Search Results (262)

The construction industry faces persistent challenges, including low productivity, high waste generation, and resistance to technological innovation. Off-site modular construction, supported by Building Information Modeling (BIM), emerges as a promising strategy to address these issues and advance sustainability goals. This study aims to evaluate the practical impacts of industrialized off-site construction in the Brazilian context, focusing on cost, execution time, structural weight, and architectural–logistical constraints. The novelty lies in applying the methodology to a high standard, mixed-use multifamily building, an atypical scenario for modular construction in Brazil, and employing a MultiCriteria Decision Analysis (MCDA) to integrate results. A detailed case study is developed comparing conventional and off-site construction approaches using BIM-assisted analyses for weight reduction, cost estimates, and schedule optimization. The results show an 89% reduction in structural weight, a 6% decrease in overall costs, and a 40% reduction in project duration when adopting fully off-site solutions. The integration of results was performed through the Weighted Scoring Method (WSM), a form of MCDA chosen for its transparency and adaptability to case studies. While this study defined weights and scores, the framework allows the future incorporation of stakeholder input. Challenges identified include the need for early design integration, transport limitations, and site-specific constraints. By quantifying benefits and limitations, this study contributes to expanding the understanding of off-site modular adaptability of construction projects beyond low-cost housing, demonstrating its potential for diverse projects and advancing its implementation in emerging markets. Beyond technical and economic outcomes, the study also frames off-site modular construction within the three pillars of sustainability. Environmentally, it reduces structural weight, resource consumption, and on-site waste; economically, it improves cost efficiency and project delivery times; and socially, it offers potential benefits such as safer working conditions, reduced urban disruption, and faster provision of community-oriented buildings. These dimensions highlight its broader contribution to sustainable development in Brazil. Full article

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency. Full article

Seismic retrofitting of reinforced concrete (RC) structures is essential for improving resilience and extending service life, particularly in regions with outdated building codes. However, selecting the optimal retrofitting strategy requires balancing multiple interdependent sustainability criteria—economic, environmental, and social—under expert-based uncertainty. This study presents a fuzzy hybrid multi-criteria decision-making (MCDM) approach that combines DEMATEL, DANP, and TOPSIS to represent causal interdependencies, derive interlinked priority weights, and rank retrofit alternatives. The assessment applies three complementary life cycle-based tools—cost-based, environmental, and social sustainability analyses following LCCA, LCA, and S-LCA frameworks, respectively—to evaluate three commonly used retrofitting strategies: RC jacketing, steel jacketing, and carbon fiber-reinforced polymer (CFRP) wrapping. The fuzzy-DANP methodology enables accurate modeling of feedback among sustainability dimensions and improves expert consensus through causal mapping. The findings identify CFRP as the top-ranked alternative, primarily attributed to its enhanced performance in both environmental and social aspects. The model’s robustness is confirmed via sensitivity analysis and cross-method validation. This mathematically grounded framework offers a reproducible and interpretable tool for decision-makers in civil infrastructure, enabling sustainability-oriented retrofitting under uncertainty. Full article

This paper presents a procedural method aimed at protecting maritime critical infrastructure, which is essential for the functioning of developed nations. A novel approach, developed by the authors, is introduced—focusing on the behavioral analysis of vessels to enable early identification of suspicious maritime activity and to prevent damage or destruction to key infrastructure elements. An integrated system is proposed, combining real-time electronic surveillance with continuous access to and analysis of data from both national and international databases. Drawing inspiration from medical sciences, a screening-based methodology has been developed. Data on vessels collected from various sources are processed according to the criteria adopted by the authors, using a multi-criteria decision analysis (MCDA) approach. MCDA is a decision-support method that considers multiple criteria simultaneously. It allows for the comparison and evaluation of different options, even when they are difficult to compare directly. This characteristic is used to select high-risk vessels for further monitoring. An initial classification of a vessel as suspicious does not constitute proof of criminal activity but rather serves as a trigger for further coordinated actions. Data on vessels is collected from the AIS (automatic identification system) and platforms that store vessel history. The AIS is a powerful tool that processes parameters such as a ship’s speed and course. This article presents sample results from surveillance and pre-selection analyses using the AIS, followed by a multi-criteria assessment of the behavior of vessels identified through this process. The results are presented both graphically and numerically. The authors conducted several scenarios, analyzing different groups of vessels. Based on this analysis, recommendations were developed for the interpretation of the findings. Full article

Multidimensional clustering of large-scale multi-view data is an important topic because it makes possible to combine a variety of manifestations of a complex information set. Nevertheless, comparing and selecting the most suitable deep clustering method is not an easy task, especially when several opposing criteria are applied. Multi-criteria decision-making (MCDM) techniques provide systematic approaches to making such judgments, although they are often limited in their ability to handle uncertainty, imprecise judgments, and interdependencies in practice. To solve these problems, this paper suggests a circular Fermatean fuzzy technique order preference by similarity to ideal solution (CFF-TOPSIS) method, which combines improved fuzzy modeling with MCDM to make the decision-making process accurate and sound. By exploiting the intrinsic symmetry of TOPSIS, where distances to positive and negative ideal solutions are treated symmetrically, the proposed model integrates five evaluation criteria for assessing clustering adequacy, including clustering accuracy, scalability, computational complexity, robustness, and interpretability, to critically evaluate five alternative clustering methods based on the input of three decision-makers. This measurement is performed efficiently by the CFF-TOPSIS method based on the uncertainty and subjective judgment contained within circular Fermatean fuzzy sets (CFFSs). The model is reliable and superior to existing models, as confirmed by sensitivity and comparative analyses. The suggested approach provides a systematic and flexible method for making decisions in complex big-data settings, while maintaining symmetry in the evaluation of alternatives and criteria. Full article

Football performance results from the dynamic interaction between physical, tactical, technical, and psychological dimensions—each of which also influences player well-being, recovery, and readiness. However, integrated monitoring approaches remain scarce, particularly in youth and sub-elite contexts. This systematic review screened 341 records from PubMed, Scopus, and Web of Science, with 46 studies meeting the inclusion criteria (n = 1763 players; age range: 13.2–28.7 years). Physical external load was reported in 44 studies using GPS-derived metrics such as total distance and high-speed running, while internal load was examined in 36 studies through session-RPE (rate of perceived exertion × duration), heart rate zones, training impulse (TRIMP), and Player Load (PL). A total of 22 studies included well-being indicators capturing fatigue, sleep quality, stress levels, and muscle soreness, through tools such as the Hooper Index (HI), the Total Quality Recovery (TQR) scale, and various Likert-type or composite wellness scores. Tactical behaviours (n = 15) were derived from positional tracking systems, while technical performance (n = 7) was assessed using metrics like pass accuracy and expected goals, typically obtained from Wyscout^® or TRACAB^® (a multi-camera optical tracking system). Only five studies employed multivariate models to examine interactions between performance domains or to predict well-being outcomes. Most remained observational, relying on descriptive analyses and examining each domain in isolation. These findings reveal a fragmented approach to player monitoring and a lack of conceptual integration between physical, psychological, tactical, and technical indicators. Future research should prioritise multidimensional, standardised monitoring frameworks that combine contextual, psychophysiological, and performance data to improve applied decision-making and support player health, particularly in sub-elite and youth populations. Full article

While traditional disposal of solid waste from the global wine industry causes significant environmental burden and hazards, a range of value-added by-products can be produced from the grape marc. This review focuses therefore on crucial sustainability-enhancing technologies for pomace dewatering and separation, which constitute a mandatory stage in obtaining storage-stable by-products and final value-added commodities. A number of dryers and separators were considered for pre-treatment of wet grape marc and analysed in terms of their design characteristics, functionality, feasibility, throughput and efficiency. A multi-criteria decision analysis was carried out to compare, rank and select the equipment which is most suitable for the purpose. It was found out that the rotary drum dryer and the drum screen separator with internal blade rotor are the best candidates to fulfil the technology requirements, while the flowsheet that includes an initial separation followed by drying of the resulting fractions is a rather attractive option. Valorising grape waste worldwide contributes substantially to achieving the United Nations Sustainable Development Goals for responsible consumption and production, mitigating climate change, caring for health and well-being, preserving land life and combating hunger. Full article

Driven by global climate change and sustainable development, the coordinated development of multiple industries based on photovoltaic energy in the “Desert-Gobi-Wilderness” region has become the key to achieving sustainable development, as well as transforming and upgrading the energy structure. However, the site selection decision for “Photovoltaic +” multi-industry integration, which takes into account economic, social and ecological benefits in a complex ecological environment, is still a key difficulty that restricts the feasibility and scalability of the project. This study first identified and systematically analyzed six “PV +” multi-industry integrations suitable for development in China, including “PV + sand control”, “PV + agriculture”, “PV + agriculture + tourism”, “PV + animal husbandry”, “PV + animal husbandry + tourism”, and “PV + tourism”. Then, a site selection decision framework for “PV +” multi-industry integration consists of three parts. Part 1 establishes a multi-dimensional suitability assessment system that takes into account heterogeneous data from multiple sources. Part 2 uses an integration method based on BWM-CRITIC-TODIM for priority ranking analysis, which first uses a Geographic Information System (GIS) to carry out suitability simulation for the entire region of China—identifying six alternative regions—then uses the interactive and multi-criteria decision-making (MCDM) method to prioritize the alternative areas. Part 3 carries out further sensitivity analyses, scenario analyses, and comparative analyses to verify the dynamics and scientific nature of the site selection decision framework. Finally, this study identifies regions of high suitability for development corresponding to the six multi-industry integrations. The framework is designed to help decision stakeholders achieve precise site selection and benefit optimization for “PV +” multi-industry integration and provides a replicable planning tool for achieving industrial synergy and sustainable development in the “Desert-Gobi-Wilderness” region driven by green energy. Full article

In this study, the authors present the results of a systematic literature review on applications of the technique for order of preference by similarity to ideal solution (TOPSIS) in power systems. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach was used in the identification of publications used in this research. The SCOPUS database was utilized to locate the publication, and a total of 78 articles published between 2014 and 2024 were included in the review. A bibliometric analysis was performed, and reports were given on the annual number of publications and the top 10 cited journals. The main themes emerging from the content review of the publications were types of TOPSIS approaches, calculation of weights in multi-criteria decision-making (MCDM) problems, energy markets applications, renewable energy technologies assessment, heating and cooling systems combined with power systems, power system operation strategies, power system stability assessment, power system operations planning, and other power systems applications. Research trends and developments in the area were analyzed to identify the existing gaps. Proposed future research areas were identified based on trends and gaps presented. Full article

Accelerating the low-carbon transition of China’s coal-fired power sector is essential for advancing national sustainability goals and fulfilling global climate commitments. This study introduces an integrated, data-driven analytical framework to facilitate the sustainable transformation of the coal power sector through coordinated unit-level retirements, new capacity planning, and targeted retrofits. By combining a comprehensive unit-level database with a multi-criteria evaluation framework, the analysis incorporates environmental, technical, and economic factors into decision-making for retirement scheduling. Scenario analyses based on the China Energy Transformation Outlook (CETO 2024) delineate both baseline and ideal carbon neutrality pathways. Optimization algorithms are employed to identify cost-effective retrofit strategies or portfolios, minimizing levelized carbon reduction costs. The findings reveal that cumulative emissions can be reduced by 10–14.9 GtCO₂ by 2060, with advanced technologies like CCUS and co-firing contributing over half of retrofit-driven mitigation. The estimated transition cost of 6.2–6.7 trillion CNY underscores the scale of sustainable investment required. Sensitivity analyses further highlight the critical role of reducing green hydrogen costs to enable deep decarbonization. Overall, this study provides a robust and replicable planning tool to support policymakers in formulating strategies that align coal power sector transformation with long-term sustainability and China’s carbon neutrality commitments. Full article

De novo metastatic breast cancer (dnMBC) accounts for 3–10% of newly diagnosed cases, with 20–40% presenting as a bone-only metastatic disease, which can achieve survival outcomes exceeding 10 years with multimodal therapy. However, the role of multimodal therapy remains controversial in the guidelines. Objective: This study aims to identify dnBOMBC subgroups to develop a pragmatic staging system for guiding locoregional therapy decisions. Materials and Methods: Data from the MF07-01 phase III randomized trial (2021, median follow-up time (mFT): 40 months (range 1–131)) and the BOMET prospective multi-institutional registry trial (2021, mFT: 34 months (range 25–45)) were combined for analysis, including only patients who presented with bone-only metastases. Exclusion criteria were patients under 18 and those with a history of prior cancer or cancer metastases. Patients with missing data and positive surgical margins were excluded. Out of 770 patients, 589 were included. Survival analyses were first conducted according to molecular subgroups, after which patients were further stratified by hormone receptor status, human epidermal human epidermal growth factor receptor 2 (HER2) status, tumor grade, and clinical T (cT) stage. Group A (GrA) included hormone receptor (HR)-positive, low- or intermediate-grade tumors at any cT; HR-positive, high-grade tumors with cT0–3; or any HER2-positive tumors. Group B (GrB) included HR-positive, high-grade tumors with cT4 disease or any triple-negative (TN) tumors. Results: The hazard of death (HoD) was 43% lower in GrA than in GrB. Median OS was 65 months (39–104) for GrA patients and 44 months (28–72) for GrB patients (HR 0.57, 95% CI 0.41–0.78, p = 0.0003). Primary tumor surgery (PTS) significantly improved OS in GrA patients, regardless of the number of metastases (solitary: HR, 0.375, 95% CI 0.259–0.543, p < 0.001; multiple: HR 0.435, 95% CI 0.334–0.615, p < 0.001). Conversely, GrB patients did not experience a significant benefit from PTS. Conclusions: This study demonstrates that GrA patients have better OS than GrB patients, and PTS reduces the HoD in GrA patients compared to systemic therapy alone. These findings support using a modified staging system in dnBOBMC to identify patients who may benefit from multimodal therapy including PTS. Full article

Plant modeling uses mathematical and computational methods to simulate plant structures, physiological processes, and interactions with various environments. In precision agriculture, it enables the digital monitoring and prediction of crop growth, supporting better management and efficient resource use. Wheat, as a major global staple, is vital for food security. However, wheat stripe rust, a widespread and destructive disease, threatens yield stability. The paper proposes wheat cultivation suitability evaluation with stripe rust disease using an agriculture group consensus framework (WCSE-AGC) to tackle this issue. Assessing stripe rust severity in regions relies on wheat pathologists’ judgments based on multiple criteria, creating a multi-attribute, multi-decision-maker consensus problem. Limited regional coverage and inconsistent evaluations among wheat pathologists complicate consensus-reaching. To support wheat pathologist participation, this study employs artificial-intelligence-generated content (AIGC) techniques by using Claude 3.7 to simulate wheat pathologists’ scoring through role-playing and chain-of-thought prompting. WCSE-AGC comprises three main stages. First, a graph neural network (GNN) models trust propagation within wheat pathologists’ social networks, completing missing trust links and providing a solid foundation for weighting and clustering. This ensures reliable expert influence estimations. Second, integrating secretary bird optimization (SBO), K-means, and three-way clustering detects overlapping wheat pathologist subgroups, reducing opinion divergence and improving consensus inclusiveness and convergence. Third, a two-stage optimization balances group fairness and adjustment cost, enhancing consensus practicality and acceptance. The paper conducts experiments using publicly available real wheat stripe rust datasets from four different locations, Ethiopia, India, Turkey, and China, and validates the effectiveness and robustness of the framework through comparative and sensitivity analyses. Full article

Low-energy bone fractures refer to injuries that occur from minimal trauma or impact. These fractures are often a result of activities, such as falls from standing height or minor accidents, where the force exerted on the bone is insufficient to cause a break under normal conditions. To design an effective orthotic splint, it is critical to select the appropriate material that mimics the mechanical properties of traditional materials like plaster, which has long been used for immobilization purposes. In this case, Ansys CES Edupack 2025 software was utilized to evaluate and identify materials with mechanical characteristics similar to those of plaster. The software provided a list of six materials that met these criteria, but selecting the most suitable material involved more than just mechanical properties. Three different multicriteria decision-making methods were employed to ensure the best choice: TOPSIS, VIKOR, and COPRAS. These methods were applied to consider various factors, such as strength, flexibility, weight, cost, and ease of manufacturing. The results of the analyses revealed a strong consensus across all three methods. Each approach identified PLA (Polylactic Acid) as the most appropriate material for the orthotic design. Following the material selection process, simulations were conducted to assess the structural performance of the orthotic splint. The results determined that the minimum thickness required for the PLA orthosis was 4 mm, ensuring that it met all necessary criteria for acceptable stresses and deformations during the four primary movements exerted by the wrist. This thickness was sufficient to maintain the orthosis’s functionality without compromising comfort or effectiveness. Moreover, a significant improvement in the design was achieved through topological optimization, where the mass of the preliminary design was reduced by 9.58%, demonstrating an efficient use of material while maintaining structural integrity. Full article

Energy efficiency (EE) is an important driver of quality of life (QoL), which is an overarching goal of sustainable development (SD). The levels of these phenomena in the European Union (EU) vary. Previous analyses presented in the literature have focused mainly on a one-dimensional view of EE and QoL. The authors of this article, given the multidimensional nature of the phenomena under study, present both categories from a holistic perspective. The purpose of this study was to identify the level of QoL in the context of EE and to compare the results of the classification of EU countries in terms of the analyzed phenomena. The study was conducted using the ELECTRE Tri method, one of the advanced techniques of multi-criteria decision analysis (MCDA). The classification procedure used made it possible to assign countries to predefined decision-making categories on the basis of preference threshold values and dominance relations to reference profiles. The 27 EU member states were analyzed on the basis of empirical data from 2023, using a set of 20 indicators characterizing EE and QoL. Countries were assigned to one of five classes, differentiating the level of development in both analyzed areas. Optimistic and pessimistic approaches were used to assess the stability of the classifications. The analysis showed the presence of countries with consistent results (e.g., Poland and Germany), extreme countries (Ireland and the Netherlands—high QoL with low EE; Romania and Croatia—inversely), as well as non-unique cases (e.g., Malta, the Czech Republic/Czechia, and Finland). The spatial approach indicated regions requiring special support. The results of the study can be a useful tool to support the process of designing public policies aimed at integrating social, economic, energy, and environmental goals within SD. Full article

This study assesses local stakeholders’ perceptions regarding how a Mediterranean cereal-based cropping system could transition to a more sustainable production, focusing on the identification of the most suitable alternatives for their diversification. Fifty-four stakeholders from the Aragon region in Spain, including farmers, technical advisors, public agricultural officers, local researchers, and experts from environmental NGOs, were consulted. Their responses were analysed using multi-criteria decision-making techniques to order their preferences for different farming practices and diversification strategies. Stakeholders’ responses suggest a priority for balancing soil conservation with the economic viability and continuity of farms. This is evident not only in its consideration as a priority objective but also in their preferences for farming practices, where their implications for farm profitability, especially through the choice of less costly alternatives, are a main concern. This economic rationale also influences their choice of crop diversification alternatives, with a preference for short (two-year) rotations in rainfed cereals and double cropping in irrigated cereals, showing a consideration of the balance between environmental and economic sustainability, and for diversification crops that farmers are already familiar with, aiming both to reduce the uncertainties linked to new crops and to minimise the need for technical support. Full article