Search Results (101)

Supplier selection is one of the critical processes that entail multiple complex deliberations. The selection of an appropriate alternative supplier is a highly intricate process, primarily due to there being multiple criteria which are exceptionally subjective. This paper aims to develop a practical framework for choosing a suitable precast supplier by integrating the Value Engineering (VE) concept, Stepwise Weight Assessment Ratio Analysis (SWARA), and the Weighted Aggregated Sum Product Assessment (WASPAS) technique. This paper introduces a novel method to estimate the quality weights of alternative suppliers’ criteria (CQW) by linking factory capacity with the coefficients of the nine significant criteria, computed using principal component analysis (PCA). None of the formal studies make this link directly. The framework’s findings were validated by comparing its results with an expert assessment of five Saudi supplier alternatives. The results revealed that the framework’s results agree with the expert’s judgment. The method of payment criterion received the highest weight, indicating that it was considered the most important of the nine criteria identified. Combining PCA and VE with the WASPAS technique resulted in an unprecedentedly effective selection tool for precast suppliers. Full article

Identifying and classifying mathematical errors is crucial to improving the teaching and learning process, particularly for first-year engineering students who often struggle with foundational mathematical competencies. This study aims to select the most appropriate theoretical framework for error classification by applying the Analytic Hierarchy Process (AHP), a multicriteria decision-making method. Five established classification models—Newman, Kastolan, Watson, Hadar, and Polya—were evaluated using six pedagogical criteria: precision in error identification, ease of application, focus on conceptual and procedural errors, response validation, and viability in improvement strategies. Expert judgment was incorporated through pairwise comparisons to compute priority weights for each criterion. The results reveal that the Newman framework offers the highest overall performance, primarily due to its structured approach to error analysis and its applicability in formative assessment contexts. Newman’s focus on reading, comprehension, transformation, and encoding addresses the most common errors encountered in the early stages of mathematical learning. The study demonstrates the utility of the AHP as a transparent and replicable methodology for educational model selection. It addresses a gap in the literature regarding evidence-based criteria for designing diagnostic instruments. These findings support the development of targeted pedagogical interventions in mathematics education for engineering programs. Full article

This research evaluates four hydrogen (H₂) production technologies via water electrolysis (WE): alkaline water electrolysis (AWE), proton exchange membrane electrolysis (PEME), anion exchange membrane electrolysis (AEME), and solid oxide electrolysis (SOE). Two scoring and ranking methods, the MACBETH method and the Pugh decision matrix, are utilized for this evaluation. The scoring process employs nine decision criteria: capital expenditure (CAPEX), operating expenditure (OPEX), operating efficiency (SOE), startup time (SuT), environmental impact (EI), technology readiness level (TRL), maintenance requirements (MRs), supply chain challenges (SCCs), and levelized cost of H₂ (LCOH). The MACBETH method involves pairwise technology comparisons for each decision criterion using seven qualitative judgment categories, which are converted into quantitative scores via M-MACBETH software (Version 3.2.0). The Pugh decision matrix benchmarks WE technologies using a baseline technology—SMR with CCS—and a three-point scoring scale (0 for the baseline, +1 for better, −1 for worse). Results from both methods indicate AWE as the leading H₂ production technology, which is followed by AEME, PEME, and SOE. AWE excels due to its lowest CAPEX and OPEX, highest TRL, and optimal operational efficiency (at ≈7 bars of pressure), which minimizes LCOH. AEME demonstrates balanced performance across the criteria. While PEME shows advantages in some areas, it requires improvements in others. SOE has the most areas needing enhancement. These insights can direct future R&D efforts toward the most promising H₂ production technologies to achieve the net-zero goal. Full article

As technological progress and population growth continue to drive rising energy demand, renewable energy has emerged as a key focus of the global energy transition due to its environmental sustainability. However, in suitability assessments and site selection for green energy projects such as photovoltaic (PV) power generation, key criteria such as supply–demand balance and land price are often inadequately considered, despite their direct impact on decision outcomes. Moreover, excessive reliance on expert judgment for weighting, along with the neglect of inter-criterion relationships, introduces uncertainty. Combined with the presence of ill-posed problems, these issues limit the practical value of the evaluation results. This study integrates economic cost–benefit analysis into the evaluation criteria system alongside climatic and geographical criteria, constructing a set of 11 spatial indicators, including global horizontal irradiation (GHI), land prices, and regional power demand, to support PV site selection. Furthermore, a comprehensive evaluation framework is proposed that combines geographic information systems (GIS), multi-criteria decision analysis (MCDA), fuzzy comprehensive evaluation (FCE), and machine learning (ML). The framework enables the collaborative optimization of expert-constrained and data-driven criteria weighting. A national suitability zoning map for PV power plants was developed and validated against actual construction cases. The results demonstrate that the proposed methodology outperforms traditional approaches, achieving a 0.1178 improvement in weight determination compared to expert-based methods, producing a photovoltaic suitability map that more accurately reflects actual construction trends, thereby providing better and more effective support for PV site planning. Full article

Selecting an appropriate business development model is central to strategic decision-making in economic and business management. These models shape sustainable growth, long-term scalability, and strategic flexibility. Existing evaluation methods rely on heuristic or qualitative judgments that lack transparency, reproducibility, and sensitivity to evaluation criteria. To address these limitations, this study introduces a hybrid multi-criteria decision-making (MCDM) framework that integrates VIKOR, entropy weighting, and simulation to evaluate 35 business development models derived from 245 real-world cases. The evaluation covers six strategic criteria: scalability, adaptability, risk exposure, financial sustainability, implementation complexity, and market relevance. Entropy weighting assigns criterion importance based on data variability, and simulation generates input sets for sensitivity and stability analysis. Results highlight Cross-Border Investment, Tiered Access, and Crowd-Backed models as top-performing strategies across multiple dimensions. By combining multiple tools in a unified framework, the research advances MCDM methodology and supports strategic business development planning under uncertainty. This contribution strengthens both academic insight and managerial practice in economics and business management. Full article

This research proposes a robustness analysis model for the frequency performance of grid-forming converter systems (GFMCS). Based on the derived sensitivity and complementary sensitivity functions, a system robustness identification framework is established. This framework includes two key frequency performance indices: sensitivity and robust stability. Sensitivity analysis is used to describe the system’s sensitivity to external disturbances. In addition, the judgment criterion is proposed to quantitatively and intuitively identify the robustness of the GFMCS. Furthermore, the influence mechanisms of the damping and inertia coefficients on robustness are examined comprehensively. This study finds that when the damping coefficient is small, and the inertia coefficient is large, the system is more prone to oscillatory instability, resulting in longer stability time as well as poorer robustness. Finally, the theoretical analysis is validated experimentally. Full article

In multicriteria decision-making (MCDM), methods such as TOPSIS are essential for evaluating and comparing alternatives across multiple criteria. However, traditional normalization techniques often struggle with datasets containing outliers, large variances, or heterogeneous measurement units, which can lead to skewed or biased rankings. To address these challenges, this paper proposes an adaptive, cluster-based normalization approach, demonstrated through a real-world logistics case study involving the selection of a host city for an international event. The method groups alternatives into clusters based on similarities in criterion values and applies logarithmic normalization within each cluster. This localized strategy reduces the influence of outliers and ensures that scaling adjustments reflect the specific characteristics of each group. In the case study—where cities were evaluated based on cost, infrastructure, safety, and accessibility—the cluster-based normalization method yielded more stable and balanced rankings, even in the presence of significant data variability. By reducing the influence of outliers through logarithmic normalization and allowing predefined cluster profiles to reflect expert judgment, the method improves fairness and adaptability. These features strengthen TOPSIS’s ability to deliver accurate, balanced, and context-aware decisions in complex, real-world scenarios. Full article

Decision makers (DMs) are often viewed as autonomous in the majority of multicriteria group decision making (MCGDM) situations, and their psychological behaviors are seldom taken into account. Once more, we are unable to prevent both positive and negative flows of varying alternative preferences due to the nature of attributes or criteria in complicated decision-making problems. However, DMs’ perspectives are likely to affect one another in complicated MCGDM issues, and they frequently use subjective limited rationality while making decisions. The multicriteria quantum decision theory-based group decision making integrating the TODIM-PROMETHEE II strategy under linguistic Z-numbers (LZNs) is designed to overcome the aforementioned problems. In our established technique, the PROMETHEE II controls the positive and negative flows of distinct alternative preferences, the TODIM method manages the experts’ personal regrets over a criterion, and the quantum probability theory (QPT) addresses human cognition and behavior. Because LZNs can convey linguistic judgment and trustworthiness, we provide expert LZNs for their viewpoints in this work. We determine the criterion weights for each expert after first obtaining their respective expert weights. Second, to represent the limited rational behaviors of the DMs, the TODIM-PROMETHEE II approach is introduced. It is employed to determine each alternative’s dominance in both positive and negative flows. Third, a framework for quantum possibilistic aggregation is developed to investigate the effects of interference between the views of DMs. The views of DMs are seen in this procedure as synchronously occurring wave functions that affect the overall outcome by interfering with one another. The model’s efficacy is then assessed by a selection of renewable energy case studies, sensitive analysis, comparative analysis, and debate. Full article

Introduction: We conducted a comprehensive comparative analysis of the Okazaki, Umehara, and American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) classification criteria for diagnosing immunoglobulin G4-related disease (IgG4-RD). Materials and Methods: A retrospective study was conducted in a single tertiary hospital, using expert clinical judgment as the gold standard. We compared the diagnostic accuracy of the Okazaki, Umehara, and ACR/EULAR criteria in a cohort of 41 patients with suspected IgG4-RD. We assessed sensitivity, specificity, and positive and negative predictive values for each criterion, and conducted a separate analysis based on four IgG4-RD subtypes. Results: A total of 30 patients were confirmed to have IgG4-RD and 11 were identified as mimickers. The Umehara criteria demonstrated the highest sensitivity (83.33%), followed by the ACR/EULAR 2019 (66.67%) and Okazaki (60.0%) criteria. All three criteria exhibited 100% specificity, with overall diagnostic accuracy ranging from 70% to 88%. The areas under the curve (AUC) were 0.917 (Umehara), 0.800 (Okazaki), and 0.833 (ACR/EULAR 2019), indicating significant diagnostic effectiveness (p < 0.000). Subtype analysis revealed that the Umehara and ACR/EULAR 2019 criteria were more effective in diagnosing pancreato-hepato-biliary involvement (subtype 1), while the Okazaki and ACR/EULAR 2019 criteria were more effective in diagnosing retroperitoneal fibrosis and/or aortitis (subtype 2). Conclusions: Our study provides valuable insights into the diagnostic performance of the Okazaki, Umehara, and ACR/EULAR criteria for a cohort of patients with suspected IgG4-RD. The Umehara criterion demonstrated the highest sensitivity, suggesting its potential utility for screening purposes, while all three criteria showed consistent specificity. Full article

Solid waste disposal methods within indigenous communities present unique challenges and opportunities for sustainable development. However, the current knowledge on solid waste management focuses on formal waste collection systems, neglecting the practices and sustainability aspects of solid waste management in indigenous communities. Thus, it becomes imperative to undertake research studies that evaluate the sustainability of these practices as they play a pivotal role in ensuring sustainable development. The current study systematically evaluates the views and judgments associated with the sustainability aspects of indigenous waste management practices in the rural communities of South Africa using the Analytic Hierarchy Process (AHP) model. The data analysis was carried out using the AHP model. The findings of this study showed that the rural communities of Bushbuckridge Local Municipality prioritize the sustainability of the environment (weight: 0.590) over the economic (weight: 0.240) and social sustainability (weight: 0.165) based on the AHP evaluative framework. The validity of the priorities was tested through the computed degree of consistency (<10%) and an eigenvalue of 5.107. Furthermore, according to the assessment in the current study, the AHP evaluative framework dominantly prioritizes the sub-criteria of environmental sustainability (composting) at a responding rate of over 70% almost across all indigenous communities except for Acornhoek (30%), Casteel (25%), and Mambumbu (24%). Likewise, the sub-criterion of social sustainability, which is associated with communal cleaning labor, was found to be of extreme importance (60%), outperforming taboos (10%) that are anchored in cultural and spiritual beliefs. With a response rate > 50%, waste trading proved to be of economic efficacy. Using the AHP model to evaluate the sustainability aspects associated with indigenous solid waste management practices addresses a substantial gap in the comprehension of the role of indigenous knowledge towards sustainability in the discipline of solid waste management. However, it also offers a valuable sustainability perception that is associated with indigenous waste disposal methods that local governments and policymakers should include for consideration in integrated waste management plans. This can lead to the development of waste disposal programs that are well-coordinated and in accordance with indigenous sustainable waste management practices that advance the circular economy and promote environmental protection. Full article

Selecting the most preferred software for teaching power systems engineering at the undergraduate level is a complex problem in developing countries, and it requires making an informed decision by compromising on various criteria. This study proposes a multi-criteria framework to determine the most preferred software solution for instructing undergraduate power system modules using the Fuzzy-ARAS (additive ratio assessment) method and expert opinions. Twelve evaluation criteria were used to evaluate eight widely used software packages. A questionnaire was designed to capture views from professionals in academia and industry on the criteria weights and ranking of software options. Linguistic terms were used to represent the experts’ judgment, and weights were assigned to each criterion. The Fuzzy-ARAS multi-criteria decision approach was applied to obtain ratings for each software alternative. Based on the result, MATLAB emerged as the most preferred software for instructing power systems analysis, whereas MATPOWER (V 8.0) was rated as the least preferred choice. In addition, the Fuzzy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach was used, producing a separate ranking; the most preferred software was MATPOWER, while the least preferred software was NEPLAN (V 360 10.5.1). A new coefficient that combines the findings of the two approaches was suggested to reconcile the ranks. The combined ranking aligns with the result of the Fuzzy-TOPSIS method by returning MATLAB as the most preferred, while the least preferred software was NEPLAN. This study significantly contributes to the choice of software for undergraduate power systems analysis instruction by providing direction to educators and institutions looking for software solutions to improve undergraduate power systems analysis education. Full article

The error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) equivalent models, and can promote the standardization process of equivalent methodology. Due to differences in power system conditions and model evaluation standards in different countries, the form and indexes of error thresholds of WF equivalent models have not been unified yet. This paper proposes a theoretical method for quantifying the minimum risk of error threshold of WF equivalent models based on the Bayes discriminant criterion. Firstly, the Euclidean errors of WF equivalent models in different periods are quantified, and the probability density distributions of the errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the prior probability of a valid WF equivalent model, and the different losses caused by the missed judgment and misjudgment of the model validity to power systems are taken into account. Thirdly, the minimum risk quantification model of error threshold is established based on the Bayes discriminant criterion, and the feasibility of the proposed method is verified by an actual WF with numerical examples. Compared with the existing error thresholds, the proposed error threshold can more quickly and accurately determine the validity of WF equivalent models. Full article

Deep learning-based object detection has become a powerful tool in dress code monitoring. However, even state-of-the-art detection models inevitably suffer from false alarms or missed detections, especially when handling small targets such as hats and masks. To overcome these limitations, this paper proposes a novel method for dress code monitoring using an improved YOLOv8n model, the DeepSORT tracking, and a new dress code judgment criterion. We improve the YOLOv8n model through three means: (1) a new neck structure named FPN-PAN-FPN (FPF) is introduced to enhance the model’s feature fusion capability, (2) Receptive-Field Attention convolutional operation (RFAConv) is utilized to better capture the difference in information brought by different positions, and a (3) Focused Linear Attention (FLatten) mechanism is added to expand the model’s receptive field. This improved YOLOv8n model increases mAP while reducing model size. Next, DeepSORT is integrated to obtain instance information across multi-frames. Finally, we adopt a new judgment criterion to conduct real-scene dress code monitoring. The experimental results show that our method effectively identifies instances of dress violations, reduces false alarms, and improves accuracy. Full article

Open-pit mining seriously damages the original vegetation community and soil layer and disturbs the carbon cycle of vegetation and soil, causing instability in the mining ecosystem and decrease in the carbon sequestration capacity of the mining area. With the deepening of environmental awareness and the influence of related policies, the ecological restoration of open-pit mines has been promoted. The mining ecosystem is distinct owing to the disperse distribution of mines and small scale of single mines. However, the carbon sequestration capability of mines after ecological restoration has not been clearly evaluated. Therefore, this study evaluated the carbon sequestration capacity of restoration mines, taking the mines of the Yangtze River Basin in Jurong City, Jiangsu Province as the research objects. Firstly, the visual effects of the vegetation and soil in their current status were determined through field investigation, the methods for sampling and data collection for the vegetation and soil were selected, and the specific laboratory tests such as the vegetation carbon content and soil organic carbon were clarified. Meanwhile, the evaluation system consisting of three aspects and nine evaluation indexes was established by using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The process of evaluation included the following: the establishment of the judgment matrix, calculation of the index weight, determination of the membership function, and establishment of the fuzzy membership matrix. Finally, the evaluation results of the restoration mines were determined with the ‘excellent, good, normal and poor’ grade classification according to the evaluation standards for each index proposed considering the data of the field investigation and laboratory tests. The results indicated that (1) the evaluation results of the mines’ carbon sequestration capacity were of excellent and good grade at a proportion of 62.5% and 37.5%, which was in line with the field investigation results and demonstrated the carbon sequestration capacity of all the restored mines was effectively improved; and (2) the weights of the criterion layer were ranked as system stability > vegetation > soil with the largest value of 0.547, indicating the stability of the system is the main factor in the carbon sequestration capacity of the mines and the sustainability of the vegetation community and the stability of soil fixation on the slope. The proposed evaluation system effectively evaluates the short-term carbon sequestration capability of the restoration mining system according to the visual effects and the laboratory testing results, objectively reflecting the carbon sequestration capacity via qualitative assessment and quantitative analysis. The evaluation method is relatively applicable and reliable for restoration mines and can provide a reference for similar ecological restoration engineering. Full article

Background: Symmetry is a special kind of balance. This study aims to systematically explore and apply the role of balanced composition in aesthetic judgments by focusing on balanced composition features and employing research methods from computational aesthetics and neuroaesthetics. Methods: First, experimental materials were classified by quantifying balanced composition using several indices, including symmetry, center of gravity, and negative space. An EEG experiment was conducted with 18 participants, who were asked to respond dichotomously to the same stimuli under different judgment tasks (balance and aesthetics), with both behavioral and EEG data being recorded and analyzed. Subsequently, participants’ data were combined with balanced composition indices to construct and analyze various SVM classification models. Results: Participants largely used balanced composition as a criterion for aesthetic evaluation. ERP data indicated that from 300–500 ms post-stimulus, brain activation was more significant in the aesthetic task, with unbeautiful and imbalanced stimuli eliciting larger frontal negative waves and occipital positive waves. From 600–1000 ms, beautiful stimuli caused smaller negative waves in the PZ channel. The results of the SVM models indicated that the model incorporating aesthetic subject data (ACC = 0.9989) outperforms the model using only balanced composition parameters of the aesthetic object (ACC = 0.7074). Conclusions: Balanced composition is a crucial indicator in aesthetics, with similar early processing stages in both balance and aesthetic judgments. Multi-modal data models validated the advantage of including human factors in aesthetic evaluation systems. This interdisciplinary approach not only enhances our understanding of the cognitive and emotional processes involved in aesthetic judgments but also enables the construction of more reasonable machine learning models to simulate and predict human aesthetic preferences. Full article