Power, Control, and Data Processing Systems

Abstract Social networking platforms have emerged as a focal point for academic and practical research, driven by their growing influence in modern society. Among various analytical tasks, link prediction has gained prominence as a critical challenge in social network analysis. This study examines three primary link prediction strategies: feature-based methods, Bayesian statistical models, and probabilistic relational models. Acknowledging the significant challenge of class imbalance in link prediction, we explore a combination of algorithmic techniques, advanced data preprocessing, and effective feature selection methods to improve predictive outcomes. Our research specifically focuses on coauthorship networks, leveraging topological attributes and enhanced data mining practices to extract meaningful patterns. Through extensive experimentation, we evaluate the performance of different approaches, emphasizing decision trees and Naive Bayes classifiers. These models consistently outperform alternatives in terms of predictive accuracy, particularly when assessed using F-measure and AUC metrics. Notably, our findings underscore the critical role of robust data preprocessing in achieving superior results, highlighting its potential to mitigate the impact of class imbalance. This study contributes valuable insights to the field of link prediction, offering practical guidance for developing more effective algorithms and addressing challenges in real-world social network applications.

Abstract The global challenges posed by population growth, increasing energy demand, and the depletion of fossil fuel resources have intensified the need for sustainable energy solutions. In this context, renewable energy sources—particularly solar energy—have emerged as key alternatives due to their sustainability and environmental compatibility. This study analyzes and optimizes a photovoltaic (PV) system aimed at reducing building energy consumption in the hot and humid climate of Bushehr, Iran (latitude 28.92°N). Simulations were conducted using the TRNSYS software, evaluating the influence of factors such as building insulation, window-to-wall ratio, and panel tilt angle on system performance. The results indicate that south-facing PV panels installed at an optimal tilt angle of 25° can reduce building energy consumption by up to 25%. During summer months, the system decreased cooling loads by as much as 20% and partially met heating demands in winter. Additionally, the annual energy cost savings shortened the payback period to 8.33 years, while reducing annual CO₂ emissions by approximately 1.17 tons. These findings highlight the significance and effectiveness of PV systems in reducing energy consumption and environmental impact in similar climatic regions.

Abstract This study investigates the feasibility of integrating biogas derived from municipal solid waste with solar energy in a hybrid power plant located near a municipal landfill site. The hybrid system comprises photovoltaic panels and a gas turbine that currently operates on natural gas. The primary objective of this research is to replace natural gas with biogas in order to enhance energy security, reduce greenhouse gas emissions, and improve sustainable waste management. Detailed data on the volume and characteristics of municipal waste were collected, and the biogas production potential from the landfill site was calculated. Simulations were conducted using PVsyst and HOMER software. Integrating biogas as a sustainable and reliable energy source compensates for the intermittency of solar energy and ensures a more consistent electricity generation throughout the year. This system not only reduces dependence on fossil fuels but also contributes to the sustainable management and control of municipal waste. The results of this research demonstrate the high potential of this hybrid system in reducing operational costs, improving energy efficiency, and mitigating environmental impacts. Furthermore, this project serves as an educational platform for students and researchers, offering opportunities to study renewable energy technologies. The study presents innovative solutions for the optimal utilization of local resources and proposes a model for similar projects in other regions, which could lead to a more sustainable and efficient energy future.

Abstract With the increasing global demand for clean and renewable energy sources, research on solar cells—particularly perovskite/silicon tandem solar cells—has grown rapidly in recent years. Due to the toxicity of lead present in the structure of perovskite solar cells, which hinders their commercialization, it is necessary to look for alternative elements to replace lead, thereby reducing toxicity and enabling commercial viability. In this study, a computational approach is employed to investigate a multi-junction silicon-perovskite solar cell with the structure: FTO/TiO₂/CH₃NH₃GeI₃/c-Si(n)/c-Si(p)/c-Si(p⁺). In the simulation, the absorber layer CH₃NH₃PbI₃ was replaced with CH₃NH₃GeI₃ as the perovskite absorber in order to improve efficiency and overall solar cell performance. Analysis of the simulation results showed that using CH₃NH₃GeI₃ led to improved performance compared to the initial structure, increasing the power conversion efficiency to 28.98%. Other optimized parameters of the proposed multi-junction solar cell include: Short-circuit current density: 43.51 mA/cm² ,Open-circuit voltage: 0.78 ,Fill Factor: 85.10%

Abstract When taking into account the prevailing COVID-19 pandemic scenario, by early detecting COVID-19, we can formulate an effective treatment plan and make decisions regarding disease containment. As a result of this issue, Artificial Intelligence (AI) specialists have been encouraged to develop models that employ deep learning techniques in COVID-19 detection. These models diagnose infection severity rapidly and economically. The present study proposes a Deep Convolutional Neural Network (CNN) model based on PSO, which helps identify COVID-19 infections by chest Computerized Tomography (CT) scans. Moreover, we demonstrate how pre-trained models can classify the disease through transfer learning. Initially, the random search is used to identify an optimal CNN model. The transfer learning strategy presents an analysis of several popular pre-trained models. The optimal CNN model inherits several layers from these previously trained models, and we then fine-tune the selected optimal CNN model accordingly. The proposed architecture is built using three pre-trained models with the highest quality. PSO algorithm is applied to estimate how each pre-trained model will affect the ultimate detection of the suggested model. To train the model, we analyzed two publicly available datasets—COVID-CT and SARS-CoV-2—applying distinct pre-processing techniques to each. According to the experimental results, our PSO-based configuration optimization performed well on this dataset and can achieve better results with more training data. As a result of extensive parameter tuning, the proposed model can identify COVID-19 with an accuracy of up to 90.32%. This model will facilitate the detection and diagnosis of COVID-19 promptly.

Abstract In modern microgrid systems, maintaining frequency stability in the presence of renewable energy sources remains a critical challenge. This paper presents a resilient frequency control strategy that incorporates virtual inertia to mitigate instability issues arising from the fluctuations of renewable generation. The proposed method is based on the Robust Polynomial-Based Control Design (RPCD), which is tailored for microgrid applications to handle inherent system uncertainties. In addition, the H∞ control method is utilized as a benchmark to evaluate system performance under disturbances. The study considers distributed generation from solar and wind sources and analyzes the system’s dynamic behavior in both grid-connected and islanded operating modes. In the islanded scenario—triggered by events such as voltage drops or frequency mismatches—the performance of the developed RPCD-based scheme in preserving system frequency is thoroughly evaluated. Furthermore, to investigate potential enhancements in local control performance, a version of the system with optimized PID parameters using the Shuffled Frog Leaping Algorithm (SFLA) is also examined. The results confirm that the proposed controller, with and without optimization, successfully ensures system stability under diverse and challenging operational conditions.