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 ATTENTION DISTANCE PROBLEM: THEORETICAL ANALYSIS OF LONG-RANGE DEPENDENCIES IN TRANSFORMERS
Transformer-based language models have revolutionized natural language processing, but their ability to model long-range dependencies remains a significant challenge. This paper presents a theoretical investigation into what we term the "attention distance problem"—the fundamental limitations of transformer architectures in capturing relationships between tokens separated by large distances in a sequence. We formalize this problem mathematically, analyzing how attention mechanisms degrade as token distance increases, and establish theoretical bounds on information retention across layers. Our analysis reveals that the effectiveness of attention decays at different rates depending on architectural choices, with implications for model design. We introduce analytical models to examine signal propagation through transformers and identify key bottlenecks in multi-head attention mechanisms. Based on this theoretical foundation, we propose principled approaches to improve long-range modeling capabilities without increasing computational complexity. Our findings provide a unified framework for understanding various approaches to extending context length in transformers, offering insights into the design of more efficient architectures for long-context language modeling.
STRICT LIABILITY OR FAULT-BASED REGIMES FOR AI-CAUSED HARM? A DOCTRINAL ANALYSIS ACROSS COMMON LAW AND CIVIL LAW SYSTEMS
Scientists have entered a new scientific era which uses multiple genomic assessment methods to study diseases across all their molecular components. Multi-omics methods which study all genetic material in cancer and complex diseases enable researchers to identify treatment targets and drug resistance mechanisms through advanced methods which assess entire biological systems instead of studying single genetic changes. This paper provides a comprehensive synthesis of current multi-omics methodologies, computational integration frameworks, and their applications in therapeutic target discovery and resistance marker identification. The study investigates the process of creating whole-genome sequencing data, transcriptomic data, proteomic data, epigenomic data, and metabolomic data, which healthcare professionals use to discover targets that can lead to clinical applications. Case studies from lung adenocarcinoma breast cancer and haematological malignancies demonstrate how multi-omics profiling has uncovered resistance mechanisms which targeted therapies face against EGFR inhibitors and HER2-directed agents and BCR-ABL tyrosine kinase inhibitors. We describe the necessary computational infrastructure to integrate multi-omics data and the difficulties of using multi-omics in clinical oncology and the rising use of artificial intelligence for prioritizing multi-omics targets. The paper concludes with a forward-looking perspective on single-cell multi-omics and spatial transcriptomics as next frontiers in precision medicine.
Multi-Omics, Genomic Profiling, Therapeutic Targets, Drug Resistance Markers, Transcriptomics, Proteomics, Epigenomics, Precision Oncology, Machine Learning, Single-Cell Sequencing, Egfr Resistance, Systems Biology
CANCER NANOMEDICINE: UTILIZING THE ENHANCED PERMEABILITY AND RETENTION (EPR) EFFECT TO DELIVER HIGH PAYLOADS OF CHEMOTHERAPEUTIC AGENTS DIRECTLY TO TUMOR SITES
The enhanced permeability and retention (EPR) effect serves as the primary scientific basis which supports nanomedicine methods for cancer drug delivery. The EPR effect which Matsumura and Maeda first described in 1986 occurs because solid tumors develop abnormal blood vessels and do not function properly to drain lymphatic fluids. The study investigates how biological mechanisms of EPR effect to control nanoparticle platforms show different clinical application results. The study investigates how passive EPR-based targeting is improved through three methods which include active ligand-mediated targeting and stimuli-responsive release mechanisms and surface engineering techniques that use PEGylating and charge modulation. The analysis shows that EPR heterogeneity between different tumor types and patient groups together with immunological nanoparticle clearance and tumor microenvironment obstacles create a major difference between preclinical research results and actual clinical outcomes. The study evaluates new methods for improving EPR utilization which include vascular normalisation methods and ultrasound-based permeation techniques and artificial intelligence-based formulation design methods. The study ends with a perspective which describes how EPR-based nanomedicine will develop into customized cancer treatment.
Cancer Nanomedicine, Epr Effect, Nanoparticle Drug Delivery, Chemotherapy, Tumor Vasculature, Liposomes, Polymer Nanoparticles, Pegylation, Active Targeting, Tumour Microenvironment
AN ANALYSIS OF ECO COMMUNITY TOWER FOR SUSTAINABLE LIVING THROUGH VERTICAL FARMING APPROACH
The Eco Community Tower for Sustainable Living is a project involving waste-to-energy vertical farming systems with renewable energy assuming a site near the Pallikaranai marshlands in Chennai, India. This study focuses on the impact of unregulated bio-waste dumping, the loss of biodiversity, and marsh groundwater contamination.This paper presents a thorough experimental and analytical study of the proposed Eco Community Tower. First, bio-waste samples collected from the Pallikaranai marshlands were examined for their organic constituents, decomposition rates, and potential to generate methane. Laboratory anaerobic digestion systems indicated that one ton of processed marsh bio-waste can produce 90 – 110 m3 of biogas allowing the generation of 180 – 220 kWh of electricity from biogas burner generators.Along the same lines, Computational Fluid Dynamics (CFD) simulations worked on the tower's aerodynamic features and wind-resistance systems confirming 25 – 30% drag force reduction from conventional high-rise forms. Vertical farming modules were kept under controlled hydroponic systems where the average yield was 95 kg/m2/year, almost 24 times greater than traditional outdoor farming. In addition, models for rainwater aqueducts and wastewater recycling systems suggest that 65 – 70% of the freshwater needed is saved due to the systems in place.The study highlights the possibility of including waste-to – energy systems, vertical farming and biomimetic architectural approach systems within a single model of an urban high – rise. This research provides a quantitative model for policymakers, architects, and urban planners to practice high-rise ecosystems in sensitive ecological regions, achieving self-sufficient energy, food security, and environmental restoration.
Sustainability, Biomimicry, Renewable Energy, Waste Management, Eco-Friendly Architecture
REAL-TIME FRAUD DETECTION IN HEADLESS COMMERCE USING FEDERATED LEARNING
The present-day digital retail world has adopted headless commerce architectures which separate their online display systems from their backend business functions through their API-first system structure. The new system design creates difficulties for fraud detection because transaction data gets dispersed through various API connections and merchant sites, and e-commerce platforms maintain strict data protection standards which block organizations from collecting customer usage patterns that standard fraud detection methods need. The paper presents a real-time fraud detection system for headless commerce environments which uses a federated learning framework to operate. The system enables merchants to train their models together while keeping their transaction information protected, and it achieves inference times under 100 milliseconds through its edge-based model deployment system. The research results show that federated machine learning systems enable organizations to protect their private data and meet regulatory requirements while maintaining security through their advanced privacy features.
: Federated Learning, Fraud Detection, Headless Commerce, Composable Architecture, Differential Privacy, Secure Aggregation, Real-Time Inference, Api Security, Fedavg, Machine Learning Privacy
Jagadeesh Sundaramoorthy, Dr.S.Kayalvili
AN OPTIMIZED WAY OF ANALYSIS AND PREDICTION OF INTRUSION DETECTION CICIDS2017 DATASET USING ML BASED STACKING CLASSIFIER
The growing security threat of cyberattacks requires advanced intrusion detection systems capable of accurately identifying diverse threats with minimal false alarms. This study presents an optimized methodology for analysing and predicting network intrusions using the comprehensive CICIDS2017 dataset, which features realistic, contemporary network traffic. The current ML based algorithm includes CAT boost, Logistic Regression, Random Forest lacks Detection accuracy and efficiency parameters. The core detection mechanism deployed in this research employs a stacking ensemble classifier, a hybrid approach, utilizing high-performing, diverse models imbibing LightGBM+XGBoost+SVC. The implemented stacking classifier provides extensive results with Accuracy 99.9%, Precision 99.8%, Recall 99.7%, F1 Score 99.6 on provided input data which seems to be enumerate higher performance than the current available methodologies.
(IDS), Stacking Ensemble Classifier, CICIDS2017 Dataset, LightGBM, XGBoost, (SVC).
K.Sankar Ganesh, Dr.L.Arokia Jesu Prabhu, Dr. Andrews S
THE IMPACT OF NARRATIVE DISCLOSURE ON MARKET VALUE: THE MEDIATING ROLE OF FINANCIAL PERFORMANCE
Objective: This study examines the effect of narrative disclosures on market value and the mediating role of financial Performance in Iraq, based on a sample of listed banks in ISX from 2018 to 2024. In contrast to previous studies, which typically focus on a single side of narrative disclosures, this study explores multiple effects. Design/methodology/approach: The research design is a quantitative approach. Using the content analysis method regarding narrative disclosure, which utilizes a structured checklist for data generation, on the other hand, using the financial ratio analysis method for Financial Performance and Market Value indicators. The study develops hypotheses using the agency, signaling and stakeholder theories. Findings: Using regression analysis, the study examines the influence of narrative disclosures on market value and financial Performance. The results indicate that banks with more disclosure dimensions achieve higher market value. However, various models validate the explanatory power of narrative disclosures, highlighting their role in enhancing firm value by positively influencing overall financial performance. Empirical implications: The findings provide insights into how narrative disclosures can help banks' stockholders overcome transparency and symmetry of information deficiencies in making more discretionary disclosure related to future earnings of shares and general bank performance. Originality/value: This study analyses the influence of six aspects of narrative disclosure on market value and financial performance in Iraqi commercial banks.
Narrative disclosure, Market value, Financial Performance, Iraqi banks and content analysis.
Naji Afrasyaw Fatah, Dr. Naseem Yousif Hanna Allallo
AUTONOMOUS WORKFLOW OPTIMIZATION USING MULTI AGENT AI SYSTEMS AI AGENTS MANAGE STATIONS, WIP, AND TASK HANDOFFS
Industrial operations have adopted artificial intelligence because it enables them to create automated production systems that continuously enhance their efficiency through multi-agent AI systems. The existing methods for workflow management depend on unchanging scheduling systems and human operators, which cannot handle the unpredictable and changing conditions that businesses encounter in their actual operations. The research presents and evaluates a system design, which uses autonomous AI agents to oversee workstation operation, WIP inventory control, and task transfer management throughout complex manufacturing and service operations. The proposed system, which uses reinforcement learning and game-theoretic negotiation and distributed consensus mechanisms, achieves better results in throughput and cycle time and resource utilization than standard methods. The results from experimental simulations and real-world deployment case studies show that multi-agent coordination decreases average cycle time by 34% and WIP accumulation by 27% while increasing station utilization to more than 91%. The paper explores three aspects, which include emergent coordination behaviors and failure resilience and the ethical implications that arise from using autonomous agents for decision-making in essential workflow operations.
Autonomous workflow optimization, multi-agent AI systems, reinforcement learning, WIP management, task handoff optimization.
Hima Bindu Lekkala, VishnuVardhan Bandari
THERANOSTICS: INTEGRATING DIAGNOSTIC IMAGING AGENTS AND THERAPEUTIC DRUGS INTO A SINGLE MULTIFUNCTIONAL NANO-PLATFORM FOR REAL-TIME MONITORING OF TREATMENT
The field of Theranostics which combines treatment methods with diagnostic imaging through a compact nanoscale technology platform has emerged as one of the most significant advancements in current precision medicine practices. Through their ability to deliver therapeutic and imaging contrast materials via a single designed nanocarrier system theranostic platforms permit doctors to monitor drug distribution throughout the body while tracking tumor treatment results in real time which resolves the timing and location problems found in standard diagnostic procedures that follow the treatment path. The paper presents an extensive analysis of multifunctional theranostic nanosystem design principles and nanoplatform structures and imaging techniques and responsive drug delivery systems and their potential for clinical application. The study investigates fundamental classes of nanoplatforms which include superparamagnetic iron oxide nanoparticles (SPIONs) and liposomes and polymeric nanoparticles and gold nanostructures and quantum dots and hybrid systems together with their applications in various imaging techniques such as MRI and PET and CT and fluorescence and photoacoustic and ultrasound imaging. The paper discusses emerging problems which affect biocompatibility and regulatory approval and manufacturing scalability and clinical integration. The paper presents its argument that theranostic technology development will progress through AI-powered closed loop nanoplatforms which use real time imaging data to adjust treatment methods thereby creating a new form of customized medical care which uses visual diagnostics.
Theranostics, Nanoplatform, Drug Delivery, Real-Time Monitoring, Mri, Pet Imaging, Spion, Liposome, Tumor Microenvironment, Multimodal Imaging, Precision Oncology, Stimuli-Responsive Release
IMMUNOTHERAPY AND CELL THERAPY: DEVELOPING CAR-T CELL THERAPIES AND OTHER IMMUNE-BASED TREATMENTS FOR CANCER AND AUTOIMMUNE DISEASES
The introduction of Chimeric Antigen Receptor T-cell (CAR-T) treatment as a new method for treating blood cancers has developed into a successful medical solution that now shows potential for treating solid tumors and autoimmune disorders. This review presents an analysis of CAR construct molecular structures through five generational stages and examines how FDA/EMA-approved CAR-T products perform clinically while showing their safety profile and upcoming development of cellular therapies which use TCR-engineered T cells and Natural Killer (NK) cell therapies and Tumour-Infiltrating Lymphocyte (TIL) therapy and regulatory T cell (Treg) adoptive transfer for autoimmune conditions. We assess 28 key clinical trials results which include 4890 patient outcomes through response rates and complete remission rates and progression-free survival time and toxicity assessments of CRS and ICANS. The review evaluates upcoming solutions which will solve existing challenges in antigen escape and T-cell exhaustion and solid tumor penetration and manufacturing scalability. The review of CAR-T and related platforms for treating autoimmune diseases focuses on systemic lupus erythematosus and multiple sclerosis and refractory rheumatoid arthritis by discussing results from recent phase I/II clinical trials which showed long-lasting complete recovery. The study provides a translational roadmap which lists essential scientific manufacturing regulatory and health-economic barriers that must be resolved before global cellular immunotherapy treatment can reach its complete healing abilities.
CAR-T cell therapy; adoptive cell transfer; chimeric antigen receptor; tumour immunotherapy; checkpoint inhibitors; cytokine release syndrome; autoimmune disease; NK cell therapy; TIL therapy; immune tolerance
MECHANISMS DRIVING IMMUNOTHERAPY RESISTANCE IN COLORECTAL CANCER LIVER METASTASES
Colorectal cancer liver metastases (CRLM) represent one of the most immunologically hostile environments encountered in clinical oncology which prevents most patients from benefiting from immune checkpoint inhibitors (ICIs). While microsatellite instability-high (MSI-H) tumors respond favorably to anti-PD-1 therapy the predominant microsatellite stable (MSS) subtype which accounts for 80-85% of cases shows almost complete resistance to immunotherapy in liver metastatic cases. The review presents the existing research evidence about ICI resistance mechanisms in CRLM which include hepatic immune tolerance mechanisms and tumor-specific molecular changes and immune cell dysfunction and stromal changes and metabolic changes and systemic immune system loss. The main resistance factors stem from the immunosuppressive activities of Kupffer cells and hepatic stellate cells and liver sinusoidal endothelial cells which create a barrier that prevents T-cells from entering the tumor through WNT/β-catenin pathways and TGF-β which inhibits cytotoxic immune responses and the tumor environment which leads to higher regulatory T cell and myeloid-derived suppressor cell expansion and the metastatic site which causes PD-L1 to be incorrectly increased. The article examines how current approaches which target these mechanisms through LAG-3 inhibition and VEGF/VEGFR co-blockade and TGF-β pathway antagonism and locoregional liver-directed therapies will be implemented in clinical practice. The design of effective combination treatment plans for transforming immunologically cold liver metastases into treatable lesions requires an understanding of resistance mechanisms.
Colorectal Cancer Liver Metastases (CRLM), immune checkpoint inhibitors, immunotherapy resistance, tumor microenvironment, TGF-β, WNT/β-catenin, Kupffer cells, regulatory T cells, microsatellite stable, hepatic immune tolerance
DATA POISONING AND MODEL INTEGRITY THREATS IN AI SYSTEMS
The quick expansion of Artificial Intelligence (AI) systems to vital infrastructure systems and medical facilities and financial institutions and systems that operate without human input has created new security threats which differ from the common software security issues. Data poisoning and model integrity attacks represent two of the most dangerous threats because they attack machine learning (ML) pipeline systems at their most essential components. Data poisoning attacks work by damaging training data to create hidden harmful activities which can stay undetected for multiple years. The security of deployed AI systems gets further compromised through model integrity threats which include backdoor insertion and model stealing and Trojan attacks. the classification system and operational methods and real-world effects and methods to reduce data poisoning and model integrity threats. AI systems require organizations to adopt a complete security system which protects all stages of machine learning from data handling to processing data. The paper ends with research opportunities that remain open and it presents a plan to develop reliable and secure artificial intelligence systems for use in environments where adversaries operate.
Artificial Intelligence (AI), Data Poisoning, Model Integrity, Trojan attack
PREDICTING FURFURAL CHANGES IN POWER TRANSFORMERS DUE TO SHORT-CIRCUIT CURRENTS
Power transformers are among the main equipment for transferring electrical energy from production fields to consumption fields. To ensure the health of the cellulose insulation of these equipment, oil sampling was performed and the amount of furfural in the oil was determined by liquid chromatography.In this study, the prediction of the amount of furfural changes in power transformer oil due to the passage of short-circuit currents and the age of the power transformers at the time of the fault has been addressed. For this purpose, 18 power transformers were investigated over a period of 11 years and the number and intensity of short-circuit currents, the age of the power transformer during the fault, and the changes in furfural in each power transformer oil sampling period were collected. By statistical analysis and comparison of machine learning models, the best model for predicting furfural changes was obtained based on the highest coefficient of determination and the lowest mean absolute error and root mean square error. The accuracy of this model was proven by examining two 230 kV and 160 MVA power transformers with different lifetimes, as well as applying a short circuit and measuring the amount of furfural in the oil of a 400 kV and 315 MVA power autotransformer in a high-voltage substation. In addition to creating a new framework for assessing the condition of transformers, this study allows analysts to predict the amount of furfural in power transformer oil without the need for sampling and testing.
Power transformer, power transformer lifetime, furfural, machine learning.
Amir Reza Amini, Seyed Mahmoud Modaresi, Tahereh Daemi, Seyed Ziya Mazloom
GENERIC 5G NR LDPC ENCODER ARCHITECTURE OPTIMIZED FOR AREA AND THROUGHPUT
The deployment of fifth-generation wireless communication systems demands efficient error correction mechanisms capable of supporting diverse data rates and reliability requirements. Low-Density Parity-Check codes have emerged as the primary forward error correction technique for 5G New Radio data channels due to their superior performance and flexible parallelization capabilities. However, existing LDPC encoder implementations face significant challenges balancing hardware complexity, silicon area consumption, and throughput requirements across the wide range of code rates and block lengths specified in 5G standards. This paper presents a novel generic LDPC encoder architecture specifically optimized for both area efficiency and high throughput operation. Our design employs an innovative shift register network combined with optimized memory management strategies that reduce redundant computations while maintaining full compatibility with all 5G NR LDPC base graphs. The proposed architecture achieves 35% area reduction compared to conventional parallel implementations while delivering throughput exceeding 10 Gbps for typical code configurations. Synthesis results using 28nm CMOS technology demonstrate that our encoder consumes only 0.82 mm² silicon area while operating at 500 MHz clock frequency. The architecture supports all code rates from 1/3 to 8/9 and information block lengths from 40 to 8448 bits as specified in 3GPP Release 15 standards.
5G NR, LDPC encoder, hardware architecture, area optimization, throughput, error correction coding, wireless communication, base graph
Yogesh Rethinapandian, Arun Karthik Sundararajan, Kaushik Kumar, Sanjanaa Sridhar, and Smrithi Prakash
AGENTIC AI-BASED SECURE MULTI-CLOUD ENVIRONMENT WITH HOMOMORPHIC ENCRYPTION AND ADAPTIVE BANDWIDTH OPTIMIZATION
The proliferation of multi-cloud architectures has introduced significant security and performance challenges for enterprise organizations. This research presents a novel framework integrating agentic AI with homomorphic encryption and adaptive bandwidth optimization to address these challenges. Traditional multi-cloud environments struggle with data security during computation and inefficient resource allocation across heterogeneous cloud platforms. Our approach employs autonomous AI agents that dynamically manage encrypted data processing while optimizing bandwidth allocation based on real-time network conditions and workload characteristics. The framework implements fully homomorphic encryption (FHE) to enable secure computation on encrypted data without decryption, eliminating vulnerability windows during processing. Concurrently, AI agents continuously monitor network performance metrics and adjust bandwidth allocation to minimize latency and maximize throughput. Through experimental validation across AWS, Azure, and Google Cloud Platform, we demonstrate that our framework achieves 43% reduction in data exposure risk, 37% improvement in bandwidth utilization efficiency, and maintains computational overhead within acceptable limits of 15-18% compared to unencrypted operations. The agentic approach proves particularly effective in handling dynamic workload variations, automatically redistributing computational tasks and network resources without human intervention. This research contributes both to cloud security literature and practical implementation guidance for organizations seeking to leverage multi-cloud strategies without compromising data protection or performance.
Agentic AI, Multi-Cloud Security, Homomorphic Encryption, Bandwidth Optimization, Cloud Computing, Autonomous Systems, Data Privacy.
Dr N Sandeep Chaitanya, Dr. Alexei Souri, Dr Alvin Chan’s
APPLICATION OF HOMOMORPHIC ENCRYPTION AND ADAPTIVE BANDWIDTH OPTIMIZATION
Modern distributed computing environments face critical challenges balancing data security with operational performance, particularly as regulatory frameworks increasingly mandate privacy-preserving computation. This research investigates practical applications of homomorphic encryption combined with adaptive bandwidth optimization techniques to enable secure, efficient data processing across distributed infrastructures. Traditional encryption approaches require data decryption before computation, creating vulnerability windows that expose sensitive information to potential breaches. Homomorphic encryption addresses this limitation by enabling mathematical operations directly on encrypted data, eliminating decryption requirements during processing. However, computational overhead from homomorphic operations ranges from 10x to 1000x compared to plaintext processing, creating severe performance bottlenecks that limit practical adoption. This study develops an integrated framework combining fully homomorphic encryption (FHE) with dynamic bandwidth optimization algorithms that intelligently manage network resources based on real-time encryption overhead and workload characteristics. Through experimental validation across healthcare data analytics, financial transaction processing, and cloud computing scenarios, we demonstrate that strategic integration of these technologies reduces computational overhead to 18-22% while maintaining complete data confidentiality throughout processing lifecycles. Our adaptive bandwidth allocation algorithm improves network throughput by 41% compared to static configurations by prioritizing latency-sensitive encrypted operations and scheduling bandwidth-intensive transfers during optimal network conditions. The framework proves particularly effective for compliance-sensitive industries requiring GDPR, HIPAA, or PCI-DSS adherence where data exposure risks carry substantial legal and reputational consequences. This research contributes both theoretical understanding of homomorphic encryption performance characteristics and practical implementation guidelines for organizations seeking to operationalize privacy-preserving computation at scale.
Homomorphic Encryption, Bandwidth Optimization, Privacy-Preserving Computation, Data Security, Network Performance, Distributed Systems, Compliance.
The migration to cloud computing promised unlimited scalability, reduced capital expenditure, and operational flexibility. Organizations enthusiastically adopted cloud platforms, moving workloads from on-premises data centers to AWS, Azure, and Google Clo
Cloud computing has revolutionized enterprise IT infrastructure, yet escalating costs and resource inefficiencies threaten to undermine these benefits. This research examines FinOps-driven optimization models that enable organizations to balance cloud performance, cost efficiency, and business value. The study addresses the critical challenge enterprises face in managing cloud expenditures while maintaining operational excellence. Through comprehensive analysis of FinOps principles and practical optimization frameworks, we develop models that integrate financial accountability, technical efficiency, and business alignment. Our research demonstrates that organizations implementing structured FinOps practices achieve 25-40% cost reductions without compromising application performance. The study contributes both theoretical frameworks for understanding cloud financial management and practical implementation strategies for enterprise contexts. Findings reveal that successful cloud optimization requires cultural transformation alongside technical improvements, with cross-functional collaboration between finance, engineering, and business teams proving essential. This work provides organizations with actionable guidance for implementing FinOps practices that transform cloud spending from uncontrolled expense into strategic investment.
FinOps, Cloud Cost Optimization, Enterprise Applications, Cloud Financial Management, Resource Efficiency, Cloud Economics, Cost Governance
PREVENTING BIAS IN AI-BASED DECISION-MAKING: ANALYZING TECHNIQUES TO REMOVE UNFAIR PREJUDICE IN ALGORITHMIC OUTCOMES
Artificial intelligence systems increasingly influence critical decisions affecting human lives, from loan approvals to criminal sentencing. However, these systems often perpetuate and amplify existing societal biases, leading to discriminatory outcomes against marginalized groups. This research examines the sources of bias in AI decision-making systems and evaluates techniques for detecting and mitigating unfair prejudice in algorithmic outcomes. We analyze both pre-processing methods that address biased training data and post-processing techniques that adjust model outputs to ensure fairness. Through experimental evaluation of multiple bias mitigation strategies across different application domains, we demonstrate that hybrid approaches combining data correction with algorithmic fairness constraints achieve superior results compared to single-method interventions. Our findings reveal that while technical solutions can significantly reduce measurable bias, they require careful calibration to balance fairness with accuracy. The research provides practical guidance for organizations deploying AI systems in high-stakes contexts where fairness is essential for ethical and legal compliance.
Algorithmic bias, fairness in AI, bias mitigation, discriminatory AI, ethical artificial intelligence, algorithmic fairness, machine learning bias
AI-DRIVEN PREDICTIVE RISK MODELING TO IMPROVE COST EFFICIENCY AND SYSTEM RESILIENCE IN U.S. HEALTHCARE INFRASTRUCTURE
The United States healthcare system faces unprecedented challenges related to escalating costs, resource allocation inefficiencies, and systemic vulnerabilities exposed by recent public health crises. This research explores how artificial intelligence-driven predictive risk modeling can transform healthcare infrastructure by enhancing cost efficiency and system resilience. Traditional reactive approaches to healthcare management have proven inadequate for addressing complex, interconnected risks that span clinical, operational, and financial domains. We propose an integrated AI framework that leverages machine learning algorithms, real-time data analytics, and predictive modeling to identify vulnerabilities before they escalate into crises. Our research examines current risk modeling practices in U.S. healthcare systems, identifies critical gaps in predictive capabilities, and develops a comprehensive framework for implementing AI-driven solutions. The study demonstrates that predictive risk modeling can reduce preventable hospital readmissions by up to 25%, decrease operational costs by 18%, and improve resource allocation efficiency during demand surges. By analyzing patterns across clinical outcomes, supply chain dynamics, and financial performance, the proposed AI systems enable healthcare organizations to shift from reactive crisis management to proactive risk mitigation. This work contributes both theoretical frameworks for understanding healthcare system vulnerabilities and practical implementation strategies for AI-driven predictive analytics.
Artificial Intelligence, Predictive Analytics, Healthcare Risk Management, Cost Efficiency, System Resilience, Machine Learning, Healthcare Infrastructure.
Ispita Jahan, Md Hasan Or Rashid, Sonia Nashid, Khairum Nahar Pinky
ASSESSING CLIMATE CHANGE IMPACTS ON WIND POWER POTENTIAL AND ELECTRICITY GENERATION IN NORTH AFRICA
Reliable long-term planning of wind energy systems requires evaluating the effects of climate change on wind resources, especially in areas with substantial wind potential like North Africa. This study employs high-resolution regional climate models from the CORDEX framework combined with a multi-model ensemble (MME) approach to evaluate projected changes in wind speed and wind power density (WPD) under the RCP 4.5 scenario for the period 2021–2050. When model outputs are compared to ERA reanalysis data, a constant positive bias in simulated wind speeds is found, necessitating the use of bias correction methods. Model results indicate better agreement with observed conditions after rectification. According to the results, there won't be any notable long-term trends in the wind resources across North Africa. Nonetheless, there is evident seasonal variation, with fall displaying more fluctuation and summer displaying the maximum wind potential. Projected wind energy potential is more confident and less unpredictable according to the MME technique.
Wind energy; Wind power density (WPD); Climate change; CORDEX; Multi-model ensemble (MME); Bias correction; North Africa; Seasonal variability
Diaa Eldin Hassan, Fawzia Ibrahim Moursy, Gamil Gamal
DESIGN AND VALIDATION OF A 1300 W HIGH-FREQUENCY PFC–LLC BATTERY CHARGER FOR 48 V LIFEPO₄ ELECTRIC VEHICLE PACKS
Electric vehicles require the development of efficient and reliable on-board and off-board chargers. These chargers should be able to ensure the safe operation of the vehicle’s battery. In this paper, the design and validation of a 1.3 kW high-frequency switched-mode power supply (SMPS)-based electric vehicle battery charger with a wide input voltage range of 190 V to 265 V AC and a closed-loop control for the regulation of the DC-link voltage and the control of the battery charging process are presented. The proposed charger configuration includes a continuous conduction mode (CCM) active power factor correction (PFC) boost converter for current regulation, a high-frequency LLC resonant DC–DC converter for galvanic isolation, and zero-voltage switching (ZVS) of the converter. The proposed charger is designed to operate at a 51.2 V nominal battery voltage, a maximum battery charging voltage of 58.4 V, and a maximum charging current of 20 A. The control strategy of the proposed charger comprises a multi-loop control strategy with a DC link voltage control loop and a current control loop for the PFC converter, a constant current/constant voltage control strategy for the battery charger circuit, and a frequency modulation control strategy for the LLC converter circuit. The analytical results show that the input power factor is always greater than 0.98, and the efficiency is always greater than 93%. It is also shown that the output voltage ripple is within 1%, and the output current ripple is within 2.5%. Various protection schemes are provided for input over current protection, output over voltage protection, under voltage protection, overload protection, and earth leakage protection. The proposed high-frequency PFC–LLC structure provides a compact solution with low switching losses and high efficiency, which is not possible with conventional low-frequency chargers.
EV charger, switched-mode power supply (SMPS), power factor correction (PFC), LLC resonant converter, CC–CV charging, zero-voltage switching (ZVS), high-frequency power conversion, battery charger design.
Ms. Meena Andure, Prof. Neha A Zope, Mr. Om Naik
EXPERIMENTAL INVESTIGATION AND VIBRATION-BASED CONDITION ANALYSIS FOR VARYING LOAD OF GEAR BOX WITH SPUR GEAR ARRANGEMENT TO INCREASE RELIABILITY
For many industrial applications, the dependability of gearbox systems is essential, especially when spur gear combinations are involved. In order to increase the reliability of gearboxes, this study focuses on their experimental examination and vibration-based condition analysis under various load settings. Advanced signal processing techniques are used to collect and evaluate vibration data from a gearbox with spur gear arrangements by applying various load scenarios in a methodical manner. The goal of the study is to pinpoint distinctive vibration signatures connected to various operational loads and fault states. The findings show a significant relationship between load fluctuations and particular vibration patterns, which offers important information for the early identification of possible problems. These discoveries can be used to improve maintenance plans and stop unplanned malfunctions, which will extend gearbox lifespan and operating reliability. This research contributes to the field of predictive maintenance and condition monitoring, offering practical solutions for improving the reliability of mechanical systems in industrial settings.
Gearbox reliability, Spur gear arrangement, Vibration analysis, Condition monitoring, Load variation, Predictive maintenance, Signal processing, Fault detection, Mechanical systems, Industrial applications
Saicharan Suryavanshi, Patil Rupesh J., Jitendra Atmaram Hole, Nilesh S. Navagale
POST-IMPLEMENTATION ERP VALUE REALIZATION: A DECISION INTELLIGENCE FRAMEWORK
Enterprise Resource Planning systems represent substantial organizational investments, yet many organizations struggle to realize expected benefits after implementation. This research develops a decision intelligence framework specifically designed to maximize ERP value realization during the critical post-implementation phase. While extensive literature addresses ERP implementation challenges, significantly less attention focuses on extracting value after systems go live. Our framework integrates data analytics, organizational learning, and strategic decision-making into a cohesive approach that transforms ERP systems from operational tools into strategic assets. Through analysis of post-implementation patterns across multiple organizations, we identify key decision points where intelligent interventions dramatically improve value capture. The framework addresses three critical dimensions: operational optimization decisions that enhance process efficiency, strategic adaptation decisions that align ERP capabilities with evolving business needs, and continuous improvement decisions that sustain value generation over time. Our findings demonstrate that organizations applying decision intelligence principles to post-implementation ERP management achieve 60% higher benefit realization compared to those using traditional approaches. This research contributes both theoretical understanding of ERP value dynamics and practical guidance for maximizing return on ERP investments.
Enterprise Resource Planning, Decision Intelligence, Value Realization, Post-Implementation, Business Process Optimization, Change Management, Digital Transformation
ERP AS A DIGITAL BACKBONE: REDEFINING ENTERPRISE SYSTEMS FOR CONTINUOUS VALUE CREATION
Enterprise Resource Planning systems have evolved from transactional processing tools into strategic digital backbones that orchestrate organizational value creation. This research examines how modern ERP implementations transcend traditional operational efficiency goals to enable continuous innovation, real-time decision-making, and ecosystem integration. Through analysis of contemporary ERP architectures and their impact on organizational capabilities, we demonstrate that successful digital transformation requires reconceptualizing ERP not as a software package but as an adaptive infrastructure supporting diverse business models. Our findings reveal that organizations treating ERP as a digital backbone achieve 35% higher agility scores and 42% faster time-to-market for new capabilities compared to those maintaining legacy perspectives. The study identifies key architectural principles, integration patterns, and governance approaches that enable ERP systems to function as platforms for continuous value creation rather than static operational systems. This research contributes both theoretical frameworks for understanding ERP's evolving role and practical guidance for organizations navigating digital transformation journeys.
Enterprise Resource Planning, Digital Transformation, Digital Backbone, Business Agility, Platform Architecture, Continuous Innovation, Value Creation
FINOPS-DRIVEN CLOUD OPTIMIZATION MODELS FOR ENTERPRISE APPLICATIONS
BLOCKCHAIN-ENABLED TRUST FRAMEWORKS FOR ENTERPRISE INFORMATION SYSTEMS, ESTABLISHING VERIFIABLE TRUST IN DISTRIBUTED ORGANIZATIONAL ENVIRONMENTS
Enterprise information systems increasingly operate in distributed environments where traditional trust mechanisms based on centralized authority prove insufficient. This research develops a comprehensive blockchain-enabled trust framework that establishes verifiable, decentralized trust mechanisms for enterprise systems operating across organizational boundaries. The study addresses critical gaps in current enterprise architectures where trust depends on centralized intermediaries, creating single points of failure and limiting inter-organizational collaboration. Through examination of existing trust models and blockchain capabilities, we propose an integrated framework that combines cryptographic verification, distributed consensus, and smart contract automation to establish trust without centralized control. Our framework enables organizations to verify data authenticity, validate transaction integrity, and confirm participant identity across distributed enterprise ecosystems. Implementation analysis demonstrates that blockchain-enabled trust reduces dependency on intermediaries by 73%, accelerates cross-organizational transactions by 58%, and enhances stakeholder confidence in information authenticity. This research contributes to both theoretical understanding of trust in distributed systems and practical guidance for implementing blockchain-based trust mechanisms in enterprise contexts. The framework addresses real-world challenges in supply chain collaboration, multi-party business processes, and federated data sharing where traditional trust models fail.
Blockchain, Trust Framework, Enterprise Systems, Distributed Trust, Smart Contracts, Cryptographic Verification, Inter-organizational Systems, Decentralized Architecture.
IMPLICATIONS OF QUANTUM COMPUTING FOR ENTERPRISE CYBERSECURITY AND DATA INTEGRITY
Quantum computing represents a paradigm shift in computational capabilities that poses both unprecedented threats and opportunities for enterprise cybersecurity. This research examines the implications of quantum computing advancement on current cryptographic systems, data protection mechanisms, and organizational security frameworks. Through analysis of quantum computing developments from 2019-2024 and surveys of 280 cybersecurity professionals across various industries, this study identifies critical vulnerabilities in existing encryption standards and explores emerging quantum-resistant solutions. The findings reveal that approximately 78% of enterprises remain unprepared for quantum threats, with current RSA and ECC encryption systems facing potential compromise within the next 10-15 years. The research demonstrates that quantum computers could break 2048-bit RSA encryption in approximately 8 hours once sufficiently powerful machines become available, compared to billions of years required by classical computers. This study contributes to cybersecurity literature by providing a comprehensive assessment of quantum threats, quantifying organizational preparedness gaps, and offering practical recommendations for transitioning to post-quantum cryptographic systems.
Quantum computing, cybersecurity, post-quantum cryptography, data integrity, encryption, quantum threats, enterprise security
THE IMPACT OF THE SARBANES - OXLEY ACT (SOX) ON INTERNAL CONTROL EFFICIENCY: AN EXPLORATORY STUDY OF THE OPINIONS OF A SAMPLE OF FIRST-CLASS AUDITORS
The study aims to analyze the impact of implementing the Sarbanes-Oxley Act (SOX) requirements on improving the efficiency of internal control systems within the Iraqi business environment, from the perspective of first-class auditors working in banks and companies listed on the Iraq Stock Exchange. The research's significance stems from the scarcity of Arabic studies, particularly Iraqi ones, that have addressed this topic within a context different from the American environment in which the law originated. The study seeks to assess the suitability of this legislation's requirements to the realities of auditing and control in Iraq. The study adopted a descriptive-analytical approach to construct its theoretical and applied frameworks. Data were analyzed using SPSS software through a questionnaire distributed to a sample of (96) auditors. The results demonstrated a significant positive correlation and impact between compliance with the Sarbanes-Oxley Act requirements and the internal control system. The study recommends that professional and regulatory bodies in Iraq adopt frameworks similar to the requirements of this law, taking into account the specificities of the local accounting environment, to enhance the efficiency and effectiveness of internal control systems.
Sarbanes-Oxley act (SOX), Internal Controls, Coso.
Safaa Nawzad Ahmed, Rizgar Abdullah Sabir Jaf
BENCHMARKING AI-DRIVEN ANOMALY DETECTION: FROM TREE-BASED ENSEMBLES TO GRAPH NEURAL NETWORKS IN IOT AND TRADITIONAL SYSTEMS
The rapid expansion of cyber-physical systems and network-based digital infrastructures necessitates the evolution of intrusion detection mechanisms into more flexible, intelligent, and scalable architectures. Traditional signature-based systems prove inadequate against zero-day attacks, high-volume data flows, and the dynamic nature of IoT-based traffic, as they are limited to reacting solely to known threats. Machine learning and deep learning methods transcend these limitations by strengthening anomaly-based detection processes and identifying unknown attacks through the extraction of statistical models of normal behaviour. Predicated on technical trends from the 2020–2025 period, this study presents a comparative analysis of classical and deep learning approaches and introduces a hybrid methodology addressing contemporary challenges such as data imbalance, explainability, real-time performance, and resource efficiency. Models based on LSTM, CNN, Autoencoder, GAN, XGBoost, and Random Forest were evaluated on extensive datasets, and their generalization capacities were tested across IoT, IIoT, and traditional network scenarios. The results indicate that while no single algorithm maintains dominance across every scenario, multi-layered hybrid models demonstrate high accuracy, low false-positive rates, and strong generalization performance. These findings suggest that AI-supported intrusion detection systems will become a fundamental component of future defence architectures, highlighting the critical importance of developing holistic, multi-stage solutions in this domain.
Anomaly Detection, Deep Learning, Intrusion Detection Systems, Machine Learning, Network Traffic Analysis
Anomaly Detection, Deep Learning, Intrusion Detection Systems, Machine Learning, Network Traffic Analysis.
LFC AND RESERVE PREQUALIFICATION FOR SYNCHRONIZATION WITH CONTINENTAL EUROPE SYNCHRONOUS AREA
This paper presents a comprehensive analysis of the LFC framework essential for the synchronization of EPSs with CE. The primary objective of this study is to ensure and maintain the stability and reliable operation of autonomously functioning EPSs by analyzing the technical requirements and prequalification procedures for FCR and LFC reserves. Numerical simulations were conducted to evaluate the system’s dynamic behavior under disturbance conditions. Results show that after a 100 MW active power loss, the system frequency dropped to 49.82 Hz and was restored to 50 Hz within 9 minutes using FCR and aFRR. A more severe 200 MW loss caused a minimum frequency of 49.68 Hz, requiring additional mFRR activation, with recovery achieved in 18 minutes. Under extreme scenarios (up to 600 MW loss), the frequency exceeded the critical threshold value of 49.2 Hz, with full restoration achieved within 36 minutes through the sequential activation of FCR, aFRR, mFRR, and RR. In addition, long-term simulations confirmed that the FRCE remained within the SOGL-defined Level 1 limit for 75% of the time and within the stricter Level 2 limit for 96% of the time, thereby fulfilling CE quality requirements. The study further examines the prequalification procedures necessary for reserve units, including technical testing, communication interface validation, and energy reservoir management for LER providers. These ensure both technical robustness and non-discriminatory participation in balancing markets. The findings demonstrate that successful synchronization with CE requires not only coordinated reserve activation but also harmonized prequalification rules and continuous performance monitoring, thereby bridging the gap between theoretical models and practical implementation.
Balancing service provider, Electrical Network, Frequency restoration control error, Frequency restoration reserve, Limited energy reservoir, Load frequency control, Synchronization.
Ramūnas Deltuva, Miglė Kriuglaitė, Roma Račkienė, Konstantinas Otas