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.
THE IMPACT OF PREDICTIVE INTELLIGENCE AND REAL-TIME DATA INSIGHTS ON CORPORATE FINANCIAL SUSTAINABILITY
Purpose: This study examines the impact of Predictive Intelligence (PI) and Real-Time Data Insights (RDI) on Corporate Financial Sustainability (CFS), while controlling for organizational factors, industry factors, and AI-related risk factors. The research aims to determine whether these analytic capabilities (PI and RDI) individually and jointly contribute to long-term financial resilience and performance in modern organizations. Theoretical Framework: The study is grounded in three complementary theoretical perspectives: the Resource-Based View, which conceptualizes PI and RDI as strategic organizational capabilities; Dynamic Capability Theory, which explains how sensing (PI) and seizing/reconfiguration (RDI) functions enable firms to adapt to changing environments; and the Data-Driven Decision-Making perspective, which emphasizes the role of timely, analytically processed data in improving decision quality and financial outcomes. Methodology: A quantitative, cross-sectional research design was adopted, with data collected from 320 managerial and analytical professionals across medium and large organizations in multiple countries and industries, including Financial Services, Manufacturing, Information Technology, and Retail/Trade. Reliability and validity tests, including Cronbach's alpha, Exploratory Factor Analysis (EFA), and Harman's Single Factor Test for common method bias, were conducted, and measurement methods were adapted from validated scales. Key Findings: The results provided strong support for all three hypotheses. PI had a significant positive effect on CFS (β = 0.410, p < 0.001, ΔR² = 0.144), and RDI similarly demonstrated a significant positive influence (β = 0.370, p < 0.001, ΔR² = 0.133). When entered together, PI and RDI jointly explained an additional 19.7% of variance in CFS (ΔR² = 0.197, p < 0.001), confirming their complementary and synergistic contributions. Among controls, OF consistently showed a strong positive effect, IF was positive but marginally significant, and AR exhibited a significant negative relationship with CFS across all models. Originality/Value: This study contributes to the literature by empirically distinguishing PI and RDI as separate but complementary analytics capabilities, demonstrating that their combined use provides a more comprehensive explanation of financial sustainability than either construct alone. It also highlights the importance of managing AI-related risks to realize the full potential of analytics investments. The findings offer practical guidance for organizations seeking to leverage AI-enabled predictive and real-time analytics for long-term financial resilience.
Predictive Intelligence, Real-Time Data Insights, Corporate Financial Sustainability, Business Analytic, Financial Risk Management.
AUTONOMOUS CONTROL OF VARIABLE SPEED THREE PHASE INDUCTION MOTOR DRIVE FOR EV/AERO-SPACE APPLICATIONS
Induction motor drives are widely used in variable speed for electric vehicles, aerospace control applications. Due to advances in power-electronic switching devices, it is possible to control the speed of an induction motor. The microcontrollers are programmed with a unit sine curve for the 1st 256 locations. By addressing the microcontroller at different speeds, various sine-frequency outputs can be generated. Since the microcontroller's output is digital, an appropriate digital to analog converter is used to get sine wave. This voltage is being modulated with a high carrier frequency. A comparator compares the pulse width modulation output with the feedback voltage from the current transformer motor terminals. This comparator output is given to the D-flip-flop to get 2 complementary outputs for the inverter legs. Similarly, for all 3 phases, control voltages are generated, and the outputs are passed through appropriate dead-zone generators, which are then applied to the MOSFET gates. Suitable opto-couplers are used to isolate the control signal from high voltages. The inverter output is given to the motor terminals. Current Transformers are used for each phase and fed back to the comparator for control purposes. The system has been tested for various frequencies from 30 - 62.5Hz. The mixed signal oscilloscope records the phase, line voltages across motor terminals and the snubber inductor waveforms in bitmap and csv format. The csv format is used to do FFT analysis to get frequency domain results to identify fundamental and higher harmonic frequency voltages present in the phase and line voltages. The higher harmonic components voltage may produce negative torque in the motor, hence it is necessary to identify. The result shows that there is no overshoot at the terminal voltage of phase and line in all the speeds of induction motor due to proper snubber circuits.
Fast Fourier Transformation. Induction Motor, Pulse Width Modulation, Sine Modulation
Chandra Shekar S M, Reddy Sudharshana K, Prakash S Ari, Shameer N, A Ramachandran
DESIGN AND PERFORMANCE ANALYSIS OF A COMPACT FREQUENCY-RECONFIGURABLE VARACTOR-LOADED PRINTED MONOPOLE ANTENNA WITH DEFECTED RADIATING ELEMENT AND PARTIAL GROUND PLANE FOR WIRELESS APPLICATIONS
The rapid advancement of wireless communication technologies has created a growing demand for compact, efficient, and adaptable antenna systems capable of supporting multiple communication standards. Emerging applications such as fifth-generation (5G) networks, wireless sensor networks, Internet of Things (IoT) devices, cognitive radio systems, and intelligent communication platforms require antennas that can operate over different frequency bands while maintaining stable radiation characteristics. Consequently, the development of compact antennas with frequency reconfiguration capability has become an active area of research. Among the various antenna configurations reported in the literature, printed monopole antennas have gained considerable attention owing to their simple structure, low profile, lightweight nature, ease of fabrication, and compatibility with printed circuit technologies. In addition, printed monopole antennas generally exhibit wide impedance bandwidth and nearly omnidirectional radiation characteristics, making them attractive for modern wireless communication systems[3]. However, conventional monopole antennas are typically designed for fixed-frequency operation, limiting their adaptability in dynamic spectral environments.
Dr. Chetan. S, Ashwini Garaddi, Dr. Kiran B, Dr. Rajendra R. Patil, Dr. Shashi T J, Dr. Jagadeesha. S
A HYBRID INTERNET OF THINGS AND MACHINE LEARNING APPROACH FOR A USER-FRIENDLY PRECISION AGRICULTURE FRAMEWORK.
Objectives: To develop an IoT and Machine Learning (ML) based decision-making framework that enables farmers to make informed decisions through data-driven approaches on the suitable type of crop to be planted, corresponding to respective soil conditions. Additionally, a Nutrient management approach to maintain soil health and optimize healthy yields. Methods: Two primary models have been implemented: a Crop Recommendation model that suggests crops suitable for the respective soil type by analysing soil health using real-time crucial parameters such as nitrogen (N), phosphorus (P), potassium (K), pH, rainfall, temperature, and humidity obtained via a NPK sensor, and a Nutrient Sufficiency Classification model that analyses the sensed nutrient content and classifies nutrient sufficiency (low, sufficient, high) for N, P, and K. The output of the Nutrient Sufficiency Classification model is linked to a rule-based feedback system that provides suggestions or solutions for the detected shortcomings to improve soil health. Findings: The Crop Recommendation model demonstrated an accuracy of 90.35% and the Nutrient Sufficiency Classification model exhibited an accuracy of 85.12%. Soil parameters analysed by the model, which include: nitrogen (N), phosphorus (P), potassium (K), pH, Rainfall, Temperature, and humidity, were either manually given as input through the web-based interface or a combination of real-time sensing and third-party APIs, including Google and weather APIs, that auto-populated the input fields in the user-friendly HTML-based web interface. The entire system was validated for its performance. The predictive results of the ML model were displayed on the web interface. Novelty: This study implements a practical and deployable framework with a real-time sensing IoT architecture to sense nutrient content in the soil, an ML framework for data analysis with an integrated rule-based feedback engine to auto-populate the web interface that gives crop recommendation outputs and actionable feedback in case of nutrient deficiencies.
IoT, Machine Learning, Crop Recommendation, Nutrient Sufficiency Classification, third-party APIs.
Punya Prabha V, M.D. Nandeesh, Tejaswini S, Jaseema Feroze Mohideen
COMPARATIVE ANALYSIS OF VISION TRANSFORMER AND EFFICIENTNET FOR FOOD RECOGNITION WITH BMI-AWARE DIETARY MONITORING
Dietary-related diseases and obesity impose a substantial global health burden, yet accurate and continuous dietary monitoring remains largely manual and inaccessible. This paper presents a comparative analysis of two lightweight deep learning architectures—Vision Transformer (ViT-tiny) and EfficientNet-B0—for automated food image classification and personalized calorie estimation. Both models are trained under identical conditions on 65 food categories derived from the Food101 dataset, comprising 48,750 training and 16,250 validation images pre-processed to 128×128 resolution. Phase I (ViT-tiny) achieves 76.17% top-1 and 93.49% top-5 validation accuracy with 5.5M parameters, while Phase II (EfficientNet-B0) achieves 77.56% top-1 and 93.91% top-5 accuracy with 4.1M parameters. Phase III benchmarks GPU inference throughput on an NVIDIA T4, with ViT-tiny achieving 2,918 img/s at 5.06 ms single-image latency and EfficientNet-B0 achieving 2,480 img/s at 8.65 ms. A personalized nutritional engine integrates the Mifflin–St Jeor basal metabolic rate equation with WHO BMI-category-adjusted gram-level serving recommendations, delivering user-specific caloric guidance across four weight categories. Experimental results demonstrate that EfficientNet-B0 achieves superior classification accuracy with fewer parameters, while ViT-tiny offers a 1.7× latency advantage suited for real-time edge deployment, establishing complementary efficiency-accuracy trade-offs for practical food recognition and dietary monitoring applications.
: Food recognition, Vision Transformer, EfficientNet, multi-label classification, personalized nutrition, BMI, calorie estimation, deep learning, transfer learning, Food101.
Punya Prabha V, M.D. Nandeesh, Manjunath C. Lakkannavar, Lavanya Y.S., Asha K, Vidya M, A. Vaibhav Sagar
PERFORMANCE ENGINEERING FOR LLM INTEGRATED APPLICATIONS.
The integration of large language model (LLM) application programming interfaces into the critical request path of production software introduces a class of performance-engineering problems that classical deterministic system design does not anticipate. Unlike conventional remote procedure calls, an LLM completion call is stochastic in output, autoregressive in structure, and highly variable in latency, since response time scales with generated token count, provider queueing conditions, and model routing decisions rather than fixed computation. This paper characterizes the sources of non-determinism and latency variability in LLM-integrated systems and proposes a structured performance-engineering and testing framework organized around four pillars: token-streaming instrumentation, adaptive timeout strategy, retry-storm-resistant backoff design, and fault-injection-based resilience testing. The framework treats time-to-first-token (TTFT) and inter-token latency (ITL) as first-class service-level indicators, applies deadline propagation across multi-hop agentic call chains, and formalizes retry budgets, jittered exponential backoff, and circuit breakers as the minimum viable resilience surface for AI-dependent services. A comparative synthesis of resilience patterns and a latency-budget taxonomy for four common LLM interaction classes are presented. The discussion argues that performance testing for LLM-integrated applications must shift from throughput-oriented load testing toward probabilistic, chaos-driven validation that treats variable latency and partial failure as expected operating conditions rather than exceptional ones.
Performance Engineering, Large Language Models, Non-Determinism, Latency Testing, Token Streaming, Timeout Strategy, Retry Storms, Circuit Breaker, Chaos Engineering, Resilience Patterns.
THE ROLE OF TRANSFER LEARNING IN ACCELERATING MACHINE LEARNING DEPLOYMENTS
Transfer learning has emerged as one of the most transformative paradigms in modern machine learning, fundamentally reshaping how practitioners build and deploy intelligent systems. By leveraging knowledge acquired from solving one problem and applying it to a related but distinct task, transfer learning dramatically reduces the data, compute, and time requirements that have historically constrained ML deployments. This paper provides a comprehensive survey of transfer learning techniques, their theoretical foundations, practical deployment benefits, and the challenges that remain. We examine case studies across computer vision, natural language processing, healthcare, and industrial applications, and quantify the acceleration effects on model training cycles, labeling costs, and time-to-production. Our analysis demonstrates that pre-trained model pipelines can reduce training time by 60–90% and cut labeling requirements by as much as 80%, making production-grade AI accessible to organizations that previously lacked the resources to participate. We also discuss limitations, failure modes, and emerging directions including meta-learning and federated transfer, concluding that transfer learning will remain a cornerstone capability as the field matures.
Transfer Learning, Fine-Tuning, Domain Adaptation, Pre-Trained Models, Deployment Acceleration, Foundation Models, Few-Shot Learning, Knowledge Transfer.
ACOUSTIC PEST DETECTION AND NOISE REDUCTION TECHNIQUES FOR PRECISION AGRICULTURE
In this paper, we have provided a noninvasive acoustic sensing system that can be employed for detecting agricultural pests through the analysis of their bio-acoustic signals. The proposed system focuses on Tribolium confusum, also known as the confused flour beetle. For collecting the bio-acoustic signals generated by the insects, sensitive microphones were used while maintaining controlled experimental conditions. The collected signals were then filtered in MATLAB using FIR and IIR band pass filters in order to reduce environmental noise from wind and agricultural machinery. The next step was extracting spectral information from the signals and classifying them into respective categories employing deep learning models such as Convolutional Neural Network (CNN) and Support Vector Machine (SVM). Experiments confirmed that the background noise can be efficiently reduced using the described algorithm with almost 10 dB improvement in signal intensity.
Acoustic Pest Detection, FIR Filter, IIR Filter, SNR, MATLAB, Noise Reduction, Precision Agriculture
Dr. Avinash G M, Dr. Dilna U, Dr. Keerthi Kumar N, Sunil D M
FINANCIAL STATEMENT ANALYSIS AND STOCK VALUATION: AN EMPIRICAL EVIDENCE ON THE VOLATILITY OF DEPOSIT MONEY BANKS LISTED ON NIGERIAN STOCK MARKET
This study examines the connection between financial statement analysis and stock valuation of deposit money banks (DMBs) listed on the Nigerian Exchange Group market volatility. The study adopted a dynamic panel structure of stock price over the period 2020–2024 and employed the two-step system Generalized Method of Moments (GMM) estimator. The result indicates a mixed outcome across the explanatory variables. Earnings per share (EPS) exhibits a positive but statistically insignificant effect on stock price (coefficient = 0.0003562, p-value = 0.209), suggesting that EPS does not significantly drive stock valuation within the sampled banks. Also, return on investment (ROI) displays a negative and slightly insignificant relationship at the 5% level (coefficient = −0.1560306, p-value = 0.056), while it achieves statistical significance at 10% level, demonstrating limited explanatory power in forecasting stock price movements. Firm size, however, shows a statistically significant negative effect on stock price (coefficient = −0.0965265, p-value = 0.002), inferring that rises in bank size are linked with declines in stock price, probably due to inefficiencies or market sensitivities of big organisations. The conclusion infer that firm-specific financial indicators may not be the main factors of stock price volatility in Nigerian DMBs, with external macroeconomic and market-specific factors possibly playing further overriding role. The study recommends greater financial disclosure quality and greater investor reliance on inclusive market indicators outside traditional accounting metrics for informed investment decisions.
Earnings per Share; Financial Statement Analysis; Stock Market Volatility; Stock Valuation
Adekunle A. Adeyemi*, Michael S. Ogunmuyiwa, Taophic O. Bakare, Jamiu A. Akindele, Jide Ibikunle, Abubakar Zik-Rullahi Ahmed
THEORETICAL FRAMEWORK FOR A PROTOTYPE MEASUREMENT AND VERIFICATION (M&V) METER THAT SUPPORTS BASIC M&V 2.0 PRINCIPLES
A theoretical framework is presented for a prototype Measurement and Verification (M&V) meter. The development of the framework was guided by the regulations, standards, and operating procedures applied in South Africa. The possibility of its further development for other M&V markets, or for more advanced M&V methods than presented here, is not excluded. Longstanding fundamental considerations associated with the classical approach toward M&V, such as process transparency and data integrity, are provided for in the framework as are newer considerations associated with the more recent approaches of M&V 2.0, such as data granularity and real time reporting. The equipment of the industry standard data acquisition path is combined in a single node, and the extent of the quality control path is reduced to one piece of equipment. The framework is supported by the development of Simulink models that provide some basic functionality. Simulation results show that the models perform their intended functions. The models will be interconnected appropriately during prototyping in order to function as the sub-models of a larger model, and additional sub-models will be developed for more complex functions. An introductory overview of the existing and future models is presented. Details will be presented at a more advanced stage of development.
Advanced measurement and verification, M&V meter, Quality control path
Marius Bekker, Jan Harm Christiaan Pretorius
DEVELOPMENT OF LONG SHORT-TERM MEMORY –RECURRENT NEURAL NETWORK MODEL FOR THE ASSESSMENT AND PREDICTION OF VOLTAGE SAG IN ADO-EKITI SECONDARY DISTRIBUTION NETWORK
Voltage sag is a significant power quality disturbance that adversely affects the reliable operation of sensitive equipment and overall industrial productivity in secondary distribution networks. In the Ado-Ekiti metropolis, Nigeria, recurring voltage instability driven by load fluctuations, feeder overloading, and network topology limitations necessitates the adoption of advanced, data-driven monitoring and forecasting approaches. This study presents a predictive framework based on a Long Short-Term Memory–Recurrent Neural Network (LSTM-RNN) architecture designed to capture nonlinear temporal dependencies associated with voltage variations. The model was developed and validated using high-resolution historical measurements obtained from six distribution feeders. To enhance generalization and reduce overfitting, regularization techniques including L2 weight decay, dropout, and early stopping were incorporated during training. Model performance was evaluated using standard statistical indicators, including Root Mean Square Error (RMSE) and the coefficient of determination R², alongside operational criteria for disturbance detection defined by voltage sag conditions below 0.8p.u. The results indicate strong predictive performance, with R² values ranging from 0.83 to 0.93 across the studied feeders.In particular, the model achieved an R² value of 0.89 and a Mean Absolute Error (MAE) of 1.53 V for the Adebayo feeder, demonstrating effective tracking of seasonal voltage variations and recurring instability patterns.These findings confirm that the proposed LSTM-RNN framework provides a reliable and scalable approach for power quality assessment in low-voltage distribution systems, supporting improved operational planning and enhanced network reliability in developing power infrastructure.
Development, Distribution, Neural Network, Recurrent, Voltage Sag
Adeoye, O.S. , Olulope, P. K., Olajuyin, E.A., Olajiga, B. O., Adebimpe, A.M., Akinyede, J.A., Olusuyi, K., O., Olayiwola, O. P.
RECOMMENDER SYSTEMS IN SOCIAL NETWORKS USING CROWDSOURCING
The rapid growth of content in social networks has made it hard for users to find relevant information and has raised the need for effective recommender systems. Collaborative filtering is one of the most widely used recommendation approaches, yet it faces the challenges of data sparsity and cold start. The aim of this study was to design and evaluate a hybrid recommender system that strengthens collaborative filtering with crowdsourced data. The study followed an applied and developmental design, and the data were gathered from an active social-network group in the health-news domain. The implicit data were drawn from the users' behavioral traces, and the explicit data were obtained from a researcher-made questionnaire across four dimensions, namely satisfaction, relevance, transparency, and trust. The model performance was measured with precision, recall, the F1 score, coverage, diversity, and prediction error. The findings showed that crowdsourcing increases data richness and density and raises the density of the interaction matrix from 6.3 percent to 28.9 percent. The structured integration of explicit and implicit data also lifted the precision of the optimized version to 0.45 at the top ten recommendations and its recall to 0.44, while lowering the prediction error. These improvements enhanced recommendation quality for low-activity users and eased the cold-start problem. Overall, integrating crowdsourcing with collaborative filtering provides a human-centered and effective framework for improving recommendation in social networks.
Collaborative Filtering, Crowdsourcing, Explicit Data, Recommender System, Social Network.
Masoumeh Ansarifar, Erfaneh Noroozi, Mehdi Hosseinzadeh
THE AI-AUGMENTED PRODUCT LIFECYCLE: HOW GENERATIVE AND AGENTIC AI TRANSFORM IDEATION, PRIORITIZATION, PROTOTYPING, AND TESTING
The integration of generative and agentic artificial intelligence (AI) into product development processes is catalyzing a fundamental transformation in how organizations conceptualize, prioritize, build, and validate digital and physical products. This paper presents a comprehensive analytical framework that examines the impact of large language models (LLMs), multimodal generative AI, and autonomous agent systems across the four canonical stages of the product lifecycle: ideation, prioritization, prototyping, and testing. Drawing on a synthesis of 64 peer-reviewed publications, industry case studies, and empirical performance benchmarks from 2018 to 2024, this investigation establishes that generative AI accelerates ideation throughput by 3.1 to 5.4 times compared to traditional brainstorming methodologies, reduces time-to-prototype by up to 68%, and improves defect detection rates in automated testing by 41% relative to rule-based quality assurance frameworks. We further characterize the emergent role of agentic AI systems—those capable of goal-directed, multi-step reasoning and tool use—in enabling closed-loop product iteration at unprecedented speed. A comparative performance matrix across seventeen enterprise AI deployment contexts is presented, alongside an analysis of organizational readiness dimensions, ethical risks, and human-AI collaboration models. The paper concludes with a strategic roadmap for product organizations seeking to deploy AI-augmented workflows, identifying critical capability gaps and proposing a tiered adoption model.
Generative AI; Agentic AI; Product Lifecycle Management; Ideation; Prototyping; Large Language Models; AI-Augmented Design; Software Testing Automation; Human-AI Collaboration; Digital Product Development
A FEW-SHOT COPYRIGHT ATTRIBUTION FOR GAME IMAGE BASED ON META-LEARNING
The rapid proliferation of digital game content has increased the need for reliable methods to identify the copyright ownership of game images, particularly in scenarios where only a limited number of labeled samples are available for each title, character, or visual asset. Conventional image classification approaches often require large-scale annotated datasets and show limited generalization across heterogeneous game domains, such as variations in genre, art style, rendering quality, and post-processing. To address these challenges, in this paper, we propose a few-shot copyright attribution framework for game images based on meta-learning. The proposed method is designed to learn transferable visual representations and fast adaptation strategies from episodic training tasks, enabling effective attribution of previously unseen or weakly represented game image classes. In addition, a cross-domain learning setting is considered to improve robustness against stylistic and content-level discrepancies among game datasets. The framework aims to distinguish copyright-related visual sources by capturing discriminative fine-grained patterns while maintaining generalization under data scarcity. In this paper, we have simulated a few-shot attribution scenario on a game image benchmark and evaluated the proposed framework using standard recognition and attribution measures. The results are expected to demonstrate that meta-learning provides a practical and scalable solution for copyright attribution in game images, offering strong potential for digital copyright protection, content monitoring, and forensic analysis in the gaming industry.
Meta-learning; Game image analysis; Copyright attribution; Few-shot learning
STUDY OF DRAG ON CONDUCTING FLUID FLOW PAST AN IMPERMEABLE CIRCULAR CYLINDER IN COMPOSITE REGIONS WITH MAGNETIC FIELD
The analytical study of viscous, incompressible, conducting fluid flow in multiple regions past and through an impermeable cylinder embedded in a sparsely packed porous medium in presence of transverse magnetic field is presented. Modified Brinkman equation and Stokes equation are used as governing equations. Here we have computed Tangential velocity component, Normal velocity component, streamlines and drag coefficient. The effect of dimensionless parameters on the fluid flow have been analyzed and presented graphically.
Modified Brinkman equation, Porous Parameter, Hartmann Number, Modified Bessel’s equations, Magnetic permeability, Drag coefficient
Gopinatha K.R., Gnaneshwari P, Dhananjaya M, Mallika K.S.
DEEP LEARNING FOR POWER SYSTEM STABILITY
Power system stability has emerged as a critical challenge in modern smart grids owing to increasing penetration of renewable energy sources, complex load dynamics, and the growing scale of interconnected networks. Traditional methods such as time-domain simulation, continuation power flow, and energy function analysis, while theoretically sound, suffer from computational bottlenecks that preclude real-time deployment. This paper proposes a novel hybrid deep learning framework that combines Long Short-Term Memory (LSTM) networks with one-dimensional Convolutional Neural Networks (1D-CNN) to assess voltage stability in real time. The model simultaneously captures temporal correlations in power system measurements through LSTM layers and extracts local feature patterns through convolutional filters. Trained on simulated datasets derived from IEEE 39-bus, 118-bus, and 300-bus test systems, as well as real SCADA measurements from the Northern Indian grid, the proposed LSTM-CNN achieves a classification accuracy of 98.8%, an AUC-ROC of 0.9891, and an average inference time of 4.9 ms per sample. These results represent significant improvements over Support Vector Machines (SVM), Random Forests, Deep Neural Networks (DNN), and stand-alone LSTM baselines. Ablation studies, sensitivity analyses, and feature importance rankings confirm the robustness and interpretability of the proposed framework. The findings strongly support the deployment of deep learning-based tools in energy management systems (EMS) for automated, near-instant stability monitoring.
Power System Stability; Deep Learning; LSTM; Convolutional Neural Network; Voltage Collapse; Real-Time Assessment; Smart Grid; Feature Extraction; SCADA
POST-QUANTUM SECURITY FOR CLOUD-BASED DIGITAL TWINS: A FRAMEWORK FOR NEXT-GENERATION HEALTHCARE AND SMART CAMPUSES
The proliferation of cloud-based digital twins across critical infrastructures—including hospital patient-monitoring systems and university smart campuses—has created an urgent need to secure the continuous, high-fidelity data streams that bind physical assets to their virtual counterparts. Conventional public-key cryptography, based on integer factorization and elliptic-curve discrete logarithms, is increasingly exposed to future cryptographically relevant quantum computers (CRQCs) capable of executing Shor's algorithm, leaving digital twin telemetry, control commands, and identity credentials vulnerable to “harvest-now, decrypt-later” attacks. This paper proposes a post-quantum security framework for cloud-based digital twins that integrates NIST-standardized lattice-based primitives—the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM, FIPS 203) and the Module-Lattice-Based Digital Signature Algorithm (ML-DSA, FIPS 204)—into a hybrid classical/post-quantum key-establishment and twin-state authentication protocol. The framework is evaluated across two representative deployments: a remote patient-monitoring healthcare digital twin and a multi-building smart campus digital twin, using a cloud-edge testbed instrumented with resource-constrained IoT gateways. Comparative benchmarking against RSA-2048 and ECDSA P-256 baselines shows that a hybrid X25519+ML-KEM-768 handshake adds only modest latency overhead while providing quantum resistance, and that ML-DSA-65 signatures, despite larger payloads, remain compatible with the sub-second synchronization budgets required by both domains. Results across more than 5,000 simulated concurrent digital twins indicate that post-quantum digital twin security is practically achievable today through careful protocol design, algorithm-tier selection, and crypto-agile architecture, offering healthcare and campus operators a concrete migration pathway ahead of the arrival of cryptographically relevant quantum computers.
Post-Quantum Cryptography, Digital Twins, Cloud Security, Healthcare IoT, Smart Campus, Lattice-Based Cryptography, ML-KEM, ML-DSA, Quantum-Safe Architecture, Crypto-Agility.
ZERO TRUST IN THE CLOUD: ARCHITECTING SECURE, IDENTITY-FIRST FRAMEWORKS FOR DISTRIBUTED ENTERPRISE ENVIRONMENTS
The dissolution of the enterprise network perimeter, accelerated by multi-cloud adoption, remote and hybrid workforces, and the proliferation of application programming interface (API)-driven microservices, has rendered perimeter-centric security architectures structurally incapable of protecting distributed digital estates. Zero Trust, first articulated by Kindervag and formalized in NIST Special Publication 800-207, replaces implicit network-location trust with continuous, identity-centric verification of every access request. Yet most operational Zero Trust deployments remain fragmented, combining point identity and access management (IAM) products, virtual private networks, and static conditional-access rules without a unifying architectural or analytical framework capable of reasoning over the full identity-resource relationship graph in real time. This paper presents ZTIF (Zero Trust Identity Framework), an identity-first reference architecture that fuses a continuously updated identity-resource graph with a Graph Attention Network (GAT) for contextual access-risk scoring and an ensemble anomaly detector for behavioral deviation, orchestrated through a software-defined policy enforcement layer that operationalizes the NIST 800-207 policy decision point/policy enforcement point (PDP/PEP) model across multi-cloud infrastructure. ZTIF was evaluated against a simulated eighteen-month telemetry corpus comprising 2.1 million daily authentication and authorization events across 41 cloud services and 6,400 identities (human and non-human). The proposed framework achieves an identity-risk detection AUC of 0.968, an F1-score of 0.94 for anomalous access identification, and reduces mean time to contain (MTTC) lateral-movement incidents from 487 minutes under conventional segmentation to 29 minutes. Regulatory and standards compliance coverage across NIST SP 800-207, ISO/IEC 27001:2022, SOC 2 Type II, PCI DSS v4.0, GDPR Article 32, and the CISA Zero Trust Maturity Model improves from a pre-implementation baseline average of 38% to 90.5%. These results indicate that identity-first, graph-native Zero Trust architectures constitute a necessary technical foundation for defensible security posture in distributed, cloud-native enterprise environments.
Zero Trust architecture, identity and access management, cloud security, distributed enterprise environments, graph attention network, continuous authentication, micro-segmentation, policy enforcement, NIST SP 800-207, regulatory compliance, identity-first security, lateral movement detection.
The rapid integration of renewable energy sources, proliferation of distributed energy resources (DER), and the growing complexity of interconnected smart grids have placed unprecedented informational and cognitive demands on power system operators. Traditional automation tools — energy management systems (EMS), supervisory control and data acquisition (SCADA), and rule-based expert systems — while reliable, lack the adaptive reasoning, natural language interface, and contextual knowledge synthesis required for emerging operational scenarios. This paper proposes a comprehensive framework for deploying Large Language Models (LLMs) across core power system operation tasks, including real-time fault diagnosis, short-term load forecasting, contingency analysis, anomaly detection, natural language-to-control-action translation, and automated incident report generation. A domain-specific fine-tuned model, PowerBERT, is developed by continual pre-training and supervised fine-tuning of a 340M-parameter transformer on curated corpora comprising IEEE technical standards, NERC reliability guidelines, SCADA operational logs, and historical fault records. Augmented with Retrieval-Augmented Generation (RAG) using a power system knowledge base, the proposed LLM + RAG system achieves 97.8% classification accuracy, a load forecasting MAPE of 2.1%, and 89 ms average response latency across six operational task categories. Comprehensive evaluations on IEEE 39-bus, 118-bus, and real-world Indian and Saudi grid datasets confirm that the proposed framework significantly outperforms support vector machines, random forests, deep learning baselines, and general-purpose LLMs. Expert evaluation of natural language outputs further demonstrates near-human fluency, accuracy, and actionability, supporting the vision of LLMs as the next-generation cognitive layer in grid control rooms.
Large Language Models; Power System Operations; Fault Diagnosis; Load Forecasting; Retrieval-Augmented Generation; Fine-tuning; Smart Grid; Natural Language Processing; Transformer Architecture; Energy Management Systems
COMPARISON OF THE EFFECTIVENESS OF MARIGOLD (TAGETES ERECTA) PLANTS AS MOSQUITO REPELLENT WITH FERMENTATION-BASED OVITRAP MOSQUITO TRAPS IN URBAN AND RURAL ENVIRONMENTS
Background: Dengue Hemorrhagic Fever (DHF) remains a major public health challenge globally, primarily vectored by Aedes aegypti and Aedes albopictus. Vector control strategies increasingly focus on eco-friendly, community-based approaches. Objective: This study aimed to compare the effectiveness of mosquito-repellent Marigold plants (Tagetes erecta) (TMPN) against fermentation-based ovitrap traps (KAOFPN) in capturing or repelling mosquitoes across urban and rural houses over a 4-day observation period. Methods: A quasi-experimental study with a comparative approach was conducted across 36 houses (18 urban, 18 rural). Ovitraps and Marigold plants were placed across 11 specific indoor and outdoor monitoring stations. Data were analyzed using Paired Sample T-Test, Levene's Test, One-Way ANOVA, and Tukey's HSD Post Hoc test, supplemented by Effect Size (Cohen’s d) calculations. Results: The results indicate that KAOFPN captured a significantly higher number of mosquitoes compared to the repellent effect of TMPN across both urban and rural settings (\(p < 0.05\)). The highest mosquito density was observed in outdoor backyard gardens (urban: mean 32.00 \(\pm \) SD 0.00; rural: mean 35.00 \(\pm \) SD 0.00 using KAOFPN). One-Way ANOVA confirmed significant differences in mosquito capturing/repelling effectiveness across stations (\(p = 0.000\)), with Post Hoc Tukey HSD identifying outdoor backyard gardens and terraces as locations with the largest vector densities. Conclusion: Fermentation-based ovitraps (KAOFPN) are highly effective tools for vector surveillance and mass trapping, whereas Marigold plants (TMPN) act as supplementary spatial repellents. Integrated vector management combining both methods is recommended for community health promotion.
Aedes aegypti, Aedes albopictus, Dengue, Marigold, Ovitrap, Vector Control.
Rustam Aji, Dewi Lusiani, Gustomo Yamistada, Jessy Novita Sari, Muhamad Abas, Mohammed Bakri, Dingse Pandiangan, Sherly Ratih Frichesarius Santy Ajhie, Roro Ajhie Ayuningtyas
MACHINE LEARNING-DRIVEN RESOURCE ALLOCATION FOR DYNAMIC WORKLOADS IN HPC SYSTEMS
High-performance computing (HPC) systems now support workloads that shift constantly in shape and intensity, from tightly coupled simulations to bursty AI training jobs. Traditional schedulers based on static heuristics struggle to keep utilization high while meeting job deadlines, and the result is idle nodes on one side and long queues on the other. This paper explores how machine learning (ML) can be used to allocate resources more intelligently in HPC environments where workloads are dynamic and hard to predict. We designed a learning-based scheduling framework that combines a job runtime predictor, a reinforcement learning agent for placement decisions, and a feedback loop that continuously updates its models from telemetry. Experiments were carried out on a simulated 512-node cluster running a mix of scientific and AI workloads drawn from public traces. Results show that our ML-driven approach reduces average job wait time by around 34% and improves overall cluster utilization by nearly 22% compared to a well-tuned backfill scheduler. Energy per completed job also dropped by around 17% because fewer nodes stayed idle at high power states. We discuss trade-offs including training overhead, cold-start behaviour, and the risk of learned bias against long-running jobs. Findings suggest that ML-driven scheduling is ready to move from research into production HPC operations, provided that operators keep humans in the loop and monitor fairness carefully. The paper contributes an empirical comparison, a practical architecture, and a set of open problems for the community to tackle next.
HPC Scheduling, Machine Learning, Reinforcement Learning, Resource Allocation, Dynamic Workloads, Cluster Utilization
ENERGY-AWARE SCHEDULING: OPTIMIZING POWER EFFICIENCY IN EXASCALE COMPUTING ENVIRONMENTS
Exascale systems have crossed a threshold that changes how we think about performance. When a single machine draws twenty to thirty megawatts at full load, every scheduling decision carries a visible cost in dollars, carbon, and cooling capacity. This paper examines how energy-aware scheduling can reduce power consumption in exascale computing environments without giving up the throughput that scientists depend on. We propose a scheduler that combines power capping, workload characterization, and dynamic voltage and frequency scaling (DVFS) into a single decision layer, guided by a predictive model of job power draw. Experiments were run on a simulated exascale-class system with 8,192 heterogeneous nodes, using workload traces derived from recent leadership-class facility logs. Our approach reduced total energy consumption by about 24% compared to a performance-first baseline, while extending average job runtime by only 4.6%. Peak power draw dropped by roughly 19%, which is significant for facilities operating under strict grid contracts. We also observed that co-scheduling workloads with complementary power profiles helped smooth the load curve and reduced cooling stress during afternoon peaks. Trade-offs remain, including the risk of throttling latency-critical jobs and the added complexity of maintaining accurate power models across evolving hardware. Findings suggest that the exascale community needs to treat energy as a first-class scheduling metric alongside utilization and wait time, and that machine-learning-informed policies can help operators do this without sacrificing scientific productivity. The paper contributes an architecture, empirical evidence, and a discussion of what still needs to be solved before energy-aware scheduling becomes standard practice at the largest facilities.
Exascale computing, energy-aware scheduling, power capping, DVFS, sustainable HPC, workload co-scheduling
PROTECTING HEALTHCARE FINANCIAL RECORDS AND DIGITAL PAYMENT ECOSYSTEMS
The convergence of healthcare and digital payment systems has created complex ecosystems that handle sensitive financial records alongside clinical data, exposing them to a wide spectrum of security threats. Healthcare financial records contain billing information, insurance claims, and payment credentials, making them highly attractive targets for cybercriminals. This paper proposes a layered security framework for protecting healthcare financial records and digital payment ecosystems against fraud, unauthorised access, and data breaches. The framework integrates strong identity controls, encryption across data lifecycles, machine learning-based fraud detection, and continuous compliance monitoring within a unified architecture. Empirical evaluation was conducted on a simulated healthcare payment environment that processed insurance claims, patient payments, and provider reimbursements, with realistic attack scenarios including claim manipulation, identity fraud, payment redirection, and data exfiltration. Results show that the proposed framework achieved a fraud detection rate of 95.2 percent with a false positive rate of 2.4 percent, while reducing the mean time to detection from forty-five minutes to under three minutes compared with a conventional baseline. Compliance audit completeness reached 97.8 percent across the evaluated regulatory dimensions. The findings demonstrate that protecting healthcare financial ecosystems requires integrated technical, operational, and governance controls operating as a coherent system rather than disconnected point defences.
Healthcare Financial Records, Digital Payment Security, Fraud Detection, Data Protection, Compliance Monitoring, Layered Security Framework
ADVERSARIALLY ROBUST AI FRAMEWORK FOR SECURING MACHINE LEARNING BASED CYBER DEFENSE SYSTEMS AGAINST EVASION AND POISONING ATTACKS IN FINANCIAL SERVICES
Machine learning has become a foundational element of cyber defence in financial services, supporting fraud detection, intrusion identification, and risk scoring at scale. However, the same models that defend financial systems are themselves vulnerable to adversarial attacks, including evasion at inference time and poisoning during training. These vulnerabilities create a paradox in which security tools designed to protect financial institutions can be manipulated into producing harmful outcomes. This paper proposes an adversarially robust artificial intelligence framework specifically designed for machine learning based cyber defence systems in the financial sector. The framework combines adversarial training, input transformation defences, certified robustness through randomised smoothing, and continuous monitoring for poisoning indicators within an integrated architecture. Empirical evaluation was conducted on simulated financial cyber defence systems exposed to a portfolio of state-of-the-art evasion and poisoning attacks across fraud detection and intrusion detection tasks. Results show that the proposed framework improved adversarial robustness by an average of 41 percent across attack scenarios while maintaining clean accuracy within two percent of the undefended baseline. Poisoning detection achieved 92.7 percent identification rate with a false positive rate of 3.1 percent. The findings demonstrate that practical adversarial robustness is achievable for financial cyber defence systems without unacceptable degradation of operational performance, supporting the responsible deployment of machine learning in high-stakes financial environments.
Adversarial Robustness, Machine Learning Security, Evasion Attacks, Poisoning Attacks, Financial Cyber Defence, Certified Defences.
OPERATIONAL ACCOUNTABILITY IN ACTION: METRICS AND MECHANISMS FOR SYSTEMIC SUCCESS
Large operational organisations — cloud platforms, hospital networks, transportation operators, financial-services back offices — routinely publish reliability targets and ownership charts, yet post-incident reviews repeatedly find that no individual or team could be held accountable for a failure at the moment it mattered: ownership was ambiguous, the relevant metric was not tracked, or the metric that was tracked was satisfied while the underlying service degraded. This paper develops a framework that treats operational accountability itself as an engineered system, composed of a formally specified metric layer and a set of mechanisms that bind metrics to named owners, escalation paths, and incentives. We model responsibility assignment as a bipartite accountability graph linking system components to accountable owners, define a metric specification format that separates a measured quantity from the threshold and cadence at which it becomes actionable, and introduce an incentive-alignment function that scores mechanism designs by their resistance to metric gaming. A reference architecture comprising a metrics pipeline, an ownership registry, an escalation engine, and a periodic review board operationalises the model without requiring a green-field replacement of existing monitoring infrastructure. We evaluate the framework through a twelve-month case study of a reliability program at a cloud services provider, in which the transition from an informal, chart-based ownership model to the full accountability framework reduced mean time to identify an accountable owner from 41% to 96% of incidents within the first hour, and reduced the repeat-incident rate from 26.5% to 8.9% of quarterly incidents. A sensitivity analysis over the escalation review-window threshold quantifies the trade-off between escalation volume and mean-time-to-resolution improvement. We conclude with a discussion of the organisational, computational, and measurement-theoretic limitations of the approach, including its exposure to Goodhart-type metric gaming, and outline directions for adaptive, telemetry-informed accountability design.
Operational Accountability, Reliability Engineering, Service-Level Objectives, Ownership Registry, Incident Management, Incentive Alignment, Goodhart's Law, Escalation Mechanisms.
BEYOND THE BLUEPRINT: AUTOMATED WORKFLOW ENFORCEMENT AND POLICY GOVERNANCE
The rapid adoption of large language model (LLM) agents and autonomous orchestration platforms has transformed how organizations design and execute multi-step business processes. Yet a persistent gap separates the workflow blueprint — the diagram, playbook, or prompt chain an organization designs — from what autonomous systems actually do at runtime. Agentic systems routinely deviate from intended sequences, bypass approval gates, invoke unauthorized tools, or violate regulatory and organizational policy under distribution shift, ambiguous instructions, or adversarial prompting. This paper presents a framework for Automated Workflow Enforcement and Policy Governance (AWEPG) that closes this gap by treating policy compliance as a first-class, machine-verifiable property of workflow execution rather than a documentation artifact. The framework combines a declarative Policy Specification Language (PSL) compiled into runtime guards, a formal workflow state-machine representation that constrains agent transitions to policy-admissible paths, and a three-tier enforcement pipeline spanning pre-execution validation, in-flight interception, and post-hoc audit. Evaluated across four enterprise workflow testbeds spanning finance approvals, healthcare intake, IT change management, and customer data handling, the proposed framework reduces the policy violation rate from 14.2% (ungoverned LLM agents) to 0.9%, while adding only 96 ms of median enforcement latency. Longitudinal deployment over twelve weeks demonstrates that adaptive policy learning further compounds these gains without manual rule authoring. The results establish automated workflow enforcement as a necessary governance layer for agentic AI deployment at enterprise scale.
Workflow Enforcement; Policy Governance; Agentic AI; Large Language Models; Compliance-as-Code; Business Process Automation; Runtime Guardrails; State Machines; Regulatory Technology; AI Safety
KINEMATIC OPTIMISATION OF SOFT ROBOTIC MANIPULATORS FOR MEDICAL INTERVENTION
Soft robotic manipulators are increasingly investigated for minimally invasive medical intervention because their intrinsic compliance allows safe interaction with delicate anatomical structures while their continuum architecture enables navigation through tortuous natural or surgically created pathways. However, the same compliance that confers safety also introduces kinematic redundancy, nonlinear actuation-to-shape mappings, and configuration-dependent stiffness that complicate motion planning and control. This paper presents a unified kinematic optimisation framework for tendon-driven and hybrid-stiffness soft manipulators intended for confined-space medical procedures such as laparoscopy, endoluminal navigation, and transoral surgery. Building on piecewise constant-curvature (PCC) and Cosserat-rod continuum formulations, we develop a multi-objective optimisation formulation that simultaneously maximises reachable dexterous workspace, minimises tip positioning error under external tissue-contact loading, and regulates task-space stiffness through auxiliary backbone insertion. The Jacobian-based manipulability and compliance-ellipsoid formulations are used to construct differentiable cost terms suitable for gradient-based and evolutionary optimisation. A representative case study—navigation of a dual-segment tendon-driven manipulator toward a simulated pancreatic target through a constrained laparoscopic port—is used to illustrate the framework. Results indicate that joint kinematic-and-stiffness optimisation improves the trade-off between reachability and positional accuracy relative to single-objective curvature optimisation, while remaining computationally tractable for pre-operative planning. The paper concludes with a discussion of open challenges, including real-time re-optimisation under tissue deformation and the incorporation of contact-rich Cosserat models into the optimisation loop.
Soft Robotics, Continuum Manipulators, Kinematic Optimisation, Cosserat Rod Theory, Minimally Invasive Surgery, Compliance Ellipsoid, Tendon-Driven Robots
MACHINE LEARNING ENHANCED CREDIT RISK ASSESSMENT MODELS FOR COMMUNITY AND REGIONAL BANKS: IMPROVING LENDING DECISIONS THROUGH ALTERNATIVE DATA AND PREDICTIVE ANALYTICS
Community and regional banks play a critical role in providing credit to small businesses, farmers, and individuals in markets that larger national banks often underserve. However, their credit risk assessment practices have traditionally relied on conventional bureau scores and limited financial data, which can lead to suboptimal lending decisions for borrowers with thin credit files or unconventional financial profiles. This paper proposes a machine learning enhanced credit risk assessment framework specifically designed for community and regional banks, integrating alternative data sources with predictive analytics to improve lending decisions. The framework combines traditional credit bureau data with alternative signals including transaction history, utility and rental payment records, business cash flow patterns, and digital footprint indicators. A layered modelling approach uses gradient boosting for primary risk scoring, neural networks for sequential behavioural analysis, and explainability tools to support regulatory and operational requirements. Empirical evaluation was conducted on a simulated lending portfolio reflecting community bank characteristics, with outcomes measured against conventional credit scoring baselines. Results show that the proposed framework improved discrimination performance from an AUROC of 0.74 to 0.86, reduced the false rejection rate for creditworthy applicants by 31 percent, and increased approval rates for thin-file borrowers by 28 percent while maintaining default rates at acceptable levels. The findings demonstrate that machine learning combined with alternative data offers community and regional banks a credible path to better lending decisions and expanded financial access.
Credit Risk Assessment, Alternative Data, Community Banking, Machine Learning, Predictive Analytics, Financial Inclusion
REDEFINING BUSINESS PROCESS MANAGEMENT: THE SYNERGY OF AI AND INTELLIGENT AUTOMATION
Business process management (BPM) has, for two decades, organised enterprise work around explicit process models, deterministic routing rules, and human judgement at every point the model does not anticipate. Robotic process automation (RPA) extended this paradigm to the desktop, mechanising well-defined, rule-based tasks, but left the harder, judgement-dependent steps — interpreting an unstructured claim narrative, weighing ambiguous evidence, drafting a context-sensitive response — to human operators, capping the share of any given process that automation could realistically absorb. This paper argues that the recent maturation of machine learning for process mining and prediction, combined with the reasoning and language capability of large language models (LLMs), enables a qualitatively different architecture for BPM, in which prediction, judgement, and language understanding are embedded directly into the process runtime rather than bolted on as isolated point solutions. We formalise an AI-augmented process as a conventional workflow extended with a decision-intelligence layer that scores each pending step for predicted outcome and model confidence, a continuous process-learning loop that retrains on operator feedback rather than remaining static after initial deployment, and a human-in-the-loop governance mechanism that routes low-confidence or high-stakes decisions to a human reviewer without silently automating around them. A reference architecture composed of a predictive-monitoring service, a cognitive task-automation layer built on retrieval-augmented LLM inference, an adaptive routing engine, and a review console is described, together with the data-lifecycle, legacy-integration, and explainability considerations required to deploy it responsibly. We evaluate the framework through a nine-month case study of intelligent automation in insurance claims processing, presented through two figures: a comparison of process performance across traditional BPM, RPA-augmented BPM, and the proposed AI-augmented configuration, and a learning-curve and confidence-threshold sensitivity analysis. Straight-through processing rose from 22% under traditional BPM to 79% under the full framework, median cycle time fell from 46 to 6.5 hours, and residual error rate at a high confidence threshold fell below 1.5%, while the sensitivity analysis quantifies the trade-off between automation coverage and human-escalation volume. We conclude with a discussion of the organisational, technical, and governance limitations of the approach and outline directions for adaptive, increasingly autonomous process management that nonetheless preserves meaningful human oversight.
Business Process Management, Intelligent Automation, Robotic Process Automation, Large Language Models, Process Mining, Predictive Process Monitoring, Human-In-The-Loop, Hyper Automation
FRAMEWORKS FOR MITIGATING SUPPLY CHAIN DISRUPTIONS AND MATERIAL PRICE VOLATILITY IN SUSTAINABLE BUILDING CONSTRUCTION
The sustainable building construction sector faces compounding vulnerabilities from globally interconnected supply chains, in which material price volatility, geopolitical disruptions, and sustainability certification requirements converge to impose substantial cost overruns and schedule delays. Industry data consistently indicate that 62–74% of sustainable construction projects experience material-related budget deviations exceeding 12%, with lead-time uncertainty for certified low-carbon materials extending 40–60% beyond conventional substitutes. This paper introduces the Supply Chain Mitigation and Resilience (SC-MR) Framework, an integrated architecture combining ensemble machine learning price forecasting, dynamic multi-tier supplier diversification, real-time disruption monitoring, and sustainability-aligned procurement orchestration. SC-MR models the joint distribution of commodity price trajectories, geopolitical risk indices, supplier capacity signals, and certification-compliance constraints through a gradient-boosted ensemble augmented by a demand-adjusted temporal correction layer. Agent-based discrete-event simulations across 120 projects and 18 material categories demonstrate: Mean Absolute Percentage Error (MAPE) of 4.8% for 4-week price forecasts; on-time material delivery improvement from 61.2% to 91.3%; budget cost deviation reduction from 18.4% to 4.1%; and a 34.6% reduction in embodied carbon attributable to substitution guidance. SC-MR achieves these performance gains while maintaining full alignment with LEED v4.1, BREEAM 2018, and EDGE certification requirements, establishing the first integrated supply-chain resilience architecture validated across sustainability-certified construction contexts.
Supply Chain Resilience, Material Price Volatility, Sustainable Construction, Procurement Optimization, Ensemble Forecasting, Disruption Mitigation, Embodied Carbon, Leed, Breeam, Supplier Diversification, Green Building Materials, Construction Risk Management.