Search Results (166)

Agricultural development is vital for East Africa’s economic growth, yet the region faces significant disparities and systemic barriers. A critical problem exists due to the lack of an integrated quantitative framework to systematically comparing agricultural capacities and facilitate optimal resource allocation, as existing studies often overlook combined internal and external factors. This study proposes a comprehensive multi-criteria decision-making (MCDM) model to assess, categorize, and strategically profile the agricultural development capacity of 18 East African countries. The method employed is an integrated Entropy-MAIRCA model, which objectively weighs six criteria (the food production index, arable land, production fluctuation, food export/import ratios, and the political stability index) and ranks countries by their distance from an ideal development state. The experiment applied this framework to 18 East African nations using official data. The results revealed significant differences, forming four distinct strategic groups: frontier, emerging, trade-dependent, and high risk. The food export index (C4) and production volatility (C3) were identified as the most influential criteria. This model’s contribution is providing a science-based, transparent decision support tool for designing sustainable agricultural policies, aiding investment planning, and promoting regional cooperation, while emphasizing the crucial role of institutional factors. Full article

Climate change, the consequences of which have been more intense than ever in the last few decades, makes the need for sustainable transportation even more imperative. The promotion of public transportation and the discouragement of private car use are among the main priorities of sustainable transport planning in modern urban areas. However, the selection of the most appropriate transport project, apart from significant opportunities, is also accompanied by significant challenges, especially under the demand of compromising—often conflicting—social, environmental, and economic criteria, as well as different stakeholders’ interests. The aim of the present paper is to provide decision analysts and policy-makers with a decision-support tool for the prioritization and optimum selection of public transport projects for an urban area within the framework of sustainability. For this purpose, a comprehensive inventory of criteria for the evaluation of urban public transport systems (alternatives), along with a standardized table with the relevant performance of the most common alternatives (i.e., metro, tram, monorail, and BRT) are provided based on international literature review. A multi-criteria decision-aiding methodology based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), allowing for the direct exclusion of an alternative not meeting certain “binding” criteria from further evaluation, thus saving time, effort and cost, taking into account different stakeholders’ interests and preferences, as well as the particularities and special characteristics of the study area, is then proposed and tested through a theoretical case study. Full article

In this paper, we propose the use of virtual labs (VLs) as a solution to bridge the gap between theory and practice in physics education. Through an experiment conducted in two towns in the Democratic Republic of the Congo (DRC), we demonstrate that our proposed lab (BRVL) is more effective than global alternatives in correcting misconceptions and ensuring compliance with the current curriculum in the DRC. We combine Conjoint Analysis (from SPSS) to weigh selected criteria—curriculum compliance, knowledge construction, misconception correction, and usability—alongside eight MCDA methods: AHP, CAHP, TOPSIS, ELECTRE I, ELECTRE II, ELECTRE TRI, PROMETHEE I, and PROMETHEE II. Our findings show that, among six VLs, BRVL consistently outperforms global alternatives like Algodoo and Physion in terms of pedagogical alignment, curriculum compliance, and correction of misconceptions for Congolese schools. Methodologically, the respondents are consistent and in agreement, despite individual differences. The sensitivity analysis of the ELECTRE and PROMETHEE methods has shown that changes in parameter values do not alter the conclusion that BRVL is the best among the compared VLs. Full article

This research delves into the assessment of students’ perspectives regarding virtual internships within Brazilian organizations, a phenomenon accelerated by the global pandemic. Evaluating 78 students’ virtual internships via a survey, the study employs the Fuzzy TOPSIS Class method for analysis. Additionally, a sensitivity analysis was conducted to assess the robustness of the results. Key insights for enhancing virtual internships encompass: emphasizing application and deeper understanding of topics learned during the undergraduate course, enhancing understanding about how organizations work, and fostering comprehension of market dynamics. Among the points best rated by students are the opportunity to explore new subjects, development of soft skills, and supervisors’ competence in managing teams in virtual environments. This paper contributes methodologically by proposing a multicriteria decision-making approach to assess virtual internships. The findings serve as a valuable resource for internship supervisors in companies and higher education institutions, aiding them in guiding students through this pivotal developmental phase that shapes their future careers. Full article

Failure Mode and Effect Analysis (FMEA) is crucial for identifying risk reduction opportunities in design. This study aims to aid in the design of sophisticated medical devices by setting guidelines and addressing weaknesses in data collection and risk priority numbers (RPNs). This is achieved by developing an FMEA framework with potential efficiency and efficacy benefits for design engineers, surgeons and patients. The FMEA framework covered risk analysis and risk evaluation by integrating a cascading Delphi method to address data collection and Multi-Criteria Decision-Making (MCDM) technique to address RPN calculations. This study involved the design of a flexible fetoscope for minimally invasive fetal intervention, analyzing and evaluating risks. The cascading FMEA framework had two stages for data collection, namely risk identification by individual interview and risk evaluation by individual email. The cascading Delphi FMEA framework with MCDM identified the potential risks for the mother at the tip (risk score = 0.927) and subsequent risks such as debris loss (risk score = 0.896), material degradation (risk score = 0.896), and glue dislodging (risk score = 0.896) as critical issues. By identifying failure modes early, medical device designers can better mitigate risks during the initial design stages. Full article

This study seeks to optimize the shape of a wave absorber with an inclined porous plate using an artificial neural network (ANN) model to improve the operating efficiency and experimental accuracy of a square wave basin. As our numerical tool, we employed the dual boundary element method (DBEM) to avoid the rank deficiency problem occurring at the degenerate plate boundary with zero thickness. A quadratic velocity model incorporating a CFD-based drag coefficient was employed to account for energy dissipation across the porous plate. The developed DBEM tool was validated through comparisons with self-conducted experiments in a two-dimensional wave flume. The input features such as the inclined angle and plate length affect the performance of the wave absorber. These features have been optimized to minimize the averaged reflection coefficient and the installation space (spatial footprint) with the application of a trained ANN model. The dataset used for training the ANN model was created using the DBEM model. The trained model was subsequently utilized to predict the averaged reflection coefficient using a larger dataset, aiding in the determination of the optimal wave absorber design. In the optimization process of minimizing both reflected waves and spatial footprint, the weighting factors are assigned according to their relative importance to each other, using the weighted sum model (WSM) within the multi-criteria decision-making framework. It was found that the optimal design parameters of the non-dimensional plate length ($l/h$) and inclined angle ($\theta$) are $1.46$ and $5.34°$ when performing with a weighting factor ratio (80%: 20%) between reflection and spatial footprint. Full article

This paper presents HERMEES—Holistic Evaluation and Ranking Model for Energy Efficient Systems. HERMEES is based on a multi-criteria decision-making (MCDM) model designed to select the optimal combination of lightweight cryptography (LWC) and topology construction protocol (TCP) algorithms for wireless sensor networks (WSNs) based on user-defined scenarios. The proposed model is evaluated using a scenario based on a medium-sized agricultural field. The Simple Additive Weighting (SAW) method is used to assign scores to the candidate algorithm pairs by weighting the scenario-specific criteria according to their significance in the decision-making process. To further refine the selection, mean shift clustering is utilized to group and identify the highest scored candidates. The resulting model is versatile and adaptable, enabling WSNs to be configured according to specific operational needs. The provided pseudocode elucidates the model workflow and aids in an effective implementation. The presented model establishes a solid foundation for the development of guided self-configuring context-aware WSNs capable of dynamically adapting to a wide range of application requirements. Full article

Despite the significant progress in the last few decades, road safety improvement still constitutes an imperative global need. Especially in urban areas, the improvement of road safety is an even more complicated and multi-factor problem. Every minute, a human life is lost in an urban road network in the world. Given that almost all road accidents are preventable, more effective planning toward improving road safety, as a structural element of sustainable urban mobility, is imperative. The aim of the present research is to provide decision support analysts and policy-makers with a decision-support tool that identifies and prioritizes the factors undermining road safety in an urban area, with a view to developing effective policies. For this purpose, a comprehensive inventory of factors that may undermine road safety in an urban area, as well as an inventory of relevant measures and policies, is provided, based on an international literature review. The most important factors and, subsequently, the most effective measures and policies are identified and prioritized through a multi-criteria approach (modified Delphi–analytical hierarchy process (AHP)–technique for order preference by similarity to ideal solution (TOPSIS)). The Greek urban road networks, starting from the second largest city in Greece (Thessaloniki), are selected as a case study. Problems related to limited resources not allowing for systematic surveillance and policing, making arbitrary decisions instead of adopting a scientific decision-aiding methodology, education and mentality issues, infrastructure planning and maintenance, cooperation and coordination between different authorities, and laxity of penalties are highlighted as the most important factors, based on which four sets of measures and policies are identified and prioritized. Full article

There are well-known issues in conjunction with eliciting probabilities, utilities, and criteria weights in real-life decision analysis. This article explores various computationally efficient methods for generating weights in multi-criteria decision support systems. Therefore, it constitutes an aid for MCDA modellers and tool designers in selecting surrogate methods for criteria weights. Given the challenges in eliciting precise criteria weights from decision-makers, this study evaluates a range of techniques for automatically generating surrogate weights, focusing on both ordinal and cardinal ranking approaches. With a thorough inquiry methodology never before used, we examine automatic multi-criteria weight-generating algorithms in this article. The methods tested include traditional rank-based models such as rank sum (RS), rank reciprocal (RR), and rank order centroid (ROC), alongside newer approaches like the sum reciprocal (SR) and cardinal sum reciprocal (CSR). The results show that the SR approach for the ordinal case and the CSR method for the cardinal case perform better in terms of robustness than other methods, even including the promising new geometric class of methods. It is also shown that linear programming (LP) performs poorly when compared to surrogate weight models. Additionally, as expected, the cardinal models perform better than the ordinal models. Unexpectedly, though, the well-established LP model’s performance is worse than previously thought. Full article

The development of biogas technology is essential as a renewable energy source, aiding global initiatives in sustainable energy production and waste management. Geographical, technological, and economic factors significantly vary the efficiency and viability of biogas facilities by area. This study compares the techno-economic, social, and environmental impacts of biogas plants in Germany and Pakistan using a multicriteria decision-making method that combines the Analytic Hierarchy Process and SMARTER. This research has determined the weighting factors and then assessed the comparative performance of six selected biogas facilities based on five different scenarios: (i) comprehensive base-case, (ii) environmental performance, (iii) economic performance, (iv) social performance, and (v) per-kW energy efficiency. Three of these biogas facilities are in Pakistan (a low–medium-income developing country) and three in Germany (a high-income developed country). The findings of the study indicate that technical performance is the most heavily weighted criterion, playing a crucial role in determining the overall sustainability scores. Germany’s Bioenergie Park Güstrow stood out as the leading performer, achieving sustainability scores of 63.1%, 72.9%, and 73.0% across the comprehensive base-case, environmental, and per-kW efficiency scenarios, respectively. In the same scenarios, the Gujjar Colony Biogas Plant in Pakistan recorded the lowest scores of 25.4%, 43.2%, and 53.0%. The plants selected from a developed country showed a progressive score of high impact towards sustainability in most of the scenarios. In contrast, plants selected from a developing country showed low bioenergy deployment due to various factors, highlighting the gaps and flaws in achieving optimized energy generation and sustainable growth. The critical techno-economic and socio-environmental findings of the study are vital for policymakers, industry, engineers, and other relevant stakeholders seeking to enhance the performance, scalability, and sustainability of biogas technologies across developing and developed economies. Full article

This research provides a comparative analysis of different methods of weighting criteria used in the investigation of site suitability of existing onshore wind farm projects. The ranking of this suitability was performed by integrating various multi-criteria decision-making (MCDM) techniques. The assessments of the site suitability of such projects considered several criteria, including wind velocity, distance from high-electricity grids, slope, distance from road networks, installed capacity, distance from protected areas, years of operation, and distance from settlements. Both subjective and objective methods were used to compute criteria weights and compare the results, which is the main contribution of the paper. This is especially significant, as criteria weighting in the wind farm siting literature is mainly focused on subjective methods, and therefore the criteria weights are provided by subjective judgments. In this study, 374 existing onshore wind farm projects in Greece served as alternatives, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was employed to rank their suitability. The results show very high positive correlations in the rankings of both the evaluation criteria and the alternatives when subjective methods are used. Using objective weighting methods may provide a robust solution when expert judgement is missing, and the CRITIC method seems to present a high correlation with subjective MCDM methods regarding the ranking of alternatives. Various MCDM methods could be used to assess the weighting of criteria in challenges related to site suitability of renewable energy projects, as they can aid in the selection of the most sustainable sites while minimizing the downsides and maximizing the benefits of each method. Full article

Financial Risk Management (FRM) is crucial for organizations navigating complex and volatile economic conditions, as it aids in identifying and mitigating potential losses. Conventional FRM approaches are inadequate because they do not incorporate vagueness and variability in financial data. To overcome these challenges, this research presents interval-valued picture fuzzy measurement alternatives and rankings according to the Compromise Solution (IVPF-MARCOS) method. The IVPF-MARCOS method ranks investment strategies under uncertainty by assessing ten distinct investment options across seven key factors, including market risk and return on investment. It evidences its usefulness in enhancing decision-making, increasing accuracy in FRM, and developing Multi-Criteria Group Decision-Making (MCGDM) methodologies involving aggregation operators that are symmetric in nature. Consequently, this research establishes a compelling need to adopt improved fuzzy techniques in formulating the FRM to achieve more robust financial strategies. Full article

Decision-making involves selecting the best alternative based on evaluation criteria while considering environmental impacts. The translation of environmental factors into quantifiable mathematical expressions is challenging due to the inherent uncertainties. Decision-makers can address the subjective characteristics of alternatives by incorporating fuzzy set theory into decision-making processes where uncertainty and ambiguity exist. Game theory is introduced as another approach to enhance the robustness of decision-making models, leading to more informed and flexible decision outcomes. This approach promotes strategic thinking and aids decision-making by allowing individuals to visualize the potential consequences of different decisions under various conditions. This study proposes a fuzzy multi-criteria decision support system that provides a structured framework to address the complexities of agricultural land selection. The decision support system employs a two-person zero-sum game to identify the optimal land management option, considering the strategic interactions between players. The results from the payoff matrix reveal the equilibrium point, providing an ideal solution for more effective land use planning decisions. Full article

The planning of greener, more accessible, and safer cities is the focus of several strategies that aim to improve the population’s quality of life. This concern for the environment and the population’s quality of life has led to the implementation of active mobility policies. The effectiveness of the mobility solutions that are sought heavily depends on the identification of the main factors that favor their use, as well as how adequate urban spaces are in minimizing existing difficulties. This study presents an automated geographic information system (GIS) decision support tool that allows the identification of the level of suitability of urban transportation networks for the use of active modes. The tool is based on the determination of a set of mobility indices: walkability, bikeability, e-bikeability, and active mobility (a combination of walking and cycling suitability). The indices are obtained through a spatial multi-criteria analysis that considers the geometric features of roads, population density, and the location and attractiveness of the city’s main trip-generation points. The treatment, representation, and study of the variables considered in the analysis are carried out with the aid of geoprocessing, using the spatial and network analysis tools available in the GIS. The Model Builder functionality available in ArcGIS^® was used to automate the various processes required to calculate walking, cycling, and e-biking travel times, as well as the mobility indices. The developed tool was tested and validated through its application to a case study involving the road network of the urban perimeter of the medium-sized city of Covilhã, Portugal. However, the tool is designed to be applied with minimal adaptation to different scenarios and levels of known input information, providing average or typical values when specific information is not available. As a result, a flexible and automated GIS-based tool was obtained to support urban space and mobility managers in the implementation of efficient measures compatible with each city’s scenario. Full article

Photovoltaic (PV) systems are important components of the global shift towards sustainable energy resources, utilizing solar energy to generate electricity. However, the efficiency and performance of PV systems heavily rely on cleanliness, as dust accumulation can significantly obstruct their effectiveness over time. This study undertook a comprehensive literature review and carried out multiple interviews with experts in the PV systems field to propose a map for selecting the optimal PV cleaning method for PV systems within MENA region. These factors, covering meteorological conditions, the local environment, PV system design, module characteristics, dust deposition attributes, exposure time to dust, and socio-economic and environmental considerations, were employed as criteria in a Multi-Criteria Decision-Making (MCDM) model, specifically, an Analytic Network Process (ANP). The results indicate that partially automated cleaning is the most suitable method for existing utility-scale PV projects in the MENA region. The findings provide robust guidelines for PV system stakeholders, aiding informed decision-making and enhancing the sustainability of PV cleaning processes. Full article