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[Objective] The purpose of health status evaluation of the traction power supply equipment is to provide references for the operation, repair and maintenance of this equipment in urban rail transit. [Method] A health status evaluation model for urban rail transit traction transformer is established based on AHP (analytic hierarchy process) and EWM (entropy weight method). The hierarchical structure for evaluating the health status of the traction transformer is constructed based on the operation indicator data of the traction transformer. The subjective weights of the evaluation indicators are calculated by using AHP through the expert judgment matrix, and the weights of the test indicators are solved by using EWM and then combined with the subjective weights. The health status score of the traction transformer is calculated according to the evaluation indicators to determine its overall health status, thereby predict its remaining service life and evaluate the impact of different maintenance methods on the service life. With the monitoring data of the traction transformer in a certain traction substation as an example, the health status of the traction transformer is evaluated. [Result ＆ Conclusion] The effectiveness of the proposed health status evaluation method for traction transformer is verified through examples. The evaluation method realizes unification of the subjective and objective factors of equipment status detection, and can be popularized and applied to the health status evaluation of other urban rail transit DC power supply equipment. [ABSTRACT FROM AUTHOR]

［Objective］ The purpose of health status evaluation of the traction power supply equipment is to provide references for the operation, repair and maintenance of this equipment in urban rail transit. ［Method］ A health status evaluation model for urban rail transit traction transformer is established based on AHP (analytic hierarchy process) and EWM (entropy weight method). The hierarchical structure for evaluating the health status of the traction transformer is constructed based on the operation indicator data of the traction transformer. The subjective weights of the evaluation indicators are calculated by using AHP through the expert judgment matrix, and the weights of the test indicators are solved by using EWM and then combined with the subjective weights. The health status score of the traction transformer is calculated according to the evaluation indicators to determine its overall health status, thereby predict its remaining service life and evaluate the impact of different maintenance methods on the service life. With the monitoring data of the traction transformer in a certain traction substation as an example, the health status of the traction transformer is evaluated. ［Result & Conclusion］ The effectiveness of the proposed health status evaluation method for traction transformer is verified through examples. The evaluation method realizes unification of the subjective and objective factors of equipment status detection, and can be popularized and applied to the health status evaluation of other urban rail transit DC power supply equipment.

This study aims to develop an evaluation indicator system for the high-quality development of aquaculture in China, guided by relevant national policies. It uses the entropy weight method (EWM) and the CRITIC method to conduct an empirical assessment of major fishing provinces, providing a reference for the formulation of science-based industry policies. The research constructs a comprehensive evaluation framework based on five key dimensions: industrial scale and efficiency, green development, technology and infrastructure, industrial integration and transformation, and industry management and services. Taking local conditions into account, the study employs diversified evaluation methods for an in-depth analysis. The results reveal significant performance differences across provinces in these dimensions. Shandong leads in industrial integration and transformation, Guangdong excels in green development, and Jiangsu stands out in technological innovation and industry management. The evaluation system developed in this study provides a scientific basis for promoting the high-quality development of aquaculture and is of great significance for driving industrial transformation and upgrading, as well as supporting the sustainable development of fisheries globally. [ABSTRACT FROM AUTHOR]

Socioeconomic backwardness represents the antithesis of development, characterized by the inability to meet essential needs and limited access to education, healthcare, and employment opportunities within a society or region. This study delves into the multifaceted factors contributing to socioeconomic backwardness, employing a multi-criteria decision-making (MCDM) approach to address this complexity. Fourteen socio-economic, demographic, and educational indicators are analyzed to assess backwardness, with their relative importance determined using the Entropy Weight Method (EWM). The study focuses on 31 Gram Panchayats (GPs) within the Sali watershed, ranking them based on their degree of backwardness to enable targeted policy interventions. The Weighted Sum Method (WSM), Weighted Product Method (WPM), and Weighted Aggregated Sum Product Assessment (WASPAS) were applied to rank the GPs. Based on these analyses, Lachhmanpur, Patrasayer, Gobindadham, Mankanali, and Bhaktabundh were identified as the most backward GPs, with backwardness scores ranging from 0.70 to 0.85. Conversely, Hamirhati, Pearbera, Dhulai, Hat-asuria, and Dihipara emerged as the most developed, with scores ranging between 0.15 and 0.30. Key factors driving backwardness included the prevalence of scheduled tribe populations (over 40% in highly backward GPs), unirrigated land areas (exceeding 60% of total land), and the presence of disabled household members (greater than 15% of households in backward GPs). The results highlight pronounced spatial inequalities in the Sali watershed, driven by agricultural dependency, poor irrigation infrastructure, and limited socioeconomic opportunities. To mitigate backwardness, the study recommends poverty alleviation, employment generation, community development, infrastructure upgrades, land use planning, improved irrigation, and agricultural support initiatives. [ABSTRACT FROM AUTHOR]

Abstract Throughout the history of coal mining in all countries of the world, large areas of goaf have been left behind, and sudden collapses and surface subsidence of large areas of goaf may occur, especially for mining areas with long mining cycles. The northern new district of the Liaoyuan mining area has been subjected to nearly half a century of mining activities, accompanied by a gradual accumulation of disasters, which have occurred frequently in recent years. In order to assess the stability of the goaf in the study area, this paper proposes a hybrid decision-making multi-factor integrated evaluation method. The distribution of underground goafs was determined using geophysical exploration techniques (seismic survey and transient electromagnetic method) and geological drilling exploration. First, an evaluation index system was established based on the specifications of the goaf, the ecological and geological environment, and the mining conditions; the system included 14 indicators. Two weight calculation methods, AHP-EWM, were employed to determine the comprehensive weight of each indicator by combining subjective and objective weights on the basis of improved game theory. Subsequently, the fuzzy comprehensive evaluation method was utilised to complete the stability rating of each block in the study area, and MapGIS and ArcGIS were employed to complete the drawing of the stability zoning map of the northern new district goaf. The study area was divided into three zones of stability, basic stability and instability, according to the critical value. These zones accounted for 23.03%, 36.45% and 40.52% of the total area of the study area, respectively. The comprehensive on-site investigation revealed a decrease in the size and number of collapse pits and the rate of damage to the houses from the unstable zone to the stable zone. This indicates that the results of the division are consistent with the actual situation. The classification results are consistent with the actual ground disaster situation, thus verifying the rationality and validity of the evaluation method. The results indicate that the stability of the study area is generally at the lower middle level.

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively. [ABSTRACT FROM AUTHOR]

In this digital age the use of social media has evolved rapidly. This has empowered customers to express their sentiments about products or services on online platforms, leading to the emergence of influential electronic Word-of-Mouth (eWM). Therefore, in various industries, webcare, which refers to customer care via online communications, is essential. In the hotel industry webcare has become an effective marketing tool and has received considerable critical attention in hospitality and business studies over the past two decades. In the field of linguistics, webcare has received increasing attention in recent years while going relatively unnoticed in the research literature of Southeast Asia. This study examines the apology strategies as webcare in rapport building in the responses to negative online reviews by hotels of three different star-ratings in Malaysia on TripAdvisor. The findings identified eight apology strategies. It was found that the strategy of offer of repair by the five-star hotels was significantly higher than the four- and three-star hotels. Compared to the four- and three-star hotels, the five-star hotels were least likely to use the strategy of explanation of situations. This study sheds light on cultural insights into webcare strategies in social media communications in the hotel industry in Malaysia. [ABSTRACT FROM AUTHOR]

In recent years, research and application of hydrogen fuel emergency power supply vehicle technologies have been steadily advancing. However, due to the highly flammable and explosive nature of hydrogen gas, the safety risk of hydrogen leakage has been taken seriously. Addressing the current inadequacies in the understanding of hydrogen leakage risks, unclear sources of risk, incomplete coverage of risk scenarios, and insufficiencies in risk assessment methods associated with hydrogen fuel emergency power supply vehicles, this study firstly establishes a multi-scenario and multi-factor coupled hydrogen leakage risk event database and an accident risk system. This effort focuses on the diversity of application scenarios, types of hydrogen leakage hazards and complexity of interfering factors. Subsequently, by refining existing risk assessment methods, a more balanced subjective-objective evaluation and more granular risk ratings approach, termed AET-RM, is proposed, which ensures finer risk grading. Finally, through this method, the types of hydrogen leakage hazards and their harm boundaries are clarified, achieving a systematic and precise graded quantitative assessment of risk sources, probabilities, and consequences. The study results indicate that the established event database contains no risk events rated as I (acceptable) or V (unacceptable). There are 37 events classified as low risk (rating II), accounting for 17.54% of the total. Medium-risk events (rating III) comprise 142 incidents, making up 67.30%, while high-risk events (rating IV) include 32 cases, representing 15.17%. The highest risk score, determined by the AET-RM method, pertains to a risk event in the power generation scenario, Contingencies: metal corrosion within the battery. • Summarized hydrogen leakage risk events and established a risk event repository. • Established a multi-scenario hydrogen leakage risk system. • Proposed a reasonable and sophisticated risk assessment method. • Analysis and evaluation of risk events for hydrogen fuel emergency power supply vehicles. [ABSTRACT FROM AUTHOR]

Smart community construction (SCC) is pivotal for urban modernization, yet extant research often neglects the residents' perceptions of these initiatives, focusing predominantly on technological advancements. A hybrid evaluation method integrating the Entropy Weight Method (EWM) and PROMETHEE II was developed to quantify residents' perceptions of SCC. Through the literature review and expert interview, the evaluation indicators for residents' perceptions of the SCC were identified, leading to the development of an evaluation indicator system consisting of 5 dimensions and 25 indicators. The hybrid evaluation method that combines EWM and PROMETHEE II was then applied to quantify the level of residents' perceptions of SCC across five communities in Shenzhen city of China, using data from 410 valid questionnaires. The results indicated that the Baolong community had the highest level of the SCC, while the Nanyuan community exhibited the lowest ranking. Sensitivity analysis confirmed the robustness of these results, thereby validating the evaluation process. Moreover, recommendations were proposed to enhance residents' perceptions of smart community initiatives. This research not only introduces a novel evaluation method that expands the body of knowledge on the SCC and residents' perceptions but also helps the government develop sustainable SCC strategies. [ABSTRACT FROM AUTHOR]

Abstract At present, a few scholars studied influencing factors, rules and mechanisms of decision-making in ethical dilemmas. Many factors have been identified, and a few rules and mechanisms have been proposed. However, due to the inability to evaluate the weight and role of each factor in decision-making, it is difficult to establish a computational decision-making model to solve ethical dilemmas. Therefore, entropy weighted method (EWM) and Attribute Value Weighted EWM (AVWEWM) are used to process 84 dilemmas respectively to evaluate the weight and role of each factor in decision-making, then decision-making models based on EWM and AVWEWM are constructed to make decisions during autonomous vehicle (AV) crashes respectively. Lastly, 40 dilemmas are designed to test both decision-making models. The test results indicate that both can make clear decision-making in 40 dilemmas. However, the decision-making by AVWEWM is more consistent with public opinion than EWM. In addition, according to the weight and role of each factor in decision-making, it can also provide a few references for traffic management and legal departments to formulate traffic laws and regulations for AV in the future.

The stability of abandoned open-pit mine slopes and their ecological environment are threatened owing to their fragile, complicated, and uncertain characteristics. This study establishes a novel evaluation indicator system for enhancing mine design and environmental protection insight. The weights in the system are assigned using a combined method, which consists of the game theory, the interval analytic hierarchy process (IAHP), and the entropy weight method (EWM). The IAHP is optimized by the improved radial movement optimal (IRMO) algorithm and the simulated annealing (SA) algorithm to ensure calculation stability and efficiency. Meanwhile, a two-dimensional cloud model (TDCM) is developed to obtain the slope resilience level and visualize the result. This comprehensive evaluation method is applied to three abandoned mine slopes in the Yellow River Basin, and the results demonstrate that the method can provide crucial insights for rational mine slope stabilization and ecological restoration. [ABSTRACT FROM AUTHOR]

At present, a few scholars studied influencing factors, rules and mechanisms of decision-making in ethical dilemmas. Many factors have been identified, and a few rules and mechanisms have been proposed. However, due to the inability to evaluate the weight and role of each factor in decision-making, it is difficult to establish a computational decision-making model to solve ethical dilemmas. Therefore, entropy weighted method (EWM) and Attribute Value Weighted EWM (AVWEWM) are used to process 84 dilemmas respectively to evaluate the weight and role of each factor in decision-making, then decision-making models based on EWM and AVWEWM are constructed to make decisions during autonomous vehicle (AV) crashes respectively. Lastly, 40 dilemmas are designed to test both decision-making models. The test results indicate that both can make clear decision-making in 40 dilemmas. However, the decision-making by AVWEWM is more consistent with public opinion than EWM. In addition, according to the weight and role of each factor in decision-making, it can also provide a few references for traffic management and legal departments to formulate traffic laws and regulations for AV in the future. [ABSTRACT FROM AUTHOR]

A multi-objective decision method is proposed based on the combining orthogonal test, entropy weight method (EWM) with TOPSIS (technique for order preference by similarity to an ideal solution) in this article. The method is more objective and efficient than traditional methods in the design of green residential buildings. A villa in Nanjing was taken as an example, and the cost, building energy consumption, and daylight factors were used as decision-making indexes. A total of six control factors were selected: terrain elevation, window area, building orientation, the opening ratio of exterior windows, roof structure, and window glass material. Each factor was designed with three levels. First, a design scheme index system was constructed, and the orthogonal experimental design was used to select representative design schemes. Then, the EWM and the TOPSIS method were used to determine the weight of each index, a comprehensive evaluation of the residential building design scheme was conducted, and, finally, the optimal scheme was confirmed via range analysis. The results show that the multi-objective decision-making model based on the orthogonal experiment EWM-TOPSIS is suitable for green residential building design and the optimal solution obtained is "15 m elevation, a 1200 × 1500 mm size vacuum glass, an orientation of 170° and an external window opening ratio 45% with a 40 mm thick expanded insulation board roof". [ABSTRACT FROM AUTHOR]

Smart community construction (SCC) is pivotal for urban modernization, yet extant research often neglects the residents’ perceptions of these initiatives, focusing predominantly on technological advancements. A hybrid evaluation method integrating the Entropy Weight Method (EWM) and PROMETHEE II was developed to quantify residents’ perceptions of SCC. Through the literature review and expert interview, the evaluation indicators for residents’ perceptions of the SCC were identified, leading to the development of an evaluation indicator system consisting of 5 dimensions and 25 indicators. The hybrid evaluation method that combines EWM and PROMETHEE II was then applied to quantify the level of residents’ perceptions of SCC across five communities in Shenzhen city of China, using data from 410 valid questionnaires. The results indicated that the Baolong community had the highest level of the SCC, while the Nanyuan community exhibited the lowest ranking. Sensitivity analysis confirmed the robustness of these results, thereby validating the evaluation process. Moreover, recommendations were proposed to enhance residents’ perceptions of smart community initiatives. This research not only introduces a novel evaluation method that expands the body of knowledge on the SCC and residents’ perceptions but also helps the government develop sustainable SCC strategies.

Linguistic Pythagorean fuzzy set (LPFS) combines Pythagorean fuzzy sets and linguistic term sets, which can effectively deal with fuzzy information in multi-criteria decision-making (MCDM). The entropy weight method (EWM) can reflect the objectivity of decision information, while the best-worst method (BWM) can reflect the subjectivity of decision-makers. The interactive multi-criteria decision-making (TODIM) method can describe the different preferences of decision-makers for gains and losses. In this paper, EWM, BWM, and TODIM are combined and applied to LPFS for the first time. First, we calculate the objective weight and subjective weight of each criterion through EWM and BWM and combine them to get the final weight to balance subjectivity and objectivity. Then, this paper selects the best scheme through TODIM sorting. In conclusion, the LPFS-EWM-BWM-TODIM model is established in this paper. Finally, the paper applies this model to the selection of corporate investment strategy and green mine, verifies the effectiveness of the method, and carries out comparative analysis and sensitivity analysis, proving the rationality and robustness of the model. [ABSTRACT FROM AUTHOR]

Wind energy potential or quality serve as the primary determinants influencing the decisions of Thai farmers regarding the installation of water-pumping windmills with heights ranging from 9 to 15 m and a cut-in wind speed requirement of 4 m/s, aimed at reducing their fuel costs. To introduce a simplified calculation method as one of their decision-making tools, the combined approach of the entropy weight method with TOPSIS has been introduced to assist them in prioritizing and assessing the wind quality in their respective areas. This study focuses on the western region of Southern Thailand, known for its high agricultural productivity. Initially, only 18 out of the 227 sub-districts with a minimum monthly wind speed exceeding 4 m/s were selected for thorough investigation. Subsequently, the entropy weight method was applied to the monthly wind speed data of these 18 chosen sub-districts to calculate their monthly weight values. These monthly weight values provide a quantifiable characterization of the wind quality in these specific sub-districts, revealing variations in wind quality between seasons, with superior quality during the summer season compared to the rainy season. Following the calculation of monthly weight values, the TOPSIS technique was applied to the wind data in conjunction with these monthly weight values, resulting in the determination of performance scores (Pi) for each of the 18 sub-districts. Pi values were found to vary from 0.0641 to 0.9006. In the final step of the analysis, these 18 sub-districts were ranked based on their respective Pi values, with the implication that sub-districts exhibiting higher Pi values are more suitable for the installation of water-pumping windmills with heights ranging from 9 to 15 m compared to those with lower Pi values. [ABSTRACT FROM AUTHOR]

In the present work, the influence of four significant wire electrical discharge machining (WEDM) parameters, such as pulse duration (Ton), pulse interval (Toff), servo voltage (Sv) and wire tension (Wt) has been studied on cutting velocity (Vc), diagonal dimensional deviation (Dv) and surface roughness (Ra). Signal-to-noise ratio and analysis of variance are used to analyse the effects of four cutting parameters. An entropy weight method is used to determine the objective weights. An integrated weight-based multi-constrained optimization technique is employed to find the optimal solutions for multiple responses. Optimal solutions show that diagonal dimension deviation (Dv) ranges from 0.128 to 0.241 mm and surface roughness (Ra) ranges from 1.058 to 3.457 m for a wide range of cutting velocity (Vc). It is also found that the weight-based Pareto results are improved by 10.5% (for cutting velocity) 28.3% (for dimensional accuracy) and 13.7% (for surface finish) compared to the general Pareto results. Additionally, an important aspect, radial overcut (δ) is measured to predict the linear error present in the workpiece. Finally, CCI and SEM analyses have been carried out to evaluate the surface characteristics of the machined surface. It is evident from the machined surface that the surface is full of small craters, micro-holes, re-solidified materials and recast globules. [ABSTRACT FROM AUTHOR]

Coal mining can create a variety of environmental, ecological, and land-use problems. Subsidence areas resulting from coal mining are a common and particularly difficult problem to manage. Despite much discussion in the academic literature as well as among local and international stakeholders, there is neither a uniform standard nor a universally accepted approach for selecting an appropriate governance model for a subsidence area. In particular, the lack of quantitative evaluation methods and excessive subjectivity represent key obstacles to the effective selection of governance models for subsidence areas. This paper proposes a selection framework for a coal mining subsidence governance model that integrates the analytic hierarchy process (AHP) and entropy weight method (EWM). The model comprehensively considers the settlement characteristics of the subsidence area, its geographic location, the water index, as well as the vegetation index. These variables are used as indicators to develop an evaluation framework upon which different subsidence zones can be quantitatively analyzed. The selection framework is demonstrated using examples from three subsidence areas in the Huainan and Huaibei mining areas in China, for which relevant data were collected and processed with the help of field surveys, remote sensing images, and subsidence prediction software. Applying the novel selection framework, the most suitable governance model for each subsidence area was obtained and determined to be consistent with the recommendations of an academic panel composed of multiple experts. The novel selection framework has high efficacy and potential to overcome the problem of subjectivity in the selection of governance models for coal mining subsidence areas. It is also envisaged that future incorporation of the selection framework into a user-friendly software package will significantly improve the efficiency with which suitable governance models for coal mining subsidence areas are selected. [ABSTRACT FROM AUTHOR]

A multi-objective decision method is proposed based on the combining orthogonal test, entropy weight method (EWM) with TOPSIS (technique for order preference by similarity to an ideal solution) in this article. The method is more objective and efficient than traditional methods in the design of green residential buildings. A villa in Nanjing was taken as an example, and the cost, building energy consumption, and daylight factors were used as decision-making indexes. A total of six control factors were selected: terrain elevation, window area, building orientation, the opening ratio of exterior windows, roof structure, and window glass material. Each factor was designed with three levels. First, a design scheme index system was constructed, and the orthogonal experimental design was used to select representative design schemes. Then, the EWM and the TOPSIS method were used to determine the weight of each index, a comprehensive evaluation of the residential building design scheme was conducted, and, finally, the optimal scheme was confirmed via range analysis. The results show that the multi-objective decision-making model based on the orthogonal experiment EWM-TOPSIS is suitable for green residential building design and the optimal solution obtained is “15 m elevation, a 1200 × 1500 mm size vacuum glass, an orientation of 170° and an external window opening ratio 45% with a 40 mm thick expanded insulation board roof”.

As a crucial paradigm for addressing urbanization-related problems, smart community construction is in full swing, and its goal is to enhance residents' sense of gain. Prior studies have not been able to account for all aspects of smart community construction, especially the evaluation tools from the perspective of residents' sense of gain. Therefore, this paper seeks to establish a comprehensive evaluation framework for residents' sense of gain in the smart community through the integrated method, which includes the entropy weight method (EWM), the fuzzy comprehensive evaluation (FCE), and the obstacle degree model (ODM). For the purpose of verifying the feasibility of the evaluation framework, 31 smart communities in 6 Chinese cities (Shenzhen City, Putian City, Huizhou City, Dongguan City, Zhengzhou City, and Luoyang City) were selected. The results indicated that the weight of "Cultural activities for the elderly" indicator is the highest while the "Overall design" indicator is the lowest. In addition, Putian City had the best performance, but Shenzhen City ranked last among the six cities. Moreover, among the 31 communities, the Fengshan community in Putian City performed the best while the Xinglong community in Luoyang City performed the worst. Several suggestions are proposed to improve residents' sense of gain in smart communities, such as enhancing the quality of healthcare services, meeting the needs of the elderly through multiple channels, and enriching business services. This study not only innovates the evaluation method of smart community construction from the perspective of residents' sense of gain but also provides suggestions for promoting the sustainable development of the smart community and enabling residents to feel more satisfied. [ABSTRACT FROM AUTHOR]

As a financial innovation model to promote sustainable economic development, carbon finance promotes green and inclusive economic development by reshaping the financial system. As a big energy consumer, China has been committed to the green transformation of energy structure, and carbon finance has been regarded as an important tool to support carbon emission reduction. This paper divides carbon finance from three levels, financial macro, environmental governance, and technological development, and selects 17 indexes to measure the development level of carbon finance in China. Time series regression model and the entropy weight method (EWM) are used to analyze the impact of carbon finance on China's energy consumption structure. The empirical results show that carbon finance plays a positive role in promoting China's energy consumption structure, and developing carbon finance is conducive to the use of clean energy and the optimization of energy consumption structure in China; carbon finance can further optimize China's energy consumption structure by investing capital, resources, and technology in industries related to promoting clean energy consumption. Therefore, China should continue to develop carbon finance and promote the establishment of a unified national carbon trading market; at the same time, China should use the CCER trading mechanism to promote the development of renewable energy and enhance the endogenous driving force of energy transformation by promoting technological progress. [ABSTRACT FROM AUTHOR]

Fluctuations in the sintering process have a significant impact on the overall performance and outcomes of sintering production. It is crucial to gain a comprehensive understanding of the sintering operation in a timely manner in order to minimise process fluctuations and ensure the stability of sinter production. In this paper, a sintering process evaluation model is presented, which is based on the analytic hierarchy process–entropy weight method–technique for order preference by similarity to an ideal solution (AHP–EWM–TOPSIS) and is constructed using data from a steel plant sintering production process. Firstly, the evaluation indices input to the system are selected by combining the principle of the sintering process and actual production requirements. The subjective and objective weights of the indices are then calculated using the AHP–EWM method, and the weights are combined through the principle of minimum discriminatory information to construct the set of weighted indices. Subsequently, the sintering process was evaluated by the TOPSIS model, and the results of this evaluation were compared with the actual fluctuation situation at the sintering site. This was done by combining the model results with the differential level method. It was found that the model can accurately reflect the fluctuation situation that occurs in the sintering process. Furthermore, the comprehensive matching rate reached 95.42%, which demonstrates that the model is capable of accurately evaluating the operation of the sintering process. The statistical analysis of historical data provides a basis for the optimised operation of the sintering production process by summarising the optimal range of each parameter when the sintering operates efficiently. Ultimately, a sintering process evaluation system is constructed through the use of computer technology, thereby facilitating the transformation of sintering production into an intelligent system. The model is capable of rapidly ascertaining the actual status of the sintering process in real time. The established system is designed to facilitate the optimisation of sintering production operations and to promote long-term stability in sintering. [ABSTRACT FROM AUTHOR]

Given the problems of unclear division of evaluation factors, inadequate utilization of objective data and unreasonable distribution of weight in college performance measurement, this study extracts relevant indicators and quantitative data from the survey report of administrational institutions, and constructs a comprehensive performance measurement model based on the integration of fuzzy Delphi method (FDM), Entropy weight method (EWM), and grey relational analysis (GRA). The study seeks to identify differences in measurement analysis by comparing the weights, the performance and the rankings of the alternatives about higher vocational colleges in Zhejiang province. Finally, the results show the following: (1) by FDM, 33 indicators about colleges' performance are selected to form the evaluation system of the college’s performance, (2) the indicators' weights are obtained through EWM and GRA, and a nonparametric test shows that there is no significant difference between the two types of weights, (3) the grey relational degrees of the alternatives are obtained and ranked on the basis of comprehensive evaluation model. By nonparametric test, there is a significant difference between the two types of relational degrees. On the contrary, no significant difference is found in the ranking of relational degrees, (4) based on the analysis results, this study further compares the performance of alternatives in different forms. There are significant differences between the performance of public colleges and private colleges, while no significant differences are observed between the performance of vocational colleges in Hangzhou and non-Hangzhou. Given the reliability and validity of the model, the comprehensive measurement model provides a relatively objective reference for college governance and administrational institutions, and also becomes an effective tool of colleges' evaluation to assist and improve the management practice in educational institutions.

Stirring tanks are regarded as pivotal equipment for solid-liquid suspension operations in industrial processes such as adsorption and crystallisation, and its performance is complex in relation to the pulp phase, structure and process conditions. However, the traditional design method for stirring tank structure is sophisticated and time-consuming, which greatly limits the design efficiency. To address the high cost and inefficiency of traditional design methods, this paper performed a multi-objective optimal design of stirring tank based on numerical simulation, surrogate model, NSGA-II, Entropy Weighting Method and Analytic Hierarchical Process. Firstly, the flow field state of stirring tanks with different structural dimensions and operating parameters was simulated with computational fluid dynamics (CFD), which was subsequently validated by Particle Image Velocimetry. Then, for the key parameters including stirring speed, stirrer diameter, stirrer height from the bottom and paddle pitch, experiments were designed using the Latin Hypercube method to establish a Kriging model based on the gained simulation data. Afterwards, the Pareto front was obtained from the optimization using NSGA-II with two objectives of stirring power and suspension uniformity. The result of Analytic Hierarchy Process and Entropy Weighting Method showed that the selected optimal solution can improve the suspension uniformity by 0.7 % while reducing power consumption by 48.3 % compared to the initial design. [Display omitted] • An multi-objective optimization model for stirring tank was developed. • Combination of Kriging and NSGA-II reduces the consumption of resources dramatically. • Balancing objective and subjective factors by integrating AHP and EWM. • The optimal solution reduces energy cost by 48.3 % at no loss in. [ABSTRACT FROM AUTHOR]

The benefit evaluation for lean construction implementation in construction enterprises is one of the important measures to realize the rapid development of construction enterprises, which provides theoretical basis for the managers of construction enterprises to accurately formulate enterprise development strategies. This paper constructs an index system for evaluating the performance of lean construction implementation in construction enterprises from four dimensions, including goal achievement, lean tools and techniques, waste control, and lean atmosphere. The weight of each indicator is determined by combining analytic network process (ANP) and the entropy weight method (EWM), with consideration given to the correlation among indicators. Combined with the fuzzy comprehensive evaluation method (FCE), the evaluation model of lean construction performance for construction enterprises is established. Finally, the comprehensive evaluation model is verified by taking a company of China Construction Corporation as an example to provide a reference for evaluating the implementation performance of lean construction in construction enterprises. [ABSTRACT FROM AUTHOR]

Scheduling large workflows that are faced in many business as well as scientific domains such as economy, bioinformatics, astronomy and geophysics is an important area of research in the field of cloud computing. Many studies have been made to develop efficient algorithms for workflow scheduling that deal with multiple objectives. In the recent years, multi-criteria decision making (MCDM) methods have become popular for solving such multi-objective problems in various areas like risk management, climate change, renewable energy and so on. Particularly, the MCDM method called technique for order of preference by similarity to ideal solution (TOPSIS) has drawn extensive attention due to its easy understanding, fast and simple calculation. In this paper, we present a workflow scheduling algorithm in cloud environment based on TOPSIS that integrates entropy weight method (EWM). The proposed algorithm aims at minimizing makespan, cost, and energy consumption and maximizing the reliability. The algorithm is tested on various benchmark scientific workflows. The simulation results are compared with that of the related algorithms. The comparisons show that the proposed algorithm performs remarkably well in terms of cost and energy consumption while maintaining the other parameters within considerable limits. [ABSTRACT FROM AUTHOR]

Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability. [ABSTRACT FROM AUTHOR]

As a scarce public resource, carbon emission rights are essentially a new type of development rights. The rational allocation of limited carbon emission rights is crucial to international climate governance. On the basis of the multi-index method allocation model, this paper proposes a global carbon emission rights allocation model based on FAHP-EWM-TOPSIS, which uses fuzzy analytic hierarchy process and entropy weight method respectively. Determine the subjective weight and objective weight of the evaluation indicators, and use the idea of minimizing the difference to find the optimal proportion of the subjective and objective weights, and then obtain the optimal combination weight, and finally combine the TOPSIS method to score and calculate the reasonable distribution of rights and interests of countries around the world. The results show that the fair share of most countries in the world is between 1% and 2%. Compared with other carbon emission rights allocation strategies, this model takes into account the more comprehensive distribution principles, and the differences between different countries are small, which can better reflect the principle of fairness. The research results provide a new scheme for the allocation of global carbon emission rights, which has certain reference value for future global climate governance.

The subject of this study is an evaluation of the amount of evaporation from water surfaces (VVH), measured using EWM devices in two cities of different sizes, and located approximately 80 km from each other – Prague and Pilsen. The results were analyzed in the context of urban phenomena, which are pronounced especially in Prague, and also in the context of meteorological and morphological conditions in those locations. It was found that higher amounts of evaporation were measured at the meteorological station in Pilsen. The difference between the average sum of VVH per season (1st May to 30th September) between 2005 and 2014 for the two locations is 33.3 mm. The difference between daily average values was 0.2 mm. Given the suburban nature of the two locations where measurements were taken, it was not possible to draw any conclusions about the effect of the urban heat island on the rate of evaporation and values of VVH. Factors significantly influencing VVH are surface roughness, which is higher in urban environments than in open landscapes. Based on the results it was concluded that at both a regional and a local scale, the rate of evaporation is more affected by wind speed than thermal conditions. The measured VVH values differ, not just because of the urban dimension of the two cities compared, but especially as a result of different topoclimatic location of the two stations.

Prioritizing the prevention and control of non-native invasive species requires understanding where introductions are likely to occur and cause harm. We developed predictive models for Eurasian watermilfoil (EWM) (Myriophyllum spicatum L.) occurrence and abundance to produce a smart prioritization tool for EWM management. We used generalized linear models (GLMs) to predict species occurrence and extended beta regression models to predict abundance from data collected on 657 Wisconsin lakes. Species occurrence was positively related to the nearby density of vehicle roads, maximum air temperature, lake surface area, and maximum lake depth. Species occurrence was negatively related to near-surface lithological calcium oxide content, annual air temperature range, and average distance to all known source populations. EWM abundance was positively associated with conductivity, maximum air temperature, mean distance to source, and soil erodibility, and negatively related to % surface rock calcium oxide content and annual temperature range. We extended the models to generate occurrence and predictions for all lakes in Wisconsin greater than 1 ha (N = 9825), then prioritized prevention and management, placing highest priority on lakes likely to experience EWM introductions and support abundant populations. This modelling effort revealed that, although EWM has been present for several decades, many lakes are still vulnerable to introduction.

Emergency warning message (EWM) transmission insurance is one of the most interesting topics for vehicular ad-hoc network application. Clear channel assessment (CCA) threshold is deemed to be a key factor influencing EWM dissemination performance. Considering vehicle abnormal event hazard level and EWM's important level, a new parameter, EWM priority rank, is defined. Based on EWM priority rank, a new CCA threshold self-adjusting method is proposed in this paper. Moreover, according to the proposed CCA threshold selection method, an EWM dissemination algorithm is designed. A series of experiments on the sensor network testbed are conducted to evaluate the impact of proposed method on EWM dissemination. The experimental results show that an appropriate CCA threshold is beneficial to increase global dissemination success rate and to shorten packet delivery delay. [ABSTRACT FROM AUTHOR]

In this paper, the structural strength and stability of cold-formed steel lipped channel beam-columns under bi-axial moments and axial force are experimentally investigated. The results are employed to evaluate the reliability of the current North American cold-formed steel design standard, AISI-S100-12, for predicting the strength of beam-columns, by both the effective width method (EWM) and the direct strength method (DSM). Fifty-five 600S137-54 (AISI-S200-12 nomenclature) lipped channel beam-column sections with three different lengths: 305 mm (short), 610 mm (intermediate), and 1219 mm (long) are tested under combined bi-axial bending moments and axial force to characterize the failure modes and the member capacity. A loading rig specifically designed to apply eccentric axial load, in order to provide bi-axial bending and compression to the specimens, was developed and detailed herein. The experimental observations reveal that the failure modes are highly dependent on the stress distribution applied on the cross-section by the combined actions. The results show a considerable potential for improvement in current specification approaches which utilize a simple interaction equation, as this typically results in conservative strength predictions. The potential for further improvement of the current specification for predicting the strength of cold-formed steel beam-columns is discussed. [ABSTRACT FROM AUTHOR]

Prioritizing the prevention and control of non-native invasive species requires understanding where introductions are likely to occur and cause harm. We developed predictive models for Eurasian watermilfoil (EWM) (Myriophyllum spicatum L.) occurrence and abundance to produce a smart prioritization tool for EWM management. We used generalized linear models (GLMs) to predict species occurrence and extended beta regression models to predict abundance from data collected on 657 Wisconsin lakes. Species occurrence was positively related to the nearby density of vehicle roads, maximum air temperature, lake surface area, and maximum lake depth. Species occurrence was negatively related to near-surface lithological calcium oxide content, annual air temperature range, and average distance to all known source populations. EWM abundance was positively associated with conductivity, maximum air temperature, mean distance to source, and soil erodibility, and negatively related to % surface rock calcium oxide content and annual temperature range. We extended the models to generate occurrence and predictions for all lakes in Wisconsin greater than 1 ha (N = 9825), then prioritized prevention and management, placing highest priority on lakes likely to experience EWM introductions and support abundant populations. This modelling effort revealed that, although EWM has been present for several decades, many lakes are still vulnerable to introduction. [ABSTRACT FROM AUTHOR]