Sensitivity Analysis Of Weights Research Articles

Advanced Fuzzy-Based Decision-Making: The Linear Diophantine Fuzzy CODAS Method for Logistic Specialist Selection

This study addresses the inherent uncertainty in human decision-making by leveraging fuzzy sets, which were introduced to better capture the imprecision associated with human thoughts and judgments. As an extension to traditional fuzzy sets, the Linear Diophantine Fuzzy Set (LDFS) was developed, offering a more flexible approach by relaxing existing limitations on grade values. The LDFS has found applications across various fields, demonstrating its versatility and effectiveness. In this study, we explore the application of the Linear Diophantine Fuzzy Set within the framework of the Combinative Distance-based Assessment (CODAS) method. The CODAS method stands out because it incorporates Euclidean and Taxicab distances. Decision-making should consider not only the direct distances between ideal solutions and alternatives but also the indirect distances. The foremost objective of this research is to propose a novel approach by integrating the CODAS method with the LDFS to address complex decision-making problems characterized by uncertainty and imprecision. To illustrate the practical utility of the proposed method, we apply it to a numerical example involving the selection of a logistics specialist, a critical decision in emergency logistics optimization. The research also includes a case study on a logistics specialist, highlighting the practical application of the methods discussed. Furthermore, this study provides a comprehensive sensitivity analysis of the parameters' weights to evaluate the robustness and reliability of the proposed method. The results highlight the effectiveness of the LDF CODAS method in making informed and reliable decisions under conditions of uncertainty, paving the way for future applications in other decision-making scenarios.

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots.

Architectural design space exploration of complex engineered systems with management constraints and preferences

During the architectural design phase, the decisions made have a crucial impact on developing complex systems. These decisions often limit the performance bounds and steer subsequent research directions. However, the multitude of architectural components and intricate interrelationships cause component combination explosion. Additionally, the diverse requirements of different stakeholders add complexity to determining the weight of indicators, posing challenges to complex system architecture generation and trade-off. This paper proposes a method for exploring the architectural design space in complex systems realisation, which aims to resolve issues related to the formal representation of architectures, selection within the architectural design space, and trade-offs. Firstly, the architectural design space is represented by formulating and mathematically describing a morphological matrix. Then, the construction of the Constraint Satisfaction Problem model aims to achieve a reduction in the scale of the design space. Moreover, through the evolutionary algorithm, the Pareto front is identified. Weight sensitivity analysis and the improved Technique for Order Preference by Similarity to the Ideal Solution method are combined to assist designers in finding a satisfactory architecture from the design space with many combinations. Finally, the proposed method is verified by the design of the launch vehicle’s primary and secondary separation system.

Application of Mendelian randomization to assess host gene-gut microbiota correlations in patients with esophageal cancer.

Increasing evidence suggests that esophageal cancer (ESCA) may be correlated with gut flora. However, their causal connection remains unclear. This study aimed to evaluate potential causal linkages and gene-gut microbiome associations between the gut microbiota and ESCA using Mendelian randomization (MR). We analyzed the data using genome-wide association studies. The exposure factor and outcome variable were the gut microbiota and ESCA, respectively. The MR-Egger method, weighted median, inverse-variance weighted method, heterogeneity test, sensitivity analysis, and multiplicity analysis were used for the MR analysis. And it was validated using an external dataset. Further meta-analysis was performed to validate the robustness of this relationship. Finally, we annotated single nucleotide polymorphisms in the gut microbiota that were causally associated with ESCA to explore possible host gene-gut microbiota correlations in patients with ESCA. We identified four species with potential associations with ESCA. Three of these species had a negative causal relationship with ESCA (odds ratio (OR): 0.961; 95% confidence interval (CI): 0.923-0.971; p = 0.047 for Romboutsia; OR: 0.972; 95% CI: 0.921-0.961; p = 0.018 for Lachnospira; OR: 0.948; 95% CI: 0.912-0.970; p = 0.032 for Eubacterium). A positive causal relationship was observed between one bacterial group and ESCA (OR: 1.105; 95% CI: 1.010-1.072; p = 0.018 for Veillonella). External datasets show the same trend. This is further supported by meta-analysis. None of the data showed pleiotropy, and leave-one-out analysis indicated the reliability of these findings. The gut microbiomes of patients with ESCA may correlate with the 19 identified genes. Our data indicate a potential causal link between these four gut bacteria and ESCA and identify a correlation between host genes and gut microbiota in ESCA, offering novel therapeutic options.

Comprehensive Evaluation of Hydrodynamic Retarders with Fuzzy Analytic Hierarchy Process and Improved Radar Chart

The braking performance of hydrodynamic retarders is critical for the safety and economy of heavy-duty vehicles. In order to effectively improve the braking system to meet the needs of the market, an evaluation method for quantifying the performance of hydrodynamic retarders is imperative. In this paper, ten evaluation criteria are put forward according to relevant national standards and market requirements, then the scores corresponding to these criteria are obtained by performance bench test. A fuzzy modification of the analytic hierarchy process (AHP) method is applied to determine the relative weights of the chosen evaluation criteria, where uncertain and imprecise judgments of decision-makers are translated into fuzzy numbers. Furthermore, the improved radar chart method is used to comprehensively evaluate and rank the braking performance of three types of hydrodynamic retarders as an empirical example. In addition, the weight sensitivity analysis is conducted to examine the robustness of the ranking results. The results show that the proposed method is effective and feasible. It provides a reference for multi-objective optimization control strategies and structure designs of hydrodynamic retarders.

Life cycle sustainability decision-support framework for CO2 chemical conversion technologies under uncertainties

With the emergence of numerous CO2 chemical conversion technologies to simultaneously reduce CO2 emissions and produce value-added products, it is of great importance to compare their difference and select the most sustainable routes for future development. This study quantitatively evaluated the sustainability performances of 21 alternative CO2 conversion technologies from economic, technical, and environmental perspectives and developed a novel Multi-criteria Decision-making (MCDM) model to prioritize the alternatives. To cope with the external and internal uncertainties during the decision-making, Interval-Rough Numbers (IRNs) were firstly used to deal with subjective vagueness and information incompleteness involved in the group judgements unavoidably. Secondly, DEMATEL-ANP was employed based on IRNs to specify the correlation type and degree among diverse criteria for determining the global weights accurately. Lastly, a Vector-based Algorithm method was applied to measure the alternatives’ overall performance and figure out the final ranking scores of sustainability. The results revealed that CO2 to methane, urea, methanol, dimethyl ether, and acetic acid were the top five promising conversion technologies with the highest R&D priority over the next decades from a sustainability perspective. Moreover, a detailed sensitivity analysis of criteria weights was conducted to scrutinize the effectiveness of the ranking results and to validate the reliability of the new proposed MCDM model. Furthermore, in consideration of the complexity of future technological advance, market transformation, economic and social trends, this life cycle sustainability decision-support framework for CO2 conversion technologies provides a well-informed benchmark to support the screening and selection of candidate technologies including both the existing and emerging processes, and strategically explore the development opportunities and limits under uncertainties.

The m-polar fuzzy ELECTRE-I integrated AHP approach for selection of non-traditional machining processes

In the literature, there are a number of decision-making strategies that can be used to choose the best NTM processes. Chen (Citation2014) introduced a novel method to handle fuzzy data that includes multipolar uncertainty, referred to as the m-polar fuzzy set (mFS) approach. The mFS method, along with other multi-criteria decision-making (MCDM) techniques, is a good way to choose between options. An illustration of such a combination is the mFS elimination and choice translating reality-I (ELECTRE-I) . A criteria weight approach is also needed to increase the accuracy of the mFS ELECTRE-I method. The mFS ELECTRE-I method and the analytical hierarchy process (AHP) criteria weight calculation method are combined in the current work. The unique thing about this method is that it can be used to solve both MCDM and MCGDM problems by combining the mFS ELECTRE-I with the AHP criteria weight method. A single-dimensional weight sensitivity analysis is performed to confirm the technique’s stability for different criterion weights for the AHP method on alternative rank performance. The results of the NTM process selection are validated by previous research findings. EDM turned out to be the best way to create machine precise holes on duralumin alloy, and ECM turned out to be the best alternative to generate the surface of revolution in stainless steel. A C++-based soft solution that uses the mFS ELECTRE-I technique to analyze various MCDM and MCGDM problems has been developed. With the soft solution, you can fix problems with selecting the FMS, the robot, and so on.

Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons

The current work aimed to predict three critical properties: critical temperature (Tc), critical volume (Vc), and critical pressure (Pc) of pure hydrocarbons. A multi-layer perceptron artificial neural network (MLP-ANN) has been adopted as a nonlinear modeling technique and computational approach based on a few relevant molecular descriptors. A set of diverse data points was used to build three QSPR-ANN models, including 223 points for Tc, Vc, and 221 for Pc. The entire database was randomly split into two subsets: 80% for the training set and 20% for the testing set. A large number of 1666 molecular descriptors were calculated and then reduced by a statistical methodology based on several phases to retain them into a reasonable number of relevant descriptors, wherein about 99% of initial descriptors were excluded. Thus, the Quasi-Newton backpropagation (BFGS) algorithm was applied to train the ANN structure. The results of three QSPR-ANN models showed good precision, confirmed by the high values of determination coefficient (R2) ranging from 0.9990 to 0.9945, and the low values of calculated errors, such as the Mean Absolute Percentage Error (MAPE) that ranged from 2.2497 to 0.7424% for the best three models of Tc, Vc, and Pc. The weight sensitivity analysis method was applied to know the contribution of each input descriptor individually or by class on each appropriate QSPR-ANN model. Moreover, the applicability domain (AD) method was also used with a strict limit of standardized residual values (di = ±2). However, the results were promising, with nearly 88% of the data points validated within the AD range. Finally, the results of the proposed QSPR-ANN models were compared with other well-known QSPR or ANN models for each property. Consequently, our three models provided satisfactory results, outperforming most of the models mentioned in this comparison. This computational approach can be applied in petroleum engineering and other related fields to accurately determine the critical properties of pure hydrocarbons: Tc, Vc, and Pc.

Sensitivity analysis of indicator weights for the construction of flood vulnerability indexes: A participatory approach

The assessment of flood vulnerability is a complex task that involves numerous uncertainties. Within this context, sensitivity analyses are crucial to better understand the variability of vulnerability index outcomes according to different input parameters. The present study sheds light on the importance of assessing the sensitivity of different criteria weights to construct flood vulnerability indexes using the Maquiné basin (Brazil) as a case study. Specifically, we compared vulnerability scores based on weights derived from a participatory survey with 44 stakeholders to those based on an equal weighting scheme. Results helped us identify areas with low and high uncertainty and the variables contributing to this. Overall, the preference for indicator weights did not vary significantly among stakeholders with distinct socioeconomic characteristics. Furthermore, the choice of weights only had an impact on the spatial distribution of flood vulnerability in certain regions. Compared to equal weights, the flood vulnerability outcomes obtained by averaging the stakeholder scenarios were similar, indicating that the results were robust and not highly sensitive to the choice of weights. By adopting a participatory approach, we were able to consider multiple stakeholders' views, which helped to provide a more comprehensive perspective of flood vulnerability and potentially increased the acceptance of the results. Based on our findings, end-users can better understand the relative importance of each indicator and how they contribute to vulnerability. Furthermore, our results can help identify points where stakeholders disagree, which can be used to facilitate dialogue and consensus building. The methodology applied is straightforward and could be easily adapted to other multi-criteria decision-making problems.

Intelligent Diagnosis Model of Alzheimer's Disease Based on PSO Algorithm Optimized BP Neural Network

In this paper, an intelligent diagnosis model of Alzheimer's disease is established based on the PSO algorithm optimized BP neural network. After descriptive analysis and normalization of patient-specific information characteristic data, the model was established by parameter selection of BP neural network, optimization of BP neural network by PSO algorithm and sensitivity analysis of neural network connection weights, and the intelligent diagnostic model of Alzheimer's disease was tested. The results showed that the model has strong applicability in terms of the degree of Alzheimer's disease in patients, and provides a certain reference basis for accurate diagnosis of the developmental stage of Alzheimer's disease.

An Intuitionistic Fuzzy Consensus WASPAS Method for Assessment of Open-Source Software Learning Management Systems

Ineffective evaluation of open-source software learning management system (OSS-LMS) packages can negatively impact organizational effectiveness. Clients may struggle to select the best OSS-LMS package from a wide range of options, leading to a complex multi-criteria group decision-making (MCGDM) problem. This evaluates OSS-LMS packages based on several criteria like usability, functionality, e-learning standards, reliability, activity tracking, course development, assessment, backup and recovery, error reporting, efficiency, operating system compatibility, computer-managed instruction, authentication, authorization, troubleshooting, maintenance, upgrading, and scalability. Handling uncertain data is a vital aspect of OSS-LMS package evaluation. To tackle MCGDM issues, this study presents a consensus weighted sum product (c-WASPAS) method which is applied to an educational OSS-LMS package selection problem to evaluate four OSS-LMS packages, namely ATutor, eFront, Moodle, and Sakai. The findings indicate that the priority order of alternatives is Moodle > Sakai > eFront > ATutor and, therefore, MOODLE is the best OSS-LMS package for the case study. A sensitivity analysis of criteria weights is also conducted, as well as a comparative study, to demonstrate the effectiveness of the proposed method. It is essential to note that proper OSS-LMS package evaluation is crucial to avoid negative impacts on organizational performance. By addressing MCGDM issues and dealing with uncertain information, the c-WASPAS method presented in this study can assist clients in selecting the most appropriate OSS-LMS package from multiple alternatives. The findings of this study can benefit educational institutions and other organizations that rely on OSS-LMS packages to run their operations.

Macro-site selection and obstacle factor extraction of biomass cogeneration based on comprehensive weight method of Game theory

Biomass energy can reduce the consumption of fossil fuels and reduce environmental pollution. The project site selection affects the production efficiency and economic benefits. Firstly, this study proposes a macro-site selection decision framework for biomass cogeneration considering carbon emission reduction. Secondly, this paper innovatively uses Nash equilibrium as the coordination goal, and introduces Game Theory to determine the optimal combination weight of Best-Worst Method (BWM) and Criteria Importance Though Intercriteria Correlation (CRITIC). Further thinking is needed to improve the comprehensive closeness measurement method, establish a Technique for Order Preference by Similarity to an Ideal Solution and Grey Relational Analysis (TOPSIS-GRA) site selection model that reflects distance and curve shape, and sort the plans. And then explore the barriers to improving the site selection environment. Finally take five cities in China as an example. The feasibility and the stability of the site selection decision-making framework in practical applications are verified by comparative analysis and weight sensitivity analysis. The results show that carbon emission is the primary barrier factor in the criterion layer. In the sub-criterion layer, transportation costs, carbon emission pressure, carbon benefits, industrial policies, and heating rate are the common obstacle factors in each region. To promote the long-term healthy development of the project, this study provides a theoretical reference for the new construction and expansion of biomass cogeneration projects, and has guiding significance for improving the location suitability of the project.

Research on Evaluation Method of Digital Project Cloud Model Considering Weight Sensitivity

Digitization is a driving force for social development and corporate innovation. Digital projects have become an indispensable part of the sustainable development of enterprises. However, due to the imperfect decision-making system of digital projects and the lack of experience of traditional enterprises’ digital projects, the decision-making of digital projects is an unavoidable challenge in the digital transformation of enterprises. For the digital project decision of the STATE GRID Corporation of China, this paper conducts a sensitivity analysis of digital project evaluation index weights based on cloud model theory, on top of historical successful project experience to support digital project decision-making. Firstly, this paper establishes a comprehensive evaluation index system for digitalization projects from five aspects: economic efficiency, interconnection, intelligent management, value release, and development innovation. The coefficient of variation method is used for index screening, and the weight intervals are formed by four subjective and objective assignment methods. Then, the LSOM model is established to generate the weight values in the interval, and, finally, the sensitivity of digital project comprehensive evaluation indexes is analyzed based on the cloud model to select the most robust index weights for project evaluation and choose the optimal project. The feasibility of the proposed method is verified by arithmetic examples.

Management of Al3+ Residue in the Soil by Mapping Soil Capability in Retaining and Transporting Al3+ in the Farmland of Trang Bom District, Vietnam

The assessment of soil capability in retaining and transporting chemical substances is necessary, especially currently, with the overuse of chemical products for crop production. Depending on the soil properties, these chemicals may bound on soil particles or release and transport in the soil solution. In this study, we developed maps of the capability of soil to retain and transport Al3+, thereby evaluating the main soil factors affecting Al3+ fate in the agricultural land of Trang Bom District, Dong Nai Province, Vietnam. Information and data of the factors slope, soil texture, pH, organic matter, and ferrallitisation were processed and analyzed. The GIS tool was applied in combination with the analytical hierarchical process (AHP) to create the maps. Four hundred simulation runs were performed for criteria weight sensitivity analysis to explore the dependency of the resultant maps on the weights of the input factors. Sampling soil data were used to validate the accuracy of information given by the resultant maps. Results from the two maps show that the soils in the area have high capability in retaining and transporting Al3+. Ninety nine percent of the soils in the area have medium to high capability of Al3+ retention and about 65% of the soils have medium to high capability of transporting Al3+. For the agricultural land, about 65% of the land ranked as having a high to very high soil Al3+ retention capability and about 58% of the land ranked as having a medium to high capability of transporting Al3+. These maps can support the process of decision-making in identifying the appropriate dose and frequency of the chemical products that are applied on each soil capability zone; in this case study, the products contain aluminum. The accumulation of Al3+ in the soil, especially in the high Al3+ retention capability soil, can cause soil degradation and can cause negative effects on plant growth.

A Fuzzy Multi-Criteria Method for Sustainable Ferry Operator Selection: A Case Study

This study is motivated by the Zhuhai municipal government, which needs to select a sustainable ferry operator. Previous research has ignored the evaluation and selection of ferry operators. In addition, since ferry operator evaluation involves conflicting qualitative and quantitative criteria, and there may be uncertainty and ambiguity in the evaluation of criteria by experts, a fuzzy multi-criteria decision-making (MCDM) approach is required to address this challenge. To this end, this paper proposes an integrated MCDM framework model to evaluate and select the best ferry operator. First, a ferry operator evaluation index system with 15 sub-criteria is constructed according to literature and expert opinions; then the fuzzy analytic hierarchy process (FAHP) is used to determine the subjective weight of the criteria, and the entropy weight (EW) method is used to calculate the objective weight of the criteria. We use the linear weighting method to obtain the comprehensive weights of the criteria; finally, the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) method is adapted to determine the best ranking of the alternatives. This paper takes the Wanshan Islands in Zhuhai as a real case study to verify the proposed FAHP-EW-FTOPSIS method. The results show that the proposed method can be effectively applied to the evaluation and selection of ferry operators. Sensitivity analysis of criteria weights demonstrates the effectiveness and robustness of the proposed framework model. Key findings based on the research provide management insights that can benefit relevant stakeholders. This is the first paper to study the evaluation and selection of ferry operators. Hence, the evaluation index system and integrated framework model proposed in this paper can make important contributions to the evaluation of ferry operators.

Associations between Genetically Proxied Inhibition of Lipid-Lowering Drug Targets and Serum Micronutrients among Individuals of European Descent: A Mendelian Randomization Study

BackgroundLimited and inconclusive data exist concerning the associations between lipid-lowering drugs and serum micronutrient concentrations. MethodsWe conducted Mendelian randomization (MR) analyses to explore the associations between lipid-lowering drug targets and serum micronutrients. Single-nucleotide polymorphisms in genes encoding molecular targets of LDL cholesterol-lowering therapies were selected as instrumental variables for 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR; target of statins), proprotein convertase subtilisin/kexin type 9 (PCSK9; target of PCSK9 inhibitors), and Niemann-Pick C1-Like 1 (NPC1L1; target of ezetimibe). Exposure data were extracted from a published genome-wide association study (GWAS) of lipids in 188,577 European individuals, with outcome data obtained from the Integrative Epidemiology Unit (IEU) GWAS database (https://gwas.mrcieu.ac.uk). Overall, age and sex information were not calculable from the summary-level GWAS data. MR analyses were performed using the inverse-variance weighted method and MR sensitivity analysis methods. ResultsWe found genetically proxied inhibition of HMGCR to lower iron (effect, –0.16; 95% CI: –0.27, –0.06; P value = 0.003), zinc (effect, –0.83; 95% CI: –1.36, –0.31; P value = 0.002), magnesium (effect, –0.17; 95% CI: –0.27, –0.06; P value = 0.003), potassium (effect, –0.17; 95% CI: –0.27, –0.06; P value = 0.002), genetically proxied inhibition of NPC1L1 to increase calcium (effect, 0.28; 95% CI: 0.10, 0.46; P value = 0.003), retinol (effect, 0.25; 95% CI: 0.07, 0.44; P value = 0.01), and genetically proxied inhibition of PCSK9 to increase vitamin D (effect, 0.10; 95% CI: 0.07, 0.12; P value = 1.8 × 10–19). These associations were robust in MR sensitivity analyses. However, the associations between genetically proxied inhibition of HMGCR and NPC1L1 and the micronutrients were not consistent in multiple comparisons. ConclusionsOur results provide evidence that statin use may lower serum concentrations of iron, zinc, magnesium, and potassium, PCSK9 inhibitors may increase serum vitamin D, and ezetimibe may increase serum calcium and retinol concentrations.

A Novel Multi-Criteria Assessment Approach for Post-COVID-19 Production Strategies in Vietnam Manufacturing Industry: OPA–Fuzzy EDAS Model

The global manufacturing supply chain has been disrupted by the negative impacts of the pandemic. In Southeast Asia, Vietnam’s manufacturing industry is one of the most strongly integrated with global and regional supply chains. The production strategies in the “new normal” are the key solution to the survival and sustainable development of manufacturers. This study aims to develop a two-stage framework to investigate the impacts of COVID-19 and the post-COVID-19 production strategies for Vietnam’s manufacturing industry. As a theoretical contribution, this study proposes a novel and robust integration approach, which combines the Ordinal Priority Approach (OPA) and Fuzzy Evaluation Based on Distance from Average Solution (Fuzzy EDAS), for the first time. The negative impacts of the pandemic were identified and weighted by the OPA method. Then, production strategies were comprehensively evaluated using the Fuzzy EDAS method. Findings indicate that digitization and on-site renewable energy are the most essential recovery strategies for manufacturing in Vietnam. These findings are validated by comparisons with the results of recent multiple criteria decision-making (MCDM) methods. Furthermore, weight sensitivity analysis reveals different suitability of strategies for short-term and long-term negative impacts. As a managerial implication, the multi-scenario ranking results help managers to make resource-allocation decisions for the implementation of post-COVID-19 production strategies.

Research on the evaluation of China's photovoltaic policy driving ability under the background of carbon neutrality

The realization of carbon neutral goal is inseparable from the development of new energy industry, and scientific and effective policy support can accelerate the progress of the goal. In this paper, the policy driven ability of China's photovoltaic industry in the background of carbon neutral is evaluated. Firstly, the evaluation system is established by the improved diamond model. Then, the policy evaluation standard is formulated according to the Interval Type-2 Fuzzy sets (IT2FS). Finally, the weight of each index is determined by using the fuzzy OWA operator weighting method (F-OWA). Then, the policy driving ability of China's photovoltaic industry is evaluated by fuzzy matter-element extension method (F-MEEM), and the effectiveness of the evaluation results is further verified by weight sensitivity analysis. According to the evaluation results, policy support plays an important role in the development of photovoltaic industry. This paper also gives policy suggestions for the development of China's photovoltaic industry under the background of carbon neutral from the macro, meso and micro perspectives.

A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application

The paper develops a goal programming-based multi-criteria methodology, for assessing different machine learning (ML) regression models under accuracy and time efficiency criteria. The developed methodology provides users with high flexibility in assessing the models as it allows for a fast and computationally efficient sensitivity analysis of accuracy and time significance weights as well as accuracy and time significance threshold values. Four regression models were assessed, namely the decision tree, random forest, support vector and the neural network. The developed methodology was employed to forecast the time to failures of NASA Turbofans. The results reveal that decision tree regression (DTR) seems to be preferred for low values of accuracy weights (up to 30%) and low accuracy and time efficiency threshold values. As the accuracy weights tend to increase and for higher accuracy and time efficiency threshold values, random forest regression (RFR) seems to be the best choice. The preference for the RFR model however, seems to change towards the adoption of the neural network for accuracy weights equal to and higher than 90%.

GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia

The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in the Nitra river basin had not yet been assessed through MCE-AHP. Therefore, the methodology used can be useful, especially in terms of the preliminary flood risk assessment required by the EU Floods Directive. The results showed that classification techniques of natural breaks (Jenks), equal interval, quantile, and geometric interval classified 32.03%, 29.90%, 41.84%, and 53.52% of the basin, respectively, into high and very high RFP while 87.38%, 87.38%, 96.21%, and 98.73% of flood validation events, respectively, corresponded to high and very high RFP. A single-parameter sensitivity analysis of factor weights was performed in order to derive the effective weights, which were used to calculate the revised riverine flood potential (RRFP). In general, the differences between the RFP and RRFP can be interpreted as an underestimation of the share of high and very high RFP as well as the share of flood events in these classes within the RFP assessment. Therefore, the RRFP is recommended for the assessment of riverine flood potential in the Nitra river basin.