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The adoption of cloud-based building information modelling (Cloud-BIM) presents a complex landscape of potential benefits and challenges for architectural design enterprises in China. While similar opportunities and obstacles exist globally, the unique economic, technical, and regulatory landscape of China necessitates a focused analysis. This study addresses the research gap by investigating the factors influencing Cloud-BIM development in China, utilizing a novel hybrid FDelphi-FANP-TOPSIS-CEA approach. Following a systematic literature review, the interval-valued fuzzy Delphi method (FDelphi) was used to identify 4 primary and 14 secondary factors influencing Cloud-BIM adoption. A fuzzy analytic network process (FANP) was then used to prioritize these factors, revealing technology factors to be the most impactful, with interoperability holding the top position among the secondary factors. The technique for order preference by similarity to ideal solution (TOPSIS) method was further used to identify Cloud-BIM, with objects metasearch being the most favorable alternative among the four potential approaches. Finally, a causal effect analysis (CEA) was used to explore the cause-and-effect relationships between the identified factors, providing a deeper understanding of the underlying dynamics. This research offers valuable insights for architectural design enterprises in China considering Cloud-BIM implementation. By highlighting the key influencing factors, prioritizing their impact, and identifying the most suitable approach, this study equips practitioners with actionable knowledge to navigate the complex decision-making process. Additionally, the novel methodology contributes to the advancement of research in Cloud-BIM adoption, providing a robust framework for future studies in similar contexts. [ABSTRACT FROM AUTHOR]

Forest fire risk mapping is an essential measure for forest fire management. Quickly and precisely assessing forest fire risks, rationally planning fire risk zones, and scientifically allocating firefighting resources are of great significance for mitigating the increasingly severe threat of forest fires. This study utilized the random forest (RF) algorithm and the Fuzzy Analytic Network Process (FANP) to conduct a forest fire risk-zoning study in the protection and development belt of Wuyishan National Park. The findings revealed that some areas in the western and southern parts of this region have relatively high fire risk levels. Particularly, forest fire prevention and control in the western area need to be strengthened to prevent potential hazards to Wuyishan National Park. The accuracy of the FANP model was as high as 88.5%; areas with fire risk levels of grade 3 and above could control 98.44% of forest fires, and the proportion of areas with fire risk levels of grade 4 and above was 33.41%, which could control 65.63% of forest fires. This finding indicates that the FANP has preferable applicability in small-scale forest fire risk zoning and can offer more reliable decision-making support and reference basis for regional forest fire management. [ABSTRACT FROM AUTHOR]

The rapid advancement of intelligent technologies, including sensing devices, artificial intelligence, and the Internet of Things, has significantly accelerated the progress in industrial technology, particularly within the medical enterprise sector. Wearable innovations for health management have introduced novel approaches to physiological monitoring and early disease detection, contributing to an improved quality of life. In the context of sustainable development, wearable devices demonstrate considerable potential for supporting long-term healthcare solutions, particularly in the post-pandemic era, where the demand for smart health solutions continues to rise. This study aims to identify critical product design indicators for wearable devices that align with sustainable health management goals. Utilizing expert questionnaires and employing a combination of the Fuzzy Delphi Method and the DEMATEL-based Analytic Network Process (ANP), this research systematically evaluates the key factors influencing wearable device design. The findings highlight three primary aspects, six criteria, and 16 design indicators, with pivotal factors including "Compatibility", "Foresight", "Integration", "Comfort", "Appearance", "Customization", and "Intelligence". These indicators provide a comprehensive framework for developing wearable devices that address diverse user needs while promoting individual well-being and sustainable health management. This study offers valuable insights into the design and development of wearable devices that support sustainable healthcare practices, advance social responsibility, and strengthen preventive care initiatives. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG). Design/methodology/approach: The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects. Findings: The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level. Practical implications: This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation. Originality/value: The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment. [ABSTRACT FROM AUTHOR]

This study investigates the determinants of financial distress from the corporate social responsibility (CSR) perspective and examines how poor CSR performance leads to financial distress in enterprises. Based on theoretical analysis, we select predictive indicators and construct an early warning indicator system for predicting financial distress from a CSR standpoint. Additionally, we develop a novel dynamic financial distress prediction (FDP) model using decision‐making trial and evaluation laboratory (DEMATEL)‐based analytic network process (DANP), variable weights with penalty (VWP), and maximal entropy ordered weighted average (MEOWA) weight methods to assess corporate financial status. Furthermore, to evaluate the accuracy of our model, we apply it to Chinese listed companies for empirical analysis using a sample of 1142 listed Chinese companies spanning 2011–2023. The results demonstrate that our developed FDP model exhibits higher predictive accuracy compared to previous models, suggesting that poor CSR practices can contribute to corporate financial distress while significantly enhancing FDP performance. [ABSTRACT FROM AUTHOR]

The purpose of this study is to identify and analyze the application barriers and internal mechanisms of BIM technology in green building projects. It is found that BIM technology can effectively integrate information and optimize management, but its application still faces economic externalities and requires government intervention to promote its development. Through the analysis of five aspects of technology, economy, management, policy and environment, we identified 16 influencing factors and focused on nine key factors, such as insufficient technology maturity and high-cost issues. Research shows that policy and management factors play a fundamental role in BIM adoption and influence economic effectiveness through complex interactions. Based on the ISM-ANP coupling model, we propose that the government should encourage technological innovation and personnel training to promote the effective application of BIM technology in green buildings. This study provides a theoretical reference for the application of BIM and green building on a global scale. [ABSTRACT FROM AUTHOR]

Resident engagement is highlighted as a crucial aspect that directly impacts the residents' well-being of life in the process of sponge-style old community renewal (SOCR). However, there is a lack of in-depth study on exploring the classification of resident engagement clusters in the SOCR and developing a comprehensive evaluation system to measure this engagement. Therefore, this paper categorizes resident engagement into distinct clusters and develops an evaluation method for quantifying resident engagement in the SOCR. First, this study identified seven types of engagement clusters in the SOCR drawing from educational psychology and Arnstein's citizen engagement ladder theory. Second, an evaluation indicator system with 5 primary indicators and 21 secondary indicators was developed through a literature review and expert interviews. Third, a hybrid evaluation method combining the Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II) was established to effectively quantify resident engagement in the SOCR. Finally, 16 typical communities from five Chinese cities were selected as study areas to examine resident engagement in the SOCR. The results demonstrated that the full engagement cluster achieved the highest scores at the individual level, while the non-engagement cluster recorded the lowest scores. At the city level, Shanghai attained the highest overall engagement score, whereas Chizhou recorded the lowest. Several strategies were proposed to improve resident engagement in the SOCR. This study not only contributes to the body of knowledge on resident engagement in the SOCR but also provides guidance for fostering resident engagement in sponge city construction. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the impact of big data analytics (BDA) on the performance of healthcare supply chain management (HCSCMP) by examining both overall efficiency improvements and identifying critical success factors for effective implementation. Through a systematic literature review, the research investigates how BDA enhances real-time decision-making within healthcare supply chains (HCSCs) and identifies the key enablers required for successful BDA adoption. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs’ efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

Sub-watershed prioritization is essential for developing watershed management plans that maximize impact with minimal resources. This study used a multicriteria decision-making approach to rank sub-watersheds by degradation status in the Huehuetan River Basin, Chiapas, Mexico. The eight sub-watersheds in the basin were classified using the Analytic Network Process (ANP) model, evaluating morphometry, hydrology, hillslope stability, soil water saturation, land-use change, and socioeconomic factors. The results identified hydrology and land-use change as the most influential criteria, with weights of 20.62% and 19.82%, respectively, driven mainly by surface runoff and deforestation. Swtr 08 and Swtr 07 were identified as the highest-priority sub-watersheds, covering 24.31% of the basin area, with 55.31% of Swtr 08 classified as unstable and showing a combined high-vegetation loss of 16.46 km2. The entire watershed showed an annual vegetation loss rate of 146 ha year−1. Increasing the weighting by 50% resulted in greater variability in priority rankings, with runoff and low vegetation showing maximum global ranges of −44.33% and 30.25%, respectively, instability decreasing by 33.94%, and peak flow increasing by 18.20%. These findings emphasize the need for focused interventions in the vulnerable subwatersheds of the upper basin to manage runoff, curb deforestation, and reduce soil instability. [ABSTRACT FROM AUTHOR]

In recent decades, the issues of ecology and environmental sustainability have become a global concern in contemporary urban design. Among various urban elements, water features play a significant role in improving the ecological characteristics of their surrounding environment, especially in hot and arid areas. The aim of this study is to evaluate the ecological characteristics of urban water features comprehensively and quantitatively, which has been overlooked in previous studies, taking their physical characteristics into account. To this end, a multicriteria decision-making method, an analytic network process, was proposed to quantitatively evaluate the ecological characteristics of water features. In this approach, four ecological criteria—microclimate, biodiversity, greenery, and human wellbeing—and ten physical characteristics of water features were considered. Twenty-one experts were asked to complete a questionnaire for pairwise comparisons of all ecological criteria and the influence of physical characteristics. The results showed that vegetation and scale, with the relative influencing values of 0.255 and 0.188, respectively, were identified as the most decisive features influencing ecological criteria. Conversely, texture, with a value 0.023, had the least impact. Moreover, it was shown that water features have the greatest impact on the microclimate compared with other ecological criteria. The results were used to compare water features at the Koohsangi Transregional Park, located in a hot and arid city of Iran. The results of this study lead to a framework that can help urban designers integrate ecological criteria into water feature planning to enhance urban ecology and sustainability. [ABSTRACT FROM AUTHOR]

As demonstrated by the existing literature, lean production and management can contribute to the improvement of firm performance. However, there are many companies that struggle to apply its ethos and practices. The key point is that lean production differs from traditional mass production in many ways. Other than that, numerous studies have shown that business management systems must take into account both soft power and hard power. The main purpose of this study is to use the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) tools to find out the soft and hard power factors, rank their importance in identifying the key success factors for the introduction of a lean production system, and assist in making the company's transformation smoother and more successful. The research results verify that a lean production system needs to take into account both soft power and hard power. Lean management in this study concludes the following priorities of critical factors: In hard power (technical dimension): (1) 5S, (2) seven major wastes, (3) solutions to lean production-related issues, (4) storage location management and warehouse management, (5) single minute exchange of die, and (6) total productive maintenance; In soft power (management dimension): (1) teamwork, (2) communication, (3) leadership, (4) culture, (5) initiative, and (6) employee training. The combination of soft power and hard power can improve the success rate of lean management system introduction. [ABSTRACT FROM AUTHOR]

In the later stages of oilfield development, the decline in oil well production and the increase in development costs, attributed to issues such as insufficient liquid supply, necessitate the implementation of intermittent pumping measures. However, current methods for selecting these wells lack comprehensiveness in the decision-making process. This article proposes a novel method for selecting intermittent pumping wells utilizing the analytic network process (ANP) and fuzzy logic. Initial surveys identified the main factors influencing intermittent pumping effectiveness. The ANP was employed to screen and integrate six core factors, including submergence depth and water cut. Subsequently, a fuzzy-logic-based model was developed, incorporating trapezoidal and rectangular membership functions to establish detailed correlations among the factors. The model's efficacy was validated and tested using real-world data from the oilfield. Results indicate that the model's assessments of intermittent pumping wells align closely with professional engineering judgments. This approach not only provides clear guidance for well selection but also demonstrates high scalability and adaptability across different oilfields by adjusting membership functions, thereby showcasing significant practical value. [ABSTRACT FROM AUTHOR]

The risk of both fatal accidents and non-fatal injuries in the construction industry is significantly high in most countries. To reduce this construction safety risk, the proper use of personal protective equipment (PPE) is one of the major measures on the jobsite. In this research, in order to comprehensively assess the PPE plans, a three-phase framework was proposed to identify the optimal solution for PPE planning from a set of alternatives. As a result, four main criteria and fifteen sub-criteria were identified based on a systematic literature review, and a decision-making model integrating the analytic network process (ANP) and VIekriterijumsko KOmpromisno Rangiranje was developed. As the assessment information in the survey was incomplete and vague, the fuzzy sets theory was adopted to transform the linguistic terms into fuzzy numbers for evaluation. The model further calculated the weight of each criterion and prioritized the potential PPE plan alternatives. Finally, the presented model was implemented in a case study to verify its feasibility and applicability for practical construction management. The proposed method enables the selection of the most compromising solution as the optimal PPE plan. This research assists decision-makers and safety planners at construction workplaces to improve the overall safety performance and reduce accident risks, which significantly contributes to construction safety management and practice. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) methods provide a framework for addressing sustainable forest management challenges, especially under climate change. This study offers a systematic review of MCDM applications in forest management from January 2010 to March 2024. Descriptive statistics were employed to analyze trends in MCDM use and geographic distribution. Thematic content analysis investigated the appearance of MCDM indicators supplemented by Natural Language Processing (NLP). Factorial Correspondence Analysis (FCA) explored correlations between models and publication outlets. We systematically searched Web of Science (WoS), Scopus, Google Scholar, Semantic Scholar, CrossRef, and OpenAlex using terms such as 'MCDM', 'forest management', and 'decision support'. We found that the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the most commonly used methods, followed by the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the Analytic Network Process (ANP), GIS, and Goal Programming (GP). Adoption varied across regions, with advanced models such as AHP and GIS less frequently used in developing countries due to technological constraints. These findings highlight emerging trends and gaps in MCDM application, particularly for argan forests, emphasizing the need for context-specific frameworks to support sustainable management in the face of climate change. [ABSTRACT FROM AUTHOR]

Interdependent criteria play a crucial role in complex decision-making across various domains. Traditional methods often struggle to evaluate and prioritize criteria with intricate dependencies. This paper introduces a generalized method integrating the analytic network process (ANP), the decision-making trial and evaluation laboratory (DEMATEL), and the consistent fuzzy analytic hierarchy process (CFAHP) in a fuzzy environment. The Drazin inverse technique is applied to derive a fuzzy total priority matrix, and we normalize the row sum to achieve the steady-state fuzzy priorities. A numerical example in the information systems (IS) industry demonstrates the approach's real-world applications. The proposed method derives narrower fuzzy spreads compared to the past fuzzy analytic network process (FANP) approaches, minimizing objective uncertainty. Total priority interdependent maps provide insights into complex technical and usability criteria relationships. Comparative analysis highlights innovations, including non-iterative convergence of the total priority matrix and the ability to understand interdependencies between criteria. The integration of the FANP's network structure with the fuzzy DEMATEL's influence analysis transcends the capabilities of either method in isolation, marking a significant methodological advancement. By addressing challenges such as parameter selection and mathematical complexity, this research offers new perspectives for future research and application in multi-attribute decision-making (MADM). [ABSTRACT FROM AUTHOR]

The survivability of out-of-hospital cardiac arrest patients in China is notably low, at less than 1%, primarily due to difficulties in accessing nearby Automated External Defibrillator (AED) devices during emergencies. Therefore, the strategic deployment of AEDs is crucial for improving patient survival. This study adopts a novel hybrid methodology that integrates the Fuzzy Analytic Network Process (FANP) and Grey Relational Analysis (GRA) to assess and prioritize the critical factors for optimizing AED deployment in China. In the FANP model, the rate of cardiopulmonary resuscitation training among individuals in the area, the rate of AED training among individuals in the area, and records of AED device usage within the area are key factors for improving the effectiveness of AED deployment. Furthermore, in the GRA model, the factors with higher gray relational degrees are Visibility, Social Environment, and Accessibility. This analysis sheds light on which secondary indicators can have the most significant impact on improving specific primary indicators. The insights from this study can guide the optimization of AED deployment strategies in China and offer practical solutions for enhancing the deployment of relevant public medical facilities. [ABSTRACT FROM AUTHOR]

The multi-functional integrated RF system (MIRFS) is a crucial component of aircraft onboard systems. In the testability design process, traditional methods cannot effectively deal with the inevitable differences between system designs and usage requirements. By considering the MIRFS's full lifecycle characteristics, a new testability allocation method based on multi-criteria decision-making (MCDM) is proposed in this paper. Firstly, the testability framework was constructed and more than 100 indicators were given, which included both different system-level and inter-system indicators. Secondly, to manage parameter diversity and calculate complexity, the basic 12 testability indicators were optimized through the Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) method. Thirdly, the detailed testability parameters were obtained by using the Decision-Making Trial and Evaluation Laboratory and Analytic Network Process (DEMATEL-ANP) to reduce the subjectivity and uncertainty. Finally, an example was utilized, and the results show that the MCDM method is significantly better than traditional methods in terms of accuracy and effectiveness, which will provide a more scientific basis for the MIRFS testability design process. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are essential in establishing renewable energy systems. The implementation of ESS, particularly in countries that have only recently begun their shift toward renewable energy, however, is far from fully realized. Hence, this presents an opportunity to compare different types of ESS using the SPELL framework that identifies criteria based on the social, political, economic, legal, and logistic factors that influence the successful implementation of such systems in countries with developing energy infrastructures, and this is explored in the Philippine context. This study proposes a multi-criteria decision-making method built on a hybridized Best-Worst Method, Analytic Network Process, and Decision-Making Trial and Evaluation Laboratory (BWM-ANP-DEMATEL) framework, allowing the interdependence in evaluating the importance of criteria such as price perception, public understanding of ESS, legal frameworks, and supply chain readiness to be considered in the design of the model. It was found that the levelized cost under the economic factors is considered as the most critical criterion for implementation based on both BWM-ANP-DEMATEL (w = 0.1565) and BWM (0.2027) only, representing the methods performed with and without considering influence, while perceived health under social factors is the least critical (0.0477, 0.0286). Application of the selection model on various types of ESS showed that battery-based energy storage systems, particularly lithium-ion batteries, are prioritized, followed by pumped hydro storage due to their relatively affordable cost and more widely available infrastructure compared to most other ESS considered in the study. Meanwhile, flywheel storage is the lowest ranked due to lower familiarity and legal framework for its implementation. [ABSTRACT FROM AUTHOR]

Background: This research endeavors to enhance supplier selection processes by combining the Analytic Network Process (ANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies, with a specific focus on sustainability criteria. Method: Initially comprising 21 sub-criteria derived from prior research, the selection criteria are refined to 17, eliminating redundant elements. The core principle guiding this refinement is the comprehensive coverage of economic, social, and environmental dimensions, essential for sustainable supplier evaluation. Results: The study's outcomes underscore the paramount importance of economic criteria (0.0652) in supplier selection, followed by environmental (0.0343) and social dimensions (0.0503). Key sub-criteria contributing significantly to this evaluation encompassed consistent product quality, competitive raw material pricing, proficient labor capabilities, recycling potential, punctual delivery performance, and effective waste management practices. Conclusions: These sub-criteria are thoughtfully integrated into the sustainable assessment framework, aligning seamlessly with the economic, environmental, and social criteria. [ABSTRACT FROM AUTHOR]

The aim of this study was to study the impact of artificial intelligence (AI) on enterprises in terms of strategy, technology, business operations, and organizational management. This study used grounded theory analysis to identify the influencing factors of AI technology application maturity in Chinese enterprises. Taking Chinese film and television enterprises as an example, this study constructed an AI technology application maturity evaluation index system for enterprises based on the analytic network process (ANP) and evaluated the application maturity of AI technology in enterprises in terms of enterprise strategy, technology, business operations, and organizational management. To comprehensively evaluate and empirically analyze the application maturity of enterprise AI technology, this study calculated the index weight based on the ANP, and combined it with the fuzzy comprehensive evaluation method to construct a comprehensive evaluation model. The research results showed that intelligence strategy was the element that was believed to be most affected by the maturity of enterprise AI technology. For technology, intelligence technology and equipment were the elements that were believed to be affected the most. For business operations, smart shooting was the element that was believed to be affected the most. With respect to organizational management, corporate culture was the element that was believed to be most affected. The results showed that the proposed methods for evaluating the application maturity of enterprise AI technology are scientific and effective. The results of this study provide a reference for promoting the application of AI, implementing the intelligence transformation, and enhancing the core competitiveness of enterprises. [ABSTRACT FROM AUTHOR]

In the context of transitioning energy structures, thermal power generation is adapting to peak load regulation, highlighting the need for comprehensive studies on boiler combustion characteristics under varying load conditions. This study focuses on a 660 MW tangentially fired boiler, evaluating its combustion and pollutant generation at different operational loads: boiler maximum continuous rate (BMCR), turbine heat acceptance (THA), 75% of THA, and 40% of THA. The findings reveal a general decline in temperature, and nitrogen oxide concentration as the load decreases. However, at 40% THA, increased oxygen mole fractions lead to higher carbon dioxide and sulfur dioxide levels compared to other conditions. Through the Analytic Network Process, each parameter's impact is evaluated and scored to identify the most effective burner and deflection angle combinations. Optimal configurations are identified: a 10-degree rightward adjustment for ABDE layer burners at BMCR and a 10-degree leftward adjustment for BD layer burners at 40% THA, both enhancing combustion performance and reducing pollutants at the furnace outlet. For THA and 75% THA conditions, the industrial standard combination is recommended.

Analytic Network Process adalah salah satu metode yang dapat digunakan untuk memilih alternatif terbaik dari beberapa alternatif. Pengambilan sampel pada penelitan ini dilakukan dengan memberikan angket untuk semua siswa kelas 12 IPA. Kemudian memberikan kuisioner perbandingan berpasangan dari kriteria dan subkriteria kepada kepala cabang LBB Primagama untuk memperoleh bobot dari masing-masing subkriteria. penilaian kinerja pengajar menggunakan rating scale berdasarkan kriteria dan subkriteria kepada siswa yang menjadi responden penelitian berdasarkan angket yang telah diberikan. Data penelitian ini merupakan persepsi siswa kelas 12 IPA terhadap pengajar kelas 12 IPA bimbingan belajar Primagama berdasarkan 5 kriteria yaitu prapembelajaran, penguasaan materi, strategi pembelajaran, dan interaksi serta subkriteria dari masingmasing cluster kriteria. Berdasarkan hasil Analytic Network Process (ANP) dapat ditarik kesimpulan bahwa kinerja pengajar di bimbingan belajar Primagama di tahun 2021 yang kinerja mengajarnya paling baik yaitu pengajar I dengan bobot yang dimiliki sebesar 4.747, kemudian pengajar A yang kinerja mengajarnya terbaik kedua dengan bobot yang dimiliki sebesar 3.979, lalu pengajar dengan kinerja mengajar terbaik ketiga yaitu pengajar G memiliki bobot sebesar 3.703, dan pengajar yang kinerja mengajarnya pada urutan keempat yaitu pengajar E dengan bobot yang dimiliki sebesar 2.798. Dengan lima kriteria utama sebagai prioritas dalam penilaian kinerjanya yaitu melaksanakan pembelajaran dengan runtut dengan bobot sebesar 0.1730, menyampaikan materi pembelajaran dengan jelas dengan bobot sebesar 0.1510, menunjukan penguasaan materi pembelajaran dengan bobot sebesar 0.1240, menunjukan sikap terbuka terhadap respon siswa dengan bobot sebesar 0.1170 dan menguasai kelas dengan bobot sebesar 0.1100 Analytic Network Processis one method that can be used to select the best alternative from several alternatives.Taking The sample in this research was carried out by providing a questionnaire for all 12th grade science students. Then, a Pairwise comparison questionnaire of criteria and subcriteria will be delivered to the head of the LBB branch Primagama to obtain the weight of each subcriteria. Teacher performance assessment using a rating scale based on criteria and sub-criteria for students who become research respondents based on the questionnaire that has been given. This research data is the perception of grade 12 science students towards the 12th-grade science teacher Primagama tutoring based on 5 criteria: pre-learning, mastery of the material, learning strategies, and interactions as well as sub-criteria for each cluster criteria. Based on the results of the Analytic Network Process (ANP), it can be concluded that the performance of teachers in Primagama tutoring in 2021 with the best teaching performance is teacher I with a weight of 4,747, then teacher A has the second best teaching performance with a weight of 3,979 , then the teacher with the third best teaching performance, namely teacher G, has a weight of 3,703, and the teacher whose teaching performance is in fourth place, namely teacher E, has a weight of 2,798. With five main criteria as priorities in assessing performance, namely carrying out learning coherently with a weight of 0.1730, conveying learning material clearly with a weight of 0.1510, showing mastery of learning material with a weight of 0.1240, showing an open attitude towards student responses with a weight of 0.1170 and mastering class with a weight of 0.1100

This study aims to evaluate the impact of big data analytics (BDA) on the performance of healthcare supply chain management (HCSCMP) by examining both overall efficiency improvements and identifying critical success factors for effective implementation. Through a systematic literature review, the research investigates how BDA enhances real-time decision-making within healthcare supply chains (HCSCs) and identifies the key enablers required for successful BDA adoption. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs’ efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

The use of big data analytics (BDAs) allows for the collection, management, and analysis of large volumes of data, which helps in making real-time decisions. This study aims to assess how the application of BDA impacts the performance of healthcare supply chain management (HCSCMP). Through a systematic literature review, the research explores how BDA enhances efficiency within healthcare supply chains (HCSCs) and identifies the critical factors enabling successful BDA implementation. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs' efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

The Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), and Analytic Network Process (ANP) methods are widely used for decision-making across various fields, and have shown success in numerous applications within human resource management (HRM). The purpose of this study is to present a systematic literature review on the applications of AHP, FAHP, and ANP in human resource management. The search process encompassed two main online databases, Scopus and Web of Science. This research covers a total of 180 application papers. To help readers extract quick and meaningful information, references are summarized in tabular format. The results showed that these methods have been applied in various domains of HRM such as performance management and appraisal, selecting human resources, talent attraction and retention, intellectual capital, workplace safety, reward management, e-HRM, green HRM, etc. To the best of our knowledge, no comprehensive research review has yet been conducted on the applications of AHP in HRM. [ABSTRACT FROM AUTHOR]

The laboratory is a high-risk place for scientific research and learning, and there are many risk factors and great potential for harm. Hazardous chemicals are important to consider and are the key objects to monitor in a laboratory. In recent years, hazardous chemical fire accidents have occurred in laboratories in various industries, bringing painful lessons and making it urgent to strengthen the safety management of hazardous laboratory chemicals. In this study, a semi-quantitative comprehensive risk assessment model for hazardous chemical laboratory fires was constructed by combining the bowtie model, three-dimensional risk matrix, and analytic network process (ANP). This study applied this method to the management of hazardous chemicals at the TRT Research Institute; evaluated the probability, severity, and preventive components of the corresponding indicators by constructing different index systems; and calculated the evaluation results using the weight of each index. The evaluation results show that the comprehensive likelihood level is 2, the comprehensive severity level is 3, the comprehensive preventive level is 3, and the final calculated comprehensive risk level is tolerable (II). Based on the results of the risk assessment, the corresponding control measures that can reduce the fire risk of hazardous chemicals in the laboratory are proposed according to the actual situation at the TRT Research Institute. [ABSTRACT FROM AUTHOR]

The consistency analysis in AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process) methods is a critical element that helps evaluate how consistent the pairwise comparisons of criteria or decision alternatives made by the decision-maker are. Inconsistent evaluations can lead to erroneous or misleading results. The article presents a simple method of graphical analysis of the consistency of these evaluations (occurring in the so-called pairwise comparison matrix). The proposed concept of spatial visualization of the relative positioning of the compared pairwise criteria or decision alternatives offers analysts, on the one hand, a straightforward and visually appealing way to formulate always consistent evaluations. On the other hand, it facilitates the identification (seeing), understanding, and correction of undesirable inconsistencies in situations where the classical method of evaluation formulation, recommended by Saaty [1], has been used, where each compared pair of elements is considered separately. [ABSTRACT FROM AUTHOR]

The health sector is one of the components of development, social welfare and economic growth. The purpose of this study was to develop an economic evaluation model of the environmental and health costs of occupational diseases by hybrid approach. To achieve the study goal, a taxonomy of economic evaluation model of the environmental and health costs of occupational diseases has been developed. The Delphi method was used to identify health and environmental criteria and the analytic network process (ANP) method was used to weigh the sub-criteria. Finally, health and environmental cost were estimated based on the available information. Naft Subspecialty Hospital in Tehran, Iran (NSHT), was selected as the place of case study. In this study, eight and eleven sub-criteria were identified in the health and environmental sector, respectively. The ANP results indicated that the medicine and medical equipment cost criteria with a weight of 0.312 in the Medical sector, and the special and infectious waste cost criteria with a weight of 0.085 in the environmental sector were the most significant cost criteria in NSHT. Furthermore, the parametric model findings indicated that 99.84 and 0.16% of the total costs are associated with health and environmental costs, respectively. The findings indicated that 61.3% of the costs of the health sector were associated with the two sectors of medicine and medical equipment and the cost-of-service personnel, and 91.7% of the costs of the environmental sector are associated with wastewater treatment and the cost of electricity consumption. This study tried to present a quantitative model of the health and environmental costs of NSHT. Implemention of this integrated model can be a practical and effective step in allocating resources and prioritize interventions. [ABSTRACT FROM AUTHOR]

Automatic distribution master stations (ADMSs) are software platforms that can be used to monitor energy supply and consumption of power demand-side resources as well as to guarantee grid security. They play a vital role in maintaining the optimal performance and stability of electrical power systems. However, the stability of ADMSs is usually determined by the performance of the information interaction, which includes various challenges and complexities. Therefore, an efficient multiple-indicator filtering-based information interaction evaluation method for the ADMS is proposed in this paper. The proposed method first utilizes the principal component analysis (PCA) to filter the indicators of the ADMS. Then, the data envelopment method and the analytic network process (ANP) are employed to remove the invalid indicators and to determine the indicator weights. In this regard, the secondary screening of the indicators is completed, the effective indicators are obtained, and the accuracy of the evaluation system is improved. Results of the simulation show that the proposed method works well when evaluating information interaction performance, and it offers satisfactory performance evaluation of ADMSs. [ABSTRACT FROM AUTHOR]

The concurrent engineering approach necessitates integrating material selection into the product design to effectively align with client specifications. Premature product failure, leading to substantial losses, frequently arises as a consequence of inadequate material selection due to conflicting demands. The Multi-Criteria Decision Making (MCDM) procedures are essential for making wise decisions since choosing materials is complicated. This study employs fuzzy analytic network process (FANP) techniques to determine which bio-composite filaments will be the most effective for Fused Deposition Modeling (FDM). The requirements and available factors of egg carton packaging material determine the selection criteria for bio-composite filaments. These factors serve as the foundation for identifying ten essential features. The acquired data showed that the sugar palm fiber/polylactic acid composite (SPF/PLA) 7.5 wt.% fiber loading exhibited the highest priority score, 19.80%. The kenaf/acrylonitrile butadiene styrene (Kenaf/ABS) composite, with a fiber loading of 7.5%, exhibited the lowest ranking, scoring 4.4%. Subsequently, a sensitivity analysis was conducted to further corroborate the findings. It was observed that the SPF/PLA 7.5 wt.% fiber loading consistently ranked highest throughout all four examined scenarios. The study determined that a bio-composite filament material with a weight ratio of 7.5% SPF/PLA fiber loading is the optimal choice for utilizing FDM technology in the design of egg carton packaging. [ABSTRACT FROM AUTHOR]

As the world is more focused on developing products and services, which are sustainable, special attention is needed in the design and manufacture of machine tools. There is a multitude of elements that contribute to the sustainability of machine tools. Moreover, the elements are interdependent with each other. The presence and absence of each element can be measured by different indicators. This paper analyzes the influence of elements and their indicators on the sustainability of machine tools. Sustainability elements and their indicators have been identified first. Further, a new hybrid model was developed to find the interdependencies between the sustainability elements for machine tools and determine the priorities of the indicators. Two structural models, i.e., Decision-Making Trial and Evaluation Laboratory and Analytic Network Process, have been combined to establish levels and intensities of interdependence as well as the relative weight of selected indicators. The results classify the indicators into the receiver, dispatcher, and neutral groups and also point out the indicators with feedback dependencies, i.e., indicators that depend on themselves. The methodology can be applied for evaluating as well as comparing the sustainability of different machines. The observations would be helpful to the designers of machine tools to make strategies for improving the sustainability of their machines. [ABSTRACT FROM AUTHOR]

Flash flood is the most recurrent natural threat in northeastern India, especially during the peak of the monsoon season. In recent decades, the frequency of flash floods has considerably increased in the Himalayan watersheds, causing enormous human suffering, infrastructure damage, ecosystem disruption, and economic losses. Considering the exponential rise in the frequency of flash flood events, identifying the Flash Flood Vulnerable Zones (FFVZs) is one of the most crucial findings to limit their negative consequences. In this study, grey fuzzy analytic hierarchy process-based model integrated with the geographic information system is implemented to assess the flash flood vulnerability in the Ranikhola watershed, East Sikkim, India. Additionally, a novel Flash Flood Vulnerability Index (FFVI) is proposed to determine the flash flood vulnerability that takes into account twelve natural and anthropogenic parameters. Further, the FFVI map is classified into three FFVZs: low, moderate, and high. The effectiveness of the methodology implemented is judged by the high EV value (6.4286) and the comparison parameter. The harshest conclusion of this analysis, the major cities, such as Gangtok, Ranipool, and Singtam, are designated high FFVZ. The sensitivity analysis results show the LULC has the maximum positive influence on high FFVZ. [ABSTRACT FROM AUTHOR]

Urbanization without planning causes concerns about biodiversity loss, congestion, housing, and ecosystem sustainability in developing countries. Therefore, resilience and regeneration following urbanization are critical to city planning and sustainable development. Integrating multi-criteria decision-making methods (MCDM) with geographic information systems (GIS) can be a promising method for analyzing city resilience and regeneration. This study aims to use two MCDMs, the Analytic Network Process (ANP) and the Best–Worst Method (BWM), to evaluate the resilience of metropolitan neighborhoods in Tehran. Fourteen criteria were selected to represent the city's resilience, and the weights of two models were evaluated for their spatial patterns using GIS. The results showed that the building age was the most important criterion in both methods, while the per capita green space was the least important criterion. The weights of the most important criterion, the building age, for the ANP and BWM, were 19.56 and 18.98, respectively, while the weights of the least important criterion, the per capita green space, were 2.197 and 1.655, respectively. Therefore, the MCDM with GIS provides an approach for assessing city resilience and regeneration priority. [ABSTRACT FROM AUTHOR]

The power industry is currently undergoing a rapid transformation toward the maximum utilization of renewable energy resources. In grid-connected renewable energy systems, enhancing the voltage stability during the fluctuations in renewable energy outputs can be achieved using a transformer with built-in on-load tap changing. It is one of the main traditional voltage regulation techniques utilized to improve the voltage profile in the power grid. Due to the unpredictable nature of renewable energy penetration, the frequent operation of an on-load tap changer (OLTC) may increase significantly. Despite the proposed methods used to optimize the location and size of renewable generation, there is a lack of empirical or detailed studies that have been looking at the issue of tap changers, mainly the impact of renewable energy storage allocation. The main aim of this paper is to find a compromise approach to mitigate unnecessary tap-changing operation time by wisely siting and sizing the renewable energy storage system (RESS). Initially, a generalized method is proposed to formulate an analytical optimization framework coupled with a renewable storage index. This approach is employed to evaluate the behavior of OLTC in a grid-connected renewable energy system. The formulation is then used to model a decision-making approach to confirm the location and size of RESS, using the analytic network process (ANP) with entropy and technique for order of preference by similarity to an ideal solution (TOPSIS). The performance of the proposed approach is evaluated and compared using the IEEE benchmark networks. Aspects in relation to the predictions of future operations about the management of renewable energy storage in coordination with voltage regulators in the smart grid context, that will be the subject matter of additional studies are also discussed. [ABSTRACT FROM AUTHOR]

Pairwise comparisons have been a long-standing technique for comparing alternatives/criteria and their role has been pivotal in the development of modern decision-making methods such as the Analytic Hierarchy/Network Process (AHP/ANP), the Best-Worst method (BWM), PROMETHEE and many others. Pairwise comparisons can be performed within several frameworks such as multiplicative, additive and fuzzy representations of preferences, which are particular instances of a more general framework based on Abelian linearly ordered groups. Though multiplicative, additive and fuzzy representations of preferences are widely used in practice, it is unknown whether decision makers are equally precise in the three aforementioned representations when they measure objective data. Therefore, the aim of this paper is to design, carry out and analyse an experiment with over 200 respondents (undergraduate university students) from two countries, Czechia and Italy, to compare precision of the respondents in all three representations. In the experiment, respondents pairwise compared (by approximation) the areas of four geometric figures and then, the imprecision of their assessments was measured by computing the distance with the exact pairwise comparisons. We grouped the respondents in such a way that each participant was allowed to deal with a unique type of representation. The outcomes of the experiment indicate that the multiplicative approach is the most precise. [ABSTRACT FROM AUTHOR]

With the advancement of information technology, the Internet is pivotal in today's society, serving as a global connectivity platform. Leveraging Internet technology within an enterprise can improve operational efficiency and curtail costs. However, traditional Internet platform selection methods cannot simultaneously handle quantitative and qualitative information, fuzzy semantics, and incomplete expert-provided information. To address these limitations, this study integrated the fuzzy analytic hierarchy process (FAHP) and the weighted aggregated sum product assessment (WASPAS) approaches to tackle Internet platform selection problems within an incomplete information environment. To demonstrate the validity of this research approach, this study utilized a construction industry Internet platform selection case to confirm the efficacy of the proposed novel fuzzy analytic hierarchy process-based method. Comparative analysis against the weighted sum model (WSM), weighted product model (WPM), FAHP, and typical WASPAS approaches was conducted with numerical verification, revealing that the proposed method in this study effectively manages comprehensive information and yields more rational outcomes for construction industry Internet platforms. [ABSTRACT FROM AUTHOR]

Introduction: Performance evaluation is essential in defining supply chain strategies because the firm can find out how the business processes in the supply chain are running and formulate strategic policies. Kayumas coffee is one of Indonesia’s specialty coffee with a unique aroma and flavor originating from Kayumas Village, Situbondo District, East Java. Kayumas coffee supply chain needs to be evaluated because of the lack of competitiveness with other specialty coffees. Strategies are needed to improve supply chain performance, improve product quality, and reduce costs. This study aims to configure the supply chain of Kayumas specialty coffee structure, evaluate its performance, and determine performance improvement strategies. Methods: This study uses descriptive analysis to configure the supply chain structure and flow. Analysis of supply chain performance based on Supply Chain Operation Reference (SCOR) model then weighted with Analytic Network Process (ANP). Performance improvement strategies were formulated by a discussion with experts and then weighted with ANP. Results: The research results show that the Kayumas specialty coffee supply chain comprises farmers, farmer groups, distributors or resellers, and consumers. There are 6 performance indicators (metrics) to measure performance: perfect order fulfillment, order fulfillment cycle time, flexibility, total supply chain management cost, cost of goods sold, and cash-to-cash cycle time. Performance measurement shows that Kayumas specialty coffee supply chain performance is in the inferior category. Conclusion: The strategy needed to improve supply chain performance by reducing cost is a partnership between supply chain members to reach a price agreement.

This study assessed the service efficacy of Nanjing’s urban open space (UOS) system, emphasizing the complex structural characteristics of UOS services, specifically, the impact of interactions between different subsystems on the overall service efficacy of the UOS system. The objective was to understand whether and how a UOS system can effectively and efficiently meet urban needs under constrained conditions. The dimensions of UOS services and the interrelationships between services offered by different subsystems were examined, and a structural model for assessing the service efficacy of the UOS system was developed. A multidimensional analytical approach was applied to quantify and characterize the relationships between subsystems and identify and determine the weighting of subsystem analysis indicators. The findings indicate that subsystems and their respective indicators differ in importance across dimensions, influence each other, and collectively form the overall service efficacy of a UOS system. Furthermore, while service efficacy across different administrative districts in Nanjing’s main urban areas generally remains moderate, significant differences in evaluation values exist among districts and subsystems, offering direction for future urban planning and resource allocation. This study offers detailed insights into the efficiency and optimization of UOS services, helping urban planners improve UOSs globally.

In order to scientifically evaluate the command capability of a joint combat command organization, the paper establishes a command capability indicator system for the joint combat command organization, analyzes the interrelationships between indicators at various levels and the interrelationships between elements within the indicator group, and uses Super Decisions software to establish a relationship diagram of command capability evaluation indicators for the joint combat command organization, obtaining the weight of the final indicator system, collects two groups of command organization exercise evaluation data and calculates the evaluation results of capability. By analyzing the weight of indicators, it is pointed out that to improve the command capability of joint operations organizations, it is particularly important to focus on three key points: planning and decision-making capabilities, intelligence reconnaissance capabilities, and operational control capabilities.

In the context of a burgeoning knowledge economy, enterprise intellectual capital has emerged as a pivotal asset for organizational growth. Evaluating it requires a comprehensive and robust index, yet there is no standard methodology for such assessments. Here, we propose an index for evaluating enterprise intellectual capital. We use the Delphi method to delineate a scientific decision structure. A grey-based decision-making trial and evaluation laboratory (DEMATEL) is coupled with an analytic network process (ANP)—i.e., grey DEMATEL-based ANP (GDANP)—to determine the relative weight of indicators. Then, we use the technique for order preference by similarity to an ideal solution to validate the effectiveness and applicability of the proposed evaluation index based on data on thirty new-technology companies in China. This study bridges a critical gap in academic discourse, and we discuss the practical implications for the strategic management of intellectual capital in corporate settings. [ABSTRACT FROM AUTHOR]

Modeling and linguistic representation in the form Interval Type-2 Fuzzy have better accuracy than Type-1 Fuzzy. The type-2 fuzzy set involves more uncertainty than the type-1 fuzzy set. The degree of fuzzy membership is used to explain uncertainty and ambiguity in the real world. This study presents the type-2 Fuzzy Analytic Network Process (ANP) method to determine the weight of each attribute based on the level of interest and the extension method of type-2 Fuzzy TOPSIS to handle problems based on the value of the fuzzy type-2 attribute. Decision-making is based on the assessment of several experts called Multi-Criteria Group Decision Making (MCGDM), using type-2 Fuzzy geometric mean aggregation function. The membership function in this research is type-2 fuzzy based on the trapezoid. The contribution is a hybrid method Type-2 Fuzzy TOPSIS with Fuzzy Type-2 ANP group-based with new metric intervals on fuzzy type-2 for decision making. The results are a hybrid type-2 FANP and FTOPSIS decision-making model to support the best decision-making. Based on a comparison of the accuracy of trapezoid model 1, model 2, and model 3, the best accuracy result is model 3, which is 84%. The research benefits by presenting a hybrid Type-2 Fuzzy TOPSIS and ANP method that improves decision-making accuracy and better handling uncertainty and ambiguity than Type-1 Fuzzy systems. [ABSTRACT FROM AUTHOR]

This paper presents a method for identifying causal relationships between strategic objectives within a strategy map of a Balanced Scorecard. Strategy maps are modeled as a network of strategic objectives (nodes) and causal relationships (directed arcs). The nodes are also grouped into clusters that represent the perspectives of a Balanced Scorecard: (a) Finances, Clients, Internal Processes and Growth and Learning. The method uses the Analytic Network Process (ANP) to establish the importance of every relationship and uses a multi-objective integer linear programming model to select the relationships to be included within a strategy map of a company. The method provides a method that optimizes the selection of the relationships to be included in a strategy map. An illustration of the application of the method in a manufacturing company is presented. [ABSTRACT FROM AUTHOR]

In dealing with the occurrence of earthquakes, urban communities and people within the system should primarily consider the geological background, secondary hazards caused by earthquakes, countermeasures to mitigate earthquake disasters, community connectivity, and the impact of various indicators and criteria on the earthquake-proof capacity of the community. It is important to comprehensively assess these factors to ensure that a community is well-prepared to withstand an earthquake. This study takes Shenzhen (China) as an example to conduct a preliminarily evaluation of a method of earthquake-proof capacity during an earthquake, with the results reflecting the comprehensive capacity of communities. The evaluation framework focuses more on effectively improving the disaster response capacity and resilience of urban communities. [ABSTRACT FROM AUTHOR]

The coordinated development of land urbanization and population urbanization is crucial for the advancement of new urbanization. The study applied the entropy weight method and coupling coordination degree model, taking Shaanxi—a province in China characterized by a moderate pace of economic development and volume, along with distinct geographic and demographic features within its region—as the subject. It assessed the coordination conditions of these two types of urbanization from both macro and micro scales during the years 2010–2022. Utilizing the Analytic Network Process (ANP), the study ranked and analyzed the causes of issues stemming from uneven development, thus connecting a crucial link from theoretical analysis to decision-making implementation. The results showed that: (1) The province's land urbanization index was between 0.075 and 0.203, whereas the population urbanization index ranged from 0.221 to 0.408, with the development of the former significantly lagging behind the latter. (2) The coupling degree between land and population urbanization ranged from 0.835 to 0.854, with a coordination degree between 0.148 and 0.306. This indicated that a close connection had been formed between the two, yet a benign coupling relationship had not been established, displaying a spatial distribution characterized by "high in the middle, low in the north and south". (3) The limitation on further urban expansion was identified as the primary issue to be addressed (with a weight of 0.324), followed by insufficient infrastructure (with a weight of 0.261). The extent of ecological environmental damage was comparatively lower (with a weight of 0.225), and the degree of social injustice was the lowest (with a weight of 0.191). Therefore, to alleviate the problems associated with the imbalanced development between land urbanization and population urbanization, measures such as optimizing land spatial layout, enhancing urban ecological service functions, and strengthening the central cities' radiating effect should be implemented. [ABSTRACT FROM AUTHOR]

Cooperative alternatives need complex multi-criteria decision-making (MCDM) consideration, especially in resource allocation, where the alternatives exhibit interdependent relationships. Traditional MCDM methods like the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) often overlook the synergistic potential of cooperative alternatives. This study introduces a novel method integrating AHP/ANP with Shapley values, specifically designed to address this gap by evaluating alternatives on individual merits and their contributions within coalitions. Our methodology begins with defining problem structures and applying AHP/ANP to determine the criteria weights and alternatives' scores. Subsequently, we compute Shapley values based on coalition values, synthesizing these findings to inform resource allocation decisions more equitably. A numerical example of budget allocation illustrates the method's efficacy, revealing significant insights into resource distribution when cooperative dynamics are considered. Our results demonstrate the proposed method's superiority in capturing the nuanced interplay between criteria and alternatives, leading to more informed urban planning decisions. This approach marks a significant advancement in MCDM, offering a comprehensive framework that incorporates both the analytical rigor of AHP/ANP and the equitable considerations of cooperative game theory through Shapley values. [ABSTRACT FROM AUTHOR]

This study proposes an approach for simultaneously treating fuzziness and randomness when conducting safety risk assessments of nuclear power plant building projects (NPPBPs). First, the interval type-2 fuzzy set (IT2FS) and analytic network process (ANP) were employed to establish an IT2FS-ANP method for modifying the solution for the weight vector and determining the mutual relationships between the assessment indexes and their weights. Second, the IT2FS and cloud model (CM) were used to establish the IT2FS-CM method to modify the solution for the expectation value. Third, the IT2FS-ANP and IT2FS-CM methods were combined to establish a model for ranking the safety risks and determining the overall safety risk level. Finally, the approach was applied to an NPPBP in northeast China to validate its applicability. [ABSTRACT FROM AUTHOR]

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Introduction: Global climate changes, primarily manifested as global warming and rapid urbanization, exacerbate extreme weather events. Statistics indicate that floods are among the most prevalent and catastrophic natural disasters (Safiah Yusmah et al., 2020: 552). Urban floods caused by heavy rainfall have evolved from gradual accumulation to sudden surges (Masozera et al., 2007: 299; Hallegatte et al., 2013: 802). Therefore, urban flood resilience, crucial for flood control and disaster reduction, has garnered increasing research focus (Obrist et al., 2010: 284; Xu et al., 2018: 5298). The Sendai Framework for Disaster Risk Reduction (2015-2030), endorsed by the World Conference on Disaster Risk Reduction in 2015, and the 2018 Beijing Resilient City Development Plan, underscore the importance of enhancing urban resilience to disasters (Sun et al., 2022: 1). To address the growing risk of natural events, it is essential to strengthen capacities that enable vulnerable communities to better prepare for, cope with, and recover from adverse effects. In disaster management literature, this strategy is commonly referred to as resilience. Resilience, when applied to communities, is defined as “the ability of a community to prepare for, plan, absorb, recover, and adapt to actual or potential adverse events in a timely and efficient manner, including rebuilding and improving essential functions and structures.” A resilient community incurs fewer losses and recovers more swiftly from hazardous events (Cutter et al., 2014: 65; Abdali et al., 2022: 6). Theoretical Framework: The concept of resilience originated from Holling’s seminal work in ecology, aimed at understanding the instability and dynamics of nature. According to Holling, resilience is “the measure of a system’s ability to absorb change and disturbance while maintaining the same relationships between populations or state variables.” However, resilience is not simply “the ability of a system to return to equilibrium after a temporary disturbance” (Holling, 1973: 14-17). In ecological literature, two definitions of resilience emerged: one that encompasses continuity, change, and unpredictability in a nonlinear and non-equilibrium system (ecological resilience), and another that focuses on efficiency, stability, and predictability in a single equilibrium system (engineering resilience) (Holling, 1996: 25; Folke, 2006: 256). Engineering resilience emphasizes rapid and efficient recovery post-disturbance, while ecological resilience is about absorbing changes and ensuring continuity (Pickett et al., 2004: 373). Methodology: This study employs a combined multi-criteria decision-making approach using the Analytic Network Process (ANP), DEMATEL technique, and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE). The DEMATEL-ANP model identifies internal relationships between indicators, allowing for pairwise comparisons and weight evaluation, while PROMETHEE ranks solutions based on their proximity to the ideal solution, with the best solution being closest to the ideal point (Chen et al., 2011: 909; Chiu et al., 2013: 49; Thor et al., 2013: 27; Ju et al., 2015: 348-349). Results and Discussion: Resilience in Khorramabad exhibits a clear spatial pattern, with higher resilience in affluent upper city areas and lower resilience in less affluent lower city areas. This disparity highlights the inequitable distribution of urban facilities and services, emphasizing the need for equitable development and access to vital resources. Economic resilience is surprisingly lower in the city’s economic center due to deteriorated infrastructure and the residence of lower socioeconomic strata. Institutional resilience is higher in central areas, benefiting from the concentration of governmental, private, and grassroots organizations. Peripheral areas suffer from inadequate infrastructure, resulting in lower overall resilience. Social capital resilience lacks a specific spatial pattern, with most areas rated as medium to low, indicating weak community connections. Environmental resilience shows that city center areas are more prone to flooding, while higher resilience is observed in different parts of the city, possibly due to land permeability and river positions. Comparative analysis reveals that regions 1, 4, 7, 13, and 17 have the lowest resilience, while regions 23 and 14 rank high. This analysis provides a comprehensive understanding of resilience levels at the city scale, identifying areas requiring further intervention. Conclusion: Mapping the results reveals distinct spatial patterns of resilience and identifies hotspots needing more intervention. Central and western regions, with lower resilience, require more attention. Environmental and infrastructural factors are key, directly relating to land use and planning. The findings can assist Khorramabad’s urban planning organizations in integrating disaster resilience into urban planning, transforming reactive plans into proactive adaptation strategies. Additionally, identifying potential hotspots can aid emergency management institutions in effective disaster risk management. Enhancing social resilience is crucial for community capacity building to prepare for, respond to, and adapt to climate change impacts, guiding local stakeholders in fostering fair development and equitable resource access. [ABSTRACT FROM AUTHOR]