In recent decades, the strategic placement of capacitors for compensating inductive reactive power has been extensively investigated by network operators and researchers globally, owing to its profound impact on minimizing power losses, improving voltage regulation, and enhancing overall voltage stability. The installation of shunt capacitors has been demonstrated to significantly improve the efficiency and performance of power systems by regulating voltage levels at load points, as well as at distribution and transmission system buses. This approach not only reduces inductive reactive power but also corrects the system’s power factor, thereby optimizing energy utilization. In this study, the optimal sizing and placement of capacitor banks within a specific section of the Duhok city distribution network were systematically analyzed. The Electrical Transient Analyzer Program (ETAP) software was employed to simulate and evaluate power losses and voltage drops both before and after capacitor installation. The findings reveal a marked improvement in the voltage profile across the network, accompanied by a substantial reduction in power losses. These results underscore the critical role of capacitor banks in enhancing the operational efficiency of distribution networks, providing a robust framework for future implementations in similar systems. The methodology and outcomes presented herein offer valuable insights for network operators seeking to optimize power system performance through reactive power compensation.
This study investigates the role of Artificial Intelligence (AI) in sustainable education through a bibliometric analysis, aiming to explore research trends, key contributors, citation analysis, co-authorship, and thematic developments in the field. As AI becomes increasingly integrated into Education, it is crucial to understand its impact on learning personalization, institutional efficiency, and sustainability. The study also identifies research gaps and provides recommendations for future exploration. The study employs a bibliometric and content analysis methodology using Scopus data. Two hundred seventy-six documents (2016-2025) were analyzed through descriptive statistics, citation analysis, co-word analysis, and co-authorship networks, utilizing VOSviewer and Biblioshiny for data visualization. The analysis examines publication trends, top-cited articles, leading institutions, and international collaborations to map the intellectual landscape of AI in sustainable education. The findings indicate a significant increase in AI-related publications after 2019, reflecting growing global interest. India, the USA, and China lead research output, while Sustainability (Switzerland) and Lecture Notes in Networks and Systems are the most prominent publication sources. The co-authorship analysis highlights strong global research collaborations, with the UK, Brazil, and China playing key roles. Thematic clustering reveals four major research areas: AI-driven Environmental Education, AI in Education, sustainable education frameworks, and AI's technical advancements in learning systems. This study provides a comprehensive, macro-level bibliometric analysis that maps global research dynamics, identifies intellectual structures, and visualizes collaborative networks in AI and sustainable education. Despite its contributions, the study has several limitations. First, while Scopus offers broad and reputable coverage of peer-reviewed literature, the exclusive reliance on this database limits the inclusion of potentially relevant studies indexed in other databases such as Web of Science (WoS). This may restrict the diversity and comprehensiveness of the findings. Future research should consider cross-validating results using multiple databases to ensure a more holistic understanding of AI in sustainable education. Second, the exclusion of non-English publications may limit the diversity of perspectives. Third, the study primarily focuses on journal articles and conference papers, excluding books and institutional reports that might offer more profound insights.
This study investigates the extent to which renewable energy adoption contributes to achieving a sustainable competitive advantage in Jordanian small and medium-sized enterprises (SMEs) through enhanced sustainability practices. A quantitative research design was employed, utilizing data collected from 467 administrative personnel across 43 SMEs operating in diverse industries to ensure representativeness. Structural equation modeling (SEM) was conducted using SmartPLS 4 to examine both the direct and indirect effects of renewable energy adoption on corporate sustainability practices and its subsequent impact on long-term competitiveness. The findings indicate that integrating renewable energy into business operations significantly strengthens sustainable competitive advantage by improving operational efficiency, reducing costs, and enhancing corporate reputation. Furthermore, the results highlight the role of renewable energy adoption in reinforcing sustainability initiatives, thereby aligning environmental stewardship with strategic business objectives. These insights provide valuable implications for SMEs seeking to enhance market positioning through sustainability-driven strategies. Additionally, the study contributes to the existing body of knowledge on corporate sustainability and strategic management by elucidating the mechanisms through which renewable energy facilitates long-term competitive positioning. Practical recommendations are offered to policymakers and business leaders to support the effective implementation of sustainability initiatives within the SME sector.
The domination of cyberspace technologies in inter-human communications is obvious because of their ultra-rapidness and enormous data capacity. Human-intensive use of cyberspace increased the magnitude of streamed data through its nodes, created by two sources: human users and AI. While humans can control their generated data, it proves impossible to control AI due to its super intelligence along with their self-developing abilities, enabling it to produce unlimited volumes of data. It is known that cyberspace depends on physical infrastructure, which is inherently limited. Despite investments to expand capacity, overloading this infrastructure with unlimited data creates critical functionality issues. Additionally, the presence of uncontrollable AI elements leads to unpredictable outcomes. Ultimately, this results in AI dominating cyberspace, a phenomenon known as cyber singularity.
The ultimate consequences of AI cyber singularity motivated the study to recall a similar phenomenon in astrophysics: gravitational singularity. Using general relativity theory, the research analyses the dilemma of data overload in cyberspace and its effects, drawing parallels between outer space and cyberspace. It aims to illustrate AI's acquisition of cyber singularity according to astrophysics laws on gravitational singularity, providing an innovative perspective for scientists and scholars studying cyberspace.
This article aims to discuss the evolution over the centuries of the role and social position of those mastering the technologies of their time. We suggest that the Industrial Revolution, the rationalization of technical and managerial processes, then the rise of IT, the ascent of cryptocurrencies and finally the emergence of the neoliberal state have lifted a fringe of these individuals to the top of the social hierarchy. Among the “technology masters”, we distinguish three families: those who remain at the service of the State and the established order, those who have exploited, consciously or not, the withdrawal of the neoliberal State to offer services and innovations formerly assumed by the public sector, and finally those who have consciously taken advantage of this same withdrawal and the recognition they enjoy in society to propose other models (free software, open source, crypto anarchism, technical alternatives, etc.).
This paper presents two synthetic estimations of the Gini coefficient at a municipality level for Colombia in the years 2000 - 2020. The methodology relies on several machine learning models to select the best model for imputation of the data. This derives in two Random Forest models where the first is characterized by containing Dominant Fixed Effects, while the second contains a set of Dominant Varying Factors. Upon these estimations, the Synthetic Gini Coefficients for both models are inspected, and public links are generated to access them. The Dominant Fixed Effects models is rather “stiff” in contrast to the Varying Factor model. Hence, for researchers it is recommended to use the Synthetic Gini Coefficient with Varying Factors because it contains greater variability across time than the Dominant Fixed Effects models.
Accurate estimation of tree height is fundamental to sustainable forest management, particularly in regions such as Kumrat Valley, Pakistan, where Deodar Cedar (Cedrus deodara) serves as a vital ecological and economic resource. Conventional height estimation models often exhibit limitations in capturing the inherent complexity of forest ecosystems, where multiple environmental factors interact non-linearly. To address this challenge, a hybrid predictive framework integrating fuzzy inference systems (FIS) and multiple linear regression (MLR) has been developed to enhance the accuracy of height estimation. The FIS model incorporates key environmental and physiological parameters, including trunk diameter, soil quality, temperature, and rainfall, which are classified into fuzzy sets—low, medium, and high—corresponding to distinct growth rates (slow, normal, fast) and developmental stages (early, average, late). This classification enables a nuanced representation of environmental variability and tree growth dynamics. Complementarily, the MLR model quantifies the statistical relationships between these variables and tree height, yielding an R² value of 0.85, an adjusted R² of 0.64, and a statistically significant p-value of 0.04. The integration of fuzzy logic with regression analysis offers a robust, data-driven approach to height prediction, effectively addressing the uncertainties associated with environmental fluctuations. By leveraging both rule-based inference and quantitative modeling, this method provides valuable insights for precision forestry, contributing to the sustainable management and conservation of Deodar Cedar in Kumrat Valley.
In recent years, frequent natural disasters and public emergencies have emphasized the importance of emergency material distribution path planning. Aiming at the problems of neglecting the differences in the urgency of the demand at the disaster-stricken points and the lack of distribution fairness in traditional research, this study proposes an emergency material distribution path planning method that integrates the priority assessment of the disaster-stricken points and multi-objective optimization. First of all, a two-level evaluation system is constructed from the dimensions of disaster degree and material demand, including the number of rescue population and other indicators, and the combined weights are calculated by combining the subjective and objective methods of hierarchical analysis (AHP) and entropy weighting, so as to quantify the urgency coefficient of the demand at each disaster site and break through the limitations of the traditional “nearby distribution” mode. On this basis, a vehicle path planning model is established with the dual objectives of minimizing the total distribution cost and vehicle load balance, and the elite strategy non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the problem. Scenario analysis is carried out with the background of public health emergencies in Jingzhou City, and the effectiveness of the model is verified based on the actual data of 64 medical material demand points. The simulation results show that the total distribution distance and vehicle load balance are optimized after optimization. Finally, it is suggested in conjunction with the current situation of emergency material distribution in China. Through the quantification of demand urgency and multi-objective collaborative optimization, this study provides theoretical basis and practical reference for improving the fairness, timeliness and resource utilization efficiency of emergency logistics, and has important application value for improving disaster relief decision-making.
With the rapid advancement of modern robotics and artificial intelligence, intelligent picking robots have been widely adopted in agricultural production. Global path planning techniques have been applied to crop harvesting, such as oranges, apples, tea leaves, and tomatoes, yielding promising results. This study focuses on the path planning problem for a robotic arm used in premium tea leaf picking. Experimental simulations reveal that the Ant Colony Optimization (ACO) algorithm performs particularly well in solving small-scale Traveling Salesman Problems (TSP), as it can incrementally construct initial paths and, with properly tuned parameters, produce higher-quality solutions and achieve faster convergence compared to other algorithms. However, the traditional ACO algorithm tends to fall into local optima and suffers from slow convergence. To address these challenges, this paper proposes a dynamically optimized ACO algorithm that enhances the pheromone update rules and optimizes the $\alpha$ and $\beta$ parameters during the search process. These parameters are updated according to the optimization results, and a ranking factor is introduced to prevent the optimal picking path from being overlooked. The proposed method demonstrates superior performance over the traditional ACO algorithm in terms of path quality and convergence speed.
The increasing pace of urbanization has heightened the need for urban systems that are both sustainable and resilient. While extensive research has been conducted on these two concepts, the interplay between them remains insufficiently explored. In particular, sustainability is often associated with efficiency—maximizing resource utilization—whereas resilience emphasizes redundancy, ensuring the presence of backup systems to mitigate risks. To address this critical gap, a comprehensive framework is proposed that integrates these dual objectives within urban land-use planning. Geospatial technologies and multi-criteria decision analysis are employed to systematically assess the balance between efficiency and redundancy in urban environments. A machine learning (ML)-based classification of land use and built-up area changes, combined with demographic and infrastructural data, is utilized to quantify these factors. The proposed approach provides urban planners and policymakers with an adaptable decision-making tool, enabling context-specific prioritization of efficiency or redundancy based on local requirements. In high-density urban areas experiencing rapid expansion, efficiency is emphasized to optimize land and resource use, whereas in regions vulnerable to environmental hazards, redundancy is strategically incorporated to enhance resilience without undermining overall urban functionality. The flexibility of this method offers a significant advantage over rigid, predefined planning policies that may not be suited to specific urban contexts. By facilitating informed decision-making, the framework enhances risk management, optimizes resource allocation, and supports the development of customized urban strategies, ultimately improving long-term urban performance under diverse developmental scenarios.
The Location-Routing Problem (LRP) involves the simultaneous determination of optimal facility locations and vehicle routing strategies to fulfill customer demands while adhering to operational constraints. Traditional formulations of the LRP primarily focus on delivery-only scenarios, where goods are allocated from designated warehouses to customers through a fleet of vehicles. However, real-world logistics often necessitate the simultaneous handling of both deliveries and pickups, introducing additional complexity. Furthermore, inherent uncertainties in demand patterns make precise parameter estimation challenging, particularly regarding the quantities of goods received and dispatched by customers. To enhance the realism of the model, these demand variables are represented using fuzzy sets, capturing the uncertainty inherent in practical logistics operations. A mathematical model is developed to account for these complexities, incorporating a heterogeneous fleet of vehicles with capacity constraints. The optimization of the proposed fuzzy capacitated LRP with simultaneous pickup and delivery is conducted using a Genetic Algorithm (GA) tailored for fuzzy environments. The efficacy of the proposed approach is validated through numerical experiments, demonstrating its capability to generate high-quality solutions under uncertain conditions. The findings contribute to the advancement of location-routing optimization methodologies, providing a robust framework for decision-making in uncertain logistics environments.
Open Access
