pipelines, enterprise applications face a growing challenge: securing dynamic, distributed, and often multi‑cloud infrastructure against rapidly evolving threat vectors. Traditional perimeter‑based defenses are inadequate, as they assume static, trusted boundaries that no longer exist. This paper investigates the integration of Cyber Intelligence, Blockchain, and Artificial Intelligence (AI) within a Zero‑Trust DevSecOps framework to secure cloud‑native enterprise applications. The objectives are: (i) to design an architecture that embeds zero‑trust principles into the DevSecOps lifecycle; (ii) to incorporate cyber intelligence (threat intelligence, anomaly detection) and blockchain (for tamper‑proof logs / identity / trust mechanisms); (iii) to apply AI/ML to both predictive threat detection and adaptive policy enforcement; (iv) to evaluate the effectiveness of this integrated framework via metrics on security, performance, compliance, and overhead. Methodologically, a prototype framework is developed incorporating blockchain for immutable audit trails and decentralized identity management; AI modules for behavior‑based anomaly detection and policy automation; and threat intelligence feeds to inform policies. The framework is applied to cloud‑native applications using containers (e.g., Kubernetes), microservices, and multi‑tenant CI/CD pipelines. Simulated attacks (e.g., supply‑chain attacks, unauthorized lateral movement, credential misuse, container escape) are executed, and the system’s responses are compared to baseline DevSecOps pipelines without the integrated zero‑trust + blockchain + AI additions. The results indicate substantial improvements: detection of anomalous behavior with high recall (~ 90‑95%) and precision (~ 88‑92%); significant reduction in mean time to detect (MTTD) and mean time to respond (MTTR) threats; audit trail integrity ensures non‑repudiation; compliance with zero‑trust policies enforces least privilege and microsegmentation with acceptable performance overhead (~ 10‑20%) in latency. Trade‑offs include increased complexity, computational overhead for blockchain consensus / storage, additional resource consumption for AI inference, need for skill sets, and potential latency / scaling bottlenecks. <span style="font-size: 10.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; color: black; mso-themecolor: text1; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">In conclusion, integrating cyber intelligence, blockchain, and AI under a zero‑trust DevSecOps framework offers strong promise for improving security posture of cloud‑native enterprise applications. The study highlights best practices, key trade‑offs, and a road map for organizations wishing to adopt such architectures. Future work should consider scaling to large‑scale multi‑cloud production environments, improvements in blockchain scalability, privacy in AI components, explainability, regulatory compliance, and usability for developers.
Copyright (c) 2025 Bankim Chandra Chattopadhyay (Author) https://creativecommons.org/licenses/by/4.0
AI and IoT-Integrated Smart Agriculture: Predictive Crop Health Monitoring and Sustainable Resource Optimization using Cloud Platforms
Agriculture faces increasing pressures from climate change, population growth, environmental degradation, and the depletion of natural resources. To meet rising food demand sustainably, modern farming must become more datadriven, resilient, efficient, and adaptive. Smart agriculture, integrating Internet of Things (IoT) sensors, cloud platforms, and artificial intelligence (AI), offers promise for realtime monitoring of crop health, early disease and pest detection, precise irrigation and fertilizer control, and overall resource optimization. This paper proposes a comprehensive framework combining IoT sensors deployed in the field (soil moisture, temperature, humidity, spectral imaging, leaf health), AI models in the cloud for predictive crop health monitoring (disease, pest, nutrient deficiency), yield forecasting, and resource optimization (water, fertilizer, energy). The architecture also includes feedback loops to send recommendations to farmers via mobile/web dashboards, actuators for irrigation/fertilization control, and alerting in case of anomalies. We conduct both simulation experiments and a pilot field deployment. Key evaluation metrics include disease/pest detection accuracy, forecasting error (for yield and stress), water usage reduction, fertilizer use reduction, timeliness of detection (latency), system reliability, and user acceptability. Our results show that predictive crop health monitoring with AI achieves detection accuracies of ~9098% for common diseases/pests; yield forecasting errors (MAE/RMSE) are reduced by ~2540% compared with baseline statistical methods; water usage for irrigation is reduced by ~3045%, fertilizer usage by ~2035% with maintained or increased yield; early alerts allow interventions that cut disease spread significantly; cloudbased analytics enable scale, remote access, and integration of multisource data (sensor + imaging + weather). Tradeoffs include issues of data privacy, connectivity and latency, dependency on sensor quality, cloud costs, robustness to environmental variability, and fairness in access for smallholder farmers. The study discusses how to mitigate these via edgecloud hybrid architectures, lightweight models, transfer learning, data augmentation, local capacity building, and flexible hardware. We also explore socioeconomic, policy, and regulatory challenges. In conclusion, AIIoT smart agriculture systems on cloud platforms can significantly improve crop health, reduce resource consumption, and support sustainable farming—especially when carefully designed for environmental, infrastructural, and socioeconomic constraints. Future work will include larger scale deployments, extension to multispectral / hyperspectral sensing, robust models for new diseases, better user interfaces, and exploring federated/cloud hybrid or edgeAI solutions to reduce dependence on connectivity.
Smart Agriculture, Internet of Things (IoT), Cloud Computing, Artificial Intelligence / Machine Learning, Crop Health Monitoring, Disease / Pest Detection, Predictive Yield Forecasting, Resource Optimization (Water, Fertilizer), RealTime Monitoring, Sustainable Farming.
Data-Driven Decision Intelligence: Leveraging AI and Cloud ERP Analytics for Strategic Enterprise Agility and Risk Governance
Organizations are increasingly operating in volatile, uncertain, complex, and ambiguous (VUCA) business environments. To remain competitive, they must be agile, make fast & accurate strategic decisions, and effectively govern risk. Cloudbased ERP systems hold vast operational, financial, supply chain, and human resource data. Coupled with Artificial Intelligence (AI) and advanced analytics, these data assets can enable decision intelligence: the ability to anticipate trends, simulate scenarios, and guide strategic action. However, realizing this promise requires overcoming significant challenges: data silos, data quality, latency, model trust, risk of bias, regulatory compliance, and alignment with governance frameworks. In this paper, we propose a comprehensive framework titled AIDriven Decision Intelligence for Cloud ERP Analytics aimed at enabling enterprise agility and strengthened risk governance. The framework integrates modules for realtime analytics, predictive forecasting, scenario modeling, prescriptive recommendations, and continuous risk assessment. It builds on cloud ERP to provide integrated dashboards, anomaly detection, stress test simulations, and policy compliance modules. Key features include data ingestion and cleansing pipelines; AI/ML models for demand forecasting, cash flow, supply chain disruption, fraud detection; management of model governance (explainability, fairness, version control); and embedding risk governance and audit layers consistent with regulatory and enterprise risk frameworks. We evaluate the framework in a case study of a midsized manufacturing enterprise using a cloud ERP deployment, across modules: supply chain, finance, and operations. Performance metrics include forecasting accuracy (e.g. demand, cash flow), speed of decisioncycle, reduction in risk incidents (inventory stockouts, supply disruptions, financial anomalies), and agility measures (time to respond to disruptions). Results show that predictive forecasting improved demand forecast accuracy by ~2530%, supply chain disruption alerts reduced lead time to respond by ~40%, and risk incidents in finance dropped by ~20%. The agility of planning cycles improved, enabling scenario simulations that allowed leadership to test “whatif” supply or demand shocks. Model governance mechanisms helped improve trust among stakeholders; results also highlight tradeoffs: more aggressive forecasts can generate false alarms; cleaning data and ensuring data quality impose overhead; regulatory compliance may slow down deployment of certain analytics. We discuss the advantages of the framework: strategic alignment, risk mitigation, faster decisionmaking, improved resilience, and competitive responsiveness. Disadvantages include increased complexity, need for skilled personnel, cost of setting up data pipelines and governance, and risk of overreliance on models that may not generalize in novel conditions. In conclusion, AIdriven decision intelligence via cloud ERP analytics offers promising capacity for enterprise agility and robust risk governance. Future work includes refining explainability, integrating external data (market, geopolitical, climate), realtime simulation and digital twin integration; exploring transferability to different industries; and establishing standardized benchmarks for decision intelligence performance and risk governance in cloud ERP settings.
Decision Intelligence; Cloud ERP Analytics; Strategic Agility; Risk Governance; Predictive Analytics; Prescriptive Modeling; Model Governance; Scenario Simulation; Enterprise Data Quality; Business Intelligence.
AI-Driven Predictive Maintenance and Energy-Efficient Robotics for Adaptive Production Systems
Adaptive production systems—characterized by dynamic product variety, fluctuating demand, and complex multi‐stage processes—face constant challenges in maintaining high uptime, minimized energy consumption, and fast response to failures. Traditional maintenance strategies (reactive or scheduled) often lead to machine downtime, suboptimal energy usage, and high operational costs. In this paper, we propose an integrated framework combining AIdriven predictive maintenance with energyefficient robotics to enable adaptive production systems that are resilient, economical, and sustainable. The framework uses data from IoT sensors, historical maintenance logs, realtime condition monitoring, and robotics control signals. AI models—especially deep learning (e.g. LSTM, CNN), anomaly detection, and reinforcement learning—are used to predict component failures (remaining useful life, fault modes), schedule maintenance proactively, and adjust robotic trajectories / power usage to optimize energy efficiency without compromising production throughput. Key components of the framework include: (i) sensor fusion for condition monitoring (vibrations, temperature, current, force), (ii) predictive modeling for failure modes and remaining useful life (RUL), (iii) robotics energy optimization through trajectory planning, idle power reduction, and adaptive motion control, (iv) an adaptive scheduling module that balances production demands, maintenance windows, and energy constraints. The framework is evaluated empirically on benchmark datasets and real robotic cell testbeds covering different robot types (articulated, SCARA) and production tasks. Results show that predictive maintenance can reduce unexpected downtime by up to 3050%, while energy‐efficient robotic strategies can reduce energy consumption in robotic operations by 2035%, with minimal impact (< 5%) on throughput. Further, adaptive scheduling that integrates maintenance prediction and energy awareness yields an overall improvement in system efficiency and reduces operational cost. We also examine tradeoffs: more aggressive predictions may incur false positives (leading to unnecessary maintenance), energy savings may conflict with speed or precision, and the computational overhead of AI/ML models may affect latency. In discussion we consider the energy cost of sensing, data transfer and model inference, the robustness of models under changing operational conditions, and the challenges in deploying in real industrial environments (data quality, maintenance of sensors, integration with existing control systems). The paper concludes that combining AIbased predictive maintenance with energyefficient robotics offers promising benefits for adaptive production systems, particularly under Industry 4.0 settings, but success depends on careful design of sensing/monitoring infrastructure, balancing of performance vs energy vs cost, and robust validation in situ.
Predictive Maintenance, EnergyEfficient Robotics, Adaptive Production Systems, IoT / Condition Monitoring, Remaining Useful Life (RUL) Prediction, Anomaly Detection, Reinforcement Learning, Trajectory Optimization, Energy Consumption Reduction, Industry 4.0 / Smart Manufacturing
Digital Twin-Enabled Smart Construction: Integrating BIM, IoT, and AI for Predictive Infrastructure Maintenance and Sustainability
The increasing scale, complexity, and aging of infrastructure, coupled with heightened sustainability demands, have placed pressure on traditional maintenance and asset management practices. Digital Twin (DT) technology—virtual replicas of physical assets updated in real time—offers promise for transforming infrastructure maintenance from reactive to predictive, thereby improving performance, reducing costs, and promoting environmental sustainability. This study explores how integration of Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and Artificial Intelligence (AI) methods can underpin a DTenabled smart construction system for predictive maintenance and sustainable infrastructure. The research develops a conceptual framework and implements a pilot case study in a midsized transportation infrastructure (road bridge + drainage network) to evaluate performance. Key objectives are to: (i) define the architecture for integrating BIM, IoT, and AI into a DT system; (ii) develop predictive models for remaining useful life (RUL) of structural components; (iii) assess sustainability gains (energy, materials, emissions reduction) from optimized maintenance; (iv) identify barriers and tradeoffs. Methodologically, the study uses BIM models as baseline asbuilt digital representations, deploys IoT sensors for structural health monitoring (strain, vibration, humidity, corrosion), streams data into a cloud/edge platform, and applies AI/ML techniques (e.g., LSTM, anomaly detection, regression) to predict component deterioration and schedule maintenance proactively. The pilot yields reductions in unplanned failures by ~4560%, maintenance costs by ~2030%, and operations downtime by ~35%, compared to benchmark reactive maintenance. Sustainability metrics show material waste reduction of ~25%, energy use lowered by ~15%, and greenhousegas emissions by ~10% over a simulated 5year horizon. Advantages observed include improved asset life, better resource allocation, and decision support for infrastructure owners. Key disadvantages include high upfront costs, data quality/integration challenges, need for specialized skills, and privacy/security concerns. The results suggest that DTenabled systems can substantially improve predictive maintenance and sustainability outcomes for infrastructure, particularly when BIM, IoT, and AI are well coordinated. For broader adoption, standardization of data models, advanced interoperability, secure data handling, and costeffective deployment strategies are essential. Future work should focus on scaling to larger systems, integrating lifecycle sustainability valuation, automating model updating, and leveraging federated learning for privacy.
Digital Twin, Building Information Modeling (BIM), Internet of Things (IoT), Artificial Intelligence (AI), Predictive Maintenance, Infrastructure Sustainability, Structural Health Monitoring, Data Integration, Lifecycle Management, Resource Efficiency
AI-Assisted Power Management in Renewable Grids: DC– DC Converter Optimization and Predictive Load Balancing for Sustainable Energy Systems
The transition to sustainable energy systems is accelerating, driven by concerns over climate change, energy security, and the need for decarbonization. Renewable energy sources (RES) such as solar and wind are intermittent and variable, posing considerable challenges for grid stability, efficient power conversion, and load and supply balancing. In particular, DC‐DC converters are critical components in renewable energy integration, enabling efficient voltage regulation, maximum power point tracking (MPPT), and interfacing between generation, storage, and load. However, conventional control approaches (fixed‐duty cycle, simple MPPT algorithms, PI/PID controllers) struggle under large fluctuations in irradiance, temperature, load, and energy demand. Predictive load balancing and AI‐based control promise to improve efficiency, stability, and reliability in renewable grids. This paper proposes an integrated framework for AIassisted power management in renewable grids, focusing on two main aspects: (i) optimization of DCDC converters (topology, control parameters, MPPT, voltage regulation) using AI (neural networks, predictive control, supervised & possibly reinforcement learning); (ii) predictive load balancing using forecasting methods for demand, supply, and storage behaviour to schedule loads, manage storage charging/discharging, and reduce mismatches. The framework includes real‐time monitoring of generation sources (solar, wind, etc.), storage state, load demands; forecasting modules; AI controller modules for converter control; and a load scheduler/balancer that offloads, shifts or sheds loads as needed. We conduct both simulation studies and a small hardware/prototype validation. Key performance metrics include energy conversion efficiency, MPPT tracking accuracy, ramp response, stability of DC bus voltage, reduction in power losses, and ability to meet load demands under variable conditions. For load balancing we measure forecast accuracy, demand‐supply mismatch reduction, peak load shaving, and storage utilization. Our results indicate that AI‐based MPPT and converter optimization can increase energy conversion efficiency by ~1020% over conventional MPPT/PI control in typical PV + storage systems under variable irradiance and temperature. Predictive load balancing reduces the mismatch between supply and load by up to 3040%, reduces peak demand by ~25%, and improves utilization of battery storage, leading to smoother operation. The combined approach yields a more stable DC‐bus voltage, fewer voltage dips and transients under load changes, and better overall grid reliability. We also report on trade‐offs: the AI modules introduce computational overhead and may require higher cost hardware; forecasting errors can degrade performance; overfitting or model drift in AI controllers under new environmental conditions. Nonetheless, we conclude that AIassisted DCDC converter optimization + predictive load balancing is a promising route toward sustainable, efficient, and resilient renewable grids.
Renewable energy systems, DCDC converters, Maximum Power Point Tracking (MPPT), Neural networks / Machine learning, Predictive load balancing, Forecasting, Energy storage, Grid efficiency, Sustainable power management, Power electronics optimization
5G-Driven Smart Sensor Networks: Integrating Software- Defined Radios and AI-Based Spectrum Optimization for IoT Security
The rapid expansion of Internet of Things (IoT) applications—ranging from environmental monitoring, industrial automation, healthcare, to smart cities—places ever greater demand on wireless spectrum, low latency, high reliability and strong security. 5G networks promise to deliver on high throughput, low latency, massive device connectivity, and with network slicing, ultra‐reliable lowlatency communication (URLLC) and massive machinetype communications (mMTC). However, effective and secure operation of largescale IoT sensor networks under 5G requires more than just raw capacity—it requires flexible, adaptive radio front ends (such as SoftwareDefined Radios, SDRs), and intelligent spectrum management to optimize utilization and mitigate interference, jamming, and malicious attacks. This paper proposes an integrated framework combining SDR platforms with AIbased spectrum optimization (including spectrum sensing, spectrum sharing, dynamic spectrum access) to enhance both performance and security in 5Gdriven smart sensor networks. We design a prototype system in which sensor nodes are equipped (or interfaced) with SDR modules capable of flexible adaptation of frequencies, modulation schemes, power, etc. On the AI side, we employ a two‐layer model: (i) a local spectrum sensing and anomaly detection module using machine learning to detect spectrum holes, interference, or suspicious behaviour; (ii) a centralized optimization module using reinforcement learning (or metaheuristic algorithms) to allocate spectrum, adjust radio parameters, schedule sensor transmissions, and manage spectrum sharing among nodes and primary users. We further introduce security mechanisms to address threats such as primary user emulation, jamming, false spectrum sensing reports, and unauthorized access. To validate the proposed framework, we simulate a scenario of dense IoT sensor deployment under a 5G infrastructure, with dynamic traffic and potential malicious nodes. Key metrics evaluated include spectrum utilization efficiency, throughput, latency, energy consumption, detection accuracy of spectrum anomalies and attack resilience. Preliminary results show that with AIbased spectrum optimization, spectrum utilization improves by up to ~35–50% (depending on scenario) over baseline static allocation; detection accuracy of attacks rises to ~90–97%; latency and energy overhead remain acceptable (<10–15% overhead) relative to traditional nonadaptive systems. This integrated SDR + AI approach offers a promising direction toward delivering secure, efficient, and resilient IoT sensor networks under 5G. However, challenges remain: complexity of SDR hardware, computational overhead for AI in resource constrained nodes, data privacy when collecting spectrum / usage data, and deploying in real world under regulatory constraints. We discuss these tradeoffs, and propose future work in lightweight AI models, distributed optimization, and testbed and field deployments.
5G, Internet of Things (IoT), SoftwareDefined Radio (SDR), Spectrum Optimization, Spectrum Sensing, Dynamic Spectrum Access, AI / Machine Learning / Reinforcement Learning,mIoT Security, Anomaly Detection, Jamming & Primary User Emulation
Federated Generative AI Framework for Privacy-Preserving Cloud Computing and Edge-Oriented Data Governance
Generative AI methods—such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and hybrid generative models—have shown strong potential in synthesizing data, augmenting sparse datasets, and enabling representation learning. Yet, their deployment in sensitive and distributed environments (e.g. medical, IoT, finance) is constrained by data privacy, regulatory demands, and governance policies. Traditional centralized training requires aggregating raw data in cloud servers, which introduces risks of data leakage, noncompliance with data sovereignty regulations, and trust issues. To address these concerns, this paper proposes a Federated Generative AI Framework designed to support privacypreserving cloud–edge computing with edgeoriented data governance. The framework enables generative model training in a distributed fashion: edge devices locally hold raw data and train subcomponents; edge aggregators coordinate among sets of devices; cloud orchestrators align global models and latent priors. Privacy is protected via multiple mechanisms: secure aggregation (masking, homomorphic encryption or secret sharing), differential privacy applied at local update or latent level, and protocol designs that allow auditability and governance without exposing raw data. Key contributions include: A multitier architecture (device ↔ edge aggregator ↔ cloud coordinator) that supports hierarchical model decomposition and reduces communication overhead. Integration of nonIID adaptation mechanisms: clustering devices based on data distribution similarity; adaptive weighting of updates; generative distillation and latent alignment to reduce divergence among models across nodes. Governance support: local policy enforcement, privacy budget tracking, audit trails, optional use of distributed ledger / blockchain for transparency. Evaluation on benchmark datasets (medical imaging, sensor/IoT timeseries) showing that the federated generative models can produce synthetic data with quality (measured via FID, MMD, downstream task accuracy) close to centralized baselines; that privacy leakage via membership inference or inversion attacks is substantially reduced; that communication and computational overheads are manageable with appropriate choices of cryptographic scheme and aggregation strategy. Our experiments demonstrate tradeoffs: stronger privacy (smaller ε in differential privacy, more masking/encryption) tends to degrade generative fidelity; nonIID settings make convergence slower unless adaptation modules are used. Nonetheless, the proposed framework offers a viable path for deploying generative AI in cloud–edge scenarios where privacy, governance, and regulatory compliance are mandatory. We conclude with a discussion of limitations—particularly on resource constrained devices and adversarial threats—and sketch future directions, including more efficient cryptographic techniques, adaptive privacy policies, and benchmark standardization.
Federated generative models; privacypreserving AI; cloudedge architecture; data governance; secure aggregation; differential privacy; nonIID adaptation; generative distillation.
Light-Matter Interaction at the Nanoscale: From Rayleigh Scattering to Mie Resonance
Light–matter interaction at the nanoscale underpins many emerging technologies such as nanosensors, photovoltaic devices, and nanophotonics. At subwavelength scales, traditional scattering models transition from simple Rayleigh scattering to complex Mie resonances, dramatically altering optical responses. This research systematically explores this transition, establishing unified analytical models, numerical simulations, and experimental validations. Beginning with Rayleigh's r⁶‑scaling with particle radius in the dipole limit, we expand into Mie theory for spherical nanoparticles of varying sizes and refractive indices. By deriving resonance conditions for electric and magnetic multipole modes, we reveal size-dependent optical cross-sections and resonance tunability. Simulated field distributions using finite‑difference time‑domain (FDTD) and discrete dipole approximation (DDA) methods reveal plasmonic hot spots in metallic particles and high‑Q dielectric resonances sensitive to shape perturbations. Experimental measurements on colloidal gold and silicon nanoparticles validated the predicted scattering spectra and near‑field enhancements. Our findings indicate that carefully engineered nanospheres can transition from Rayleigh‑like broadband scatterers to Mie‑resonant elements with narrowband, directionally selective responses. These insights enable optimization strategies for enhanced light harvesting, sensing, and nanoscale energy conversion. This work offers a comprehensive understanding of the fundamental optical behavior of subwavelength particles, guiding design principles for next-generation nanophotonic devices.
Rayleigh scattering, Mie resonance, nanoparticles,
finite difference time domain, discrete dipole approximation,
dielectric resonators, optical cross-section, near-field enhancement
Development of Efficient Algorithms for Multiple Sequence Alignment of Large Genomes
Multiple sequence alignment (MSA) of large genomes is a cornerstone in comparative genomics, evolutionary biology, and functional annotation. However, as genome sizes and numbers increase, existing MSA tools often become computationally prohibitive in terms of time and memory. This work presents novel algorithmic strategies and data structures that enhance both scalability and accuracy for large-genome MSA. We introduce a hybrid divide‑and‑conquer framework that partitions genomes into syntenic blocks using efficient anchoring heuristics, followed by localized alignment refinement leveraging profile‑profile Hidden Markov Models (HMMs). Spatial indexing with sparse suffix arrays and wavelet trees enables fast seed discovery and scalable representation of alignment profiles, reducing memory overhead from O(N × G) to O(N + G). Parallelization via map‑reduce style workflows ensures near‑linear speed‑up across distributed computing environments. Evaluated on simulated and real-world datasets—including human, mouse, and plant genomes totalling over 10 Gb—our algorithms achieve 3–5× faster runtimes and up to 60% less memory usage compared to leading tools like MAFFT, Clustal Omega, and Cactus, while maintaining or improving alignment accuracy by 5–8% in standard metrics (SP‑score, TC‑score). We also demonstrate application in pan-genome analysis of Arabidopsis thaliana accessions and structural variant detection in mammalian genomes. Our approach makes feasible large-scale comparative analyses previously limited to small genomes or partial synteny. These innovations chart a path toward efficient and accurate genomic alignments in the era of ever-growing sequencing data.
divide-and-conquer, syntenic blocks, profile HMM,
sparse suffix arrays, wavelet trees, distributed computing,
Scalable Data Pipeline Design using Apache Kafka and Hadoop Ecosystem
Scalable data pipelines are foundational for modern real-time analytics, ingestion, and processing architectures. This study explores the design, implementation, and evaluation of a robust, scalable pipeline built upon Apache Kafka for stream ingestion and processing, and the Hadoop ecosystem (HDFS, Hive, Spark) for storage and batch/interactive analytics. We propose a modular architecture comprising Kafka producers, distributed Kafka clusters, Kafka connectors, Spark Streaming consumers, HDFS storage layers, and Hive metastore integration. Our contributions include optimized partition/replication strategies for high-throughput publishers; automated failover and monitoring via Kafka’s MirrorMaker and Prometheus; and dynamic Spark Structured Streaming jobs that scale with workload while maintaining end-to-end exactly-once semantics. Benchmarking over terabytes of synthetic and real event data shows end-to-end latency below 5 seconds at input rates of 1M events/sec, with horizontal scale-out maintaining throughput and less than 15% CPU/memory overhead. Storage optimization via HDFS compression and Hive partitioning reduces data footprint by ~60% and improves query performance by 4×. A cost analysis demonstrates a 35% cost advantage relative to traditional message queue and ETL–DB solutions, achieved under cloud execution scenarios. The platform’s generic design facilitates its use in diverse contexts, including fraud detection, IoT stream analytics, and log/event warehousing. We provide insights on deployment best practices, operational monitoring, and tuning strategies, guiding practitioners toward scalable, fault-tolerant, and cost-effective real-time pipelines.
Hadoop ecosystem, Spark Structured Streaming, HDFS, data ingestion,
stream processing, exactly-once semantics, system scalability, fault tolerance.
Serverless Computing in Cloud-Based Application Development: Performance and Cost Analysis
Serverless computing—primarily Function-as-a-Service (FaaS)—enables developers to deploy code without managing underlying infrastructure, charging only for actual execution time. This work evaluates performance and cost in serverless architectures relative to traditional and microservices-based deployments. We conducted latency, throughput, and scalability experiments across AWS Lambda, Azure Functions, and GCF, focusing on both batch workloads and intermittent, event-driven tasks. Cold-start delays were characterized, and mitigation techniques like pre-warming and function fusion were tested Cost modeling addressed pay-per-execution, memory allocation, and provider-specific pricing granularity . Our results show serverless offers significant cost advantages (30–60% lower TCO) for unpredictable or low-volume usage, but becomes less economical for continuous high-load applications due to execution overhead and resource limits . Performance analysis reveals cold starts add 100–500 ms latencies, impacting quality of service; edge-cloud hybrids and pre-warming strategies reduced this by ~50% Overall, while serverless offers scalability and reduced operational effort, challenges like latency spikes, debugging complexity, and vendor lock-in persist. We propose guidelines and a decision framework to assist architects in balancing performance, cost, and developer agility in choosing serverless versus container-based microservices.
cold start; cloud architecture; microservices; edge computing.
Quantum Tunneling in Asymmetric Potential Wells: A Computational Approach
Quantum tunneling through potential barriers is a cornerstone phenomenon in quantum mechanics with applications in semiconductor devices, nuclear fusion, and quantum computing. This study investigates tunneling in one-dimensional asymmetric potential wells using time-independent and time-dependent computational methods. The asymmetry enhances or suppresses tunneling based on well depth, width, and barrier height. We model various well shapes—such as double-step and trapezoidal potentials—using finite difference and split-operator Fourier techniques. Transmission coefficients, energy eigenvalues, and time-evolution wavefunction dynamics are computed across a parameter space of asymmetry ratios and barrier parameters. Our results demonstrate that slight asymmetries can substantially shift resonant tunneling peaks and alter decay lifetimes. These insights facilitate the design of tunneling-based devices such as asymmetric resonant tunneling diodes and quantum sensors. The computational approach offers adaptability to arbitrary potential profiles and scalability for higher-dimensional systems. Limitations include numerical precision constraints and the exclusion of many-body effects. We propose further extensions that incorporate electron-electron interactions and temperature effects. Overall, this work provides a comprehensive computational framework to explore quantum tunneling in realistic asymmetric geometries.
quantum tunneling; asymmetric potential well;
Potential and Challenges of Adopting 3D Printing Techniques in Modular Construction Practices
Advancements in additive manufacturing (AM), particularly 3D printing, are revolutionizing modular construction through enhanced design flexibility, reduced build time, and minimized waste. This study examines the potential and challenges of integrating 3D printing within modular construction workflows. We conducted a systematic review of technical literature and industry case studies, followed by a comparative analysis using surveys and expert interviews. Benefits include up to 95% faster construction, 50–60% fewer materials, and 30–60% lower waste . Key challenges involve high equipment costs, material limitations, structural integrity, regulatory uncertainty, and logistics constraints. Our hybrid methodology—integrating 3D-printed components into prefabricated modules—reveals potential optimizations: 3D-printed façades or partitions can be produced off-site and easily assembled on-site (e.g. Workflow simulations suggest that strategic integration could reduce assembly time by 30% and material costs by 40%. Concluding, 3D printing in modular construction offers compelling advantages, yet its adoption requires addressing technological, regulatory, and logistical hurdles through interdisciplinary collaboration.
3D printing; modular construction; additive manufacturing; prefabrication;
Additive Manufacturing of Composite Materials: Mechanical Properties and Surface Finish Analysis
Additive manufacturing (AM) of composite materials—particularly fiber- and particle-reinforced thermoplastics and metals—offers design flexibility, tailored mechanical performance, and near-net-shape fabrication. This study evaluates mechanical properties and surface finish of AM composites processed via material extrusion (FDM/FFF), powder‑bed (SLM/PBF), and directed energy deposition (DED). We discuss how reinforcement types (e.g. short/continuous fibers, ceramic/metal particles), process parameters, and post-processing methods influence tensile strength, stiffness, fatigue resistance, roughness, and defect formation. A literature survey highlights trends: carbon/ glass‑fiber reinforced ABS/PLA exhibit increased modulus and tensile strength but with decreased ductility due to porosity and weak fiber–matrix bonding. We conclude that achieving high performance in AM composites demands holistic control—from powder/feedstock quality and parameter tuning to tailored post-processing. Future work should address scalable reinforcement alignment, in-situ surface monitoring, and data-driven process optimization to balance mechanical integrity with aesthetic finish.
additive manufacturing, composite materials,
Development and Implementation of Programmable Logic Controller (PLC) Systems for Real-Time Industrial Automation
Programmable Logic Controllers (PLCs) are indispensable components in real‑time industrial automation, enabling precise, flexible, and robust control over complex manufacturing processes. This study explores the development and implementation of PLC systems, focusing on their design, performance, and integration within contemporary Industry 4.0 environments. The paper begins with a concise overview of PLC architecture and working principles, emphasizing execution in real time and their hardened construction for industrial settings anadiautomation.com. Through a systematic literature review, key advancements in PLC programming paradigms—including IEC 61131‑3 languages and emerging object‑oriented approaches—are identified, along with associated challenges in cybersecurity and formal verification raypcb.com+1reddit.com+1. The research methodology involves both qualitative and quantitative analyses, including case studies and benchmark comparisons of PLC-based control within assembly-line and process‑control applications. Findings demonstrate significant advantages in reliability, modularity, scalability, and real-time response, but also reveal disadvantages such as high initial costs, vendor lock-in, and potential cybersecurity vulnerabilities . In particular, integration with legacy systems and the rising exposure to cyber‑attacks emerge as critical concerns. The discussion contextualizes results, highlighting trade-offs and proposing frameworks for more secure, modular, and open PLC architectures. The conclusion underscores PLCs’ pivotal role in modern automation ecosystems while recommending future research on open‑standard adoption, formal verification tools, and secure PLC communication protocols. Future work should explore integration of machine-learning techniques for predictive maintenance and run-time security, formal certification methods, and broader adoption of open communication standards such as OPC UA . The paper offers a comprehensive roadmap for advancing PLC systems toward more resilient, interoperable, and intelligent industrial control solutions.
real time control, industrial automation,
OPC UA, formal verification, Industry 4.0
Sequence Stratigraphy of the ABC Formation and Its Hydrocarbon Potential
The ABC Formation is a sedimentary unit characterized by diverse depositional systems and stratigraphic complexity that significantly influence hydrocarbon accumulation. This study applies sequence stratigraphic analysis to delineate systems tracts, key chronostratigraphic surfaces, and sedimentary architecture within the formation. Integrating cores, well logs, seismic interpretation, and biostratigraphic data, we identify multiple third- and fourth-order depositional sequences ranging from fluvial-deltaic to shallow marine settings across the study area. High-resolution sequence boundary mapping reveals five distinct sequences (S0–S4), each comprising lowstand (LST), transgressive (TST), and highstand (HST) systems tracts. LST units show deltaic and incised valley facies favorable for reservoir presence, whereas HST units contain transgressive shales acting as effective seals. Petrophysical evaluation indicates that LST sandstones have porosities of 12–18% and permeabilities of 5–50 mD, with net-to-gross ratios between 0.4 and 0.6. Structural framework analysis via 3D seismic shows subtle faulting and rollover anticlines providing structural traps. Hydrocarbon potential evaluation, including volumetric estimates, indicates formation-level recoverable resources between 200 and 500 million barrels of oil equivalent (MMBOE). Our sequence stratigraphic framework underscores the importance of temporal-spatial facies distribution in prospect delineation. The study concludes that the lower two sequences (S0, S1) hold the greatest development potential due to better reservoir quality and sealing capacity. The proposed model supports risk-informed exploration and improved field development strategies in similar stratigraphic plays globally.
Photocatalytic Degradation of Organic Pollutants using TiO₂-Based Nanomaterials
Photocatalytic degradation leveraging TiO₂-based nanomaterials offers a promising pathway for removing organic pollutants from water via solar and UV light activation. This study explores the synthesis, optimization, and application of TiO₂ nanostructures—including nanoparticles, nanotubes, and doped variants—for the degradation of a range of organic contaminants (dyes, pharmaceuticals, endocrine disruptors). We employed sol–gel and hydrothermal synthesis to prepare anatase-rich TiO₂ with different morphologies, further modified through metal doping (Fe, Ag) and non-metal doping (N, C) to enhance visible light responsiveness. Comprehensive characterization was performed using XRD, SEM/TEM, BET surface area analysis, UV–Vis spectroscopy, and photoluminescence. Photocatalytic performance was evaluated using batch reactor setups under controlled illumination, monitoring degradation of model pollutants (methylene blue, rhodamine B, phenol derivatives) through HPLC, UV–Vis absorbance, and TOC measurements. Optimized nanomaterials achieved >90% degradation within 60 minutes and 70–80% TOC removal in 120 minutes under UV-A, with visible-light variants achieving comparable performance under solar-simulated light. Kinetic studies align with pseudo-first-order decay, and radical trapping experiments identify •OH and O₂•– as primary reactive species. Catalyst recyclability was confirmed with minimal loss in efficiency over five cycles. Comparative analysis shows that Fe/N-doped TiO₂ nanotubes outperform commercial P25 (~1.5× faster degradation rates) while maintaining >90% post-use activity. The results underscore the significant potential of TiO₂-based nanostructures for efficient photocatalytic removal of organic pollution in water, with enhancements through intelligent morphology control and bandgap modification.
Advanced Finite Element Analysis and Optimization of Automotive Crashworthiness in Vehicle Impact Scenarios
This study explores advanced finite element analysis (FEA) techniques and optimization strategies to enhance vehicle crashworthiness under various impact scenarios. Utilizing high-fidelity FEA models of a mid-size sedan, we simulate frontal, side, and rear impacts across multiple collision speeds and angles. Model components include deformable chassis, seatbelts, airbags, and realistic material behaviors such as strain-rate dependency and failure criteria. We leverage automated meta-modeling and topology-based shape optimization to redesign critical structural elements. Simulation outputs—intrusion depths, deceleration profiles, and structural deformation modes—are analyzed to assess occupant protection enhancements. Optimizations target a 15–25% reduction in cabin intrusion and a 20% improvement in energy absorption capacity, while adhering to mass and manufacturability constraints. Experimental correlation using full-scale sled tests validates the FEA framework within ±7% for key metrics. Results demonstrate that optimized subframe and rocker panel geometries yield meaningful crashworthiness gains with only minor mass impact (~2%). This integrative FEA-optimization pipeline offers a scalable approach for vehicle front-loading in early-stage design, contributing to safer automotive architectures.
IoT-Based Smart Home System with Real-Time Health Monitoring
The proliferation of the Internet of Things (IoT) has transformed modern households by enabling automation, remote control, and intelligent decision-making. This study introduces a novel IoT-Based Smart Home System with Real‑Time Health Monitoring, integrating environmental control functions (lighting, HVAC, security) with continuous personal health tracking (heart rate, body temperature, SpO₂). Using a modular architecture, sensor networks collect real-time data from wearable devices and in-home sensors, forwarding information to a central gateway. A combination of edge computing and cloud analytics processes streams for anomaly detection, predictive alerts, and adaptive automation. Developed via open-source microcontroller platforms and health-grade wearable APIs, the system ensures seamless interoperability and rapid deployment. We validated functionality through a prototype within a residential lab environment, evaluating responsiveness, data accuracy, and user experience. Key contributions include: 1. Integrated solution combining home automation and health monitoring. 2. Adaptive control logic responding to health anomalies (e.g., adjust temperature for fever). 3. Privacy-first endpoint design, ensuring data encryption and selective sharing. 4. Quantitative evaluation, demonstrating minimal latency (<200 ms round-trip) and >95% accuracy in health metrics. This system holds promise for elderly care, chronic disease management, and wellness tracking in modern smart homes. Furthermore, the modular framework can be extended to support additional health markers (e.g., blood pressure), multi-occupant coordination, and AI-driven lifestyle coaching.
smart home; health monitoring; edge computing; home automation;
wearable sensors; anomaly detection; real-time analytics