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Found 5152 publications
Dictionary-based assessment of European Sustainability Reporting Standard (ESRS) disclosure topics
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
As the imperative for sustainable business practices and corporate responsibility has grown, the analysis and integration of Environmental, Social, and Governance (ESG) issues into corporate strategies has become a crucial aspect of business strategy. The paper assesses the ESG reporting preparedness of companies in the Central and Eastern European (CEE) region by analyzing their compliance with the European Sustainability Reporting Standards (ESRS). The study assesses the variability in disclosures across ESG pillars and examines their relationship with financial metrics using a test of independence and bootstrapped multiple regression. By employing an automated text analysis methodology on sustainability reports from top-performing companies, including Hungary, the Czech Republic, Poland, Austria, Slovenia, and Romania, the research identifies significant differences in reporting scores across various ESG disclosure topics. The results indicate that Climate Change (E1) scores are higher than those of other topics, suggesting an uneven emphasis on different aspects of sustainability. Furthermore, the analysis reveals that larger companies tend to achieve higher ESG scores, reflecting their greater resources for comprehensive and transparent reporting practices. The research contributes to the understanding of ESG reporting practices in the CEE region and highlights the importance of improvement in sustainability reporting to foster greater transparency and comparability. The findings suggest policy initiatives to encourage balanced reporting across all ESG topics and that companies, particularly smaller ones, could benefit from capacity-building efforts to enhance their reporting capabilities.
Open Access: Yes
A hybrid physics-informed neural and explainable AI approach for scalable and interpretable AQI predictions
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Air Pollution is a critical environmental issue affecting public health, climate, and ecosystems. However, accurately predicting and classifying Air Quality Index (AQI) levels across different regions remains a challenging task due to the complex nature of air pollution patterns. Conventional and ensemble ML and DL models often fail to capture the physical laws goverming the air pollution, which leads to inaccurate predictions. This study addresses these issues by introducing an approach that employs Physics-Informed Neural Networks (PINN) with Explainable AI (XAI) techniques for AQI classification (AirSense-X). The proposed approach utilizes PINN for regression, along with mapping for classification and XAI for interpretation. PINN ensures that the model learns from physical laws governing air quality rather than relying solely on data. The dataset utilized in this study is a publicly available dataset containing the AQI data at daily levels from various stations across multiple cities in India. The proposed AirSense-X approach achieves an accuracy of 98 %, with 97 % precision, 95 % recall, and an F1 score of 0.96, ensuring reliability. Similarly, the confusion matrix for the proposed approach indicated that the model correctly classified 21,306 and misclassified 268 instances. The key focuses of this study include: • Introducing a novel approach, AirSense-X, which employs PINN for accurate AQI prediction and XAI for enhanced interpretability. Additionally, the study also involves comparative analysis with conventional and ensemble ML and DL models. • Employing structure mapping technique for classification based on the predicted AQI values. • Integrating physical laws governing air pollution using a PINN model enhances prediction accuracy and ensures that the model learns beyond relying on data-driven insights.
Open Access: Yes
Analysis of Wireless Communications for Smart Grid: MABAC Model Based on Complex Propositional Picture Fuzzy Sugeno Weber Power Aggregation Information
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
In this study, the shortcoming of the conventional procedure is demonstrated by proposing the novel technique of complex propositional picture fuzzy sets with some fundamental concepts based on algebraic and Sugeno Weber norms. In addition, the authors classified the different types of power operators based on Sugeno Weber norms for complex propositional picture fuzzy values, called the complex propositional picture fuzzy Sugeno Weber power averaging, complex propositional picture fuzzy Sugeno Weber weighted power averaging, complex propositional picture fuzzy Sugeno Weber power geometric, complex propositional picture fuzzy Sugeno Weber weighted power geometric operators and also designed their three different properties for each operator. As well, the authors designed the multi-attributive border approximation area comparison for the proposed operator. Further, wireless communication networks are playing a critical and vital role in the circumstance of development and operation of smart grids, which incorporate advanced technologies to enhance the capability, efficiency, and sustainability of electricity distribution. Finally, the designed techniques and models are applied to the wireless communications for smart grids in Taiwan. Sensitivity and comparative analysis are derived to obey the strength and competence of the developed model. This study gives an inventive decision analysis structure, which varieties a substantial contribution to wireless communication in smart grid assessment difficulties under the indeterminate situation.
Open Access: Yes
Comparison of sustainability and circularity indicators: downstream vs. upstream supply chain strategies
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
In the quest for sustainable and circular practices within supply chains, both downstream and upstream strategies play pivotal roles. This structured literature review aims to compare the indicators of sustainability and circularity between downstream and upstream supply chain strategies, evaluating their impact through practical cases and empirical studies. The downstream strategies, which focus on waste management, customer engagement, and reverse network planning, are explored alongside upstream strategies such as sourcing environmentally friendly raw materials and integrating design for circularity principles. This paper assesses the efficacy of these strategies through a comprehensive review of scholarly articles, reports, and case studies in achieving sustainability and circularity goals. The literature review reveals that downstream strategies often face challenges related to felxibility and operational efficiency while crucial for managing end-of-life products and optimizing resource utilization. Conversely, upstream strategies, emphasizing eco-friendly sourcing and circular design principles which demonstrate significant potential for long term sustainability and circularity. Practical cases illustrate how upstream interventions can lead to reduced environmental impact, enhanced resource efficiency, and increased product longevity across various industries. Furthermore, the review highlights the interconnectedness of downstream and upstream strategies within the broader supply chain ecosystem. Synergistic approaches that integrate both strategies demonstrate the highest potential for driving transformative change towards sustainable and circular supply chains. Ultimately, this review underscores the importance of integrated approaches that leverage both downstream and upstream strategies to achieve lasting environmental and economic benefits to provides insights for policymakers and researchers seeking to prioritize interventions that maximize sustainability and circularity across the supply chain.
Open Access: Yes
Generic optimal power flow solution associated with technical improvements and emission reduction by multi-objective ARO algorithm
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
In modern power engineering, the optimal operation aims to achieve the basic requirements of the electrical power grid, meet various technical and economic aspects, and preserve the environmental limits within their accepted bounds. In this line, the current paper finds the optimal operational scheduling of the power generation units that cover the load requirements, considering different frameworks of the optimal power flow (OPF) problem involving single- and multi-objective functions. Technical, economic, and emissions objective functions are considered. Artificial rabbits’ optimization (ARO) is developed to find the optimal OPF framework solution. The effectiveness of the proposed algorithm is evaluated through a comprehensive comparison study with the existing works in the literature. With six IEEE standard power systems, 22 different cases are implemented to test the ARO performance as an alternative to solve the OPF problem. Two of these systems are considered small-size systems, 30-, and 57-test systems, while the other four are large-scale power systems (IEEE 300, 1354, 3012, and 9241 test systems) to expand the validation scope of this paper. This comparison validates the scalability and efficiency of the ARO algorithm. The impact of varied population size and maximum iteration number is tested for two test systems, the most benchmarking test systems. It was proven that the routine of ARO has robust and superior competitive performance compared with others at fine convergence rates. Significant improvements are acquired in the range of 47% in the technical and economic issues by accepting the environmental concerns.
Open Access: Yes
Decision-analytics-based electric vehicle charging station location selection: A cutting-edge fuzzy rough framework
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 711-735
Description:
Electric vehicles are of great significance in supporting sustainable transportation and sustainability. In parallel with the increasing demand for such vehicles worldwide, the electric vehicle charging stations (EVCSs) market has grown dramatically. The study presents a practical model for selecting EVCS sites integrating multi-criteria decision-making (MCDM), fuzzy, and rough sets. The research aims to bridge the gap in evaluating EVCS locations by leveraging the superiorities of fuzzy and rough set theories to address vagueness effectively. Firstly, assessment criteria cover the environment, economic, technology, and social drivers. Secondly, a fuzzy Defining Interrelationships Between Ranked criteria (F-DIBR) model is applied to determine the weight values of siting factors. Last, for the first time, the Mixed Aggregation by COmprehensive Normalization Technique (MACONT) with hybrid fuzzy rough numbers (FRN-MACONT) model is proposed to obtain the ranking results. Further, a new approach for defining hybrid fuzzy rough numbers is suggested, based on an improved methodology for determining rough numbers' lower and upper limits, allowing consideration of mutual relations between a set of objects and flexible representation of rough boundary intervals depending on the dynamic environmental conditions. The study's novelties reside in deciding the importance of the driving forces used in determining the EVCS site location with a novel method, F-DIBR, and selecting the optimal site with a new FRN-MACONT approach. The results show that “economy” is the most significant criterion, whereas “system reliability” is the most critical sub-criterion. The findings also indicate that the Konak territory performs the best, whereas the Cigli territory is the second best. Comprehensive sensitivity analysis verifies the proposed framework's validity, robustness, and effectiveness. As per the research findings and analyses, some managerial implications are further discussed. The approach introduced has the potential to contribute to the green transport literature.
Open Access: Yes
Dynamic response distortion due to changing excitation frequency
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-12-01
Volume: 16
Issue: 12
Page Range: Unknown
Description:
This study addresses the distortion in system response caused by continuously changing excitation frequency. The distortion leads to reduced resonance peak amplitude and shifts the resonance frequency as well. The novelty of this work lies in providing an analytically established, model-based methodology that not only describes but also predicts and enables one to control this distortion, in contrast to existing studies that mainly describe the phenomenon characteristically [1,2]. The proposed approach incorporates the influencing parameters, such as the sweep direction and rate of linearly changing excitation frequency, and applies a first-order ODE (ordinary differential equation) formulation to approximate the distortion. This enables a sensitivity analysis across frequency and damping ranges, which has not been previously reported in the literature. The methodology is validated with experimental data from an E-drive system, demonstrating how optimal sweep rates and other test conditions can be derived from model fitting. While nonlinear effects may occur in E-drives, the present study focuses on their linear regime to isolate distortion effects. The findings provide both fundamental insights into resonance distortion and practical guidelines for improving the accuracy and reliability of swept-excitation-based NVH (noise, vibration, harshness) measurements in engineering applications.
Open Access: Yes
The impact of machine learning applications in agricultural supply chain: a topic modeling-based review
Publication Name: Discover Food
Publication Date: 2025-12-01
Volume: 5
Issue: 1
Page Range: Unknown
Description:
Machine learning (ML) has become a pivotal element in agriculture, providing groundbreaking solutions to tackle intricate issues related to productivity, sustainability, and resource management. A comprehensive examination of the current literature is crucial as the discipline evolves, allowing for the identification of significant themes, trends, and focal discussions. The current study employs latent Dirichlet allocation (LDA)-based topic modeling to examine 1114 publications regarding ML applications in agriculture, sourced from the Scopus database. The analysis indicates notable expansion in ML studies, featuring leading publications across various interdisciplinary fields. Six primary areas have been identified: precision agriculture and remote monitoring, molecular and food composition analysis, food systems and agricultural applications, quality assurance and adulteration detection, advanced financial and technological applications in ML, and predictive modeling for agricultural success and efficiency. Every topic is examined to highlight its contributions and possible avenues for further investigation. The analysis offers theoretical perspectives on the interdisciplinary aspects of ML in agriculture, along with practical applications for farmers, agribusiness experts, policymakers, and technologists. This study represents the first thorough review of ML applications in agriculture utilizing the LDA approach. It provides a current and comprehensive understanding of the field, while also uncovering emerging areas and opportunities for future exploration.
Open Access: Yes
Generic multidimensional economic environmental operation of power systems using equilibrium optimization algorithm
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The economic emission load dispatch (EELD) problem is one of the main challenges to power system operators due to the complexity of the interconnected power systems and the non-linear characteristics of the objective functions (OFs). Therefore, the EELD problem has attracted significant attention in the electric power system because it has important objectives. Thus, this paper proposes the equilibrium optimization algorithm (EOA) to solve the EELD problem in electrical power systems by minimizing the total fuel cost and emissions, considering system and operational constraints. The OFs are optimized with and without considering valve point effects (VPE) and transmission system loss. The multi-OF, which aims to optimize these objectives simultaneously, is considered. In the proposed EOA, agents are particles and concentrations that express the solution and position, respectively. The proposed EOA is evaluated and tested on different-sized standard test systems having 10, 20, 40, and 80 generation units through several case studies. The numerical results obtained by the proposed EOA are compared with other optimization techniques such as grey wolf optimization, particle swarm optimization (PSO), differential evolution algorithm, and other optimization techniques in the literature. To show the reliability of the proposed algorithm for solving the considered OFs on a large-scale power system with and without considering different practical constraints such as VPE, ramp-rate limits (RRL), and prohibited operating zones (POZs) of generating units, the proposed EOA is evaluated and tested on the 140-unit test system. Also, the proposed multi-objective EOA (MOEOA) successfully acquires the Pareto optimal front to find the best compromise solution between the considered OFs. Also, the statistical analysis and the Wilcoxon signed rank test between the EOA and other optimization techniques for solving the EELD problem are performed. From numerical results, the total fuel cost obtained without considering VPE using the proposed EOA is reduced by 0.1414%, 0.1295%, 0.6864%, 5.8441% than the results of PSO, with maximum savings of 150 $/hr, 78 $/hr, 820 $/hr, and 14,730 $/hr for 10, 20, 40, and 80 units, respectively. The total fuel cost considering VPE is reduced by 0.0753%, 0.2536%, 2.8891%, and 3.6186% than the base case with maximum savings of 80 $/hr, 158 $/hr, 3610 $/hr, 9230 $/hr for 10, 20, 40, and 80 units, respectively. The total emission is reduced by 1.7483%, 12.8673%, and 7.5948% from the base case for 10, 40, and 80 units, respectively. For the 140-unit test system, the total fuel cost without and with considering VPE, RRL, and POZs is reduced by 6.4203% and 7.2394%, than the results of PSO with maximum savings of 107,200 $/hr and 126,400 $/hr. The total emission is reduced by 2.5688% from the base case. The comparative studies show the superiority of the EOA for the economic/environmental operation of the power system by solving the EELD problem with more accuracy and efficiency, especially as the system size increases.
Open Access: Yes
