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, 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.
UNDERSTANDING THE RELATIONSHIP BETWEEN DATA GOVERNANCE AND BUSINESS ANALYTICS SUCCESS: A CASE STUDY OF GLOBAL CORPORATIONS
This paper explores the relationship of data governance to the success of business analytics within multinational corporations. With a mixed-methods approach involving both quantitative and qualitative data derived from a Structural Equation Model of the data, the study has revealed the essential aspects of data governance for the success of business analytics. The data for the study was derived through a series of quantitative surveys of data governance officials and surveys sent to a total of 250 firms worldwide. The essential variables that measure the data, such as data quality management, compliance with policies, and the influence of data governance frameworks, were quantitatively measured and analyzed. The findings through the SEM analysis showed a positive relationship between data governance variables and the success of business analytics, with the regression coefficients for data quality management (β=0.42), policy compliance (β=0.36), and the influence of data governance frameworks (β=0.49), all having a significance of <0.001. The quantitative data of the study's findings were further supported and supplemented with a thorough examination of the quantitative data of the critical themes derived through a thorough analysis of the quantitative data of the study's findings derived through a thorough examination of the data of the qualitative themes of the study.
Data Governance, Business Analytics, Data Quality Management, Regulatory Compliance, Structural Equation Modeling (SEM), Decision-Making.
Muhammad Adnan, Md Аsikur Rаhmаn Chy, Emran Hossain, Md Imtiaz Faruk, Farhana Karim, Syed Mohammed Muhive Uddin, Mohammed Majid Bakhsh, Sraboni Clara Mohonta*
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
REPLACING NITROGEN CHEMICAL FERTILIZERS WITH VERMICOMPOST: A SUSTAINABLE STRATEGY TO ENHANCE ROSMARINUS OFFICINALIS L. PERFORMANCE UNDER WATER STRESS
A split-plot experiment using a randomized complete block design was conducted in Karaj, Iran (2023–2024) on Melissa officinalis (lemon balm), focusing on sustainable fertilization and irrigation. Three irrigation regimes were tested: optimal (I1), irrigation after 40% water depletion (I2), and after 55% depletion (I3). Six fertilizer treatments ranged from no fertilizer (F1) to varying mixes of urea and vermicompost (F2–F6). Results showed that higher water stress reduced seed yield, essential oil yield, chlorophyll content, and photosynthesis. Vermicompost improved soil quality and boosted plant biochemical and photosynthetic performance. This highlights the value of eco-friendly practices to enhance medicinal plant productivity while conserving resurces. Plain Language Summary (lemon balm), a valuable medicinal plant. The research aimed to develop sustainable cultivation practices by testing various combinations of water and fertilizers to determine which conditions promote better growth and higher essential oil production. Results showed that increased water stress reduced seed yield, oil content, and chlorophyll levels. However, using vermicompost (worm-produced organic fertilizer) improved plant health, enhanced photosynthesis, and supported better growth even under mild stress conditions.
Rosmarinus officinalis, essential oil, carotenoid, drought stress.
Mehdi masoudi, Farshad Ghooshchi*, Hamid-Reza Tohidi-Moghadam, Meysam Oveysi, Mohammad Nasri
DESIGNING AN E-LEARNING CURRICULUM MODEL BASED ON THE COMPONENTS OF ACTIVE CITIZENSHIP EDUCATION IN THE ELEMENTARY SCHOOL
Background and Objectives: With the expansion of digital technologies, e-learning plays an important role in cultivating active citizens. The aim of this research is to design an e-learning curriculum model based on the components of active citizenship education in the elementary school stage. This study was conducted using a mixed-methods approach. Methods: The qualitative section was conducted using grounded theory, interviews with curriculum planning experts, and analysis of documents and reputable scientific sources. The quantitative section involved collecting data from 207 teachers and school principals in Hamadan province, which were analyzed using confirmatory factor analysis and partial least squares structural equation modeling (PLS-SEM). Findings: The qualitative findings identified barriers to designing and implementing the e-learning curriculum at three levels: causal (e.g., weaknesses in e-learning and critical thinking), contextual (e.g., lack of interactive learning environments and access inequality), and intervening (e.g., cultural resistance and teacher skill gaps). “Active citizenship education in e-learning” emerged as the central concept, requiring transformative strategies, teacher empowerment, and purposeful use of digital technologies. The quantitative results confirmed the model’s validity and structure, with significant relationships between constructs and strong predictive power (R²). Conclusion: The overall conclusion indicates that the proposed model, with precise planning, teacher training, and strengthening of infrastructure, can be an effective step toward cultivating active and responsible citizens in the digital society of the elementary education stage.
E-learning, Digital Technologies, Active Citizenship Education, Mixed-Methods Research.
Zohreh Ghasemi Honar, Farzaneh Vasefian*, Mehdi Keshavarzi
ENHANCING ORGANIZATIONAL RESILIENCE: INTEGRATING CYBERSECURITY RISK MANAGEMENT INTO INFORMATION SYSTEMS GOVERNANCE
This article examines the enhancement of organizational resilience through the integration of cybersecurity risk management into Information Systems (IS) governance. Organizations are finding it harder to keep their operations safe as technology grows more important and cyber threats get more complicated. This study employs systems theory to offer a comprehensive framework that integrates business objectives with IT strategy, highlighting the necessity of a robust and flexible cybersecurity posture. The research utilizes an extensive analysis of contemporary cybersecurity literature, established frameworks, and industry practices, providing a pragmatic guidance for enterprises to efficiently mitigate cyber threats. The suggested Cybersecurity Resilience Framework combines governance principles, ongoing monitoring, stakeholder involvement, and human behavior variables to create a complete solution. The findings of this study reveal that firms employing automated detection systems have an average response time of 20 minutes, in contrast to 31 minutes for those utilizing manual detection methods. Additionally, businesses with automated systems had less downtime (4 hours instead of 6 hours) and less of an effect on their finances ($150,000 instead of $250,000). The study also found that companies that followed recognized frameworks like NIST and ISO were better at finding threats (more than 80% of the time) and lost less money (around 20% of the time). The Return on Security Investment (ROSI) analysis showed that companies that made smart investments in cybersecurity saved a lot of money, with ROSI percentages between 28% and 61%. Also, firms showed that they were better at finding and responding to threats, as shown by their Cybersecurity Effectiveness Scores (CES), which showed that they were ready to do business. In general, this framework gives businesses a strong plan for dealing with the ever-changing world of cybersecurity while keeping their operations running.
Cybersecurity Risk Management, Information Systems Governance, Organizational Resilience, Cybersecurity Resilience Framework, Automated Threat Detection, Return on Security Investment (ROSI)
Syed Nazmul Hasan, Partha Chakraborty*, Md Talha Bin Ansar, Abdullah Al Zaiem, Niropam Das, Ahmed Shan-A-Alahi, Jobanpreet kaur
ANALYSIS OF ECONOMIC GROWTH AND COMPETITIVENESS IN SANTANDER: CHALLENGES AND OPPORTUNITIES
Economic growth and competitiveness are essential pillars for the prosperity of a region. In this context, Colombia faces significant challenges, ranking 64th in the global competitiveness ranking and showing GDP growth rates below 4% in recent years. This research aims to analyze the relationship between the competitiveness and economic growth indicators of Santander in the period 2013-2020. To do so, a non-experimental methodology of a longitudinal, transversal, descriptive and correlational nature is used, with a quantitative approach, using secondary data such as the departmental competitiveness index of the Private Competitiveness Council. The statistical tools include the mean, standard deviation, minimum and maximum values, the Shapiro-Wilk normality test and Pearson and Spearman correlation coefficients as appropriate. The results show a slowdown in Santander's economic growth in recent years, although its GDP per capita exceeds the national average and that of departments such as Antioquia. In terms of competitiveness, Santander stands out compared to Antioquia in areas such as ICT, environment, secondary education and labor market. It is concluded that some competitiveness indicators, especially in public service, health, education, business, labor market, financial sector, domestic market, exports and innovation, are related to Santander's GDP. Keywords: Competitiveness, GDP
Competitiveness, GDP, economy, Santander, public policies.
Isabel Cristina Rincón Rodríguez*, Javier Carreón Guillén, Julio E Crespo, Albeiro Beltrán Díaz, Jorge E. Chaparro Medina
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON RISK MITIGATION IN IT PROJECT MANAGEMENT: A MANAGEMENT INFORMATION SYSTEMS APPROACH
This paper considers the combination of Artificial Intelligence (AI) and the IT project management field of project management risk in the context of Management Information Systems (MIS). In IT projects, risks are identified, analyzed, and responded to with varying degrees of efficiency. This study explores the use of AI tools (including machine learning regression and neural networks) to improve the processes of AI-enhanced tools integrated into established risk management processes. Improved decision-making capabilities are the result. Performance evaluation tools such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) as well as measures of precision, recall and the F1 score were the metrics of the study to measure the efficiency and effectiveness of AI as contrasted with the AI tools and the implementation of traditional risk systems management. AI, with respect to risk management, traditional, and non-AI systems, risk systems management design, measures, time, project success, and overall outcome, significantly AI MIS and risk management project are realized. AI systems, project management tools, and human cooperation, in project deliverables, management timely project streamline outcomes management is more project completion are combined with MIS. This paper considers project risk management and the use of project management AI tools in an organizational context.
Artificial Intelligence (AI), Risk Mitigation, IT Project Management, Management Information Systems (MIS), Risk Identification, Project Success Rate..
Shuchona Malek Orthi, Kazi Bushra Siddiqa*, Urmi Haldar, Md Abubokor Siam, Niropam Das, Partha Chakraborty, Emran Hossain, Foysal Mahmud
MIND-PRINT SECURITY: EEG-BASED BIOMETRIC AUTHENTICATION FOR CRITICAL INFRASTRUCTURE PROTECTION
Critical infrastructure security requires highly developed authentication tools that shield against advanced cyber and physical attacks. Traditional approaches like passwords, access cards, and even more sophisticated things like fingerprints and iris scans are subject to theft, spoofing, or coercion. This paper examines the application of the electroencephalogram (EEG) signal as one of the biometric modalities in ultra-secure authentication in power systems, such as control rooms, substation, or SCADA systems. EEG patterns are non-visible, non-reproducible, and generated internally, making them more secure in high-risk environments. The M3CV EEG dataset was used to develop and test a cognitive-activity-based authentication scheme to generate a unique neural response during mental arithmetic and visual monitoring tasks. The signal was pre-processed using bandpass filtering, artefact elimination, and normalisation, and feature extraction was performed across the time, spectrogram, and time-frequency spectrogram dimensions. Both conventional machine learning (SVM, Random Forest, kNN) and deep models (CNN, LSTM) were evaluated. The findings reveal that deep learning-based models have a significantly higher performance than classical ones, and in particular, LSTM demonstrated an accuracy of 96 % and an Equal Error Rate (EER) of 0.035. Robustness studies supported these findings, showing that EEG authentication resists session-to-session consistency and anxiety-related variability. The results confirm the EEG biometrics' competitive claim to be a next-generation access security tool, commensurate with power protection schemes like IEC 61850 and NERC CIP. SCADA security architecture solution, incorporating EEG-based authentication, can grant resilience to any critical infrastructure against insider threats and sophisticated spoofing attacks.
EEG biometrics, Mind-print security, Critical infrastructure protection, Cognitive-task authentication, SCADA cybersecurity, Power system protection, Deep learning for biometrics.
SRIVASTAVA, Nidhi, POLISHETTY, Rohit Kumar, JAGADAM, Naveen, RAJULAPATI, Veera Siva Prasad
PROPOSING AN ECONOMIC PLANNING MODEL TO DETERMINE OPTIMAL CAPACITY OF A FLEXIBLE WIND POWER PLANT
It is difficult but essential to decide on the cost planning and borrowing method to implement large-scale projects under uncertain time conditions. Considering the unique design of each power plant and the climatic conditions affecting its design and establishment, conducting any research for the design and implementation of special projects such as wind power plants (WPPs) requires the utiliza-tion of uncertain models for planning in order to provide appropriate financial resources and manage the use of WPPs. Tradtional design of power systems is in such a way that offer a flexible context where demand is fulfilled by mass generation. Besides, flexibility required for maintaining load-generation balance is increased by the incorporation of uncertain and variable renewable generation sources, like wind. This manuscript is an attempt to determine the sizing of the WPP capacity along with the amount of storage capacity that can provide the possibility profit for the investor, consider-ing the uncertainties and challenges available in this regard. Thus, it is essential to specify in the planning horizon the amount of load needed by the grid, the wind level of the geographical area, the wind farm’s output power and the interest rate, all of which have major uncertainties. This is possible by presenting a mixed integer model and considering the behavior and characteristics of the storage WPP and the challenges of grid flexibility. To develop this hybrid model, possibilistic programming will be used for optimization.
Flexible wind power plant, Possibility, Necessity.
Mahdi azari, Abdolreza Tavakoli, Alireza Sahab, Hossein Mahdinia Roudsari
LONG-TERM DECISION-MAKING, THE COMPOSITION OF BOARDS OF DIRECTORS: A MULTIVARIATE APPROACH
Companies must constantly adapt to new market requirements. Transformations due to various economic causes create a complex environment. Furthermore, it is known from the literature that economic markets , such as Ecuador, are subject to a low reputation for decisions to protect minority investments. These factors alter investors' perceptions of Ecuador, as they encourage the beginning of capital expropriation. This negative effect delays the growth of the country's capital market. This study performs a multivariate exploration of the decisions made by companies in the composition of their boards of directors. Based on the results, which are relevant to capital creation and boards, it was determined that there is a high positive relationship between the presence of men on the board of directors and the creation of boards. A positive relationship is also established with the presence of women on the board. Despite this, a low correlation is evident with the establishment of companies on the board. This trend persists across different management areas.
Multivariate, Emerging Markets , Boards of Directors, Decision Making, Biplot
MULTI-STAGE OPTIMIZATION OF IEEE 802.11 ACCESS POINTS PLACEMENT USING PARTICLE SWARM OPTIMIZATION BASED ON PATH LOSS
Indoor placement is critical factor in enhancing the performance of wireless networks especially in smart healthcare environments requiring accurate localization and consistent connectivity. This study proposed a multi-stage optimization framework using the Particle Swarm Optimization (PSO) algorithm to optimize the placement of IEEE 802.11 Access Points (APs) based on empirical Path Loss (PL) modelling. Initially Received Signal Strength Indictor (RSSI) data is collected across four zones in a hospital environment. A zone-specific long distance PL model is derived by optimizing the PL exponent and the standard deviation using PSO under both Line of Sight (LoS) and non Line of Sight (NLoS) conditions. Subsequently a second PSO stage determines the optimal AP location to maximize coverage and minimize interference by maximizing Signal to Interference Ratio (SIR) using the optimum PL parameters in the previous stage. The accuracy of measurement data was evaluated by calculating the mean-square error (MSE) between real PL and theoretical PL, as well as the real PL and optimized PL model. The results demonstrate that the measurement data is accurate and the optimized PL model can reduce MSE and build a reliable dataset. The minimum MSE is achieved in zone 1 which is equal to 0.28 dB. On the other hand, the optimum AP locations provide a high coverage area with less interference
Received Signal Strength Indictor (RSSI), WiFI, Path Loss(PL), Particle Swarm Optimization (PSO), Signal to noise ratio (SIR), coverage area , interference, Mean Square Error (MSE).
Rawaa Akram, Negar Majma, Aseel H. Al-Nakkash, Mohammadreza Soltanaghaei
DESIGN AND DEVELOPMENT OF A FUZZY FOURIER FIBONACCI (FFF)-BASED AI SOFTWARE MODULE FOR FUEL CONSUMPTION PREDICTION AT LIQUID FUEL STATIONS: ENHANCING IRAN’S ENERGY SECURITY
This study presents the design and development of an AI-based software module for predicting fuel consumption at liquid fuel stations in Iran, with the aim of strengthening the availability dimension of national energy security. Iran currently consumes over 110 million liters of gasoline per day, with an annual growth rate of about 4.5%, making accurate forecasting essential for sustainable energy management. The study evaluates several forecasting models, including ARIMA, LSTM, CNN, and hybrid approaches, and introduces a novel Fuzzy Fourier Fibonacci (FFF) model. The FFF model, which integrates Fourier Transform, Fuzzy Logic, and the Fibonacci Sequence, demonstrates superior predictive performance, achieving an RMSE of 0.045, MAE of 0.035, and R² of 0.98. In comparison, traditional ARIMA models produced an RMSE of 0.085 and R² of 0.89. These results show that the FFF model more effectively captures both linear and non-linear patterns in consumption data. By enabling more precise forecasts, the proposed module can help optimize distribution across more than 4,000 fuel stations nationwide, reducing shortages and improving reliability. Although renewable energy sources are expanding globally, fossil fuels remain dominant in transportation, making advanced forecasting tools crucial. The proposed AI-driven module offers not only a practical solution for Iran but also a scalable framework for other regions facing similar energy security challenges.
Seyed Mohammad Ziazadeh, Mostafa Panahi*, Ali Jamali Nazari*, Dariush Sardari
CHALLENGES AND TRENDS IN INJURY RECOVERY AND PREVENTION IN HIGH-IMPACT SPORTS: A BIBLIOMETRIC APPROACH
The practice of high-impact sports such as rugby, mountain biking, soccer, and gymnastics has increased significantly in recent decades, generating growing scientific interest in their physical and psychological consequences. Current studies reveal extensive but asymmetrically documented injuries associated with these disciplines, ranging from dental and joint trauma to concussions, muscle imbalances, and psychological disorders linked to pain or rehabilitation. This study seeks to address the research question: How can conceptual term co-occurrence network analysis identify the challenges in recovery and prevention of injuries in high-impact sports? To this end, a bibliometric co-occurrence network analysis was conducted based on 2,006 conceptual terms defined by 1,434 authors. The findings delineate four main clusters: Cluster 1: Prevention of bone and musculoskeletal injuries in high-impact sports—exercise, risk factors, and integral health; Cluster 2: Biomechanics, neuromuscular control, and prevention of lower-limb injuries in high-impact sports; Cluster 3: Concussions, neuroimaging, and cognitive sequelae in high-impact sports; Cluster 4: Knee injuries, tissue regeneration, and rehabilitation strategies in high-impact sports. These results highlight emerging research gaps that can be further explored through advanced quantitative techniques aimed at deepening conceptual structures and defining new constructs.
Injury recovery, Injury prevention, High-impact sports, Bibliometrics, Python.
NEUROEDUCATION AND MENTAL HEALTH IN HIGHER EDUCATION: THE ROLE OF ADDICTIVE BEHAVIORS IN ACADEMIC PERFORMANCE. A BIBLIOMETRIC ANÁLISIS
In the past two decades, the intersection of neuroscience, education, and mental health has given rise to an emerging field of research known as neuroeducation, which seeks to understand how brain processes influence learning and the academic performance of university students. Moreover, the increasing complexity of educational contexts has highlighted the need to integrate interdisciplinary perspectives that bring together psychology, pedagogy, and biomedical sciences. The present study aims to address the question: What trends emerge from the co-occurrence network of terms in the scientific literature on neuroeducation and mental health in higher education, and how are these trends related to the role of addictive behaviors in academic performance? To this end, a bibliometric co-occurrence network analysis was conducted on 2,213 terms defined by 4,258 authors. The results reveal four relevant clusters: Cluster 1: Neurobiological and genetic foundations of learning and mental health; Cluster 2: Neurocognitive processes and memory mechanisms in learning; Cluster 3: Neuroimaging and cognitive decline in the study of brain health; and Cluster 4: Psychoeducational interventions and digital technologies for mental health and learning. These findings open new research gaps, where scholars, through the application of quantitative techniques such as structural equation modeling, may generate novel constructs of knowledge
Neuroeducation, Mental Health, Academic Performance, Bibliometrics, Python.
Salinas Palma Alexandra, Martha Salinas Lopez
WOMEN, IMMUNOLOGY, AND THERAPY: A BIBLIOMETRIC ANALYSIS OF ADVERSE DRUG EFFECTS IN THE SCIENTIFIC LITERATURE
In recent decades, the development of immunotherapies has profoundly transformed the treatment of autoimmune, infectious, oncological, and chronic diseases. However, numerous studies agree that knowledge regarding the adverse effects of these pharmacological treatments exhibits significant gaps, especially when analyzed from a sex-differentiated perspective. Women, due to their unique immunological, endocrine, and reproductive physiology, not only present a higher prevalence of autoimmune diseases but also demonstrate distinct immunopharmacological responses, which entail an increased risk of unforeseen or underreported adverse effects. Therefore, the present study aims to address the following research question: Does a network analysis of author-defined terms allow identification of the challenges related to adverse drug effects in women within the field of immunology? To answer this question, a bibliometric analysis was conducted using a corpus composed of publications from journals ranked in the Q1 quartile of the two main databases, Scopus and Web of Science, comprising a total of 548 articles. The results allowed the identification of four main clusters concentrating the conceptual terms defined by the authors: Cluster 1 Immunoregulation and reproductive therapies in women with immunological dysfunction; Cluster 2 Oncological immunotherapy and immune-mediated adverse events with metabolic impact; Cluster 3 Vaccines, HIV, and maternal-infant immunological health in vulnerable contexts; and Cluster 4 Biological therapies and immunometabolic and perinatal impact in women with chronic diseases. These findings contribute to the scientific production on this topic, opening new avenues for research that should be further explored through quantitative studies such as factor analysis and structural equation modeling.
Women, immunology, therapy, bibliometric adverse drug effects, Python.
Johanna Nathali Delgado Ferrin, Jonathan Walter Espinoza Parraga, Karol Jomaira Vacacela Guerrero, Marissa Elena Palacios Medina and Viviana Alejandra Palma Sanchez
NUTRIGENOMICS, FUNCTIONAL FOODS, AND FOOD ENGINEERING: BIBLIOMETRIC TRENDS TOWARD HEALTHY AND SUSTAINABLE EATING
The interrelationship between nutrition, health, and sustainability has become a central focus of contemporary scientific research. The transition toward healthier and more sustainable food systems demands integrative approaches that combine nutritional knowledge, technological innovation, and molecular understanding. In this context, nutrigenomics, functional foods, and food engineering emerge as strategic pillars to address pressing challenges such as obesity, non-communicable chronic diseases, and metabolic decline, while simultaneously promoting the conservation of natural resources and equitable access to adequate nutrition. This study seeks to answer the question: Can a term co-occurrence network analysis identify emerging trends in nutrigenomics, functional foods, food engineering, and healthy and sustainable nutrition? To this end, a bibliometric analysis was conducted using a network of 1,342 conceptual terms defined by 550 authors. The results revealed four thematic clusters: Cluster 1 Nutritional bioactives, molecular regulation, and emerging technologies for health and food performance; Cluster 2 Clinical intervention and functional compounds for the prevention of metabolic and cardiovascular diseases; Cluster 3 Personalized nutrition, gut microbiota, and dietary patterns in the prevention of chronic diseases; and Cluster 4 Personalized nutrition, pharmacotherapy, and precision medicine in the management of obesity and metabolic disorders. The findings provide a foundation for the development of new lines of research, which should be further explored and formalized through the application of robust methodologies such as structural equation modeling and factorial analysis.
Nutrigenomics, functional foods, healthy nutrition, bibliometrics, Python
Minchala Hidalgo Raul Ricky, Mendoza Hidalgo Ángel César, Hidalgo Lopez Carmen Rosario, Minchala Santander Raúl Tancredo
INTEGRATING ROBOTIC PROCESS AUTOMATION INTO POWER SYSTEM CONTROL: A FRAMEWORK FOR ADAPTIVE CHANGE MANAGEMENT AND OPERATIONAL RESILIENCE
Increasingly complex modern power systems and the quest toward intelligent grid control have fueled the implementation of Robotic Process Automation (RPA) in control centres and protection systems using SCADA. The technical advantages of automation are well known; however, its organisational aspects within high-reliability environments have not been discussed. This work will discuss an Adaptive Change Management Framework to implement RPA in the power system protection and control process, which integrates real-time automation and scheduled change integration. Mixed methods are used, where technical validation of the Smart Grid Monitoring Dataset is combined with the organisational impact survey, which is simulated. The findings have indicated that by implementing RPA, there is a dominant and encouraging change in the performance metrics of the system, such as detection percentage (96.7%), false positives (reduced to 1.8%), and average response time (0.45 seconds). At the same time, the completion rate of the training was increased to 94% in just four weeks, and confidence in the automation was enhanced by 42%, thanks to the support of direct communication channels and feedback loops. The framework will be organised into three levels: an RPA Core Engine, a Change Management Layer focusing on training and trust measures, and a Governance and Safety Layer encompassing cybersecurity, approval processes, and compliance. Such architecture guarantees technical reliability and creates operational penetration. Findings The findings provide power utilities a scale-based risk-considerate avenue of integrating automations into their mission-critical facilities. The paper closes the gap between the theoretical analysis of the digital transformation and applied engineering practice, the literature on smart grid automation, and the practical approaches to work.
Robotic Process Automation (RPA), Power System Protection, SCADA Integration, Change Management, Smart Grid Automation, Organisational Resilience, Fault Detection, Adaptive Framework, Operational Technology (OT), Grid Reliability.
Sandeep Singh, Mohammad Mushfiqul Haque Mukit, Van-Huy Chu
CHALLENGES OF EDTECH IMPLEMENTATION IN LOW- AND MIDDLE-INCOME COUNTRIES: A BIBLIOMETRIC APPROACH
The deployment of educational technologies (EdTech) in low- and middle-income countries constitutes one of the most significant contemporary challenges in global educational transformation. Despite growing recognition of its potential to democratize access to knowledge, implementation processes face deeply rooted structural, cultural, and technological limitations. This study seeks to answer the question: Does network analysis of terms allow the identification of challenges in EdTech implementation in low- and middle-income countries? Through a bibliometric co-occurrence network analysis of 1,191 conceptual terms defined by 548 authors, the results reveal four clusters: Cluster 1: Technology acceptance models and individual factors in EdTech adoption; Cluster 2: Health education and structural challenges in EdTech implementation within clinical and healthcare contexts; Cluster 3: Educational innovation, digital literacy, and technological planning in developing countries; and Cluster 4: Gamification, assessment, and competency development in emerging digital educational environments. These findings create new research gaps that require further exploration using advanced techniques such as factor analysis and structural equation modeling to transform the clusters into conceptual constructs
EdTech, low- and middle-income countries, bibliometrics, Python
Mendoza Hidalgo Angel Cesar, Gonzalez Matute Monica Maribel, Minchala Hidalgo Raul Ricky, Hidalgo Lopez Carmen Rosario
CHALLENGES AND TRENDS IN THE USE OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION, IMPACTS ON EDUCATIONAL QUALITY AND THE LEARNING EXPERIENCE: A BIBLIOMETRIC APPROACH
The emergence of artificial intelligence (AI) in educational systems has radically transformed the dynamics of learning, assessment, and pedagogical management in higher education. In recent decades, the use of intelligent technologies has shifted from a futuristic promise to a tangible reality that directly influences the educational experience of both instructors and students. This study aims to answer the following question: Does co-occurrence network analysis allow for the identification of the challenges associated with the use of artificial intelligence in higher education and its impact on educational quality and the learning experience? To address this, a bibliometric co-occurrence network analysis was conducted on 1,642 conceptual terms proposed by 1,190 authors across 273 Q1-ranked journal articles. As a result, four main clusters were identified, each concentrating the majority of the conceptual terms: Cluster 1: Educational transformation and diversity in clinical environments mediated by artificial intelligence; Cluster 2: Language learning and emotional self-regulation supported by conversational AI; Cluster 3: Immersive and gamified environments for pedagogical innovation mediated by AI; Cluster 4: Learning analytics and intelligent algorithms applied to educational innovation in scientific disciplines. The findings highlight emerging research gaps that scholars in these areas may explore through advanced quantitative methodologies, such as factorial methods and structural equation modeling, as these clusters have the potential to evolve into mature constructs depending on their application
Artificial Intelligence, Higher Education, Educational Quality, Learning Experience, Bibliometrics, Python
Barco Quiñonez Juan Carlos, Freire Lasso Johanna Mariela, Macias Arroyo Franklin Gregorio, Lazo Sulca Rafael Seleyman
CHALLENGES AND TRENDS IN THE PREVENTION OF CARDIOVASCULAR SIDE EFFECTS IN CANCER THERAPIES: A BIBLIOMETRIC APPROACH
The prolonged survival of cancer patients has highlighted a consequence that was, until recently, underestimated. Among the most frequent side effects are hypertension, arrhythmias, heart failure, and myocarditis. The present study aims to address the following questions: Can bibliometric analysis be used to evaluate trends in the scientific production related to cardiovascular prevention and side effects in cancer therapies? And can co-occurrence network analysis help identify the challenges in cardiovascular prevention and adverse effects associated with cancer treatments? To answer these questions, a bibliometric study based on co-occurrence network analysis was conducted. The evaluated corpus comprised 159 scientific articles. The results allowed for the identification of four highly relevant clusters: Cluster 1: Natural Compounds and Alternative Oncoprotective Therapies; Cluster 2: Oncological Hormonal Therapies and Associated Systemic Risks; Cluster 3: Cardioprotective Strategies and Risk Management in Cancer Patients; and Cluster 4: Advanced Diagnostics and Biomarkers in Preventive Cardio-Oncology. These findings contribute new avenues of knowledge, enabling researchers interested in this field to explore novel constructs and apply advanced factorial models. Palabras claves: Prevención cardiovascular, efectos secundarios, terapias contra el cáncer, bibliometría, Python
Prevention of cardiovascular, side effects, cancer therapies, bibliometric, Python
Alvaro Andres Falconi Loqui, Johanna Nathali Delgado Ferrin, Boris Adrian Falconi Loqui, Luis Eugenio Murillo Castillo, Nicolas Luis Ugart
A LITERATURE REVIEW ON THE APPLICATIONS OF TRI-BAND MIMO ANTENNAS IN MULTI-STANDARD SUB-6 GHZ SYSTEMS
The demand for high-speed, low-latency, and reliable wireless communication has intensified with the rollout of 5G networks. To meet these requirements, Multiple-Input Multiple-Output (MIMO) antennas have become vital, especially in the Sub-6 GHz band due to its favourable balance between coverage and data rate. This study proposes the design and simulation of a compact Tri-band MIMO antenna operating at 2.4–2.5 GHz, 3.4–3.6 GHz, and 4.6–4.8 GHz, targeting Sub-6 GHz applications such as smart cities, IoT, cloud computing, and fixed wireless access. The research addresses challenges like mutual coupling, limited bandwidth, and size constraints in current MIMO antenna systems. Using HFSS software, antenna parameters including return loss, VSWR, channel capacity loss, and isolation loss are optimized. The fabricated prototype on an FR4 substrate is evaluated using a vector network analyser. The expected outcomes include enhanced efficiency, improved isolation (≥20 dB), and a channel capacity loss below 0.1, contributing significantly to next-generation wireless communication technologies. The advancement of 5G technology has necessitated the development of compact, high-performance antennas to accommodate the demands for enhanced speed, reliability, and low-latency communications. This paper presents the design, simulation, and analysis of a compact tri-band MIMO antenna targeting the sub-6 GHz spectrum, operating specifically in the 2.4–2.5 GHz, 3.4–3.6 GHz, and 4.6–4.8 GHz frequency bands. The design tackles critical challenges including mutual coupling, limited bandwidth, and size constraints, using HFSS simulation and FR4 substrate fabrication. The proposed antenna demonstrates improved performance parameters including isolation loss >20 dB, VSWR <2, and channel capacity loss <0.1, making it suitable for modern 5G IoT, smart city, and wireless communication systems.
Tri-band MIMO antenna, Sub-6 GHz, 5G communication, Isolation loss, Channel capacity loss, Mutual coupling, HFSS, FR4 substrate, Wireless networks
Mr. Suresh Shripatrao Kambale, Dr. Anuradha Deshpande
FEMALE EDUCATORS IN THE CONSTRUCTION OF INCLUSIVE AND EQUITABLE EDUCATION: TRENDS AND CONTRIBUTIONS FROM A BIBLIOMETRIC PERSPECTIVE
This article aims to identify the trends and contributions of female educators in the construction of inclusive and equitable education in higher education. The central research question guiding this study is: Can thematic mapping analysis reveal the trends in the contributions of female educators toward building inclusive and equitable education? To address this question, a bibliometric analysis was conducted based on a corpus of 165 documents retrieved from the SCOPUS and Web of Science databases, all indexed in Q1-ranked journals. The results of the conceptual map analysis provided insights into the evolving trends in the participation of female educators in fostering inclusive and equitable education. Within the Motor Themes quadrant, findings indicate a sustained research focus on how female educators lead educational transformation processes in socially, culturally, and functionally diverse contexts. In the Niche Themes quadrant, the analysis highlights emerging research gaps, notably the need to train female educators with intercultural and gender-sensitive perspectives, positioning them as agents of change in exclusionary educational settings. The bibliometric study of the role of female educators in constructing inclusive and equitable education reveals a growing line of inquiry, supported by a solid documentary foundation and predominantly published in Q1 journals. This positioning not only underscores the scientific quality of the contributions in this field but also reflects its increasing prominence in contemporary debates on educational equity.
Female educators, inclusive education, equity, bibliometrics, Bibliometrix, Python
Jessica Yépez Verduga, Carolina Daysi Villacis Macias
POWER QUALITY ISSUES MITIGATION USING MATRIX CONVERTER-BASED WIND ENERGY CONSERVATION SYSTEM
Wind energy stands as one of the rapidly expanding sources of renewable energy worldwide. The intermittent nature of wind energy presents obstacles for integrating it into the grid, primarily because of the fluctuating power output from wind turbines. To ensure grid compatibility and enhance power quality, effective control strategies are imperative. This paper introduces a novel control scheme design for three-phase grid-connected wind energy conversion systems (WECS) aimed at optimizing power quality. By addressing the variability in wind power generation, the proposed control scheme facilitates seamless integration with the power grid, thus ensuring stable and reliable operation. Through advanced control techniques, this approach mitigates fluctuations in generator output, enhancing the overall performance and efficiency of WECS. The effectiveness of the proposed control scheme with matrix converter with different power quality parameters is demonstrated through Validation of the proposed system is verified through MATLAB/Simulink simulation showcasing its potential to strengthen the reliability and viability of wind energy as a sustainable power source.
WECS, Matrix converter, Power Quality, Induction generator Power electronics.
EVALUATING AGENT-BASED CUSTOMER SERVICE IN ELECTRONICS RETAIL – A CASE STUDY
This paper presents a comprehensive case study on the evaluation of agent-based customer service systems within the context of electronics retail, a domain characterized by high product complexity and frequent customer inquiries. The study focuses on two key dimensions of assessment: functional effectiveness and convergence behavior. Functional evaluation examines the agent’s ability to accurately understand and resolve customer queries, analyzing metrics such as intent recognition accuracy, task completion rate, response latency, and customer satisfaction. Convergence evaluation, on the other hand, assesses the system’s learning dynamics and behavioral stability over time, including its ability to reduce fallback occurrences, increase automation rates, and improve consistency in responses through iterative updates or retraining. The case study draws from a real-world deployment in a mid-sized electronics retail chain, leveraging actual customer interaction logs and agent performance data across a six-month period. A notable differentiator of this paper is the dual-pronged evaluation methodology that not only measures how well the agent performs at any given time but also how it evolves to become more efficient and context-aware over time. Additionally, the study integrates feedback loops from human agents, enabling the agent to learn from escalated cases and progressively reduce human dependency. Unlike prior works that focus predominantly on initial deployment success or chatbot usability, this paper emphasizes long-term adaptability, operational resilience, and alignment with business KPIs such as customer retention, service cost reduction, and query deflection rates. The findings aim to serve as a benchmark for organizations seeking to quantify and optimize the performance of AI-driven customer support systems in complex retail environments.
Agent-based systems, customer service, electronics retail, functional evaluation, convergence metrics, conversational AI, intent recognition, case study
Amit Chakraborty, Chirantana Mallick, Sudip Dasgupta, Saptarshi Das, Raj Kumar Keshri
CHALLENGES OF ORGANIZATIONAL STRATEGIES IN COMMUNICATION AND FEMALE LEADERSHIP: A BIBLIOMETRIC APPROACH
Sustainable Development Goal 5, which aims to achieve gender equality, reports that despite having the same abilities and skills as men and demonstrating equal effort, women receive lower compensation, thereby restricting their economic and professional growth. It is further emphasized that it will take approximately 140 years for women to achieve parity in positions of power and leadership. In this context, female leadership has become increasingly crucial in organizations, where gender diversity in top management positively influences business outcomes. This study seeks to answer the question: What are the challenges that organizational strategies face regarding communication and female leadership? To address this, a bibliometric methodology was applied to a corpus of 141 scientific articles sourced from the Scopus and Web of Science databases, prioritizing publications indexed in Q1 journals. The results, derived from a co-occurrence analysis of keywords and thematic clustering of the scientific output, identified four conceptual clusters: (1) structural barriers and professional development; (2) transformational leadership and gender-sensitive work environments; (3) cultural diversity, resilience, and regional leadership; and (4) stereotypes, representations, and symbolic challenges. These findings reveal that the challenges faced by women in leadership roles are not only structural but also symbolic and cultural, affecting their visibility, legitimacy, and capacity for action within organizations. This work contributes to the literature on gender equality by providing empirical evidence that can guide legislation and the formulation of more inclusive organizational policies.
Gender equality, bibliometrics, decision-making, women leaders