Power System Protection and Control (original—Relay), quarterly journal, is a national electro-technical journal. It has been published at home and abroad since 1973 Power System Protection and Control has been indexed in many reputable databases. They are Compendex (EI), Scopus, Chinese Science Citation Database (CSCD), Chinese Core Journal, Chinese Sci-tech Core Journal, Outstanding S & T Journals of China, RCCSE Chinese Authoritative Academic Journal (A+), The Excellent International Impact Academic Journals of China from 2014 to 2016, 2018,and 2017,2019,2020,2021 The Highest International Impact Academic Journals of China, Cambridge Scientific Abstracts (CSA), Ulrich's Periodicals Directory (UPD), Scientific Abstract (SA, INSPEC), JST, and Abstract Journal (AJ, VINITI). Editor-in-chief of Power System Protection and Control is Dr. ZHOU De-Gao, who is an Academician of Chinese Academy of Engineering and professor of Hunan University. Chairmen of Editorial Committee are Qiang LU and Shijie CHENG, who are academicians of Chinese Academy of Sciences. Editorial Committee is composed of 127 members, including 7 academicians and 10 international committee members. All of them hold posts in famous universities or research institutes at home and aboard, acquiring distinguished achievements in the field of power system protection and control. Power System Protection and Control is dedicated to presenting high-quality academic achievements reflecting scientific innovation, advanced technology in the field of Power System Protection and Control by scholars, researchers and engineers at home and abroad, and devotes herself to serving as an academic platform for researches in power industry. Aims: Paying attention to the academic development; Combining theory with practice; Being application-oriented; Serving the front-line research and production. Scope: Power System Protection and Control publishes academic papers with new techniques, new achievements, operation experiences, technical innovations and scientific management, technical regulations, or foreign advanced techniques in the field of power system protection and control including, but are not limited to, power system relay protection, power system analysis and control, power system planning, integrated energy system, new energy generation, smart substation, intelligent power transmission and consumption, influence of large-scale electric vehicles integration on power grid, micro-grid techniques, application of power electronics in power system, power system automation and remote control techniques, power system communication, electricity market, power quality, Risk Management, Risk Management in Engineering Systems, Industrial Engineering, System Engineering, Medical Radiation Engineering, etc. Main Columns: Theoretical analysis, survey, application research, design and development, engineering application, special series on power industry development and hotspot, etc. Open Access: All articles published by Power System Protection and Control are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. Power System Protection and Control will have copyright of the manuscript upon acceptance, including the right to reproduce, publish, transmit, translate and edit the work. The authors can use or share the published article under the Attribution-Non Commercial 4.0 International (CC BY-NC 4.0) license. Power System Protection and Control applies the Creative Commons Attribution-NonCommercial (CC-BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0) which permits unresticted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Power System Protection and Control has followed the OA policy of the journal since 2008, and it conforms to the definition given by DOAJ. Authorship: Researchers, designers, equipment manufacturers from power enterprises and departments. Professors and students in universities with faculties of power system and its related fields; technical staffs from power plants, substations, power transmission and distribution system and power supply companies; personnel engaged in electric power production, construction, and operation & management; individuals from the fields of metallurgy, coal mining, petrifaction, railroad construction, machining, etc.
CONCIERGE AGENT: A LOCAL HYBRID VISION-LANGUAGE FRAMEWORK FOR PRIVACY PRESERVING AUTONOMOUS COMPUTER USE
The majority of computer usage agents available today are based on cloud-computing AI models that need constant communication of screenshots and user activity with servers, which poses significant privacy risks when the task involves personal information, credentials, or documents. This paper presents Concierge Agent, a fully local hybrid computer-use agent designed for privacy-sensitive graphical user interface automation across web browsers and operating systems. The framework combines local Vision-Language Model reasoning using quantized FARA-7B with deterministic, tool constrained execution across both browser and operating system interfaces. In contrast to cloud-based agents that continuously transmit screenshots for remote inference, the proposed system keeps visual context and execution traces on the user device. The agent follows a structured See-Think-Act loop in which every proposed action passes through a multi-stage Validation Layer covering schema compliance, tool allow lists, coordinate bounds, and contextual guardrails before execution. Experimental evaluation shows that this validation-first design attains a 100% task success rate by filtering invalid actions and recovering through controlled regeneration as opposed to the 42.1% success rate for the cloud-based GPT-4o. However, this comes with a cost in execution time, which is 177 seconds for local models as opposed to 44 seconds for cloud models. The results indicate that local hybrid agents can provide an effective solution for reliable computer automation in scenarios where the local data is of primary importance, offering an alternative to cloud computing.
Computer Use Agents, Vision Language Models, Local Inference, Privacy Preserving Automation, Browser Automation, Operating System Control, Gui Agents
Gagan P, Josy Elsa Varghese, Bhargav Koduru, Anirudh C Balaji, Aby Binoy
A JOINT PROBABILISTIC FRAMEWORK TO INTEGRATE RAM SIMULATION AND ECONOMIC FORECASTING WITHOUT DOUBLE‑COUNTING DOWNTIME
Reliability, Availability, and Maintainability (RAM) simulation and economic lifecycle forecasting are routinely performed as sequential or parallel analyses in asset-intensive industries. This separation creates a systemic error: downtime events are either counted twice (once in RAM metrics, once in financial models) or counted inconsistently, leading to distorted net present value (NPV) estimates and suboptimal investment decisions. This paper proposes a joint probabilistic framework (JPF) that integrates RAM simulation and economic forecasting within a unified Monte Carlo engine, enforcing a single source of truth for downtime events across reliability and financial domains. Using a case study of a natural gas compression station with three compressor trains, we compare three methods: (1) sequential RAM-then-economic analysis (baseline), (2) parallel analysis with manual reconciliation (industry practice), and (3) the proposed JPF. Over 10,000 simulated operating years, the baseline method double-counts downtime in 34% of scenarios, overstating lifecycle costs by 18.7% on average. The proposed JPF eliminates double-counting entirely, reduces NPV estimate variance by 52%, and correctly identifies the optimal maintenance strategy (predictive vs. preventive) that sequential analysis misclassifies. The framework offers a rigorous, computationally tractable approach for integrated asset investment planning.
Availability, Downtime Cost, Economic Forecasting, Lifecycle Cost, Monte Carlo Simulation, Net Present Value, RAM Modelling.
Copyright (c) 2025 Power System Protection and Control
MIXTURE‑DISTRIBUTION MAINTAINABILITY MODELLING TO CAPTURE TAIL‑RISK DOWNTIME IN CRITICAL MACHINERY
Maintainability modelling for critical machinery (e.g., gas turbines, offshore drilling rigs, medical imaging systems, nuclear reactor coolant pumps) traditionally relies on standard probability distributions such as the lognormal or Weibull to represent repair time. However, these unimodal models often fail to account for tail-risk downtime—rare but excessively long repair events caused by cascading failures, specialized part unavailability, or non-routine troubleshooting. Such tail events, while low in probability, disproportionately contribute to operational losses, safety violations, and regulatory non-compliance. This paper introduces a Mixture-Distribution Maintainability (MDM) framework that explicitly captures tail-risk downtime by combining a primary component (representing routine, fast repairs) with a secondary, heavy-tailed component (representing rare, prolonged interventions). Using a finite mixture of two lognormal distributions—or a lognormal-Pareto composite—the model yields maintainability metrics (e.g., mean time to repair, maximum time to repair, and the 95th percentile repair time) that are robust to tail sensitivity. Through a case study on centrifugal compressor repair logs (N=1,200 records), we demonstrate that a single lognormal fit underestimates the 95th percentile repair time by 31% compared to the MDM approach. We further develop a Bayesian estimation procedure to handle sparse tail data and provide a decision rule for classifying high-risk repair modes. The proposed MDM framework enables reliability engineers to quantify tail risk, optimize spare parts inventory, and design maintenance policies that are resilient to extreme downtime events.
Maintainability, Tail Risk, Mixture Distribution, Heavy-Tailed Downtime, Critical Machinery, Repair Time Modelling, Bayesian Inference.
ARTIFICIAL INTELLIGENCE AND CLOUD-BASED HEALTHCARE DATA INTEGRATION FOR MEDICATION MANAGEMENT AND CLINICAL DECISION SUPPORT SYSTEMS
The rapid digitization of healthcare systems has generated unprecedented volumes of patient data, yet this data remains largely siloed, underutilized, and disconnected from clinical workflows. This paper presents a novel framework that integrates artificial intelligence (AI) with cloud-based healthcare data integration to enhance medication management and clinical decision support systems (CDSS). The proposed architecture leverages a hybrid cloud-edge computing model to ingest, normalize, and analyze multi-modal data from electronic health records (EHRs), pharmacy information systems, wearable devices, and genomic databases. Using a two-stage machine learning pipeline—comprising a gradient-boosted medication reconciliation model and a deep neural network for adverse drug event prediction—the system provides real-time, evidence-based recommendations at the point of care. The framework was evaluated on a de-identified clinical dataset comprising 1.2 million patient encounters across three tertiary care hospitals. Experimental results demonstrate that the proposed system achieves a medication error detection accuracy of 97.4%, an adverse drug event prediction F1-score of 94.2%, and a clinical decision alignment rate of 96.8% with expert physician panels. Furthermore, the cloud-based architecture reduced data retrieval latency by 82% compared to traditional on-premise solutions. The study concludes that AI-driven cloud integration offers a scalable, secure, and intelligent pathway toward precision medication management and proactive clinical governance.
Clinical Decision Support Systems, Medication Management, Cloud Computing, Artificial Intelligence, Healthcare Data Integration, Adverse Drug Event Prediction, Electronic Health Records.
Copyright (c) 2026 Power System Protection and Control
MACHINE LEARNING-DRIVEN VERIFICATION: USING ML TO OPTIMIZE COVERAGE ANALYSIS, PREDICT SIMULATION RUNTIMES, AND AUTO-GENERATE TESTBENCHES.
The exponential increase in design complexity of modern System-on-Chip (SoC) and Very Large Scale Integration (VLSI) systems has rendered traditional functional verification methods inadequate due to prolonged simulation times, incomplete coverage closure, and manual testbench development bottlenecks. This paper proposes a machine learning-driven verification (MLDV) framework that addresses these challenges by optimizing coverage analysis, predicting simulation runtimes, and auto-generating testbenches. The proposed methodology leverages supervised learning models—including Random Forest (RF), Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks—to analyze coverage hole patterns, estimate simulation execution time with high accuracy, and generate synthesizable SystemVerilog testbench components. Experimental evaluations conducted on open-source RISC-V and Tensor Processing Unit (TPU) verification suites demonstrate that the ML-driven approach achieves 96.4% coverage closure acceleration, 94.2% runtime prediction accuracy, and 92.8% testbench generation correctness. The Random Forest model for coverage analysis reduces verification cycles by 42%, while the LSTM-based runtime predictor achieves a Mean Absolute Percentage Error (MAPE) of 5.8%. Auto-generated testbenches attain 89% line coverage within the first three simulation iterations. Results confirm that ML-driven verification significantly enhances productivity, reduces time-to-market, and provides adaptive, intelligent verification flows for next-generation hardware systems.
Functional Verification, Machine Learning, Coverage Analysis, Simulation Runtime Prediction, Testbench Generation, System Verilog, RISC-V, SoC Verification.
PAN-SYSTEM CANCER INTELLIGENCE: INTEGRATING BLOOD, IMMUNE, MICROBIOME, AND TUMOR MICROENVIRONMENT DATA USING FOUNDATION MODELS
Cancer is no longer seen as just a disease of mutated cells. Over the last decade, researchers have come to realize that tumors live inside a much larger ecosystem that includes circulating blood biomarkers, immune cell populations, the gut and tumor microbiome, and the local tumor microenvironment. Each of these layers carries useful information, but they are often studied in isolation. This paper proposes a pan-system approach to cancer intelligence that uses foundation models to integrate data across all four of these biological layers. We worked with a curated multi-omics dataset combining publicly available cohorts and synthetic clinical records, covering nearly 4,200 patient samples across five cancer types. A transformer-based foundation model was pretrained on the combined modalities and then fine-tuned for tasks like cancer subtype classification, survival risk prediction, and treatment response forecasting. Comparisons were made against single-modality baselines and traditional multi-modal fusion models. Results show that the foundation model approach improved classification accuracy by about 11 percent and lifted the concordance index in survival prediction by nearly 9 percent compared to standard methods. The microbiome layer, often overlooked, turned out to add meaningful predictive value, especially in colorectal and lung cancers. We discuss the practical implications of moving toward integrated cancer intelligence and the challenges that come with it, including data harmonization, interpretability, and clinical validation. The work points toward a future where foundation models could serve as a backbone for personalized oncology, helping clinicians look at cancer as a whole-body phenomenon rather than a localized event.
Foundation Models, Multi-Omics Integration, Tumor Microenvironment, Cancer Microbiome, Immune Profiling, Pan-System Oncology
AI-DRIVEN LIQUID BIOPSY SYSTEMS FOR EARLY CANCER DETECTION AND PERSONALIZED ONCOLOGY
Early cancer detection remains one of the most promising strategies for improving survival outcomes, yet current screening methods are limited by invasiveness, cost, and inadequate sensitivity for early-stage disease. This research develops a comprehensive AI-driven liquid biopsy framework that integrates circulating tumor DNA analysis, circulating tumor cells characterization, exosome profiling, protein biomarkers, and metabolomic signatures from peripheral blood samples to enable multi-cancer early detection and longitudinal monitoring for personalized oncology. We employed deep learning architectures combining convolutional neural networks for sequencing data pattern recognition, graph neural networks for mutation signature analysis, and transformer models for temporal biomarker trajectory modeling across 5,843 patients including 3,267 cancer cases across twelve tumor types and 2,576 healthy controls. The foundation model achieved 91.4% sensitivity at 95.3% specificity for pan-cancer detection, with tissue-of-origin localization accuracy of 87.6% among detected cancers. Stage I cancer detection sensitivity reached 73.8%, substantially exceeding conventional screening approaches. Longitudinal monitoring demonstrated detection of molecular relapse an average of 8.7 months before radiological progression, enabling early therapeutic intervention. Integration of multi-analyte liquid biopsy features through cross-modal attention mechanisms revealed synergistic signatures where combined ctDNA variant allele frequency, CTC enumeration, and exosomal miRNA panels achieved superior performance compared to individual biomarkers. The interpretable framework identified novel early detection signatures including tissue-specific methylation patterns, clonal hematopoiesis discrimination algorithms, and tumor evolution trajectories predicting therapeutic resistance. This AI-augmented liquid biopsy system establishes a scalable, minimally invasive approach for population-level cancer screening, minimal residual disease monitoring, and real-time treatment response assessment.
Liquid Biopsy, Circulating Tumor DNA, Early Cancer Detection, AI Diagnostics, Minimal Residual Disease, Personalized Oncology
A PRACTICAL DCA–RTA–AI/ML WORKFLOW FOR OIL AND ASSOCIATED GAS PRODUCTION FORECASTING IN A SILTSTONE RESERVOIR: A CASE STUDY
This paper is framed as a field case study and applied forecasting workflow for hydraulically fractured vertical wells producing oil and associated gas from a siltstone interval in the Cambay Basin. The reservoir setting is characterized by low matrix permeability, solution-gas/depletion-drive behavior, rapid pressure loss, and dependence on hydraulic fracturing for sustained commercial deliverability. Daily production histories from Wells 101, 112, and 201 are used to integrate empirical decline-curve analysis (DCA), rate-transient-analysis (RTA) screening, and AI/ML-assisted short-term forecasting. The forecast is extended to 2030 and is conditioned by reservoir attributes including porosity, water saturation, net pay, production/drainage area, depth, bottomhole-pressure references, and oil gravity. The case study demonstrates how a transparent, auditable workflow can be constructed when complete bottomhole-pressure, PVT, and fracture-treatment datasets are not available. DCA provides the deterministic production-forecast baseline, RTA screening provides diagnostic evidence for flow-regime and data-quality assessment, and AI/ML supplies a repeatable feature-based layer for holdout testing and interpretation. The contribution is therefore not a new machine-learning algorithm, but a practical field workflow that combines engineering diagnostics, reservoir conditioning, and data-driven validation for low-permeability fractured siltstone wells.
Siltstone reservoir; hydraulically fractured vertical well; production forecasting; decline curve analysis; rate transient analysis; artificial intelligence; machine learning; oil & associated gas.
Mangesh V. Kalode, Samarth D. Patwardhan
SMART TOURISM ECOSYSTEMS: INTEGRATING DIGITAL PLATFORMS, MOBILITY DATA, AND VISITOR ANALYTICS TO IMPROVE DESTINATION COMPETITIVENESS
Tourism destinations face mounting competitive pressure as travelers gain unprecedented access to information and alternatives. This research examines how smart tourism ecosystems integrate digital platforms, mobility tracking, and visitor analytics to enhance destination competitiveness and visitor experiences. Through analysis of implementations across multiple destinations ranging from major cities to resort areas, we demonstrate that integrated smart tourism systems improve visitor satisfaction by 28-34% while increasing destination revenue per visitor by 19-26%. The study develops a comprehensive framework for smart tourism ecosystem design, examining how platforms aggregate information, how mobility data reveals visitor behavior patterns, and how analytics transform data into actionable destination management strategies. Our findings indicate that ecosystem integration matters more than individual technology sophistication—destinations successfully connecting diverse data sources and stakeholders achieve substantially better outcomes than those deploying advanced technologies in isolation. The research contributes theoretical understanding of tourism ecosystem dynamics and practical implementation guidance. Implications extend beyond tourism to smart city development, where visitor management represents one component of broader urban intelligence systems.
Smart Tourism, Digital Platforms, Mobility Analytics, Destination Management, Visitor Experience, Tourism Competitiveness, Data Integration
DATA QUALITY AS A RELIABILITY MULTIPLIER: QUANTIFYING THE IMPACT OF STANDARDIZED WORK PROCESSES ON RELIABILITY MODEL ACCURACY
In modern industrial systems, predictive reliability models are central to maintenance strategies, yet their accuracy is fundamentally constrained by the quality of input data. While significant research has focused on algorithmic improvements for fault detection and remaining useful life (RUL) estimation, the role of standardized work processes (SWP) as an enabling multiplier for data quality and, subsequently, model accuracy remains underexplored. This paper quantifies the causal relationship between adherence to SWP, resulting data integrity metrics (accuracy, completeness, consistency, timeliness), and the performance of a supervised machine learning-based reliability model. Using a controlled manufacturing case study over six months, we compare model accuracy across two regimes: conventional data logging (Phase 1) versus SWP-integrated data capture (Phase 2). Results demonstrate that implementing SWP improves overall data quality by 41.2%, which translates to a 28.6% increase in reliability model accuracy (from 67.2% to 86.4%) and a 35% reduction in false positive alarms. The proposed framework establishes data quality as a force multiplier for reliability predictions, offering a pragmatic pathway for industries to maximize returns from existing AI investments without additional algorithmic complexity.
Data Quality, Reliability Engineering, Standardized Work Processes, Predictive Maintenance, Machine Learning, Model Accuracy.
DEGRADATION‑AWARE SPARES OPTIMIZATION WITH WEIBULL/PHM UNCERTAINTY AND LOGISTICS DELAYS
Spare parts inventory optimisation for critical industrial equipment requires accurate characterisation of both equipment failure demand and the lead-time distribution over which that demand must be covered. Conventional inventory models treat failure rates as stationary Poisson processes calibrated from historical mean demand, systematically ignoring two sources of uncertainty that cause costly stockout events in practice: the time-varying, condition-dependent hazard rate structure captured by Proportional Hazard Models (PHM) and the stochastic variability of procurement lead times arising from supply chain disruptions, customs clearance delays, and supplier capacity constraints. This paper presents a degradation-aware spares optimisation framework that jointly models equipment failure demand through a Weibull-baseline PHM with continuously updated degradation covariates and procurement lead-time uncertainty through a log-normal mixture distribution calibrated from CMMS purchase-order records. The integrated demand-over-lead-time distribution is computed via numerical convolution of the PHM-driven compound Poisson demand process with the stochastic lead-time distribution, enabling derivation of optimal reorder points and safety-stock levels that correctly account for both failure-rate non-stationarity and logistics delay variability. Validation is performed on seven critical spare-part classes at a refinery facility, demonstrating a 24.7% reduction in the total sparing cost index (holding plus stockout plus ordering cost), a 59.2% reduction in critical-spare stockout probability at the target 95% service level, and a 17.8% reduction in on-hand stock levels relative to conventional static min-max policies. The proposed framework is directly implementable within SAP MM inventory modules and ISO 14224-aligned CMMS environments.
Spares Optimisation; Proportional Hazard Model; Weibull Distribution; Logistics Delay; Lead-Time Uncertainty; Degradation-Aware Inventory; Compound Poisson Demand; Reorder Point; Safety Stock; Iso 14224
DIGITAL TWIN–RAM CO‑SIMULATION FOR MAINTENANCE AND SPARING DECISIONS IN PROCESS FACILITIES
Maintenance scheduling and spare-parts inventory planning in process facilities depend critically on accurate, dynamically updated reliability, availability, and maintainability (RAM) models. Static RAM analyses — computed once from historical failure data and applied unchanged across multi-year planning horizons — fail to reflect the evolving condition of assets, the consequences of actual maintenance decisions, and the stochastic interaction between equipment failure events and spare-parts stockout risks. This paper presents a Digital Twin (DT)–RAM co-simulation framework that couples a physics-informed Bayesian RAM model with a high-fidelity Monte Carlo co-simulation engine to provide continuously updated, probabilistically rigorous inputs to maintenance scheduling and spare-parts optimisation. The DT layer ingests real-time condition-monitoring (CM) sensor streams, CMMS work-order records, and inventory transaction data to update Weibull failure-rate posteriors on both a quarterly calendar cycle and an event-triggered basis, while the Monte Carlo co-simulation propagates these posteriors into system-level availability forecasts, spare-parts demand distributions, and total maintenance cost trajectories. A stochastic spare-parts optimisation module derives dynamic reorder points and stock levels that minimise the joint probability of stockout and excess carrying cost under the updated posteriors. The framework is validated at a heavy petrochemical olefins complex in India across a 36-month monitoring and 24-month forecasting window covering 47 critical rotating and static equipment items. Results demonstrate a 78.7% reduction in availability forecast RMSE relative to static RAM, a 15.7% reduction in total maintenance and sparing cost, and a 67% mean reduction in critical-spare stockout events. The framework is directly compatible with SAP PM and OSIsoft PI data architectures and meets ISO 55000-aligned asset management requirements.
Digital twin; RAM analysis; co-simulation; spare parts optimisation; Bayesian updating; maintenance scheduling; Monte Carlo simulation; process facilities; availability forecasting; ISO 55000
HYBRID RELIABILITY MODELLING FOR ROTATING EQUIPMENT: PHYSICS‑INFORMED LEARNING WITH SPARSE FAILURE DATA
Accurate reliability estimation for rotating equipment is severely challenged by the chronic scarcity of failure data in industrial settings — assets with mean times between failures measured in thousands of hours routinely accumulate fewer than ten observed failures across their entire operational life. Classical Weibull maximum-likelihood estimation (MLE) under such sparse-data regimes produces wide confidence intervals that are operationally uninformative, while purely data-driven machine learning models lack the physical grounding to generalise reliably beyond the observed failure regime. The present paper proposes a Physics-Informed Machine Learning (PIML) hybrid framework that embeds differential-equation-based degradation physics directly into the prior structure of a Bayesian Weibull model, combining the explanatory power of first-principles mechanics with the uncertainty-quantification capabilities of Bayesian inference. Degradation physics — encompassing Lundberg-Palmgren contact fatigue, Archard tribological wear, and rotor-dynamic stress accumulation — constrain the Weibull shape and scale parameters through a physics-derived likelihood structure, regularising inference even when observed failure counts are as low as n = 3 to 5. The framework also assimilates continuous ISO 13374-compliant condition-monitoring sensor streams through a sensor-informed likelihood update, enabling real-time posterior revision without requiring new observed failures. Validation is performed on six rotating equipment types (centrifugal pump, reciprocating compressor, axial gas turbine, high-speed gearbox, rolling-element bearing, and centrifugal compressor) at a heavy petrochemical facility over a 48-month period, demonstrating a 66% mean reduction in B10 life RMSE relative to classical MLE, 91.7% empirical coverage of 90% posterior predictive intervals, and a prognostic warning horizon of 400–800 hours ahead of failure compared to 50–150 hours for threshold-based alarms. The proposed methodology is directly compatible with CMMS environments and ISO 13374 data architectures.
Physics-informed machine learning; Weibull reliability; sparse failure data; rotating equipment; Bayesian inference; degradation modelling; PIML; B10 life estimation; condition monitoring; prognostics
BAYESIAN UPDATING OF RAM MODELS FOR DYNAMIC AVAILABILITY FORECASTING UNDER REALISTIC DOWNTIME CONSTRAINTS
This paper presents a Bayesian framework for the dynamic updating of Reliability, Availability, and Maintainability (RAM) models applied to complex industrial systems operating under realistic downtime constraints. Conventional RAM analysis relies on maximum likelihood estimation (MLE) from historical failure data, which frequently fails to account for maintenance window restrictions, crew availability queues, spare-parts lead times, and other operational downtime penalties that systematically inflate effective repair durations in practice. The proposed methodology integrates conjugate Bayesian inference — implemented through Markov Chain Monte Carlo (MCMC) sampling — with an explicit downtime penalty function to revise MTBF and MTTR posterior distributions as field data accumulate. Validation is performed on three industrial system types (rotating pump, reciprocating compressor, and gas turbine) over a 24-month monitoring horizon. Results demonstrate that the Bayesian-updated model reduces forecast RMSE by 66% relative to classical MLE, improves predicted availability by up to 4.3 percentage points, and correctly captures the widening uncertainty bounds associated with planned maintenance windows and resource constraints. The framework is directly implementable within existing CMMS environments and provides actionable inputs for proactive maintenance scheduling and asset lifecycle optimisation.
RAM analysis; Bayesian updating; MTBF; MTTR; availability forecasting; downtime penalty function; MCMC; reliability engineering; maintenance optimisation; CMMS
HYBRID MARKOV–MONTE CARLO RAM MODELLING FOR MULTI‑STATE ASSETS WITH NON‑EXPONENTIAL FAILURE/REPAIR DISTRIBUTION
Reliability, Availability, and Maintainability (RAM) modelling is fundamental to asset-intensive industries. Traditional RAM techniques, particularly continuous-time Markov chains (CTMC), assume exponential distributions for failure and repair times, which rarely hold in real-world systems where Weibull, lognormal, or gamma behaviors dominate. This paper presents a hybrid Markov–Monte Carlo (HM-MC) framework for RAM assessment of multi-state assets operating under non-exponential failure and repair distributions. The proposed method combines the state-transition structure of Markov models with Monte Carlo simulation to sample sojourn times from arbitrary distributions while preserving state memory. Using a three-state industrial pump system (healthy, degraded, failed) as a test asset, we compare the HM-MC method against traditional CTMC and pure Monte Carlo over 10,000 simulated operating hours. Results demonstrate that CTMC overestimates availability by 18.3% (0.94 vs 0.79 actual) when actual failure times follow Weibull(β=1.5). The HM-MC method achieves availability estimates within 2.1% error of ground truth while reducing computational variance by 62% compared to pure Monte Carlo. The framework offers a practical pathway for accurate RAM modelling without mathematical intractability.
Availability, Markov Chain, Monte Carlo Simulation, Multi-State Systems, Non-Exponential Distributions, RAM Modelling, Reliability.
PHYSICS‑INFORMED RUL PREDICTION WITH ALEATORIC/EPISTEMIC UNCERTAINTY AND MAINTENANCE SYSTEM INTEGRATION
Accurate prediction of Remaining Useful Life (RUL) in safety-critical machinery is essential for transitioning from reactive to predictive maintenance strategies. While deep learning approaches have demonstrated empirical progress, they suffer from two fundamental limitations: the inability to extrapolate beyond training distributions and the conflation of fundamentally distinct uncertainty sources. This paper presents a Physics-Informed Neural Network (PINN) framework that integrates thermodynamic degradation models and fatigue crack propagation mechanics directly into the neural network loss function, producing RUL predictions that are both physically consistent and probabilistically calibrated. We decompose predictive uncertainty into its aleatoric component — irreducible noise arising from sensor imprecision and environmental stochasticity — and its epistemic component, quantifying reducible model uncertainty via Monte Carlo Dropout. The resulting uncertainty bounds are propagated into a cost-optimized maintenance scheduling module that selects intervention timing by minimizing the expected total maintenance cost under the full predictive distribution. Experiments on the NASA CMAPSS turbofan degradation benchmark and a real-world wind turbine gearbox dataset demonstrate that our Full PINN-UQ model achieves an RMSE of 11.6 cycles and MAPE of 7.3%, representing improvements of 59.2% and 62.0% respectively over LSTM baselines, with a calibration score of 0.93 — the highest among all seven models benchmarked. The integrated maintenance planner reduces unnecessary preventive maintenance actions by 34% while maintaining a false-negative rate below 2.1%, demonstrating clear operational value beyond point-prediction accuracy.
Physics-Informed Neural Networks, Remaining Useful Life, Aleatoric Uncertainty, Epistemic Uncertainty, Bayesian Deep Learning, Predictive Maintenance, Prognostics and Health Management
ARTIFICIAL INTELLIGENCE-BLOCKCHAIN INTEGRATED LAND REGISTRATION VERIFICATION SYSTEM USING TREE MINFLD INTEL CLASSIFIER
Today, ensuring the authenticity and security of land registration processes is a key challenge in property management systems.Traditional land registration systems often have problems like: fraudulent transactions, lack of transparency and inefficient verification processes as well as the ability to tamper with data.This study proposes an innovative approach which will integrate artificial intelligence (AI) and blockchain technology to deal with these challenges and enhance the verification of land registration records.The framework uses Tree Minfld Intel Classifier to analyze and classify land ownership data, which ensures that only land titles with legitimacy are properly identified.Data is fed in to be processed using Norma Lytix ScalerTrain. Normalizing and scaling the data set will thus increase the performance of our AI models.From transaction records the data set is generated. It must be split into a train-test split, which separates it into training sets and testing sets to gauge the model.After the split, the Tree Minfld Intel Classifier is trained based on processed data to identify patterns of legitimate and false land registrations.Then the trained model is used for prediction, classifying new land registration records as either accurate or questionable.Once the predictions have been made, the system interfaces with a blockchain node in order to store the verified land registration data safely.It uses smart contracts to record ownership details and the results of verification. It is impossible for anyone to modify this ledger which is tamper-proof but transparent and guaranteed against fraud.One of the essential problems with traditional land registers is that there is a possibility of unauthorized changes or disputes over ownership. To deal with this, an immutable block hash point is generated which serves as a cryptographic proof to ensure the data's authenticity and integrity.This mechanism makes any unauthorized changes in the information stored immediately detectable, thereby improving trust and safety.The whole experiment was done in a Python environment and used the transaction dataset to perform AI-based classification, integrate blockchain technology.Various python-based libraries and frameworks were employed to handle data preprocessing, model training, blockchain interactivity and evaluation of results.In conclusion, the integration of AI and blockchain system provides a completely decentralized way to verify land registrations that is resistant against fraud and transparent. This approach is especially beneficial for government agencies, business in real estate markets and legal institutions as it minimizes disputes, enhancing trust and ensuring careful dealings with property problems.With both traditional risks like omitting the details (causing fake documents to be generated by corrupt officials) and the need for a centralized validation mechanism to hand over land records, this model improves the reliability, efficiency and transparency of land register verification.The integration of Tree Minfld Intel Classifier-based AI prediction with immutable block hash points means that land records are still secure, verifiable and resistant to tampering.
Land Registration Verification, Artificial Intelligence, Blockchain,Tree Minfld Intel Classifier, Land Transactions, Immutable Block Hash Points, Smart Contracts in Real Estate
LEADING‑INDICATOR‑DRIVEN RELIABILITY MODELLING: INCORPORATING OPERATIONS‑DRIVEN RELIABILITY TASKS INTO FAILURE HAZARD ESTIMATION
Traditional reliability engineering has historically relied upon lagging indicators—failure data, mean time between failures (MTBF), and historical incident rates—that measure outcomes after degradation or failure events have occurred. This retrospective paradigm creates fundamental limitations for proactive risk management in complex operational environments where failure consequences carry substantial safety, financial, and reputational implications. This research introduces a leading-indicator-driven reliability modelling framework that systematically incorporates operations-driven reliability tasks into failure hazard estimation through the development of a novel Leading Reliability Hazard Index (LRHI). Based upon comprehensive analysis of 147 reliability models spanning aerospace, nuclear power, healthcare operations, and manufacturing domains, the study demonstrates that leading indicator integration achieves 94.2% hazard estimation accuracy compared to 67.8% for traditional lagging-indicator models when validated against prospective failure data (n=12,847 operational events). The proposed methodology operationalizes six categories of leading reliability indicators: predictive maintenance task completion rates, condition-based monitoring thresholds, operator-driven reliability observations, quality assurance non-conformance trends, maintenance task effectiveness metrics, and operational stressor intensity measures. Experimental validation using synthetic operations data from three industrial sectors (aviation maintenance, n=4,203 events; medical device operations, n=5,847 events; manufacturing process control, n=2,797 events) establishes that LRHI implementation enables mean hazard detection lead time of 37.2 days prior to functional failure, representing a 5.8-fold improvement over traditional hazard identification timelines. This paper thus provides a methodological foundation for transitioning reliability programmes from failure-reactive postures toward failure-predictive operational frameworks, with direct applicability to safety-critical systems where failure prevention justifies proactive reliability investment.
Leading Reliability Indicators; Hazard Estimation; Operations-Driven Reliability; Predictive Maintenance; Condition-Based Monitoring; Failure Hazard Detection; Reliability Modelling; Lagging Indicators
BENCHMARKING HALLUCINATION, VULNERABILITY EXPOSURE, AND STYLE DRIFT IN AI-ASSISTED CODE REVIEW SYSTEMS
Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed automated code review by streamlining and improving the process. AI Review Systems, however, pose significant problems such as hallucination of findings, vulnerability exposure, and style drift that can impact software reliability, security, and maintainability. Hallucinations generate unsupported review comments, vulnerability exposure is caused by not recognizing vulnerabilities or insecure suggestions, and style drift is when generated suggestions drift from existing coding style guides. This paper advocates a benchmark framework to assess such failure modes in AI-driven code review systems. The framework features a curated repository of samples, security-focused review scenarios, ambiguous review scenarios, and longitudinal evaluation to assess review accuracy, security performance, and stylistic consistency. The experimental results demonstrated that AI reviewers are good at recognising common coding defects and common security problems, but they can make mistakes with contextual reasoning, fail to provide a thorough threat analysis, and give inconsistent coding-style recommendations. The study also underscores the importance of governance structures, such as human oversight, output auditability, adversarial testing and ongoing performance monitoring. The proposed benchmark is a pragmatic way to evaluate AI-based code review tools prior to their implementation in enterprise and security-sensitive environments and to use them as software-assurance systems and not as just productivity enhancement tools.
AI-assisted code reviews, Large Language Models, Hallucination, Vulnerability Exposure, Style Drift, Software Assurance, DevSecOps, Security Benchmarking, Automated Code Review.
Microservices: “A Comparative Analysis of Monolithic vs. Microservice Architectures in High-Scalability Cloud Environments.
Background: The evolution from monolithic software architectures to microservice-based designs represents one of the most consequential paradigm shifts in enterprise software engineering of the past decade. As cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform have matured, organizations across industries have faced critical architectural decisions with far-reaching implications for scalability, operational resilience, development velocity, and total cost of ownership. Objective: This paper presents a rigorous comparative analysis of monolithic and microservice architectures across six dimensions critical to high-scalability cloud environments: horizontal scalability, fault isolation, deployment agility, operational complexity, inter-service communication overhead, and data consistency management. The analysis synthesizes empirical benchmarks, industry case studies, and architectural theory to provide practitioners and researchers with a structured decision framework. Methods: A systematic review of peer-reviewed literature (2014–2023), supplemented by publicly documented migration case studies from Netflix, Amazon, Uber, and Shopify, was conducted. Performance benchmarks from containerized deployment environments and load-testing datasets from AWS and GCP documentation were synthesized alongside theoretical analysis of distributed systems constraints. Findings: Microservice architectures demonstrate superior elasticity and fault isolation at scale, with studies showing 40–65% improvement in deployment frequency and 30–50% reduction in mean time to recovery compared to monolithic equivalents. However, microservices introduce 15–40% higher operational overhead, network latency penalties of 2–15ms per inter-service call, and substantially greater data consistency complexity. Neither architecture is categorically superior; the optimal choice is context-dependent, governed by team size, traffic patterns, organizational maturity, and scalability requirements. Conclusion: A phased migration strategy anchored by domain-driven design principles and strangler fig patterns emerges as the most evidence-supported approach for organizations transitioning from monolithic to microservice architectures. Future research should address the emerging service mesh ecosystem and its implications for distributed tracing and observability at petabyte-scale workloads.
Microservices, Monolithic Architecture, Cloud Scalability, Distributed Systems, Containerization, Kubernetes, Devops, Domain-Driven Design, Cap Theorem, Fault Tolerance
FORECASTING OF METEOROLOGICAL DATA USING AI TECHNIQUES
Weather forecasting has always been a tricky business. Even with decades of progress in numerical weather prediction, atmospheric systems remain highly nonlinear and chaotic, which makes short-term and medium-range forecasts vulnerable to error. In recent years, artificial intelligence has stepped in as a strong alternative, offering data-driven ways to capture patterns that traditional physics-based models sometimes miss. This paper looks into how AI techniques, particularly machine learning and deep learning, can be used to forecast meteorological parameters such as temperature, humidity, rainfall, wind speed, and atmospheric pressure. We worked with a historical dataset covering ten years of daily observations from a regional meteorological station and applied several models including Random Forest, Support Vector Regression, Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model. The models were trained and tested using a chronological split to avoid data leakage, and performance was measured using RMSE, MAE, and R² scores. Results show that the hybrid CNN-LSTM model outperformed the others in temperature and rainfall prediction, while LSTM alone gave very competitive results for humidity and wind speed. The study also highlights how feature engineering and proper handling of missing values matter as much as the choice of model. Overall, our findings suggest that AI techniques can meaningfully improve forecasting accuracy when combined with thoughtful preprocessing and domain knowledge. The work contributes to ongoing efforts in building reliable, low-cost forecasting tools that can support agriculture, disaster management, and energy planning, especially in regions where high-resolution numerical models are not always accessible or affordable.
Meteorological Forecasting, Artificial Intelligence, Deep Learning, LSTM, CNN-LSTM, Time Series Prediction
BEYOND THE PIXEL: ASSESSING REAL-TIME OBJECT DETECTION MODELS IN LOW-VISIBILITY AUTONOMOUS DRIVING ENVIRONMENTS
Autonomous driving has made impressive strides in the last decade, but one area where things still get tricky is low-visibility conditions. Fog, heavy rain, snow, dust, and nighttime driving can throw off even the most advanced object detection systems. This paper takes a closer look at how real-time object detection models hold up when the weather and lighting are not on their side. We tested four widely used models, namely YOLOv8, YOLOv9, Faster R-CNN, and EfficientDet, on a curated dataset that combines images from the BDD100K, Foggy Cityscapes, and Nighttime Driving Dataset. The goal was not just to find which model gives the best numbers, but also to understand how each one behaves when pixels get blurry, contrast drops, and edges disappear. We evaluated performance using mean Average Precision (mAP), inference time, and a custom robustness score we designed for this study. Results show that YOLOv8 and YOLOv9 deliver strong real-time performance, but their accuracy drops noticeably in foggy conditions. Faster R-CNN, while slower, holds up better in dense fog and low-light scenarios. EfficientDet sits somewhere in the middle. We also found that simple preprocessing tricks like contrast enhancement and dehazing can recover a fair amount of lost accuracy without slowing things down too much. The paper closes with a discussion on why current models still struggle in these environments and what directions future research could take. Our findings should be useful for anyone working on safety-critical perception systems for autonomous vehicles operating in real-world conditions.
Object Detection, Autonomous Driving, Low Visibility, YOLO, Deep Learning, Real-Time Perception.
MULTIMODAL EDUCATION ENTREPRENEURSHIP NETWORKS AND INTELLECTUAL CAPITAL FORMATION DYNAMICS
The objective of this study was to examine the relationships among multimodal education, university entrepreneurship, and intellectual capital formation through the specification and testing of a network model using Gephi. A quantitative, cross-sectional, exploratory, and correlational design was implemented with a sample of 300 university students participating in multimodal learning environments. The proposed model integrated Multimodal Education, Entrepreneurial Competencies, Innovation Orientation, Knowledge Management, Collaborative Learning, Human Capital, Structural Capital, and Relational Capital. Data were collected using a validated instrument composed of sixty indicators distributed across the principal dimensions of the model. Reliability, validity, and network analyses were conducted to evaluate the structural properties of the proposed framework. The findings revealed significant positive relationships among all variables. Multimodal Education demonstrated strong connections with Collaborative Learning, Entrepreneurial Competencies, and Human Capital. Entrepreneurial Competencies exhibited substantial associations with Innovation Orientation, Human Capital, and Relational Capital. Knowledge Management emerged as a central mechanism influencing Human Capital and Structural Capital. Collaborative Learning functioned as a strategic intermediary process facilitating knowledge exchange and competency development. Human Capital occupied the most influential position within the network, while Relational Capital represented the primary destination of knowledge flows. Network metrics indicated high density, strong clustering, and interconnected community structures, supporting the existence of an integrated entrepreneurial ecosystem. The results suggest that intellectual capital formation is a multidimensional and networked process supported by educational innovation, entrepreneurial development, collaborative interaction, and knowledge management practices. The Gephi model provided evidence that multimodal educational environments contribute directly and indirectly to the development of entrepreneurial capabilities and intellectual capital dimensions. The study advances understanding of university entrepreneurship from a network perspective and offers methodological and practical implications for educational institutions seeking to strengthen innovation, knowledge creation, and sustainable intellectual capital development.
Multimodal Education, University Entrepreneurship, Intellectual Capital, Knowledge Management, Network Analysis
NETWORK ANALYSIS OF MULTIMODAL EDUCATION AND GENDER EQUITY SOCIAL INTERVENTION
The growing integration of digital technologies into educational systems has transformed learning environments into multimodal ecosystems characterized by the interaction of technological, social, and institutional dimensions. Simultaneously, social work interventions increasingly emphasize gender equity as a strategic objective for promoting inclusion, participation, and social justice. The present study developed and tested a Gephi network model examining the relationships among Technological Accessibility, Multimodal Engagement, Digital Literacy, Collaborative Participation, Empowerment, Social Support, Institutional Advocacy, and Gender Equity Outcomes. A quantitative, cross-sectional, and correlational design was employed using data collected from participants involved in multimodal educational environments and gender-oriented intervention programs. Indicators were validated through expert judgment procedures assessing relevance, clarity, coherence, sufficiency, and theoretical congruence. Network analysis was conducted using Gephi to estimate centrality, density, modularity, clustering, and connectivity measures. The findings revealed a highly connected structure in which educational and social intervention variables formed an integrated system. Technological Accessibility was positively associated with Digital Literacy and Multimodal Engagement, while Digital Literacy and Multimodal Engagement significantly influenced Collaborative Participation. Social Support and Institutional Advocacy contributed directly to Empowerment, which emerged as the strongest predictor of Gender Equity Outcomes. The network demonstrated substantial cohesion, short communication pathways, and interconnected communities linking educational technology, collaborative participation, institutional support, and equity-related outcomes. The results suggest that gender equity emerges from the interaction of educational, technological, social, and institutional processes rather than from isolated interventions. The proposed Gephi model provides a comprehensive framework for understanding how multimodal education and social work intervention jointly contribute to equitable participation, inclusion, representation, and empowerment within contemporary educational environments.
Multimodal Education, Gender Equity, Social Work Intervention, Network Analysis, Digital Literacy
María del Rosario Molina González, Janeth Cecilia Yocupicio Leyva, Cruz García Lirios, Marco Antonio Velderrain Rodríguez, María Teresa Gaxiola Sánchez, Rosario Adalberto Mondaca Corral, Manuel Raymundo Valdez Domínguez, Jorge Luis Arellano Cruz, Alan Ama
DATA FUSION AND STABILITY ANALYSIS WITH ALPHA FAMILY METHOD APPROXIMATION ESTIMATORS FOR SATELLITE ORBIT DETERMINATION
Accurate and robust satellite orbit determination is essential for space missions, navigation, and remote sensing. This paper presents a comparative analysis of three nonlinear state estimation techniques like Extended Kalman Filter (EKF), Particle Filter (PF), and the Alpha Filter (AF) focusing on their performance in estimating satellite position and velocity under noisy conditions. A simulated orbit scenario for Low earth orbit (LEO) is used, with Gaussian noise added to sensor measurements to introduce real-world uncertainties. Here, the Satellite orbit-determination (OD) process is described and presents the results of numerical simulation of Satellite orbit estimation using Alpha Family Method Approximation estimators (AFMAE’s). Additionally, a proposed observer for a nonlinear continuous time dynamic system makes use of the corresponding matrix Riccati type differential equation and gain from the theories of the AFMAE. Then, the condition for the local asymptotic stability for the error dynamics of the observer is derived using the Lyapunov energy (LE) functional. AFMAE-based sensor data fusion methodology is introduced. A data fusion scheme that utilizes the AFMAE’s for unified state model (U7) and inertial coordinate set (IC6) orbital trajectories is presented in state vector level fusion (SVF). State vector level Data fusion is illustrated by implementing two AFME’s. All the filtering schemes and data fusion algorithms have been validated using simulated data generated in MATLAB.
Satellite orbit determination, Alpha Family Method Approximation estimators, sensor data fusion, nonlinear observer, Lyapunov energy functional and state vector level fusion.
Nisha S.L., Dr. B.K. Sujatha, Dr. J.R. Raol
The design presents the FPGA implementation of a UART data buffering system based on a synchronous FIFO with Axi4-lite interface. The design is for the Cyclone II FPGA development board EP2C5T144 device and was created using Intel Quartus Prime for synthesis and ModelSim for functional simulation, with all RTL written in synthesizable Verilog. A 50 MHz onboard oscillator powers the system, and a baud rate divider CLK_DIV = 434 allows UART operation at 115200 baud. Two 16-deep, 8-bit synchronous FIFOs buffer data between the UART receiver and transmitter, addressing the speed mismatch of serial connection 86.8 µs per byte at 115200 baud and providing data transfer. Functional simulation confirms proper UART framing, FIFO read/write operations, and timing behavior, whereas post-synthesis results demonstrate efficient resource utilization with fewer than 300 logic elements (LEs) and positive temporal slack at 50 MHz. Hardware validation using a USB-to-UART interface confirms reliable real-time data transmission and system stability.
: UART, Synchronous FIFO, FPGA, Cyclone II, Verilog, Quartus Prime, ModelSim,Serial Communication, RTL Design
Dr. S Jagadeesha, Dr Prathibha Kiran, S. Meghana, Prof Madhukar C S, Dr H N Suresh, Dr. Shivakumaraswamy G M
DATA WAREHOUSING AND TEXT ANALYTICS AS INSTRUMENTS OF CULTURAL KNOWLEDGE MANAGEMENT: IMPLICATIONS FOR DIGITAL PRESERVATION AND SOCIETAL DECISION-MAKING
The rapid proliferation of digitized cultural artifacts, archival records, and institutional knowledge repositories presents both opportunities and challenges for contemporary information management. This paper investigates the convergence of data warehousing architectures and text analytics methodologies as a dual-instrument framework for the systematic organization, retrieval, and interpretation of large-scale cultural and societal datasets. Drawing on principles from database management, distributed data systems, and natural language processing, we propose a conceptual model that enables cultural institutions, educational bodies, and public organizations to transform unstructured textual heritage into structured, queryable, and analytically actionable knowledge assets. We further examine how decision-support capabilities embedded within such architectures can inform policy formulation, resource allocation, and the preservation of intangible cultural knowledge in an increasingly data-driven society. The implications of this framework extend beyond archival science into domains of digital humanities, public governance, and enterprise knowledge management, establishing a methodological bridge between computational data science and cultural stewardship.
Data Warehousing; Text Analytics; Cultural Knowledge Management; Digital Preservation; Decision Intelligence; Database Architecture; Natural Language Processing; Digital Humanities
INTEGRATING LEAN PRODUCTION AND GREEN MARKETING FOR SUSTAINABLE COMPETITIVE ADVANTAGE IN MANUFACTURING INDUSTRIES
In the modern business environment, organizations are increasingly adopting sustainable practices to achieve long-term growth and competitive advantage. The integration of lean manufacturing and green marketing has emerged as an important approach for improving operational efficiency and environmental sustainability. Lean manufacturing focuses on waste reduction, continuous improvement, efficient utilization of resources, and productivity enhancement, whereas green marketing emphasizes eco-friendly products, sustainable branding, environmentally responsible packaging, and green consumer communication. The present study examines the integration of lean manufacturing and green marketing practices and their impact on sustainable competitive advantage in manufacturing industries. The study is based on both primary and secondary data sources. Primary data may be collected through structured questionnaires from employees, managers, and consumers, while secondary data is gathered from journals, books, sustainability reports, company records, and published research studies. The research analyzes the relationship between waste minimization, eco-friendly production practices, green consumer awareness, and sustainable business growth. Statistical tools such as correlation, regression analysis, and factor analysis may be used to evaluate the relationship among variables. The findings are expected to reveal that organizations adopting integrated lean and green strategies achieve improved environmental performance, enhanced operational efficiency, better market reputation, increased customer satisfaction, and stronger brand loyalty. The study contributes to the existing literature on sustainability management and provides practical implications for organizations seeking sustainable competitive advantage through operational excellence and environmentally responsible marketing practices.
Lean Manufacturing, Green Marketing, Sustainable Competitive Advantage, Sustainability, Waste Reduction, Green Consumer Behaviour, Eco-Friendly Practices, Operational Efficiency, Environmental Sustainability, Business Performance.
Dr. Vipul Mishra, Dr Shephali Mathur, Mr. Ravi Karan Singh, Dr. Jyoti Prakash, Dr. Manisha Kakkar
MODERN DEVELOPMENT PRACTICES: INVESTIGATIONS INTO SERVERLESS COMPUTING, LOW-CODE/NO-CODE PLATFORMS, AND DEVELOPER PRODUCTIVITY IN REMOTE ENVIRONMENTS
The software development landscape has undergone dramatic transformation in recent years, driven by innovations in serverless computing, low-code/no-code platforms, and the widespread shift to remote work environments. This research investigates how these modern development practices impact developer productivity, application quality, and organizational efficiency. Through a comprehensive analysis combining survey data from 250 developers, performance metrics from serverless deployments, and productivity assessments across remote and traditional work settings, we examine the adoption patterns, benefits, and challenges of contemporary development approaches. Our findings reveal that serverless architectures reduce infrastructure management overhead by approximately 60% while enabling faster deployment cycles. Low-code platforms accelerate application development by 40-50% for certain use cases, though they introduce constraints on customization and scalability. Remote work environments, when properly supported with collaboration tools and clear communication protocols, maintain or exceed productivity levels compared to traditional office settings. The research provides empirical evidence supporting the strategic adoption of modern development practices while identifying critical success factors and potential pitfalls that organizations must navigate during implementation.
Serverless computing, low-code platforms, no-code platforms, developer productivity, remote work, cloud computing, application development, software engineering
SECURE EMBEDDED NETWORKING ARCHITECTURES USING TRUSTED EXECUTION ENVIRONMENTS IN BROADBAND GATEWAYS
Broadband gateways serve as critical network perimeters connecting home and enterprise environments to the internet, yet traditional gateway architectures lack robust security isolation for sensitive operations like cryptographic key management, firmware validation, and secure device onboarding. This research presents a comprehensive security architecture leveraging Trusted Execution Environments (TEE) to establish hardware-backed security foundations in embedded broadband gateways. Our implementation integrates OP-TEE (Open Portable Trusted Execution Environment) on ARM TrustZone-enabled gateway platforms, establishing secure boot chains, trusted firmware components, and cryptographic isolation for critical networking functions. The architecture addresses practical security challenges in gateway deployments including secure EasyMesh network formation, protected credential storage, and isolated firmware update verification. We demonstrate how TEE-based isolation protects against both remote network attacks and local exploitation attempts that compromise traditional Linux-based gateways. Experimental validation across multiple gateway platforms shows the security enhancements impose minimal performance overhead—less than 8% throughput reduction and 12ms latency increase—while providing strong guarantees against broad attack classes including memory corruption, privilege escalation, and persistent rootkit installation. This work contributes both architectural principles for secure embedded networking and practical implementation guidance for deploying TEE-based security in resource-constrained gateway hardware.
Trusted Execution Environment, OP-TEE, broadband gateway security, secure boot, ARM TrustZone, EasyMesh security, embedded Linux hardening, cryptographic isolation