Spatial matching of ecosystem service supply and stakeholder preferences insights for regional management in the Shennongjia forest region, China.

Peste des petits ruminants (PPR) is a highly contagious disease of small ruminants that causes significant economic losses. The disease is endemic in most sub-Saharan countries, such as Nigeria. Despite decades of widespread vaccination efforts, the disease persists due to significant animal movements, particularly transhumance and commercial trade. This facilitates the rapid spread of the disease across Nigerian states, particularly in the semi-arid north, where there is frequent animal movement. This study aimed to identify high-risk areas for PPR transmission in the Plateau, Bauchi, and Kano states using rigorous, systematic multicriteria decision analysis (MCDA). The risk factors for PPR occurrence and their relative importance were identified through a literature review and consultation with PPR experts. The corresponding geographic data were collected, standardised, weighted and combined to pinpoint the areas most likely to experience disease outbreaks. The results were validated using a recent PPR seroprevalence survey conducted in the three states. Risk factors such as climate, small ruminant density, proximity to dry areas, small ruminant commercial and transhumance movements were identified as drivers of disease occurrence. Risk maps could be used to inform targeted disease control strategies.

Infectious disease prioritization exercises have been part of national and international health surveillance. They typically use multi-criteria approaches and generally involve expert groups composed of physicians and public health specialists from various disciplines. However, little is known about the impact of multidisciplinarity composition itself. This paper builds on the 2023 infectious disease prioritization exercise conducted by the French High Council of Public health (HCSP) which employed a Multi-Criteria Decision-Making (MCDM) method; a model-based re-analysis of the initial prioritization is provided. Using multilevel modelling methods, we evaluated the impact of the contributions of non-infectious diseases physicians by testing two hypotheses: i) significant differences exist in risks ratings across specialties for several criteria; ii) these differences impact the final disease ranking. Using pediatricians as a case study and comparing them to infectious disease specialists (IDS), we found that pediatricians' ratings differed significantly for six out of eight criteria. Counterfactual analyses demonstrated that excluding pediatricians' responses or simulating a panel composed entirely of pediatricians altered the final disease classification. Our findings underscore that a multidisciplinary approach to disease risk assessment facilitates a broader -and likely more accurate- consideration of population needs.

Selection of high-performance phase change materials for enhanced building thermal management using hybrid multi-criteria decision-making

Meta-heuristic algorithm based system identification and multi-criteria decision making approaches in time delay systems

Skyline operators in multi-criteria decision making: A review of characterization, comparison, and perspectives

UCD-MP-IoTIDF: A Unified Cross-domain and Multi-protocol Intrusion Detection Framework with Calibrated Deep Learning and Multi-criteria Decision Making–based Model Selection

The paper provides a roadmap to the circular economy technologies in manufacturing, with a main emphasis on recycling, re-manufacturing, reuse, and reduction of waste by manufacturing through closed-loop systems. The paper constructs a Hybrid Simulation-Optimization Model to evaluate different technologies such as re-manufacturing systems, smart recycling lines and IoT-based waste monitoring that is implemented with the help of blockchain-based traceability. This model applies Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS) to make a judgment on technologies in waste reduction, material reuse, and recycling efficiency, resource utilization, and social Acceptance. The findings indicate that the Full Model (MCDA + GIS + All Criteria) is the best in terms of efficiency, waste reduction (85%), material reuse rate (75%), and recycling efficiency (80%). It was also the most resource utilization efficiency (90) and social Acceptance (88%). The results of this model are a clear indication of the advantages of integrating MCDA and GIS to have a holistic evaluation of circular economy technologies. The Hybrid Simulation-Optimization Model also showed that there is a possibility of incorporating digital technologies, including IoT and blockchain, to make manufacturing processes more circular, waste reduction more efficient, and resource-efficient. Such results demonstrate the need to employ a multi-criteria decision-making strategy in the process of implementing a circular economy. This research will assist in establishing a systematic theory of assessing and implementing circular economy technologies in the manufacturing sector. Additional areas of focus needed in future research are the scalability of the given approach in other industries, more applications of digital solutions, and research on the economic viability of implementing the given practices at a larger scale.

Ensuring environmental sustainability has become a pressing global concern, leading to an increasing demand for efficient waste management solutions. Among these, the recycling of end-of-life tires (ELTs) presents both an opportunity and a challenge due to its environmental and economic implications. As the recycling of ELTs becomes mandatory in many countries, decision-makers must carefully evaluate and select suitable facility features during the installation process. Given the complexity and the multiple factors involved in facility selection, this problem is best approached using multi-criteria decision-making (MCDM) methods. In this study, a novel hybrid decision-making framework is proposed, integrating the interval-valued Pythagorean fuzzy Criteria Importance Through Intercriteria Correlation (CRITIC) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methods to address the ELT facility selection problem. The CRITIC method was employed to objectively determine the importance of weights of twelve evaluation criteria. The results indicate that Monthly Electricity Cost (C5) is the most influential criterion, followed by ELT Facility Capacity (C1) and Carbon Black Conversion Percentage (C4), while Monthly Labor Cost (C6) has the lowest impact. Based on the MARCOS method, the recycling alternatives were ranked as A > B > D > C, identifying Alternative A as the most suitable option. While extensive research has been conducted on fuzzy MCDM approaches, no studies have yet incorporated interval-valued Pythagorean fuzzy sets (IVPFNs) within the CRITIC-MARCOS framework for ELT recycling. This study bridges this gap by presenting an innovative method that effectively handles uncertainties in decision-making. The proposed approach enhances decision accuracy by considering both the importance of evaluation criteria and the optimal ranking of alternatives. The results provide a robust and systematic methodology for selecting the most appropriate ELT recycling facility, contributing to both sustainability and efficient resource utilization.

Selecting suitable technologies for integrated energy systems (IESs) can be likened to an apples-to-oranges comparison, given the heterogeneous factors at stake. Consequently, past research has often employed multicriteria decision analysis (MCDA) methods with a mixture of qualitative and quantitative criteria. MCDA provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. As a result, decision makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. Next, we established thresholds by applying change-point analysis, categorizing the preferences of decision makers into distinct utility functions. To demonstrate the impact of our framework, we conducted case studies on six reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our methodology mitigates the subjective bias inherent in traditional scoring approaches by using data-driven thresholds. The framework standardizes evaluations across different stakeholder preferences to guide capital-intensive energy initiatives, while explicitly accounting for uncertainty rather than single-point estimates.

Actinic keratosis (AK) is a prevalent, chronic skin condition and a precursor to cutaneous squamous cell carcinoma. Effective, patient-friendly therapies that target both visible and subclinical lesions are essential. Tirbanibulin, a topical microtubule inhibitor, is the latest treatment approved in the USA and European Union (EU) for treating non-hyperkeratotic, non-hypertrophic AK on the face and scalp. This study aimed to assess the overall value of tirbanibulin for treating AK on the face or scalp across Germany, Italy, and Spain and to identify key value drivers using a multi-stakeholder perspective. This study used a multi-criteria decision analysis (MCDA) to assess the holistic value of tirbanibulin compared with 5-fluorouracil 4% (5FU-4%) across Germany, Italy, and Spain. The validated EVIDEM MCDA framework (tenth edition) included eleven criteria related to disease burden, treatment benefits, evidence quality, and comparative outcomes. A total of 18 participants-dermatologists, payers, and patients-evaluated the treatments in a two-phase process. Phase 1 involved weighing the criteria, and phase 2 involved scoring clinical, economic, and patient-reported evidence for both treatments. Results from both phases were used to calculate an estimated value. The approach supports transparent, stakeholder-informed decision-making for AK treatment. All criteria were rated as relevant, with the greatest importance assigned to "comparative safety/tolerability," "quality of evidence," and "comparative efficacy." Tirbanibulin received positive scores across all criteria, particularly for "expert consensus/guidelines," "quality of evidence," and "size of the affected population." The final estimated value of tirbanibulin was 0.622 on a -1 to +1 scale, indicating high perceived value. Value estimations were consistent across stakeholder types, with slight country-level variations. Overall, participants recognized tirbanibulin as a valuable treatment for AK, on the basis of robust evidence, favorable safety/tolerability and patient-reported outcomes (PRO) profiles, and alignment with clinical guidelines, with similar efficacy compared with 5FU-4%.

This study evaluates the performance of an Effluent Treatment Plant (ETP) at a dairy industry in Bangladesh and investigates the potential of physicochemical pretreatment methods using chemical coagulants. Influent and effluent samples were collected three times over three seasons and analyzed for key parameters, including pH, BOD, COD, TDS, and TSS. The results confirmed that on all three occasions, the tested parameters were within the permissible discharge limits set by the Environmental Conservation Rules (ECR) 2023 of Bangladesh. ETP demonstrated high treatment efficiency, achieving maximum removal rates of 98.3% for BOD and 97.1% for COD. To assess the feasibility of pretreatment, experiments were conducted using three conventional chemical coagulants: FeSO4, PAC, and FeCl3 with lime as a coagulant aid. However, no single coagulant combination excelled in removing all pollutants simultaneously. To address this challenge, three Multi-Criteria Decision Making (MCDM) methods: AHP, TOPSIS, and PROMETHEE II were employed. These methods consistently identified Lime + FeSO4 (100mg/L + 100mg/L) as the most effective dosage, achieving 93.51% BOD, 85.50% COD, 51.71% TDS, and 93.95% TSS removal. This study underscores the utility of MCDM in wastewater treatment optimization and highlights the potential of chemical pretreatment.

BackgroundOccupational Health and Safety (OHS) training plays a central role in promoting safety culture and risk awareness among university students. While face-to-face education has traditionally been preferred, online and hybrid models have gained prominence. However, there is limited research that systematically compares these delivery methods using structured decision-making models.ObjectiveThis study aims to develop a quantitative decision-support framework to evaluate and rank face-to-face, online, and hybrid OHS basic training methods using an integrated Entropy AHP-VIKOR multi-criteria decision-making approach.MethodsThree training alternatives were evaluated based on eight pedagogical, technical, and administrative criteria. Criterion weights were determined objectively using the Entropy-based Analytic Hierarchy Process (AHP), and the VIKOR method was applied to identify the compromise solution among alternatives.ResultsThe hybrid training model achieved the lowest S, R, and Q values in the VIKOR analysis, ranking first among alternatives. Pedagogical impact, participation, and retention emerged as the most influential criteria according to Entropy AHP weighting results.ConclusionsThe integrated Entropy AHP-VIKOR model provides a transparent and objective framework for selecting OHS training methods. The findings support prioritizing hybrid delivery for university-level OHS education while reserving face-to-face components for practice-oriented modules and online components for theoretical content.

Wave energy converters (WECs) such as Oscillating Water Column (OWC) devices hold great promise for renewable energy generation, but their long-term reliability under harsh marine conditions remains a critical challenge. This paper addresses the risk assessment gap for OWC systems by introducing a novel integrated framework that bridges technical reliability with economic viability. The proposed Hexagon Risk Assessment methodology extends traditional Failure Modes and Effects Analysis (FMEA) by incorporating six evaluation criteria (including exposure, detectability, control measures, and economic impact) rather than the standard three. These criteria are weighted and aggregated using a Pythagorean fuzzy multi-criteria decision-making (MCDM) approach, providing a robust prioritisation of failure modes. Compared to previous FMEA-based or single-method studies, the Hexagon methodology offers a more comprehensive evaluation of OWC risks, capturing complex environmental and operational uncertainties. Applied to a representative case study reflecting real-world operational constraints, the methodology successfully identified the most critical failure modes, with aging degradation, resonance tuning failure, and extreme weather events emerging as top risks. Among the evaluated criteria, severity, exposure, and economic impact were found to have the greatest influence on overall risk prioritisation. The results confirm that this holistic risk assessment approach can enhance maintenance planning and design optimisation for wave energy systems, ultimately supporting cost-reduction strategies necessary for commercial deployment.

Integrating subjective–objective weighting with multi-criteria decision models for sustainable masonry material selection

Retraction Note: Picture fuzzy WASPAS technique and its application in multi-criteria decision-making

Assessing the sustainability of urban mobility requires clear indicators and robust decision-making tools, yet current knowledge remains fragmented and unevenly distributed across regions. This study conducts a structured literature review of 38 recent publications to identify the main indicators and multi-criteria decision-making (MCDM) methods used to evaluate sustainable urban mobility. Thirty-five representative indicators were identified, covering traditional sustainability dimensions (economic, environmental, and social) as well as emerging ones such as operational-technical and spatial-urban. Among the MCDM methods, the Analytic Hierarchy Process (AHP) is the most frequently applied for weighting indicators, while the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is commonly used for prioritizing alternatives. The review also highlights key research challenges, including the need for indicator sets adapted to local contexts, the generation of more region-specific information for Latin America, and the development of approaches that account for data availability and local conditions. To address these gaps, a structured expert consultation was conducted in the medium-sized Latin American city of Bahía Blanca (Argentina), resulting in a set of twelve indicators considered suitable for assessing the sustainability of the local urban mobility system. Overall, the study provides an updated overview of current practices and methodological trends in sustainable urban mobility assessment.

Land suitability analysis is a key approach for evaluating the potential of land resources for specific uses and for supporting sustainable agricultural planning. In Ethiopia, where agriculture forms the backbone of rural livelihoods, identifying suitable land for staple crops is essential to ensure food security and long-term productivity. This study evaluated the actual land suitability for enset (Ensete ventricosum) cultivation in the Hadiya Zone, Central Ethiopia, by systematically comparing the spatial distribution of key environmental factors with established enset crop requirement standards. For each parameter, spatial data were overlaid with enset-specific ecological thresholds derived from relevant literature and expert consultation. Based on the FAO land evaluation framework, all factors were classified into five suitability classes: Very Highly Suitable (S1), Highly Suitable (S2), Moderately Suitable (S3), Marginally Suitable (N1), and Permanently Not Suitable (N2), enabling the identification of spatial variability in enset suitability and supporting subsequent multi-criteria evaluation and weighted overlay analysis. The analysis evaluated criteria such as soil properties (type, depth, organic carbon content, pH, and texture), topographic situation (slope and elevation), climate variables (rainfall and temperature), and LULC. The integrated analysis revealed that enset cultivation is highly favorable across most of the study area, with 57.72% classified as highly suitable (S1), 36.89% as moderately suitable (S2), 0.16% as marginally suitable (S3), and 5.23% as currently not suitable (N1), while no areas were identified as permanently unsuitable (N2). Overall, the results highlight the strong natural potential of the Hadiya Zone for enset cultivation, although localized constraints related to soil fertility, water availability, and slope conditions may require targeted management interventions.

Advancing Big Data Adoption for Sustainability Marketing in Restaurants: A Hybrid Multi-Criteria Decision-Making Approach

ABSTRACT Supplier selection and transportation are critical to supply chain management, as they directly affect purchasing performance and material flow efficiency. However, balancing procurement and transportation costs while ensuring supply reliability remains insufficiently addressed in the literature. This paper explores the impact of transportation on supplier selection and order allocation by proposing a multi-criteria decision support model based on mathematical programming. The model minimises total costs, including purchasing, ordering, transportation, and inventory, while considering delivery quality, supplier capacity, and buyer demand, with inventory managed across multiple stages encompassing suppliers, transit, and buyer. It then determinesefficient suppliers and optimal order quantities assigned to each. To validate the model, it was applied to a real-world case in the French railway sector. The results demonstrate that integrating transportation planning into the supplier selection process reduces costs while enhancing supply chain reliability and overall performance. The study also highlights relevant directions for future research.