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.
DEVELOPMENT OF REACTIVE HEAT EXCHANGERS FOR ENHANCED GEOTHERMAL ENERGY RECOVERY (JUNE2024)
Increased interest in geothermal systems due to the rising demand of reliable cleaner energy and low carbon energy has revived focus on conventional heat exchangers where thermal efficiency and energy recovery is restrained. The paper looks at reactive heat exchangers development as a new process of improving geothermal energy production through heat transfer in union with thermochemical and reactive processes. These systems can be used to achieve greater values of effective heat capacities, higher heat transfer rates, and less exergy losses, especially with low-enthalpy geothermal resources, through the integration of reactive media, adsorption processes, or redox reactions within the structure of the heat exchanger. The article is the synthesis of the latest developments in materials, the integration plans of the system, and optimizing their performance of the reactive heat exchanger used in geothermal applications. The important technical issues such as the material durability, reaction control and reservoir- wellbore interactions are also addressed. The results demonstrate that reactive heat exchangers have a high capacity to enhance the performance, sustainability, and scalability of the upcoming geothermal energy systems.
Geothermal energy; Reactive heat exchangers; Thermochemical processes; Heat transfer enhancement; Low-enthalpy resources; Energy efficiency; Sustainable energy systems
A THEORETICAL FRAMEWORK FOR THE USE OF INTERNATIONAL ORGANIZATIONS BY MIDDLE POWERS IN FOREIGN POLICY
Middle powers are neither system-making great powers nor rule-bound small states, yet they can shape outcomes by leveraging international organizations (IOs). Despite extensive scholarship on middle powers and international organizations as separate fields, the theoretical intersection—specifically, how and under what conditions middle powers strategically employ IOs as foreign policy instruments—remains insufficiently theorized. This article develops a theoretical framework that explains how and why middle powers use IOs as foreign policy instruments under conditions of institutionalized, multilayered global governance. Adopting a qualitative theoretical methodology grounded in analytical synthesis and conceptual abstraction, the study integrates insights from middle-power scholarship, the literature on international organizations, and legitimacy theory. Building on middle-power scholarship and the literature on international organizations and legitimacy, the framework rests on three claims. First, IOs function as power multipliers that help middle powers compensate for limited material capabilities. Second, middle powers act with strategic selectivity, investing in organizations whose rules, procedures, and normative profiles align with their goals. Third, influence is generated through three interlocking mechanisms: (i) legitimacy production that lowers political costs and increases acceptance; (ii) agenda-setting and framing that shapes processes and issue definitions; and (iii) coalition-building that creates collective leverage against asymmetries. By integrating rationalist and constructivist insights, the article reframes middle powers as strategic users of institutional authority rather than passive participants. This hybrid approach transcends the rationalist-constructivist dichotomy by demonstrating that middle powers operate simultaneously through the logic of consequences and the logic of appropriateness, with context-dependent weighting between interest-based and norm-based dynamics. The framework offers an analytically portable "reading scheme" for comparative research and specifies scope conditions under which institutional strategies are more or less likely to yield influence. Specifically, the framework exhibits higher explanatory power in highly institutionalized organizations with procedural density and strong normative legitimacy production, while acknowledging its limitations in contexts characterized by great power veto structures, weighted voting systems, or dominant informal mechanisms. It also outlines avenues for future empirical tests across different organizations, issue areas, and contexts. Positioned as a middle-range theory rather than a universal model, this framework aims to provide a conceptually transferable analytical schema that can guide comparative case studies, process tracing, and qualitative comparative analysis of middle power behaviors in diverse institutional settings.
Foreign Policy, International Organizations, Power Relations, Global Governance, Political Influence.
IMPLEMENTATION OF CONGESTION AND COLLISION ALERT SYSTEM IN VEHICULAR AD-HOC NETWORK (VANET)
Major challenges in urban environments include addressing the issue of traffic congestion as well as accidents at intersections. There is strong potential for Vehicular Ad Hoc Networks (VANETs) in providing real-time traffic management, collision avoidance and informative communication between infrastructure and vehicles. An effective Internet of things (IoT) hardware and VANET communication-enabled adaptive traffic management system is proposed in this paper to enhance and facilitate the flow of traffic and safety in-road travel. Using YOLO simulations, this research has proved that real-time adaptive control of traffic signals reduces traffic congestion and waiting times while improving traffic throughput by dynamically adjusting the signal timing based on actual traffic density and queue length. The above real-time system analyzes car density using surveillance cameras installed on the road using Raspberry Pi-based controllers for dynamic control of the traffic light intervals. Moreover, this system also utilizes a TCP-based VANET protocol that identifies possible collisions and sends warning messages to oncoming vehicles. Experimental results demonstrate a 35% reduction in mean waiting time at peak load and a 92% correct alert rate with an average latency of 1.2s, confirming improved flow and safety. The results confirm an effective proposition for the low-cost and scalable system in terms of better urban mobility and street safety towards smarter transport systems.
Vehicular Ad Hoc Networks (VANETs), Vehicle-to-Infrastructure (V2I) communication, Adaptive traffic control, Collision avoidance.
Edna Elizabeth.N, Iniyavai Kooral.A, Harshini.S, Kishore Balajee.M
PREDICTION OF GLOBAL WARMING ON CROP YIELD INTEGRATING EDGE COMPUTING AND KALMAN RICCATI SUPPORT VECTOR REGRESSION
Agricultural production to a large extent depends on weather conditions. The caution of global climate change or global warming has extensively caught the curiosity of researchers as these changes are reporting negative influences on overall crop production. Edge computing in the period of digital evolution, is steadily receiving drive across several industries. In spite of cloud configuration has hitherto played a paramount role in boosting the agricultural sector, edge computing outperforms in terms of both speed and efficiency. While exploiting edge computing technique farmers rely on data to acquire enhanced control over the industry and optimize the effectiveness of their functioning that in turn results in minimized operational expenses too. Edge computing and machine learning can be employed cooperatively in agriculture to enhance data processing and decision-making. In this work an edge computing model is integrated with machine learning technique for analyzing the impact of global warming on agriculture for enhancing crop yield prediction both accurately and in a computationally efficient manner. The method is called, Kalman Riccati Spatio-Temporal Support Vector Regression (KRST-SVR) based Crop yield prediction. With the overall method being split into three layers, the first layer collects the data and is sent to the second layer. The second layer or the edge device performs the pre-processing and feature extraction by employing Kalman Bucy Riccati Filter-based preprocessing and Spatial Temporal Regression based Feature Extraction. With this the edge device sends processed, relevant and valuable data to third layer. The third layer or the cloud server with the aid of Support Vector Regression function performs the classification with minimal error, therefore producing accurate and precise prediction results. Extensive simulation is done using Edge Cloud Simulator and Python programming language and detailed comparison is made with the state-of-the-art work in terms of prediction accuracy, precision, recall, prediction time and prediction error.
Edge Computing, Cloud Server, Kalman Bucy Riccati, Spatio-Temporal Regression, Support Vector.
Vinodh P Vijayan, Biju Paul, Varghese S Chooralil, Rohaya Latip
MODERNIZING LEGACY FINANCIAL PLATFORMS FOR AI ADOPTION: AN ARCHITECTURAL PERSPECTIVE
Financial institutions worldwide grapple with aging technology infrastructures that inhibit their ability to leverage artificial intelligence for competitive advantage. This research examines the architectural challenges and strategic approaches for modernizing legacy financial platforms to enable AI adoption while managing operational risks. Through analysis of migration patterns, refactoring strategies, and risk mitigation frameworks, we identify key architectural principles that facilitate successful transformation. Our study reveals that successful modernization requires a balanced approach combining incremental migration, data architecture restructuring, and parallel system operation during transition periods. The research demonstrates that organizations achieving AI readiness prioritize data accessibility, API-driven architectures, and modular system design over complete platform replacement. We present a comprehensive framework for assessing legacy system AI readiness and designing migration paths that minimize disruption while maximizing technological capability. This work contributes practical guidance for financial technology leaders navigating the complex intersection of legacy systems, regulatory constraints, and emerging AI opportunities.
Legacy Modernization, Financial Technology, AI Readiness, Platform Architecture, System Migration, Risk Management, Digital Transformation
BLOCKCHAIN APPLICATIONS IN BANGLADESH: INTEGRATING AI AND ML FOR ENHANCED SECURITY AND EFFICIENCY
Blockchain technology promises to transform data integrity and transparency across industries. This paper examines how blockchain can be applied in four major sectors of Bangladesh – healthcare, finance, electronic voting, and pensions – to address longstanding challenges. We rewrite and enhance prior analyses with real-world data from Bangladesh and South Asia, and propose technical architectures tailored to each sector. We also explore how AI/ML techniques (for fraud detection, trust scoring, anomaly detection, etc.) can integrate with blockchain networks to further bolster security and trust. Each sector’s section presents a system architecture diagram and discusses implementation considerations. We include performance metrics (e.g. consensus throughput) and adoption trends using actual data, presented via graphs and tables for clarity. The paper provides a comprehensive and up-to-date blueprint for adopting blockchain in Bangladesh’s public and private sectors.
Blockchain, Artificial Intelligence, machine Learning, Security, Transparent applications
Farzana Bente Alam, Ratan Rana, Saikat Baul, AKM Mahbubur Rahman
POLICY-AS-CODE ARCHITECTURAL GOVERNANCE FOR CONTINUOUS COMPLIANCE IN PUBLIC-SECTOR HYBRID CLOUD MODERNIZATION
Public-sector modernization programs increasingly adopt hybrid cloud architectures where cloud-native services coexist with mission-critical legacy systems under strict regulatory, security, and privacy controls. Traditional architectural governance relies on manual compliance reviews, static documentation, and periodic audits, resulting in slow modernization cycles, inconsistent enforcement, and limited audit traceability. This paper proposes a policy-as-code architectural governance framework that expresses regulatory, security, privacy, and operational requirements as machine-verifiable architectural fitness functions integrated into continuous integration and deployment pipelines. The framework enables continuous compliance verification, automated generation of audit evidence, and traceable management of policy exceptions across hybrid and legacy environments. A prototype implementation is realized within a government case-management modernization platform, demonstrating significant reductions in governance review time, improved consistency of compliance enforcement, and enhanced audit readiness. The proposed approach establishes a practical and scalable model for continuous architectural governance in regulated public-sector cloud transformations.
Policy-As-Code, Architectural Governance, Hybrid Cloud, Continuous Compliance, Public Sector, Devsecops, Regulatory Compliance.
A CIDR ROUTING TECHNIQUE FOR PATIENT REAL TIME DIAGNOSIS WITH MILLION SQUARE ROUTING
It is essential that real time medical diagnosis need a Classless Inter-Domain Routing. The CIDR routing with masking helps to dynamically locate the patient with help of Urgent data. Urgent data in CIDR transfer and receive with the help of automatically adjusting IP prefixes and routing tables. It the fastest routing technique for patients and hospitals. Million square routing is a proposed routing technique helps to monitor patients. In million square two IP one for hospital and one for patient. Each patient have unique IP in the hospital to connect. Its faster and secure VPN. The cloud environment with multiple hospital need this routing for rapid connection and it has low response time
GSSB: A NOVEL METHOD FOR PROVIDING A HYBRID DEEP LEARNING MODEL FOR ANOMALY DETECTION IN INTERNET OF THINGS NETWORKS
In recent years, extensive research has been published to detect anomaly in the Internet of Things, but this research has not been able to respond to challenges such as limited coverage of attack types, data imbalance, lack of effective optimization mechanisms, and inefficiency of sensor nodes for deep learning, etc. This paper presents an intelligent and decentralized intrusion detection system for IoT networks using a combination of deep learning techniques, artificial data generation, and optimization algorithms. The main objective is to design a model called GSSB (GAN_SCNN_SHO_BiLSTM) that is able to accurately detect various cyber-attacks, especially unknown and zero-day attacks. In this method, to slove the problem of imbalance data from Generative Adversarial Networks (GAN) are used, after generating augmented data by GAN, the Hurst profile is extracted as a complementary statistical feature by calculating the autocorrelation and stability of the data and added to the feature vector to increase the model's ability to recognize spatial patterns and temporal dynamics. Then complex location features of network traffic with the Stacked Convolutional Neural Network (SCNN) are extracted and the time data analysis was done by the BiLSTM network. Also used to select optimal features and reduce model complexity of the model, the binary version of the Seahorse Optimization (SHO) algorithm is used. The proposed structure is designed in a Fog Computing environment and uses federated learning to preserve data privacy and models trained in sub-nodes are aggregated together. To evaluate the model, experiments were conducted on the dataset Edge-IIoT 2023 and Edge-IIoT, and performance of the model was compared with criteria such as Accuracy, Recall, Detection Rate, False Positive Rate (FPR), and F1 Score. The results showed that the proposed model managed to achieve a F1 score of 99.3%, a recall of 98.6%, and an accuracy of 99.2%, and performed better compared to other reference methods such as CNN-BiLSTM and the federated method. These results demonstrate the ability of the GSSB model to identify advanced threats and improve the security of IoT networks in real-time situations.
Waleed Abdulzahra Jalil, Sima Emadi, Enas Fadhil Abdullah, Sanaz Asadinia
AN AI-DRIVEN ADAPTIVE OPTIMIZATION FRAMEWORK FOR ENHANCING COMMUNICATION THROUGHPUT IN COMPUTER NETWORKS
Modern computer networks face unprecedented challenges in maintaining optimal throughput amid dynamic traffic patterns, varying bandwidth demands, and unpredictable network conditions. Traditional static optimization approaches prove inadequate for contemporary networks where conditions change rapidly and unpredictably. This research proposes an AI-driven adaptive optimization framework that continuously monitors network parameters and dynamically adjusts routing, bandwidth allocation, and congestion control mechanisms to maximize communication throughput. The framework employs machine learning algorithms to predict traffic patterns, identify bottlenecks, and implement real-time optimizations that adapt to changing network conditions. Through comprehensive evaluation across diverse network scenarios, we demonstrate that the proposed framework achieves substantial throughput improvements compared to conventional approaches while maintaining stability and fairness. The research contributes both theoretical foundations for adaptive network optimization and practical implementation strategies applicable to enterprise, data center, and telecommunications networks. Our findings indicate that AI-driven adaptive optimization can increase average network throughput by up to forty percent while reducing latency variability and improving overall quality of service. DOI: 10.46121/pspc.52.4.8
Network Optimization, Adaptive Systems, Machine Learning, Communication Throughput, Congestion Control, Bandwidth Management, Artificial Intelligence
AUTONOMOUS CLOUD RESOURCE OPTIMIZATION USING REINFORCEMENT LEARNING FOR FINTECH MICROSERVICES
The fintech sector's quick adoption of microservices architectures has led to resource cloud management issues never seen before. The older autoscaling models that manage the machines in areas of computing demand are based on the policy of assuming certain threshold values and therefore being reactive in their response. Such traditional policies fail to predict demand accurately and result in either server resources being wasted or services being interrupted. Thus, this research suggests an intelligent cloud resource management system based on reinforcement learning which will tune the compute power for microservices in the FinTech industry. The system consists of three main parts: an RL-based autoscaler that discovers the best scaling policies through a constant interaction with the production workloads, an NLP-based infrastructure code validator that inspects the Terraform and Ansible scripts and gives the go-ahead or not for the deployment, and a deployment risk predictor that uses the past data and anomaly classification. Analyzing the FinTech workloads systematically, we are able to show that RL-based autoscaling not only cuts infrastructure costs by 31% but also increases response time variability by 42% when compared with conventional methods. Moreover, the NLP validator is capable of detecting configuration mistakes with 89% accuracy and thereby eliminating the chances of deployment failures. Additionally, the model that predicts risks can flag deployments with 85% accuracy in terms of those likely to pose high risk before their execution. All these results point to a significant impact on organizations in the financial sector that are trying to maintain operational efficiency, reliability, and compliance in the cloud without incurring extra costs. DOI: 10.46121/pspc.53.3.15
Reinforcement learning, Cloud optimization, Microservices, FinTech, Infrastructure automation, Deployment risk, Autoscaling, NLP validation.
DESIGN AND CONTROL OF A FIRE HAWK OPTIMIZED 51-LEVEL ASYMMETRICAL MULTILEVEL INVERTER FOR IMPROVED STABILITY IN ISLANDED SINGLE-PHASE COMMUNITY MICROGRIDS
This paper introduces a novel 51-level multi-level inverter (MLI) topology, addressing the challenge of minimizing component count in renewable energy systems. The proposed design features a hybrid asymmetrical configuration with reduced switches and Direct Current (DC)sources, enhancing efficiency and reducing complexity. To optimize power extraction from photovoltaic (PV) sources, a Walrus Optimized Recurrent Neural Network (WO-RNN) is employed, accurately predicting the maximum power point (MPP) under varying conditions. A Fire Hawks Optimization (FHO) based Cascaded Tilt Fractional Order Proportional Integral-Fractional Order Tilt Derivative (C-TFOPI-FOTD) controller governs the switching pulses of the MLI, ensuring precise voltage regulation and harmonic reduction. The proposed inverter control employs FHO due to its superior exploration exploitation balance and rapid convergence compared to conventional techniques such as PSO or GA. Simulation results confirm that FHO-optimized switching reduces THD and improves efficiency, validating its effectiveness for this system. In this work, a hybrid system integrates PV and wind power is used as input source. Simulation results, conducted in MATLAB/SIMULINK under an islanded single-phase microgrid with AC load, validate the effectiveness of the proposed system. The simulations demonstrate a low Total Harmonic Distortion (THD) of 1.29% for voltage and 1.78% for current, indicating high power quality. Furthermore, the proposed inverter topology achieves a high efficiency of 95.653%, attributed to reduce switching and conduction losses. These findings confirm the theoretical analysis and practical viability of the proposed 51-level MLI and its associated control strategies for the efficient integration of renewable energy sources into microgrid systems. DOI: 10.46121/pspc.53.4.22
Fire Hawks Optimization, Cascaded Tilt Fractional Order Proportional Integral-Fractional Order Tilt Derivative controller, Walrus Optimized Recurrent Neural Network, Multi-level inverter, solar PV.
QUANTUM-ADAPTIVE NEURAL DEFENSE: A SELF-LEARNING DDOS MITIGATION FRAMEWORK USING ENTROPY-BASED TRAFFIC FINGERPRINTING AND REAL-TIME BEHAVIORAL ANALYSIS
DDoS attacks are getting smarter and use more than one method, which makes it hard for regular defenses to work. Modern mitigation systems have three big problems: they give a lot of false positives when there are real traffic bursts, they can't learn new attack patterns, and they make the user experience worse by adding a lot of latency. This paper introduces an innovative architecture known as QAND (Quantum-Adaptive Neural Defense), which utilizes adaptive neural filtering, quantum-inspired randomization, and entropy-based traffic fingerprinting to tackle these difficulties. There have been three big updates to QAND. To find granular attack detection, a multi-dimensional entropy fingerprinting approach looks at six traffic parameters at simultaneously. The source IP distribution, the destination port patterns, the packet size variation, the inter-arrival length, the TCP flag combinations, and the payload randomization are all examples of these parameters. Second, a quantum-inspired randomness generator that updates every 100 microseconds can produce 2^128 different defense setups. This stops opponents from employing reconnaissance to figure out how the system operates. Third, an adaptive neural filter can change in real time without having to go through offline retraining cycles. It does this by using gradient descent for continuous online learning, which is limited by a memory buffer. We put QAND to the test in the real world with 40Gbps of traffic and attacks including DNS amplification, SYN floods, HTTP floods, UDP floods, and Slowloris attacks. The trials showed that 97.3% of the traffic was correctly recognized under long-term 30Gbps attacks, with a false positive rate of 0.8%. The average latency was about 2.1 milliseconds. When there is a lot of traffic, QAND is 3.1–5.8% more accurate, has 1.3–3.6 times fewer false positives, and has 2.8–21.4 times less latency than other academic systems and commercial solutions like Cloudflare, F5 Advanced WAF, and Imperva. The CPU is consuming 68% of its power at 40Gbps, while the RAM is using 4.9GB, which means that it performs well in production. DOI: 10.46121/pspc.53.4.21
DDoS mitigation, distributed denial of service, quantum-inspired computing, adaptive neural networks, entropy analysis, traffic fingerprinting, real-time machine learning, network security, attack detection, anomaly detection.
Mohammad Mohammad, Aleem Mohammed, Habeeb Vulla, Atheeq C
A SHARPNESS-AWARE FEDERATED LEARNING FRAMEWORK WITH MC-DROPOUT FOR CNN-BASED MALWARE CLASSIFICATION TO MITIGATE OVERFITTING AND MODEL UNCERTAINTY IN HETEROGENEOUS ENVIRONMENTS
Machine learning techniques are not yet widely applicable in the field of malware detection, especially because of data heterogeneity, privacy issues, and the overfitting problem of the model. The presented paper introduces an innovative federated learning framework that combines CNN-based models with Sharpness Aware Minimization (SAM) and Monte Carlo Dropout (MC-Dropout) that overcome these issues. Particularly, the framework is effective operating in decentralized and non-IID settings since it encourages flat minima in the loss landscape, thus boosting model generalization and robustness. Also, predictive uncertainty can be measured due to the utilization of MC-Dropout, a crucial feature when it comes to enhancing the reliability of a decision in the field of cybersecurity. Experimental findings confirm that the suggested method remains considerably superior to the traditional techniques, having high levels of accuracy (up to 99.86%) and precision (99.87%) along with the meaningful estimation of uncertainties. The prospect of unifying the concepts of sharpness-aware optimization and uncertainty quantification under a federated architecture presents a potential solution towards having reliable and privacy-preserved malware detection in heterogenous operational environments. DOI: 10.46121/pspc.53.4.20
Sadeq Thamer Hlama, Abolfazl Gandomi, Zuhair Hussein Ali, Mohammadreza Soltanaghaei
AN ADAPTIVE OPTIMIZATION ALGORITHM FOR ENHANCING COMMUNICATION THROUGHPUT IN REAL-TIME NETWORK SYSTEMS
Modern communication networks face significant challenges in maintaining optimal throughput during dynamic operational conditions such as network congestion, bandwidth fluctuations, and unexpected system outages. This research presents an adaptive optimization algorithm designed to enhance communication speed and reliability in real-time network environments. The proposed algorithm dynamically adjusts transmission parameters based on network conditions, implements intelligent packet routing, and optimizes buffer management to minimize latency and packet loss. Through comprehensive simulation studies and real-world testing scenarios, the algorithm demonstrated a 34.7% improvement in average throughput compared to conventional static protocols. The system successfully maintained stable communication during network disruptions, with recovery times reduced by 42% during simulated outage events. Additionally, the algorithm exhibited remarkable adaptability to sudden traffic surges, maintaining quality of service even under peak load conditions. The findings suggest that adaptive optimization techniques can significantly enhance communication performance in dynamic network environments, making them suitable for applications in IoT systems, cloud computing platforms, and mobile communication networks. This research contributes to the development of more resilient and efficient communication infrastructure capable of responding intelligently to real-time network conditions. DOI: 10.46121/pspc.45.1.1
Adaptive algorithms, communication optimization, network throughput, real-time systems, bandwidth management, packet routing, network resilience.
AI-POWERED PAYMENT FRAUD SIGNATURE GENERATION AND CONTINUOUS RETRAINING METHODS
The trend of payment fraud is not only persistent but also very fast, as criminals are adopting more and more advanced techniques, which are already beyond the detection capabilities of the traditional rule-based systems. The present research introduces a detailed framework for the generation of AI-driven fraud signatures that are constantly retrained and updated according to the real-time detection and occurrence of new fraud patterns. The role of breakthrough machine learning models is shown, which would not only identify fraud signatures within the transaction data but also automatically create detection rules and keep themselves updated continuously without any human intervention. By probing into fraud detection issues in the different digital payment ecosystems, we reveal that the use of automated signature generation gives 67% faster detection time than manual rule creation and also detection rates are increased by 34%. The inclusion of a flexible retraining strategy prevents model performance from becoming ineffective over a longer period of time, which would typically happen within 3-6 months of the initial deployment. In this paper, we offer the world practical framework for the implementation of adaptive fraud detection systems that provide a good mix of accuracy, speedy response, and operational efficiency. The results are of great significance to banks, payment processors, and online retailers who deal with millions or even billions of transactions every day. This research shows that the merging of automated pattern recognition with regular model updates yields the formation of strong fraud defense systems that are able to change as the threats change. DOI: 10.46121/pspc.52.4.7
Payment fraud detection, Fraud signatures, Continuous retraining, Machine learning, Automated pattern recognition, Adaptive systems, Real-time fraud prevention
REPRODUCIBILITY OF RADIOMICS FEATURES IN 68GAPSMA-11 PET/CT IMAGES OF PATIENTS WITH METASTATIC PROSTATE CANCER FOR A PERSONALIZED TREATMENT APPROACH
Background: Prostate-specific membrane antigen= Positron Emission tomography (PSMA PET) images' manual interpretation can lead to a considerable number of missed metastatic Prostate cancer lesions. Therefore, Radiomics has been utilized as a novel high-potential method. However, its features lack sufficient evidence regarding their robustness and reproducibility. Consequently, we opted to assess such features in cases with metastatic Prostate cancer. Materials and Methods: Our study was carried out in a tertiary referral center during 2021 and 2022 on prostate cancer cases undergoing 68 Ga-PSMA-PET/CT imaging. Initially, the PET/CT scan findings were analyzed using Python's PyRadiomics tool to extract first-order and tissue-related characteristics. Then, the segmentation of the said features was manually carried out and classified into their respective categories. Their repeatability was ultimately determined by measuring the Intraclass Correlation Coefficients (ICCs). Results: A total of 150 PSMA-PET/CT images were investigated, leading to the extraction of 101 features, among which 1, 3, and 10 had excellent, good, and moderate repeatability, respectively. Out of all the ICC values, only three were statistically significant. These values were for Low Gray-Level Run Emphasis (LGLRE; ICC = 0.799, p = 0.047) and Long-Run Low Gray-Level Emphasis (LRLGLE; ICC = 0.801, p = 0.045) in the Gray-level run length matrix as well as Low Gray-Level Emphasis (LGLE; ICC = 0.906, p = 0.003) in the Gray-level dependence matrix. Conclusion: Several PSMA-PET/CT-derived radiomics features have significant metastasis-predicting values in metastatic Prostate cancer. However, future studies must assess the agreement between these features and clinical and histological parameters. DOI: 10.46121/pspc.52.4.6
Prostate cancer; Ga-PSMA-PET/CT Radiomics; reproducibility; intraclass correlation coefficient (ICC).
Hadiseh Alimoradi, Kamran Aryana, Seyed Pezhman Shirmardi, Elham Saniei, Mehdi Salehi Barough
A NOVEL REPRODUCIBLE RADIOMICS FRAMEWORK ON PSMA PET/CT FOR LESION BURDEN QUANTIFICATION: TOWARD CLINICALLY ROBUST IMAGING BIOMARKER IN METASTATIC PROSTATE CANCER
Background: Radiomics is a powerful approach in modern cancer management which offers significant potential to transform cancer management through quantitative non-invasive biomarkers in oncology and its clinical application of radiomics features. In metastatic prostate cancer, assessing disease burden is crucial for prognosis and treatment planning .However, it remains unclear whether reproducible radiomic features can reliably reflect lesion burden, including lesion number and total volume. This study developed a novel reproducibility-driven radiomics framework for PSMA PET/CT which evaluates the correlation between stable radiomic features and lesion burden. Methods: In this retrospective study, patients with metastatic prostate cancer who underwent PSMA PET/CT were included. A predefined set of reproducible radiomic features, which had previously been validated for consistency, was extracted from the metastatic lesions. Lesion burden was defined by both the number of metastatic sites and cumulative lesion volume. Correlation analysis were performed using Pearson correlation to assess associations between radiomic features and lesion burden. Results: A total of 117 metastatic prostate cancer patients were analyzed. The median age was 67.92 years. Several reproducible radiomic features demonstrated significant correlations with both lesion number and total lesion volume. The strongest associations were observed for Energy (abdominal lymph) with correlation coefficients ranging from 0.47 Conclusion: Our results show various relations between reproducible radiomic features derived from PSMA PET/CT are associated and lesion burden in metastatic prostate cancer. These findings support the potential role of radiomics as a non-invasive biomarkers for aggressiveness of disease. Furthermore, using Pearson correlation to analyze clinical relevance of these mentioned radiomics features, is an important consideration for users conducting radiomics studies in prostate cancer researches. DOI: 10.46121/pspc.53.4.19
Prostate cancer; Radiomics; PSMA PET/CT; Lesion burden; Reproducible features.
SECURE TEXT HIDING IN AUDIO STEGANOGRAPHY USING SHUFFLED ARNOLD CAT MAP BASED ENCRYPTION AND ADAPTIVE STEP SIZE QUANTIZATION INDEX MODULATION
Audio steganography offers promising solution by enabling embedding of secret messages in audio files. However, achieving high security, robustness against attacks, and minimal distortion of host audio signals remains significant challenge. This research addresses challenges of imperceptibility in hiding sensitive frequency regions of messages ensuring robustness against audio processing and encrypts message for added security. Shuffled Arnold Cat Map (SACM) and Adaptive Step Size Quantization Index Modulation (ASSQIM) are proposed to encrypt secret data and hide text in audio steganography. ASSQIM embeds secret messages by adjusting step size based on frequency analysis. Lower step size is used in sensitive frequency region to manage imperceptibility, whereas larger step size is applied in nonsensitive regions to enhance robustness against attacks. Hidden message is encrypted using SACM from cover audio, which enhances security under different attacks. Experimental analysis shows SACM-ASSQIM obtains high Normalized Correlation (NC) of 0.9999 compared to QIM with reduction in Log Spectral Distance (LSD) from 0.998 to 0.944 representing significance on enhanced robustness and security with audio-text. Additionally, SACM- ASSQIM obtains high Peak-to-Signal-Noise Ratio (PSNR) of 29.4726 dB in high-pass filter attack and 98.3547 dB on TIMIT and GTZAN datasets when determining with audio-image compared to Linear Predictive Analysis (LPA). DOI: 10.46121/pspc.53.4.18
Adaptive Quantization Index Modulation, Audio steganography, Embedding, Hiding text, Shuffled Arnold Cat Map.
PRESENTING AN INTEGRATED MODEL OF RISK MANAGEMENT AND VALUE ENGINEERING WITH EMPHASIS ON THE ROLE OF HUMAN RESOURCES IN ROAD CONSTRUCTION PROJECTS
In road construction projects, optimal risk management and value creation are of great importance. This study presents an integrated model of risk management and value engineering with emphasis on the pivotal role of human resources. The main goal is to improve project performance by identifying and controlling risks, optimizing costs, and improving quality by utilizing human resource capabilities. In this model, using qualitative and quantitative methods, project risks are first identified and prioritized, then by applying the principles of value engineering, solutions are presented to reduce costs and increase efficiency. The results show that the proposed integrated model can lead to reduced delays, resource optimization and increased sustainability of road construction projects. This model, emphasizing the role of human resources, provides a comprehensive framework for effective project management. DOI: 10.46121/pspc.53.4.17
Risk Management, Value Engineering, Human Resources, Road Construction Projects.
Siavash Mobki, Alireza Parvishi*, Peyman Hamidi, Ashkan KhodaBandehLou
PROVIDING A MULTI-LEVEL MODEL FOR RISK MANAGEMENT OF ROAD CONSTRUCTION PROJECTS BASED ON KEY STAKEHOLDERS (A HYBRID APPROACH: META-ANALYSIS AND STRUCTURAL INTERPRETIVE MODELING)
Risk management in road construction projects is of particular importance due to their technical, economic, and social complexities. This study aims to provide a multi-level model for risk management of road construction projects based on key stakeholders. In this study, a hybrid approach of meta-analysis and structural interpretive modeling (ISM) has been used. First, using meta-analysis, key risks of road construction projects were extracted and categorized from previous studies. Then, using structural interpretive modeling, the relationships between risks and the roles of key stakeholders (such as the employer, contractor, consultant, and local community) in managing these risks were analyzed. The results of the study indicate a multi-level model in which project risks are classified into three levels: strategic, operational, and environmental, and stakeholder interactions at each level are identified to reduce the negative effects of risks. This model can be used as a guide for project managers to improve decision-making and reduce uncertainty in road construction projects. DOI: 10.46121/pspc.53.4.16
Risk Management, Road Construction Projects, Structural Interpretive Modeling.
Mohsen Sadeghi Panah, Mohammad Sedigh Sabeti*, Heirsh Soltanpanah, Adel Fatemi
COMPARATIVE STUDY AND PRESENTATION OF RISK MANAGEMENT AND ASSESSMENT MODEL IN THE ARRANGEMENT OF INTERNATIONAL PARTNERSHIP CONTRACTS IN OIL AND GAS PRODUCTION
The aim of this study was to conduct a comparative study and present a risk management and assessment model in the arrangement of international partnership contracts in oil and gas production. The research method in the qualitative part is developmental in terms of purpose and descriptive in nature and comparative studies, and in the quantitative part, considering its objectives, it is an applied research, in which 41 sub-factors were identified along with 6 sub-factors, and finally the importance of each was determined by the fuzzy mapping method. Therefore, the results of the research, the critical factors for the success of risk management of oil and gas contracts are respectively: "stability, attention to governing laws and regulations and obligations" 12 times, "referral to arbitration, environmental uncertainty and contract management" 6 times each, "risk classification, contract life cycle and assessment of competitors" 4 times each. DOI: 10.46121/pspc.53.4.15
Risk management, risk assessment, oil and gas industry.
Mohsen Sadeghi Ahmadvandi, Mohammad Sedigh Sabeti*, Heirsh Soltanpanah, Adel Fatemi
INTEGRATED MODELING OF PRODUCTION, MAINTENANCE, QUALITY, AND INVENTORY IN OPEN SHOPS CONSIDERING UNCERTAINTY AND HUMAN LEARNING
In complex manufacturing environments, simultaneous decision-making in production and maintenance domains is of great importance, since the overall performance of the system is strongly influenced by the interaction among production planning, resource allocation, and maintenance policies. In this study, a multi-objective mathematical model is developed for production and maintenance scheduling in open shop systems, pursuing three main objectives simultaneously: minimizing the total system cost, reducing the makespan, and minimizing downtime caused by equipment failures. To solve this model, the NSGA-II algorithm is employed as an effective metaheuristic approach for multi-objective optimization. Moreover, three scenarios are designed and implemented to analyze model behavior across different system sizes. The obtained results indicate that as the system scale increases, both the average and dispersion of objectives rise, reflecting higher complexity and greater sensitivity of managerial decisions in larger systems. Furthermore, Pareto front analysis reveals the inherent conflict among objectives, as cost reduction is generally accompanied by increased downtime. Nevertheless, the presence of clustered regions on the Pareto front highlights balanced and efficient trade-off solutions. This research can serve as a decision support tool in real manufacturing environments and provide a foundation for the development of more advanced models in the future. DOI: 10.46121/pspc.53.4.14
Production and maintenance scheduling; Open shop systems; Metaheuristic algorithm; Uncertainty; Human learning.
Samira Karimi Sefiddashti, Majid Vaziri Sarashk*, Gholamreza Esmaeilian
OCULAR DISEASE CLASSIFICATION BY USING GLOBAL-LOCAL MULTI-LABEL CLASSIFICATION NETWORK
The timely detection of ocular fundus disease has emerged as a crucial concern in recent decades due to the lack of early-stage symptoms and the potential for severe effects such as blindness. In this context, it is imperative for physicians to accurately diagnose ocular fundus disease during its first phases, a task that is both labor-intensive and demanding. Various machine learning algorithms are employed to provide assistance to doctors through the implementation of helpful actions. Therefore, this study presents an innovative methodology for the classification of fundus pictures, encompassing eight distinct groups that include various pathologies as well as normal cases. The proposed model, referred to as the Global-Local Multi-Label Classification Network, incorporates both global and local perspectives in the learning process. This is achieved by leveraging a vision transformer for global understanding and employing convolutional layers for local analysis. The performance of this model is assessed using the ODIR-5K dataset, and the findings indicate that it outperforms existing state-of-the-art approaches across several measures. DOI: 10.46121/pspc.53.4.13
Classification, Fundus images, Vision Transformers, Deep learning, Transfer learning
Hamed Tayebi, Mehdi Jafari Shahbazzadeh*, Mahdiyeh Eslami
NETWORK SECURITY AND TYPES OF MODERN CYBER ATTACKS ON NETWORKS
Network security in this digital age is of utmost concern for any organization and individual dependent on internet-based technologies. This paper discusses the principles of network security, along with policies and practices and technologies to protect the networks from unauthorized access, misuse, and denial of service. An in-depth analysis is provided for different types of modern cyberattacks, which include understanding malware, phishing, denial of service, man in the middle, SQL injection, zero-day exploits, advanced persistent threats, and IoT-based attacks; these attacks take advantage of existing vulnerabilities in technology, human behavior, and the network infrastructure and represent major risks to data confidentiality, effectiveness, and availability. Effective network security tools of firewalls, encryption, and intrusion detection systems (IDS) evaluated in this paper, but in addition, discusses the emerging trends in other attacks, such as AI-assisted attacks, ransomware as a service, and quantum computing threats. After analyzing the impact of cyber-attacks: financial losses, reputational losses, national security, and critical infrastructure sector effects. The paper also mentions best practices to maintain network security, such as using multilayered security approaches, keeping all software updated, employee training, and using AI and ML in threat detection. Generally, the paper emphasizes the need for global cooperation, the will of governments and regulatory bodies, and the approach towards good encryption, to counter the evolving nature of cyber threats. Using proactive security approaches and working across boundaries in cooperation, organizations will build resilient network infrastructures to defend them against threats posed by 21st-century cyberattacks. The aim of this study is to create a full-fledged understanding of network security challenges and solutions that may come in handy for cybersecurity professionals and stakeholders interested in preserving the digital surroundings.
Network Security, Cyber Attacks, Encryption, AI-Powered Threats, Incident Response.
ZERO-TRUST INTELLIGENT BEAM MANAGEMENT FOR B5G/6G MMWAVE
Present a secure design beam-management framework for B5G/6G mmWave that couples convolutional neural networks (CNNs) for real-time beam selection with elliptic-curve cryptography (ECC) and AES-GCM, governed by a Zero-Trust control loop. The end-to-end pipeline is implemented in ns-3 with realistic channel and mobility models (static, pedestrian, vehicular) and evaluated using beam-alignment time, throughput, packet delivery ratio (PDR), and tail-sensitive latency metrics. Relative to exhaustive search, the CNN reduces alignment time by 60–70%. Enabling ECC adds 1.0–1.4 ms, while Zero-Trust enforcement yields 2.2–2.6 ms end-to-end overhead, predominantly confined to the upper tail (p90/p95/p99, ES₉₅); the distribution’s center (median/MAD) remains essentially unchanged. In vehicular tests, throughput progresses from 680 Mb/s (exhaustive) to 850 Mb/s (CNN), then 810 Mb/s (CNN+ECC) and 795–800 Mb/s with Zero-Trust; PDR stays within 1% of the CNN+ECC baseline. These results indicate that learning-based, crypto-hardened beam management can sustain low alignment delay and continuous trust guarantees under mobility, with operationally bounded overheads in realistic ns-3 settings.
mmWave beam management; CNN beam prediction; ECC; AES-GCM; Zero-Trust Architecture; ns-3.
DYSPROSIUM OXIDE DOPING EFFECTS ON THE PHYSICAL AND ELECTRICAL BEHAVIOR OF PRASEODYMIUM OXIDE-BASED TIN OXIDE VARISTORS
This research explores how doping praseodymium oxide (Pr6O11)-based tin oxide (SnO2) varistors with dysprosium oxide (Dy2O3) impacts their electrical and physical properties. Varistors with different concentrations of Dy2O3 were fabricated using a solid-state reaction method and then analyzed for changes in microstructure, electrical behavior, and thermal stability. The results reveal that the inclusion of Dy2O3 significantly influences grain size, morphology, and high-voltage breakdown characteristics. These adjustments result in altered varistor properties, such as breakdown voltage and nonlinear coefficients. Additionally, Dy2O3 affects sintering behavior and enhances thermal stability. This study offers valuable insights into how Dy2O3 can be used to fine-tune the electrical and physical properties of Pr2O3-based SnO2 varistors, making them better suited for specific applications.
Tin oxide varistors – Dysprosium Oxide Doping- Praseodymium Oxide- Breakdown voltage
Diaa-Eldin H. Fikry, Sayed A. Ward, Diaa-Eldin A. Mansour, Osama A. Desouky, Essam M. Shaalan
YTTRIUM OXIDE DOPING EFFECTS ON THE PHYSICAL AND ELECTRICAL BEHAVIOR OF PRASEODYMIUM OXIDE-BASED TIN OXIDE VARISTORS
In this research, the effect of doping praseodymium oxide (Pr₆O₁₁) based tin oxide (SnO₂) varistors with yttrium oxide (Y₂O₃) on the electrical and physical properties was investigated. Varistor samples of different composition making use of Y₂O₃ were produced, through a conventional solid state reaction route, and assessed using physical property distortion, electrical performance, and thermal behaviour to determine variations. The addition of varying amounts of Y₂O₃ promoted significant alterations to grain growth and surface morphology, including high-voltage breakdown limits affecting important varistor performance parameters like breakdown voltage and nonlinear current–voltage characteristics. Y₂O₃ in this investigation also increased correlation to the sintering kinetics contributing to an improvement in densification and thermal stability indicative of vitrous characterstics. This work describes the use of Y₂O₃ dopant potential to manipulate (i.e. tailor) the functional properties of Pr₆O₁₁ based SnO₂ varistors enabling subsequent application to advanced electronic protection optimization.
Tin oxide varistors – Yttrium Oxide Doping – Praseodymium Oxide – Breakdown voltage – Nonlinear coefficient – Thermal stability.
Diaa-Eldin H. Fikry, Sayed A. Ward, Diaa-Eldin A. Mansour, Osama A. Desouky
EXPLORING THE RELATIONSHIP BETWEEN WORKPLACE AVOIDANCE, JOB SATISFACTION, AND EMPLOYEE WELL-BEING IN MEXICO AND JAMAICA
This study examines the interrelationships between workplace avoidance behaviors, job satisfaction, and overall employee well-being in Mexico and Jamaica. By exploring cultural dimensions, organizational practices, and individual perceptions, the research aims to provide a nuanced understanding of how avoidance strategies influence job satisfaction and well-being in these two distinct cultural contexts. Utilizing a mixed-methods approach, the study integrates quantitative surveys and qualitative interviews to capture a comprehensive picture of employee experiences. The findings highlight significant cultural and organizational differences, offering valuable insights for multinational organizations seeking to enhance employee engagement and well-being across diverse settings.
Workplace Avoidance, Job Satisfaction, Well-Being, Mexico, Jamaica.
Marìa Norma Torres Romero, Arturo Sánchez Sánchez, Cruz García Lirios
THE CRIME OF ABUSE OF AUTHORITY AND BREACH OF LEGAL DUTY UNDER THE PENAL CODE OF THE STATE OF SONORA: A LEGAL AND ETHICAL ANALYSIS OF PUBLIC POWER MISUSE IN MEXICO
This paper analyzes the crime of abuse of authority and breach of a legal duty as defined in Article 180 of the Penal Code of the State of Sonora, Mexico. It explores the ethical and juridical dimensions of the misuse of public power, focusing on how actions or omissions by public officials erode institutional legitimacy and public trust. Using a juridical–comparative approach supported by Mexican jurisprudence and international legal theory, the study examines both the legal structure of the offense and its ethical implications for governance. The findings reveal that both abuse of authority and breach of duty constitute fundamental deviations from the principles of legality and moral responsibility. These crimes not only harm individuals but also weaken democratic accountability and ethical integrity within public administration. The research concludes that the effectiveness of sanctions depends not only on legal enforcement but also on the promotion of institutional ethics, civic participation, and transparent governance. Strengthening these mechanisms is essential to restoring public confidence and ensuring that state authority serves the collective good rather than private interests.
Abuse of authority, breach of legal duty, public ethics, governance, corruption, accountability, Mexico, criminal law.
María del Rosario Molina González, Marco Antonio Velderrain Rodríguez, Jorge Luis Arellano Cruz, Manuel Raymundo Valdez Domínguez