Search

Green agriculture refers to pushing ahead with the greening of agricultural growth, which plays an essential role in increasing land output rates and cutting down on issues such as environmental damage. This paper analyzes and evaluates the level of agricultural green growth in Shanxi Province during 2010-2021 by utilizing the entropy value method to construct an indicator system for agricultural green growth. The results indicate that the intensity of agricultural green development in Shanxi Province is favorable and outstanding. The levels of green development of agriculture in Shanxi Province are mainly affected by three aspects of green production process, green atmosphere and green production base, and the economic benefits have less influence on the green development of agriculture. In the future,Shanxi Province should continue to reduce the use of pesticides, agricultural films and chemical fertilizers, increase publicity and guidance, encourage people to change the traditional production and consumption awareness, and stimulate the development of green agriculture from the market needs. [ABSTRACT FROM AUTHOR]

Based on the sample data of 14 divisions of Xinjiang Production and Construction Corps (hereinafter referred to as the Corps) from 2010 to 2020, the evaluation index system of 5 first-level indicators and 20 corresponding second-level indicators suitable for the evaluation of the Corps' rural revitalization level was constructed, and the entropy method and comprehensive weighted TOPSIS model were used to measure and rank the 14 divisions. It is used to evaluate the implementation effect of the XPCC rural revitalization strategy. The results show that the rural revitalization level of the XPCC is generally low, but it shows an increasing trend with the change of time series. The proximity ranking of the 14 divisions fluctuated greatly in the five first-level indicators of industrial prosperity, ecological livability, village style civilization, effective governance and rich life, and the development level of rural revitalization of each division was in an unbalanced state. All divisions of the XPCC need to promote the formulation and implementation of the distinctive development plan, create a distinctive industrial system, improve the human resources system, improve the company's governance level, and promote regional revitalization according to local conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT Classroom instruction is the cornerstone of teaching methodologies, supported by educational and pedagogical research. It is the primary way students acquire knowledge and skills. The quality of the curriculum directly affects the effectiveness of personnel training. Assessing the curriculum quality is crucial for improving classroom instruction. Establishing a scientifically sound evaluation framework is essential for fostering a strong educational ecosystem and creating a sustainable mechanism for long‐term educational advancement. This study addresses the challenges in assessing course quality in application‐oriented universities. It identifies assessment parameters through expert consultation and adopts a ‘student perception’ perspective. Using the entropy weight method, the importance of each parameter is determined. A curriculum quality evaluation model based on the cloud model is then formulated. Finally, the practical application of this evaluation model is demonstrated. This approach provides a theoretical basis for refining curriculum quality evaluation systems in higher education institutions. [ABSTRACT FROM AUTHOR]

Agricultural phosphorus (P) loss constitutes a significant factor in agricultural non-point source pollution (ANSP). Due to the widespread occurrence and complexity of ANSP, emphasis on risk prevention and control is preferable to retroactive treatment, to reduce costs. Effective risk identification is an issue that needs to be addressed urgently. Henan Province, a typical intensive agricultural region in China, was used as a case study to develop a straightforward and precise model for assessing the risk of P loss. Total phosphorus (TP) emission intensity at the county level in Henan Province was estimated based on planting, livestock and poultry breeding, and rural domestic activities. Subsequently, influential factors were selected to determine the extent of P loss in rivers. Finally, the model was validated using water quality data. The results indicate that (1) TP emission and rainfall are the primary contributors to the risk of P loss, whereas vegetation coverage has negligible effects. (2) The primary sources of TP emission, in descending order of magnitude, are livestock and poultry breeding, rural domestic activities, and planting. Livestock and poultry breeding represents the largest proportion at approximately 50%. (3) High-risk areas for P loss are concentrated in the plains of the central, eastern, and northern Henan Province, while low-risk areas are mainly located in the western mountainous and hilly regions. (4) The model exhibits high accuracy with a determination coefficient (R2) of 0.81 when compared to surface water quality monitoring data. This study provides a new framework for assessing the risk of P loss in intensive agricultural settings. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the crucial role of green finance in addressing the imperative of reducing industrial carbon emissions for a sustainable global economy. Encompassing facets, such as green credit, insurance, investment, and governmental help for growth in green businesses. Our research on the strength of a comprehensive dataset covering China's 30 provinces between 2010 and 2019 employs a fixed-effects regression and heterogeneity assessment, revealing an inverse association between green finance and industrial carbon emissions. This verifies the notion that green finance serves as a deterrent to carbon emissions from the industrial sector. According to the results of this study, green financing can significantly lower the CO2 emissions from industries, which in turn can lead to an enhancement in environmental quality. Notably, our findings revealed substantial regional variations in this relationship. By proposing actionable recommendations, we advocate strategies to address regional disparities, standardize measurement protocols for green finance, optimize the environment for technological innovation, and realize industrial structures. By acknowledging these nuanced dynamics, our study not only contributes to the understanding of the impact of green finance but also offers targeted solutions to foster high-quality sustainable development in China, ensuring a more effective and comprehensive approach to mitigating carbon emissions in the industrial sector. [ABSTRACT FROM AUTHOR]

The exploitation of natural resources and the degradation of the geological environment pose dual challenges for mineral resource-based cities amidst rapid economic development and urbanization. Evaluating geoenvironmental carrying capacity is essential in measuring the harmony between human activities and the geological environment. Unfortunately, current evaluation methods do not adequately capture the intricate interplay of multiple factors, hindering a comprehensive understanding of this concept in mineral resource cities. To address this gap, this study integrates the DPSR model into the assessment of geoenvironmental carrying capacity, aligning with the characteristics and sustainable development objectives of these cities. By employing catastrophe theory, entropy method, and analytic hierarchy process, a robust evaluation index system specific to mineral resource cities is established. Using Fushun City in Liaoning Province, China, as a case study, the analysis reveals consistently high comprehensive evaluation values over the past five years, reflecting the city's actual environmental status. The research highlights enhancing the response layer as a key strategy to boost regional geoenvironmental carrying capacity. These results offer valuable insights for the planning of mineral resource cities, fostering regional sustainable development, and promoting geological environmental protection. [ABSTRACT FROM AUTHOR]

In order to promote the green, low-carbon and high-quality development of agriculture, this paper adopted the super-efficiency SBM model and the entropy method to measure the agricultural carbon emission efficiency and the high-quality development level of agriculture in the Yellow River Basin, and discussed the interaction between the two by using VAR model. The results show that a) the agricultural carbon emission efficiency in the Yellow River Basin is on the rise as a whole. The agricultural carbon emission efficiency of Gansu and Shanxi, Sichuan, Shandong, Henan, Shaanxi and Qinghai, Ningxia and Inner Mongolia is at a lower, medium and higher level respectively. b) The overall level of high-quality agricultural development shows a fluctuating upward trend. Shaanxi, Shanxi, Ningxia and Inner Mongolia, Qinghai and Gansu, Henan, Sichuan and Shandong are at a lower, medium and higher level respectively. c) The improvement of agricultural carbon emission efficiency has a significant effect on the promotion of high-quality agricultural development, while the improvement of high-quality agricultural development has no significant effect on agricultural carbon emission efficiency. Based on the research results, some suggestions were put forward to improve the efficiency of agricultural carbon emissions and promote the high-quality development of agriculture in the Yellow River Basin from the aspects of green and sustainable development. [ABSTRACT FROM AUTHOR]

[Objective] The paper aimed to explore the factors affecting the development of digital agriculture and village in Zibo, to provide effective reference recommendations for promoting the future development of digital agriculture and village, and to promote the high-quality and sustainable development of digital agriculture and village in Zibo. (Method) Based on survey data regarding the development level of digital agriculture and village in Zibo city, Shandong province, we utilized the entropy method to assess the development level of digital agriculture and village in Zibo from 2020 to 2023. Additionally, we employed the obstacle degree model to analyze the primary factors constraining the development of digital agriculture and village in Zibo. (Result] From 2020 to 2023, Zibo experienced overall growth in the development of digital agriculture and village. However, there were significant variations in the development of different sectors and regions. The main factors limiting the rapid progress of digital agriculture and village in Zibo in recent years included the digitization of livestock and poultry breeding and aquaculture, the number of online merchants, the traceability application of agricultural product quality, the level of online work, the digital application of agricultural technology promotion, the training of farmers' digital literacy, and financial investments in the digital field. Conclusion] Zibo continues to promote the construction and development of digital agriculture and village. This can be achieved by reducing regional disparities, enhancing the digitization of livestock, poultry and aquaculture, improving the traceability of agricultural product quality and safety, streamlining the online management of rural land transfer and homesteads, and advancing the adoption and utilization of digital technology. [ABSTRACT FROM AUTHOR]

One of the key service elements of travel companies operating in the road transportation sector are passenger buses. Deciding to purchase a new bus requires managers to consider various aspects, complicating the decision-making process. In this context, implementing Multi-Criteria Decision Making (MCDM) methodologies in such decision problems can provide more accurate and effective solutions. The aim of this study is to investigate the new bus purchase decision processes of travel companies operating in Turkey with MCDM methods and to determine the most suitable bus model. Ten major bus models were analyzed in the study using four different MCDM approaches based on four major criteria and 16 sub-criteria. The weights of the criteria used in the evaluation of the relevant decision problem were determined by the Entropy method. Moreover, WASPAS, EDAS, and Gray Relational Analysis were utilized separately to discover the best suitable alternative. Furthermore, the rankings generated by these several MCDM approaches were compared using Spearman Rank Correlation Analysis in the study, and they were found to be highly consistent. The study provides several key recommendations for academic research and industry practice moving forward. [ABSTRACT FROM AUTHOR]

In response to the current situation of poor adaptive learning performance in Agent production and sales negotiation and dynamic changes in negotiation environment, the author proposes a method based on machine learning algorithms. Consider the impact of conflict level, cooperation possibility, and negotiation remaining time on negotiations in a dynamic negotiation environment, and use the entropy method to determine the weights of three influencing factors and perform linear weighting. Based on the differences in current negotiation topics, a concession amplitude prediction model based on dynamic selective ensemble learning is constructed, and an optimization strategy for supply chain production and sales negotiation is proposed. The experimental results indicate that, in the adaptive negotiation strategy of a regular SVM single learning machine, the joint utility of the most successfully negotiated agents falls within the interval [0.55, 0.70], while the author's ensemble learning strategy mainly focuses on [0.6, 0.8], the author's strategy is relatively superior to ordinary learning strategies in terms of both the number of successfully negotiated agents and the joint utility. Compared with the single learning machine negotiation strategy, this strategy improves the success rate and joint utility of Agent adaptive learning, and ensures the benefits of both production and sales in the supply chain, achieving a mutually beneficial situation for both parties in cooperation. [ABSTRACT FROM AUTHOR]

This study aims to optimize the process of minimum quantity lubrication (MQL) grinding for Inconel 625 (IN 625) while enhancing its comprehensibility. The research employed experimental methods based on the Box-Behnken design to investigate critical parameters, including tangential force, surface roughness, specific energy, and apparent coefficient of friction. Further, machine learning techniques, specifically Random forest regression and Gaussian process regression (GPR), have been employed to build predictive models. These models have been assessed using metrics like R2, mean absolute error, and root mean square error. The results demonstrate that GPR outperforms other techniques in predicting the data accurately. Additionally, this study utilized multi-criteria decision-making techniques, namely TOPSIS and VIKOR, in conjunction with the entropy method to determine the optimal conditions for MQL grinding of IN 625. The optimized parameters for achieving low tangential force, high surface roughness, low specific energy, and low apparent coefficient of friction have been identified as a wheel speed of 1800 m/min, table speed of 9000 mm/min, and a depth of cut of 0.01 mm. Furthermore, a higher value of Ra (surface roughness) indicates the superior effectiveness of MQL grinding compared to dry grinding, as applying the MQL technique helps retain the sharpness of grit for a longer period of time. SEM image and EDS analysis of the ground surfaces confirm that better surface morphology has been obtained at optimized parameters of MQL grinding. [ABSTRACT FROM AUTHOR]

In order to encourage digital transformation in the traditional construction industry, the Chinese government has promoted 24 pilot cities to develop intelligent construction. The practices of intelligent construction are disparate in all 24 pilot cities. Given this context, it is important to effectively and comprehensively evaluate the level of intelligent construction in these pilot cities. This study thus evaluates the development of intelligent construction in different pilot cities. By conducting an in-depth analysis of the existing literature and policies, an evaluation system consisting of five dimensions and a total of 30 indicators is established. The entropy method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used to evaluate the development of intelligent construction in 23 pilot cities. The research findings indicate that the development of intelligent construction in different pilot cities is uneven, with clear gaps between first-tier cities and Western cities. The development of industries, the cultivation of talent, and economic growth are relatively satisfactory, while technological innovation and digital infrastructure are insufficient. Several suggestions are proposed to promote the development of intelligent construction, including expediting the construction of intelligent infrastructure, enhancing digital transformation, promoting technological innovation, and implementing talent cultivation strategies. [ABSTRACT FROM AUTHOR]

The characteristics and influencing factors of gas production in CBM wells are analyzed based on the field geological data and the productivity data of coalbed methane (CBM) wells in the Zhengbei block, and then the favorable areas are divided. The results show that the average gas production of No. 3 coal seam CBM wells in the study area is in the range of 0~1793 m3/d, with an average of 250.97 m3/d; 80% of the wells are less than 500 m3/d, and there are fewer wells above 1000 m3/d. The average gas production is positively correlated with gas content, critical desorption pressure, permeability, Young's modulus, and Schlumberger ratio, and negatively correlated with fracture index, fault fractal dimension, Poisson's ratio, and horizontal stress difference coefficient. The relationship between coal seam thickness and the minimum horizontal principal stress is not strong. The low-yield wells have the characteristics of multiple pump-stopping disturbances, unstable casing pressure control, overly rapid pressure reduction in the single-phase flow stage, sand and pulverized coal production, and high-yield water in the later stage during the drainage process. It may be caused by the small difference in compressive strength between the roof and floor and the coal seam, and the small difference in the Young's modulus of the floor. The difference between the two high-yield wells is large, and the fracturing cracks are easily controlled in the coal seam and extend along the level. The production control factors from strong to weak are as follows: critical desorption pressure, permeability, Schlumberger ratio, fault fractal dimension, Young's modulus, horizontal stress difference coefficient, minimum horizontal principal stress, gas content, Poisson's ratio, fracture index, coal seam thickness. The type I development unit (development of favorable areas) of the Zhengbei block is interspersed with the north and south of the block on the plane, and the III development unit is mainly located in the east of the block and near the Z-56 well. The comprehensive index has a significant positive correlation with the gas production, and the prediction results are accurate. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract This work offers an integrated methodological framework that integrates the capabilities of large language models (LLMs), rules-based reasoning, multi-criteria sorting, and artificial neural networks (ANN) in developing a predictive model for classifying the intensity of sensitive social media contents. The current literature lacks a holistic consideration of multiple attributes in evaluating social media contents, and the proposed framework intends to bridge such a gap. Three actions constitute the development of the framework. First, LLMs (i.e., GPT4) evaluate the social media contents under a predefined set of attributes, leveraging the power of LLMs in content analytics. Second, rules-based reasoning and multi-criteria sorting (i.e., entropy-FlowSort) determine the categories of social media contents. Lastly, the two previous actions produced a complete dataset that can be used to train a predictive model using ANN to classify sensitive social media contents. With 1100 randomly extracted social media contents and the predefined categories of violations against community standards set by Facebook, the proposed integrated methodology produces an ANN-based classification model with 86.36% prediction accuracy. Comparative analysis using Decision Trees, k-nearest neighbors, Linear Discriminant Analysis, Random Forest, and Naive Bayes classification yields the highest performance of ANN. The predictive model can be used as a decision-support tool to design moderation actions on social media contents.

Purpose – Material selection, driven by wide and often conflicting objectives, is an important, sometimes difficult problem in material engineering. In this context, multi-criteria decision-making (MCDM) methodologies are effective. An approach of MCDM is needed to cater to criteria of material assortment simultaneously. More firms are now concerned about increasing their productivity using mathematical tools. To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material. In addition, by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the inherent ambiguities of decision-makers in paired evaluations are considered in this research. It goes on to construct a mathematical bi-objective model for determining the best item to purchase. Design/methodology/approach – The entropy perspective is implemented in this paper to evaluate the weight parameters, while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system. The intermediate pipes are used to join the components of the exhaust systems. The materials usually used to manufacture intermediate pipe are SUS 436LM, SUS 430, SUS 304, SUS 436L, SUH 409 L, SUS 441 L and SUS 439L. These seven materials are evaluated based on tensile strength (TS), hardness (H), elongation (E), yield strength (YS) and cost (C). A hybrid methodology combining entropy-based criteria weighting, with the TOPSIS for alternative ranking, is pursued to identify the optimal design material for an engineered application in this paper. This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes. After that, the authors searched for and considered eight materials and evaluated them on the following five criteria: (1) TS, (2) YS, (3) H, (4) E and (5) C. The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes, on their performance and on the cost. In this structure, the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment. This essentially measures the quantity of information each criterion contribution, indicating the relative importance of these criteria better. Subsequently, the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative. The results show that SUS 309, SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider. Findings – The material matrix of the decision presented in Table 3 was normalized through Equation 5, as shown in Table 5, and the matrix was multiplied with weighting criteria ß_j. The obtained weighted normalized matrix V_ij is presented in Table 6. However, the ideal, worst and best value was ascertained by employing Equation 7. This study is based on the selection of material for the development of intermediate pipe using MCDM, and it involves four basic stages, i.e. method of translation criteria, screening process, method of ranking and search for methods. The selection was done through the TOPSIS method, and the criteria weight was obtained by the entropy method. The result showed that the top three materials are SUS 309, SUS 432L and SUS 436 LM, respectively. For the future work, it is suggested to select more alternatives and criteria. The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality (ELECTRE), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE). Originality/value – The results provide important conclusions for material selection in this targeted application, verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Objective At present, there are relatively few studies on the safety assessment of maintenance and construction of urban rail transit operation lines. In order to reduce the operation safety risk of urban rail transit lines, the safety level of urban rail transit line maintenance construction should be comprehensively evaluated to guide the operation production and management. Method Targeting the 19 maintenance work areas on 4 lines in Shanghai rail transit network, a 4 hierarchy-structured safety effectiveness evaluation index system for urban rail transit line maintenance and construction is established. Based on the actual situation of line maintenance construction indexes, the entropy weight method is improved and combined with the mutation progression theory, and a linear combination evaluation method based on improved entropy weight and mutation progression is proposed as the result. The safety effectiveness of maintenance construction in 19 mainten-ance work areas is evaluated and analyzed with this method. Result & Conclusion The linear combination evaluation method based on improved entropy weight-mutation level passes the consistency test, and the evaluation results are stable and reliable. This method effectively expands the applicability of entropy weight method and mutation level theory in the evaluation of urban rail transit maintenance construction, and realizes the dynamic comprehensive evaluation in the field of rail transit line maintenance construction.

In the modern world, a significant role in the development of the innovative economy of the state is played by the training of highly qualified specialists who can produce new innovations and knowledge and build an efficient growing economy, manifested in the growth of the country's GDP. The training of such specialists is the responsibility of higher education institutions, which in today's, mainly Ukrainian, realities are struggling for applicants, which manifests itself in the formation of stakeholders' perceptions of the university, i.e., the formation of its own image. The image of a higher education institution becomes an intangible resource on par with industrial and intellectual property. Today, higher education institutions are moving away from just teaching students and are becoming research centers in Ukraine and the world, working in the format of teaching, research, and commercialization. The reputation and perception of a higher education institution (HEI) by its stakeholders directly depend on its ability to operate effectively and serve as a link between highly qualified specialists and businesses. This creates competition between universities in shaping their own image. Along with traditional determinants, including rankings, it is now necessary to consider new factors that reflect the digital level of representation and communication of higher education institutions in the internet space. For this study, we selected higher education institutions (HEIs) with the highest positions in the Times Higher Education rankings in Ukraine and the countries of the Visegrad Group. These countries were chosen because of their geographical proximity to Ukraine, shared history and borders, and because they were among the last to become members of the European Union, which Ukraine aspires to join. This approach allows for an objective assessment of the image of Ukrainian higher education institutions in the context of Ukraine's European integration aspirations. The methodological toolkit of the study was the entropy method of analysing the performance of the selected HEIs according to the Times Higher Education ranking and key indicators of the dynamics and visibility of their websites. The results obtained for the specific weights of the image subindices indicate a 43% influence of presence on the internet. This study empirically confirms and theoretically proves that digital presence and visibility on the internet are significant factors in forming the image of higher education institutions. This indicates the need to strengthen the digital communications and representation of Ukrainian higher education institutions to improve their image. The research results can be helpful for educational institutions, politicians, and communication specialists who seek to enhance the image of their higher education institutions. The obtained results can be used to develop strategies for improving internet presence and communication practices, which will contribute to a general increase in the rating and prestige of higher education institutions.

Abstract In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

Introduction: China is the world's largest producer and consumer of vegetables, with all provinces, cities, and autonomous regions cultivating vegetables. The main production areas for winter and spring vegetables have higher accumulated temperatures, allowing for multiple planting seasons throughout the year. Therefore, this article examines the competitiveness of vegetable production in China's main winter and spring vegetable producing areas from the perspective of the dynamic evolution of comparative advantages. Methods: Drawing on statistical data from 2000 to 2018 on the development of the vegetable industry in these key provinces, our analysis incorporates methods such as the Resource Endowment Coefficient, the Comprehensive Comparative Advantage Index, the entropy method, and systematic cluster analysis to explore the evolution of competitive advantages in vegetable production across various provinces and cities. Results and discussion: The findings reveal significant regional disparities in resource endowments and overall advantages in vegetable production during the winter and spring seasons. Notably, Chongqing, Hubei Province, and Shanghai exhibit a Resource Endowment Factor (EF) averaging above 1, indicating a relative abundance of production resources and a comparative advantage. Similarly, Hainan Province, the Guangxi Zhuang Autonomous Region, Guangdong Province, Fujian Province, Shanghai, and Zhejiang Province each demonstrate an average Comprehensive Advantage Index (AAI) exceeding 1, affirming their comprehensive comparative strengths. Overall, the competitiveness of vegetable production in these regions exhibits a fluctuating yet ascending trend, with notable disparities in competitive strengths across different areas. The ranking of annual average competitiveness scores in vegetable production is as follows: Shanghai > Zhejiang > Hubei > Hunan > Guangdong > Sichuan > Chongqing > Fujian > Guangxi > Jiangxi > Hainan > Yunnan. Based on these outcomes, the study proposes tailored strategic recommendations for provinces and cities according to their unique resource endowments and comparative advantages, aiming to enhance the sector's productivity and competitive stance. [ABSTRACT FROM AUTHOR]

Under the background of the marketization of data elements, enhancing regional scientific and technological innovation capabilities has become the main driving force for urban sustainable development. It contributes to the sustainable development of the region. Based on the data of the Yunnan statistical yearbook, we built a regional science and technology innovation index system from three dimensions of science and technology innovation input, output, and benefit. Firstly, the entropy method is used to obtain the weight of each index. Secondly, TOPSIS is used to evaluate the 36 index data of each prefecture. Finally, the scores and rankings of the scientific and technological innovation ability of each prefecture are obtained. The research can effectively find the advantages and disadvantages of scientific and technological innovation in the Yunnan Province, provide data support and decision support for regional economic development, and play an important role in promoting the level of technological innovation in modern industries. At the same time, research projects can help various regions allocate various technological innovation resources reasonably, enhance their original innovation capabilities, lead the development of strategic emerging industries and future industries, and accelerate the formation of new quality productivity. [ABSTRACT FROM AUTHOR]

The sustainability of products remains a challenge, mainly due to the lack of consistent approaches for simultaneously taking into account the key criteria of the concept in the process. This research aims to develop an eco-innovative QLCA method to create new product solutions that integrate quality (customer satisfaction) and environmental impact assessment throughout the product life cycle. The QLCA method includes: (i) product prototyping according to quality and environmental criteria; (ii) prospective assessment of the quality of prototypes, taking into account customer requirements; (iii) prospective life cycle assessment of product prototypes using a cradle-to-grave approach in accordance with ISO 14040; and (iv) setting the direction of product development while taking into account the fulfilment of customer expectations and the need to care for the environment throughout the product life cycle. Owing to the lack of previous research in this area, as well as the popularity of photovoltaic (PV) panels in reducing greenhouse gases, an illustration was obtained and test of the method was carried out on the example of silicon photovoltaic panel modules (Crystalline Si PV Module). In accordance with the adopted assumptions, the results of the QLCA method test showed that the modelled PV prototypes will, in most cases, be satisfactory for customers, but they still require improvement actions to reduce carbon dioxide (CO2) emissions throughout their life cycle. These activities should be consistent so as to achieve quality that satisfies customers. The QLCA method can be used by designers, managers, and decision-makers at the early stages of design, but also during the product maturity phase for its sustainable development. [ABSTRACT FROM AUTHOR]

To quantify the chain transmission effect between disasters in assessing the emergency capacity of the disaster chain, the chain transmission relationship between disasters is quantified by superimposing (decrement) effect coefficients. The evaluation model and calculation method of emergency capacity of disaster chain are derived based on the conversion probability and threshold between disaster chains. Considering the two-dimensional emergency preparedness and recovery indicators, an index system of emergency capability assessment of the rainstorm-landslide disaster chain is constructed. Using the entropy weight method and the technique for order preference by similarity to an ideal solution (TOPSIS) method, the emergency capacity index of two single disasters, rainstorms and landslides, is calculated. The emergency capacity of the rainstorm-landslide disaster chain is obtained by combining it with the emergency capacity evaluation model of the disaster chain. An actual analysis is carried out of the typical rainstorm-landslide disaster chain in the Guangdong-Hong Kong-Macao Greater Bay Area. The results show that the emergency capability of the rainstorm-landslide disaster chain in the central Guangdong-Hong Kong-Macao Greater Bay Area shows a downward trend compared to the single landslide disaster. In contrast, the overall emergency capability of the eastern region shows an upward trend. The emergency capability of the rainstorm-landslide disaster chain in the northwest and southwest regions is consistent with that of the single landslide disaster. The results of the emergency response capability assessment of the rainstorm-landslide disaster chain are more scientific and reasonable, which can provide technical support for the assessment and improvement of the emergency response capacity of the rainstorm-landslide disaster chain. [ABSTRACT FROM AUTHOR]

[Display omitted] • Impact of green investments on environmental degradation is investigated. • Machine learning method is used. • An environmental degradation index is introduced using the entropy method. • Green investment reduces environmental degradation. The main objective of this study is to examine the impact of green investment on environmental degradation in developing countries using machine learning-based estimation combined with robustness tests of static and dynamic panel data modeling techniques. The scope of this study covers 30 developing countries for 2009–2019. This study introduces a new index of environmental degradation that uses the entropy method and includes green gas emissions and deforestation. The study addresses trade openness, the quadratic shape of economic growth, and urbanization in the context of the Environmental Kuznets Curve Hypothesis (EKC) and the Ecological Modernization Theory (EMT), in addition to green investment. This study considers the kernel-based regularized least squares (KRLS) approach, the static panel technique Driscoll & Kraay standards error method, and a dynamic panel technique system generalized moment techniques. The empirical findings from the machine learning method show that green investment significantly reduces environmental degradation with a higher coefficient resulting from the static fixed effect estimation. The study also reveals that the main hypotheses, such as EKC and EMT, are confirmed by all estimation techniques. Based on the results, the study recommends that policymakers take pragmatic steps toward green investments and increase the financing of green energy initiatives to combat environmental degradation. [ABSTRACT FROM AUTHOR]

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks. [ABSTRACT FROM AUTHOR]

By constructing an evaluation system for the high-quality development of innovation and entrepreneurship, an evaluation index system was established in this study from five aspects: the background, process, input, output, and transformation of innovation and entrepreneurship, and analytic hierarchy process (AHP) and entropy method (EM) were adopted to perform combination weighting. Then, the core of each subsystem and the comprehensive score were calculated based on the TOPSIS method, the high-quality development level of urban innovation and entrepreneurship in 19 vice-ministerial cities like Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, Shenzhen, and Chengdu in China was measured, and the innovation and entrepreneurship development level and structural characteristics were analyzed from five aspects. The results show that Shenzhen, Shanghai, Beijing, Nanjing, and Guangzhou take the lead in the high-quality development of innovation and entrepreneurship, while Xi'an, Chengdu, Hangzhou, Wuhan, Qingdao, Jinan, and Ningbo are in the medium level. Chongqing, Shenyang, Dalian, Harbin, Xiamen, and Changchun perform poorly in the development of innovation and entrepreneurship with problems of interregional large gradient difference in capacity and unbalanced development, which provides an important reference for understanding the current situation, advantages, and disadvantages of innovation and entrepreneurship education development in various economic zones. [ABSTRACT FROM AUTHOR]

In the present work an effort is also made to evaluate the optimum combination of machining factors by applying the unique combination of response surface methodology (RSM), Shannon's entropy weight measurement with VIKOR method. Experiments were conducted on Monel 400 alloy based on Taguchi L16 OA (orthogonal array) to evaluate the impact of machining parameters like voltage (V), discharge current (Ip), pulse on time (Ton) and pulse off time (Toff) on the process to obtain the desired response characteristics namely material removal rate (MRR), tool wear rate (TWR) and Dimensional Deviation (DD). The Shannon's entropy weight measurement (SEWM) method have been proposed to assess the individual weightiness for all performance characteristic and by using VIKOR method the most optimal setting of machining parameters has been evaluated for improving productivity and minimizing the cost. The optimum setting of machining parameters found by the proposed method has of Ton 6µs, Ip 9amps, voltage 50V and Toff 3µs. The discharge current and pulse on time have the significant domination over the MRR, TWR and OC ensued by voltage and pulse off time. The application of VIKOR method for solving multi response optimization problem which would be helpful to the process engineers in selecting the most optimum parameter combination for making micro hole using Micro EDM has been systematically demonstrated. [ABSTRACT FROM AUTHOR]

People's health is the basis for the progress of social civilization and an important symbol of national prosperity and national strength. Using the entropy method, spatial autocorrelation, spatio-temporal geographic weighted regression and other methods, we conducted a comprehensive evaluation of the high-quality development level of public health in Guizhou Province from 2011 to 2020 and an evolutionary analysis of the spatio-temporal divergence. The results revealed four key aspects of the state of public health in Guizhou Province. (1) The overall level of public health is constantly improving, but structural imbalance is prominent. (2) From the spatial dimension, the spatial differences in the level of public health have gradually moved from significant to a state of equilibrium. The northeastern cities were more strongly driven by the radiation of the peripheral cities, while this effect was weaker in the southwestern cities. From the time dimension, the spatial pattern shows a clear gradient, with a faster vertical growth rate. (3) The overall spatial correlation of the level of public health is weak, with more cold spots than hot spots, showing a distribution pattern of "strong in the northeast and weak in the southwest". (4) The new rural cooperative medical care participation rate, (rural) per capita health expenditure, and the electronic health record creation rate have become the most important drivers affecting the high-quality development of public health in Guizhou Province. [ABSTRACT FROM AUTHOR]

The safety of mines is a top priority in the mining industry, and a precise assessment of aquifer water levels is crucial for conducting a risk analysis of water-related disasters. Among them, the GIS-based water abundance index method is widely used in water-richness evaluation. However, the existing research lacks sufficient determination of evaluation indicator weights, specifically in the following aspects: (1) failure to consider the information differences between indicators and (2) inadequate differentiation of the relative importance of each indicator and their importance to the evaluation target. Taking the No.3 coal roof of Dananhu No.7 coal mine as the research object, six factors are selected as the main indexes for evaluating water richness. The weights were calculated using an improved entropy method and the scatter degree method, with the principle of information entropy used to reconcile the competitive and consistent relationship between the two evaluation methods. This approach resulted in the determination of combination weights for each influencing factor, highlighting the information differences between indicators and the importance of each indicator. Finally, the evaluation of aquifer water richness is partitioned by combining the GIS analysis function. The findings suggest that the Ⅰ division has low water richness, while the Ⅱ division is primarily medium-to-high water rich. The accuracy of the evaluation results exceeds 80%, providing valuable insights for hydrophobic programs for other coal-seam roofs. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Advancements in information technology have facilitated the emergence of mHealth apps as crucial tools for health management and chronic disease prevention. This research work focuses on mHealth apps for the management of diabetes by patients on their own. Given that China has the highest number of diabetes patients in the world, with 141 million people and a prevalence rate of 12.8% (mentioned in the Global Overview of Diabetes), the development of a usability research methodology to assess and validate the user-friendliness of apps is necessary. This study describes a usability evaluation model that combines task analysis methods and eye movement data. A blood glucose recording application was designed to be evaluated. The evaluation was designed based on the model, and the feasibility of the model was demonstrated by comparing the usability of the blood glucose logging application before and after a prototype modification based on the improvement suggestions derived from the evaluation. Tests showed that an improvement plan based on error logs and post-task questionnaires for task analysis improves interaction usability by about 24%, in addition to an improvement plan based on eye movement data analysis for hotspot movement acceleration that improves information access usability by about 15%. The results demonstrate that this study presents a usability evaluation model for mHealth apps that enables the effective evaluation of the usability of mHealth apps. [ABSTRACT FROM AUTHOR]

Agricultural and rural modernization is an indispensable part of China’s modernization road. This paper constructs the evaluation index system of agricultural and rural modernization level in Heilongjiang Province from three dimensions: high quality and efficiency of agriculture, rural livability and farmers’ prosperity. The entropy method is used to comprehensively measure the level of agricultural and rural modernization in Heilongjiang Province, and the development stages of different periods are determined. The results show that the development level of agricultural and rural modernization in Heilongjiang Province from 2000 to 2022 is on the rise as a whole, and it has reached the basic realization stage. The development level of high quality and efficiency of agriculture, rural industry and livability, and farmers’ prosperity and prosperity are constantly improving. According to the results, countermeasures and suggestions are put forward from three dimensions, aiming at promoting the development of agricultural modernization in Heilongjiang Province and realizing rural revitalization. [ABSTRACT FROM AUTHOR]

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively. [ABSTRACT FROM AUTHOR]

National power has been an important discriminative signal for the evolution of great power competition and international patterns in recent years, not only in terms of the relative resource scale of the state but also in terms of the efficiency of resource use. Based on the two levels of national resources and conversion mechanisms, this article uses the entropy method to establish national power comprehensive assessment index systems covering 144 countries and 38 indicators from 1990–2020. At the same time, based on power transition theory (PTT), this article uses quadratic assignment procedures to verify the relevance and interpretation of international bilateral relations in the national power gap. The conclusion is that technology and economic elements are especially important for national power, and the conversion mechanism is icing on the cake rather than a decisive factor. The competition among great powers is transformed into nontraditional competition rather than an arms race; the international pattern has gradually developed from one superpower to a multipolar trend, and the global economic and geopolitical center has transitioned to Asia. The rise of China and the recession in the U.S. are factual; as the gap between the two countries' power narrows, their international relations become increasingly tense, confirming power transition theory. [ABSTRACT FROM AUTHOR]

This research focuses on 72 approved varieties of colored wheat from different provinces in China. Utilizing coefficients of variation, structural equation models, and correlation analyses, six agronomic traits of colored wheat were comprehensively evaluated, followed by further research on different dwarfing genes in colored wheat. Using the entropy method revealed that among the 72 colored wheat varieties, 10 were suitable for cultivation. Variety 70 was the top-performing variety, with a comprehensive index of 87.15%. In the final established structural equation model, each agronomic trait exhibited a positive direct effect on yield. Notably, plant height, spike length, and flag leaf width had significant impacts on yield, with path coefficients of 0.55, 0.40, and 0.27. Transcriptome analysis and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) validation were used to identify three dwarfing genes controlling plant height: Rht1, Rht-D1, and Rht8. Subsequent RT-qPCR validation clustering heatmap results indicated that Rht-D1 gene expression increased with the growth of per-acre yield. Rht8 belongs to the semi-dwarf gene category and has a significant positive effect on grain yield. However, the impact of Rht1, as a dwarfing gene, on agronomic traits varies. These research findings provide crucial references for the breeding of new varieties. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study offers a comprehensive evaluation of urban renewal efforts in Shandong Province, China, by employing the entropy method combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). An evaluation index system was constructed to assess the weights of key factors influencing urban renewal, including the renovation of old residential areas, improvement in sewage treatment quality, development of sponge cities, adoption of clean heating, and enhancement of air quality. Analysis of 16 prefecture-level cities in 2019 reveals significant disparities in performance, with Jinan achieving the highest ranking due to notable improvements in infrastructure and environmental management. Meanwhile, other cities displayed varying levels of success, reflecting different developmental stages and capacities. The findings underscore the necessity for tailored strategies to optimize urban renewal outcomes, focusing on high-weight factors such as sewage treatment, clean heating, and residential renovation. This study provides actionable insights for policymakers, advocating adaptive renewal strategies that align with local contexts and encourage continuous innovation. The results highlight the importance of targeted policy interventions to enhance urban renewal quality across Shandong Province, promoting sustainable urban development and better living standards.

Agricultural socialized services play a key role in achieving high-quality agricultural development and promoting agricultural modernization. This paper constructs a comprehensive evaluation indicator system for agricultural socialization services in Heilongjiang Province, using statistical data from 2011 to 2022. The entropy method was applied to measure it. It is found that the level of agricultural socialized services in the province shows an overall trend of improvement. Among them, agricultural science and technology services have grown steadily, agricultural production services have grown the fastest, the slow development of agricultural financial services, and other services have fluctuated greatly and the development is relatively unstable. In order to enhance the level of agricultural socialized service construction, investment in agricultural science and technology services should be increased and the level of agricultural science and technology should be upgraded; agricultural insurance services should be improved in order to safeguard the stability of agricultural production for farmers; financial and credit services should be improved and the quality of financial services should be enhanced; and policy support should be strengthened and a favourable environment for development should be constructed. [ABSTRACT FROM AUTHOR]