Decision Method Research Articles

Use of UAVs in Earthquakes and UAV Base Location Selection for a Possible Marmara Earthquake

UAV’s are widely used in many fields today, and one of the most important of these fields is disaster management. The most significant disaster that comes to mind when mentioning disasters is undoubtedly earthquakes. UAV’s are used to support search and rescue operations before and especially after an earthquake. As Türkiye is located in an earthquake zone, earthquakes have affected and will continue to affect us from past to present. The Marmara earthquake, which is predicted to occur in the coming years, will cause great destruction considering the population density of the region. In this study, the use of UAV’s in earthquakes and the selection of the most suitable UAV base location for quick intervention without being affected by a potential Marmara earthquake was conducted using the TOPSIS method, which is one of the multi-criteria decision-making methods with criteria weights. As for the UAV class, MALE class UAV’s, which are produced by Türkiye and classified by NATO, were preferred due to their mission duration and payload capacity. While determining the alternatives, seven airports close to the Marmara region but not on the fault line were selected. The criteria and their weights were determined based on the opinions of five UAV pilots, and a total of six criteria were chosen. As a result of applying the TOPSIS method, Sivrihisar Aviation Center was determined to be the most suitable UAV base for intervention in a potential Marmara earthquake, being the closest to the ideal solution among the selected alternatives.

Multicriteria risk assessment of water inrush in underground mines with large-scale curtain grouting: a mine disaster risk reduction strategy

This paper presents a synthetic method to assess the risk of mine water inrush before and after large scale curtain grouting based on GIS platform. Firstly, nine factors were selected from three aspects: geological conditions, hydrogeological conditions and curtain grouting parameters, to construct a multi factor assessment model. The combined weight of every factor was determined using the minimum deviation comprehensive weighting method, which can reduce the polarization effect of various assessment methods in multicriteria decision. Then, the effectiveness of the synthetic method was validated through the Maoping lead–zinc mine in Southwest China. The mining area was divided into five zones, namely safe zone, relatively safe zone, transitional zone, relatively dangerous zone, and dangerous zone. The assessment results show that the relatively dangerous and dangerous zones were significantly reduced, while the safe, relatively safe and transitional zones were significantly increased after large scale curtain grouting. Finally, we analyze the impact of curtain grouting on the risk of mine water inrush and the mechanism of zoning evolution from three aspects: water source control, pathway sealing, drainage, and pressure reduction. This work provides an effective strategy for mine disaster risk reduction.

An assessment framework for smart and sustainable housing for older adults using analytic hierarchy process (AHP)

IntroductionWhile there is a call for smart and sustainable housing in general and for older adults in particular, little attention is paid to identifying the determinants of such housing and their extent of influence on the quality of life (QoL) of older adults. This study addresses the above gap by re-defining the criteria for house quality assessment, taking into account new needs of older inhabitants, while concerning digital assistive technologies.MethodsThis research uses various methods to identify and validate housing-related criteria and metrics, resulting in a transparent multi-criteria evaluation framework that accounts for the spatial needs of older adults. These include recommendations for multi-criteria decision-making method (MCDM/A), expert workshop to develop new metrics and validate sub-criteria, expert survey to prioritize criteria and sub-criteria and interviews with three employees in the construction-services sector in the Netherlands, to gain knowledge on smart and healthy environments.Results and DiscussionThe results show that age-friendliness of housing function is the most significant criterion, while availability of housing modifications for seniors most important sub-criterion. Our findings can benefit architects in designing improved age- friendly spaces, older adults in evaluating their dwellings and researchers from the field of architecture in selecting most relevant method for their study.

Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making

With the significant advancements in communication technology, group decision-making (GDM) can now be implemented online, allowing a large number of decision-makers (DMs) to participate concurrently. However, current methods for large-scale group decision-making (LSGDM) are primarily suitable for 20 to 50 DMs, and their effectiveness in scenarios involving thousands or even tens of thousands of participants has yet to be fully validated. Furthermore, as the number of participants increases, the evaluation information becomes increasingly diverse and complex. At the same time, the social networks associated with the DMs typically become sparse, making information sharing and consensus building more challenging. In light of these challenges, we develop two new methods based on cooperative games to effectively address the challenges in super LSGDM. First, we propose a two-stage semi-supervised fuzzy C-means (FCM) clustering method with trust constraints, which aims to address the issue of sparsity in relationships within large-scale social networks. This method utilizes trust relationships as reliable resources and prior knowledge to guide and supervise the clustering process. On this basis, we discuss three scenarios from the perspective of cooperative games: (i) subgroup optimal consensus adjustments in non-cooperative situations, (ii) group optimal consensus adjustments in cooperative situations, and (iii) subgroup optimal consensus adjustments in cooperative situations. Subsequently, we view the consensus adjustment allocation as a cost cooperative game problem and propose two new LSGDM consensus methods based on Nash Bargaining (NB) and Kalai-Smorodinsky Bargaining (KSB). Finally, experiments on real datasets demonstrate the superiority and reliability of our proposed LSGDM methods.

The age-adjusted Charlson comorbidity index in acetabular fracture in geriatric acetabular fractures

ObjectiveThis study aimed to analyze factors of comorbidity in older patients with acetabular fracture to know the role of disease in the treatment of this population. MethodsDesign: retrospective cohort study. SettingSingle Level 2 Trauma Center. Patient selection criteriaA prospective database of acetabular fractures in patients over 55 years between January 2011 and January 2021 was studied. The exclusion criteria were age, pathological fracture, associated pelvic or hip fracture, follow-up of less than one year, and not having a computed tomography. Outcome measures and comparisonsPatients were categorised as low (0–2), intermediate (3–4) or high (≥5) risk according to the age-adjusted Charlson comorbidity index (ACCI). The primary outcomes were risk of complications, number of complications and mortality at one year according to the ACCI scale. The difference between the pre-fracture Clinical Frailty Scale (CFS) and the one-year follow-up was also analysed. Descriptive and multivariable regression analyses were performed and ROC curves were constructed. ResultsA total of 119 patients with acetabular fracture were collected. Ninety-eight out of those met inclusion criteria for our study. In regard to ACCI groups, 22% were low-risk, 26% were intermediate-risk, and 52% were high-risk. Statistically significant differences were found according to age, sex, mechanism of injury, treatment type, surgical treatment type, Clinical Frailty Scale (CFS) score, readmissions, complications, and mortality. ROC curves showed an association between ACCI and CFS in terms of complications and mortality, with the exception of unadjusted Charlson comorbidity index. On the multivariable regression analysis, associations were found between ACCI and presence of complications (OR 1.37, 95% CI 1.06–1.77, p=0.015) and mortality (OR 1.32, 95% CI 1.04–1.67, p=0.025) and between CFS and complications (OR 2.01, 95% CI 1.30–3.11, p=0.001) and mortality (OR 1.59, 95% CI 1.08–2.35, p=0.019). No statistical correlation was established between ASA and complications or mortality. ConclusionPresurgical ACCI and CFS scales may serve as a risk assessment method in treatment decision-making for patients with acetabular fracture. An association is observed between patients with a pre-fracture ACCI score ≥5 (high-risk) and a pre-fracture CFS score >4 (mild frailty) and mortality and complications at one year.

Multi-criteria decision making beyond consistency: An alternative to AHP for real-world industrial problems

The Analytic Hierarchy Process (AHP) is a widely used method for multi-criteria decision-making that hinges on the assumption of consistency in pairwise comparisons. However, in real-world scenarios, decision-makers often struggle to provide fully consistent judgments. This article introduces a decision-making framework that operates independently of consistency. Utilizing the Skew-Symmetric Bilinear representation of preferences allows decision-makers to more accurately evaluate alternatives and criteria, making this framework more applicable in practical settings. The proposed method is validated through practical examples and an in-depth case study in the textile industry, effectively resolving a complex decision-making problem related to acquiring a data analytics tool for supplier selection. The results underscore the robustness and adaptability of this consistency-independent technique as a flexible alternative to traditional AHP methods.

A two-stage image segmentation method for harvest order decision of wood ear mushroom

A two-stage image segmentation method for harvest order decision of wood ear mushroom

Advancing Value-Based Academic-Clinical Partnership Evaluation in Physical Therapy Education: Multiattribute Utility Analysis as a Contextual Methodological Approach.

There is a need to investigate the application of systematic, scholarly methods to develop and implement a sustainable, flexible process for evaluating academic-clinical partnership effectiveness. The purpose of this study was to explore the potential for multiattribute utility analysis (MAUA) methodology to be applied in the context of academic-clinical partnership evaluation. Persistent systemic challenges related to academic-clinical partnership volume and quality require intentional strategies addressing the inherent complexities of the clinical learning environment and contextual differences between academic institutions. Multiattribute utility analysis appears to be a viable existing methodological framework based on its successful application in a wide variety of fields. Directors and Co-Directors of Clinical Education in accredited Doctor of Physical Therapy programs. The MAUA methodological framework was used to identify and quantify weighted priorities in factors contributing to partnership effectiveness as perceived by academic-clinical education programs across the United States. Survey participants were invited to share their perspectives regarding priority characteristics and assessment approaches to advance value-based partnership evaluation in physical therapy education. The products resulting from this contemporary analysis, coupled with the previous applied MAUA simulation, offer a methodology and flexible framework that may be utilized by academic-clinical education faculty to perform a value-based partnership evaluation and make decisions within the context of their institution. Clinical educator survey results also provide a new depth of insight regarding trends in the collective degree of attribute prioritization and the complexity and intersectionality across partnership priorities. The proposed processes may allow academic programs to narrow the large number of possible partnership criteria to a manageable subset, offering a way forward for those interested in pursuing a quantitative method for practical decision making. The ability to assess partnership quality using an evidence-based framework such as MAUA may allow partners to maintain, enhance, or discontinue partnerships based on a shared understanding of attribute alignment, rather than reactively coming together in practice to address challenges needing resolution.

Development of a new hybrid method for multi-criteria decision making (MCDM) approach: a case study for facility location selection

Development of a new hybrid method for multi-criteria decision making (MCDM) approach: a case study for facility location selection

Surface water potential zones delineation and spatiotemporal variation characteristics of water pollution and the cause of pollution formation in Brahmani River Basin, Odisha

Waste water reuse is an effective method for reducing the quantity of wastewater that enters the environment. As a result, the appropriateness was assessed in accordance with its composition and the global criteria for drinking water quality. This study aims to evaluate the quality of surface water of Brahmani River, Odisha, by adapting multivariate statistical approaches, namely Cluster Analysis (CA) and multi-criteria decision-making (MCDM) methods such as Simple Additive Weighting (SAW). Again, Geographical Information System (GIS) is employed to assess the drinking water's quality in relation to its application. Around 15 physicochemical parameters, gathered from seven distinct sites, following a period of 2022 till 2024. The results showed high values of EC, SO42−, NO3−, PO43−, F− and Ca2+, in three stations, while other studied parameters are within the permissible limits defined by WHO standards. The CA three distinct classifications of sites namely, low, medium, and high, based on how comparable the various physicochemical traits and pollutant levels were throughout the sampling locations. Sites H-(4–6) were included in cluster 1, site H-(3) and (7) classified under Cluster 2 and finally, H-(1–2) indicated as Cluster 3. Thus, as stated by CA, 42.86 % of tested water samples were fall into the group of good quality and about 57.14 % of examined locations resides in the poor category of water. The findings indicate which characteristics, as determined by the CA technique, were accountable for the alteration in the water quality and raise the potential of runoff from cities, farms, and industries. Subsequently, MCDM techniques has been implemented to facilitate rapid comprehension of surface water behavior and address problems with water resource management. The obtained SAW index ranges from 0.5 to 0.94. Here, H-(1), (2), and (7) grouped as highly polluted zone, and they seemed to be connected to runoff from farms and untreated industrial and municipal trash as well as local pollution sources. From this innovative study, it is found that even though the water is fit for drinking at 3 places, it is medium to highly suitable for human consumption. Therefore, there needs to be treated in some way before being used in addition to being protected against contamination. Most often, the findings indicate that appropriate management actions are important to restore the water quality of this catchment in order to support a robust and promising aquatic ecosystem. Additionally, the significance of this restoration for objective ecological policy and decision-making processes is underscored.

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

ANN-CFD research on key parameters of stirred tank based on multi-objective optimization and decision makers’ preferences

Utilizing the Multi-Objective Optimization (MOO) model and the Multi-Criteria Decision-Making (MCDM) method provides solutions for the rarely studied issue of trade-offs between energy consumption and performance in stirred tanks for decision makers across various industrial sectors. MOO optimizes predictions from a data prediction model to generate a Pareto solution set. By examining the influence of subjective weights on dependent variables, target weight proportions for different preferences are established. The MCDM method is then applied to select optimal solutions from the Pareto set for distinct industrial applications. Notably, the optimal solution for preference Np results in an 86.5% reduction in energy consumption, while the solution for preference Nq achieves an ideal state within the Pareto set. Additionally, the optimal solution for preference σ decreases the standard deviation of solids to 9.93% of the baseline value, demonstrating effective energy consumption optimization.

Use of the value-focused thinking methodology to understand health care waste management under the perspective of occupational and environmental health

The increasing generation of healthcare waste has greatly accelerated the concern about its potential negative environmental and public health effects. In this sense, the application of decision-making methods and techniques at a strategic level has become increasingly indispensable for the management of health units. This article aimed to understand the challenges in healthcare waste management (HCWM), the main mechanisms of action and values. The Value-Focused Thinking methodology was used to evidence important gaps and propose improvements in the context of decision on occupational safety and environmental protection, leading to the creation of alternatives not seen previously and guaranteeing the good ideas would be converted into actions. The structure was developed by semi-structured questionnaires, observation lists and interviews, conducted to expose the values of the main stakeholders (managers, health professionals, and cleaning professionals) of 10 public health units selected in Caruaru (Brazil). As a result, the investigation evidenced four fundamental goals for the decision context: improvement in work and environmental safety; improvement in HCWM practices; improvement in resource management; strengthening of the stakeholder communication network. From the analysis of the attributes associated with the fundamental goals, it was possible to identify actions that should be implemented with a higher priority and, in the short term, aiming at obtaining positive results, especially in the management practices and the safe management of health waste. The study resulted from an investigation that awakened the participants about aspects little questioned in everyday life. Implementing actions to segregate and recycle materials, with data monitoring and sharing, will increase the population's trust and the environment's and professionals' safety. The alternatives derived from the VFT method consider the perspectives of key stakeholders in HCWM, helping decision-makers and policymakers develop more assertive and grounded management strategies.

Cooperative control of multi-vehicle formation based on cooperative game

In this paper, an optimization decision-making method of vehicle fleet queue based on game theory is proposed. By adopting the upper and lower controller structure, the longitudinal motion stability problem of the vehicle fleet is solved. The upper controller utilizes a game control algorithm specially developed for this study. The game payoff function is constructed by considering real-time vehicle information and comprehensively evaluating factors such as expected distance, vehicle safety, and stability. The optimal expected acceleration of the vehicle is determined. Subsequently, the lower controller adjusts the engine throttle opening or brake pressure to regulate the vehicle operation according to the desired acceleration. Simulation results show that the designed game strategy can effectively reduce vehicle spacing and speed errors, thus significantly improving the stability as well as safety of fleet operation.

Exploring the climatic conditions effect on spatial urban photovoltaic systems development using a spatial multi-criteria decision analysis: A multi-city analysis

Identifying suitable locations for urban photovoltaic systems (UPVS) is crucial for achieving sustainable energy objectives and designing smart, eco-friendly cities. This study assesses the potential for UPVS expansion in eight cities across different climatic zones in Iran using a spatial multi-criteria decision-making method. Two scenarios were analyzed: the first compared spatial potential within each city, and the second compared potential between cities. The findings indicate that rooftops of the tallest buildings in densely populated areas, especially those with high solar energy output and sky view factor, hold the greatest potential for UPVS development. These locations are often near parks, commercial centers, and road networks. In the first scenario, Ardabil (5.70%), Gorgan (4.65%), Mashhad (5.46%), Tehran (8.10%), Kermanshah (5.76%), Shahrekord (3.41%), Kerman (8.67%), and Zahedan (8.56%) show significant potential for photovoltaic development. In the second scenario, cities in hot, dry climates like Zahedan and Kerman exhibit greater potential compared to cities in moderate, humid climates like Ardabil and Gorgan. Based on the analysis of this scenario, Ardabil (0.04%), Gorgan (1.49%), Mashhad (5.58%), Tehran (5.06%), Kermanshah (0.00%), Shaherkord (0.03%), Kerman (21.70%) and Zahedan (39.11%) showed a very high potential for UPVS development. The results of this study offer valuable insights for urban solar energy planning.

Agricultural sustainability assessment in Fars province of Iran through the lens of the elimination multi-criteria decision-making method

The main objective of the present research was to assess agricultural sustainability through the lens of the Elimination Method. Iranian wheat farmers in Fars province comprised the population of the study. Smapling table of Krejcie and Morgan was applied to estimate the statistically plausible sample size. A stratified sampling approach was used for selecting sampling randomly. The research instrument included a structured questionnaire. A panel of experts approved the face and content validities. Average Variance Extracted (AVE), Composite Reliability (CR), and items correlations were used to confirm the convergent validity of the constructs. The average shared squared variance (ASV) and the maximum shared squared variance (MSV) criteria were applied to examine the divergent validity of constructs. Employing the Elimination Method to assess agricultural sustainability in four counties of Fars revealed that Marvdasht is the most sustainable area for the development of wheat cultivation. Because it was able to fulfill 16 indicators out of 23 indicators of agricultural sustainability. The high level of productivity and efficiency of the wheat crop in this region is one of the main reasons for the relatively high sustainability of wheat production practices. Furthermore, the results demonstrated that Darab was the second most suitable region for developing wheat production. Based on the Elimination Method, the region was able to fulfill six indicators out of 23 agricultural sustainability assessment indicators. According to the results of ranking alternatives using the Elimination Method, the two counties of Kazeroun and Eghlid could not acceptably fulfill indicators of agricultural sustainability. In other words, it seems that these two regions are not suitable for the development of wheat production in Fars province. In the end, it was recommended that the Elimination Method can be a useful approach for urgent decision-making and agricultural managers and decision-makers in Fars province should focus more intensively on the development of wheat production in Marvdasht.

A HOLISTIC APPROACH TO THE BANKING SECTOR: PERFORMANCE AND EFFICIENCY ANALYSIS OF TURKISH BANKS USING CoCoSo AND DEA METHODS

The banking sector is a vital component of global economies, playing a critical role in ensuring financial stability by gathering savings, providing credit, and facilitating various financial transactions. Banks not only support economic growth but also act as intermediaries between surplus and deficit units within the economy. Therefore, evaluating their financial performance and efficiency is crucial for understanding the overall health of the financial system. This study analyzes the financial performance and efficiency of the top 10 banks in Turkey, which are ranked by asset size, over a 10-year period from 2013 to 2022. Using the Combined Compromise Solution (CoCoSo) method, the study ranks these banks based on various financial criteria, such as capital adequacy, asset quality, liquidity, and profitability. CoCoSo is a multi-criteria decision-making method that allows for comprehensive performance analysis by considering multiple factors simultaneously. In addition to CoCoSo, Data Envelopment Analysis (DEA) is used to evaluate the efficiency of these banks in utilizing their resources to generate financial outputs. The study’s findings reveal that privatelyowned banks, particularly Akbank T.A.Ş., consistently rank at the top in terms of both financial performance and efficiency. Akbank’s strong performance is attributed to its effective resource utilization and strategic management decisions. State-owned banks, on the other hand, generally show lower financial performance, even though they exhibit high efficiency levels. This discrepancy suggests that public banks may be focusing on non-profit-driven projects that contribute to public welfare, which in turn affects their overall financial performance. Foreign-owned banks, such as Garanti Bankası and Denizbank, also demonstrate strong financial performance but show varying degrees of efficiency, with Denizbank being relatively less efficient in resource utilization. By employing both the CoCoSo and DEA methods, this study offers a unique dual-method approach that provides a comprehensive evaluation of the Turkish banking sector. The combination of these two methods allows for a more nuanced understanding of both financial performance and efficiency, offering valuable insights for policymakers, bank management, and researchers. The study not only highlights the strengths and weaknesses of individual banks but also underscores the importance of efficient resource management for sustaining competitive advantage in a highly dynamic banking environment. The results of this study can serve as a robust framework for future research on bank performance evaluation, especially in emerging markets. Moreover, the study’s methodology can be applied to other sectors or countries to provide cross-sectional and time-series analysis, contributing to the broader literature on financial performance and efficiency.

Enhanced FMEA Methodology for Evaluating Mobile Learning Platforms Using Grey Relational Analysis and Fuzzy AHP

This study addresses a significant problem: it is difficult to choose a suitable mobile learning platform effectively since many learning platforms are readily available for users. For this purpose, the study proposes an efficient way to rank and choose the most suitable mobile learning platform by integrating risk analysis and multi-criteria decision-making methods. The selection of a suitable mobile learning platform is challenging due to the vast collection of available platforms. Traditional decision-making approaches often struggle to manage the inherent uncertainty and subjectivity in platform evaluation. To address this, we propose an enhanced methodology that combines grey relational analysis (GRA) and fuzzy analytic hierarchy process (FAHP), leveraging their complementary strengths to provide a robust and adaptive solution. The study employs ISO/IEC 9126 software quality standards to locate the most suitable mobile learning platform. FMEA is based on three risk factors: occurrence, severity, and detection. The fuzzy analytical hierarchy process (FAHP) is used to determine the relative weight of each risk factor to identify the grey risk priority number that can be calculated for each criterion. Mobile learning platforms are then ranked based on their grey risk priority number. The method was applied to five widely used mobile learning platforms with three decision-makers. In addition, the multi-criteria decision-making software was developed to aid users, educators, and administrators in their decision-making processes. The integrated FMEA-GRA-FAHP technique, using ISO/IEC 9126 standards, provides an effective way of locating the most suitable mobile learning platform and ranking them according to their reliability. This research is believed to be the only study applying an integrated FMEA-GRA-FAHP approach to evaluate the risks and quality of mobile learning platforms. The unique approach overcomes certain limitations of the standalone methods such as FMEA and FAHP, making it a valuable tool for identifying the suitability of mobile learning platforms. In addition, the study underscores the importance of inclusivity and equity in ensuring high-quality education and creating an environment conducive to lifelong learning for all.

A multi-objective optimization decision-making methodology for fostering synergies in the water-energy-food nexus

Current decision-making methods within the water-energy-food nexus (WEFN) encounter challenges in practicality, portability, scalability, and accuracy. Optimization methods, integrating site-specific data, offer promise for achieving desired outcomes and enhancing practical decision implementation within WEFN systems. However, these methods still struggle with solving multi-objective and multi-constraint problems, poor performance, and decision-making difficulties. This study developed an optimization method for WEFN systems, which integrates an evolutionary algorithm, swarm intelligence algorithm, multistage evolutionary algorithm, and post-optimization theories to address these issues. Performance results demonstrated that the proposed algorithm outperformed traditional metaheuristic optimization algorithms. Specifically, it excelled in tackling high-dimensional constrained problems, surpassing the classical NSGA series algorithms with a 1.34-fold improvement in the comprehensive performance metric HV. Subsequently, this algorithm combined with knee point post-optimization theory was applied to a typical arid region known for food and energy production. Compared with the business-as-usual scenario, the optimized schemes enhance agricultural benefits, saving 13.3 billion m3 of water over the entire planning period. Meanwhile, sustainable energy implementation would yield potential carbon emission reduction benefits of around 2.8 billion CNY. In summary, the proposed method would successfully provide a paradigm for the synergetic decision-making of WEFN systems.

Patent mining and diversity assessment of nuclear power technologies

Nuclear power technology is one of the pivotal avenues for ensuring a clean global energy transition. Bolstering technological diversity is critical for the sustainable advancement of nuclear power worldwide. However, a gap exists in investigating the trends and diversification of nuclear power technology over the past decade. This study proposes a data-driven integrated approach, leveraging descriptive statistics, co-occurrence analysis, network analysis, and multi-criteria decision-making methods to delve into the patent development, research and development (R&D) landscapes, and international layouts of nuclear power technology. Furthermore, the diversity of nuclear power technology globally and specifically within China is assessed. The findings unveil a substantial surge in nuclear power technology patents since 2020, with China, Japan, and Korea emerging as prominent R&D hubs. The R&D efforts in nuclear power technology predominantly concentrate on the design of nuclear reactor structures and cooling systems. Europe and the United States occupy significant positions within the nuclear power technology patent network. While China's nuclear power technology diversity slightly lags the global average, it has demonstrated a marginal lead since 2021. This study offers a comprehensive decision-support tool for trend analysis and diversity assessment in nuclear power technology.