The sustainability of regional water resources has important supporting data needed for establishing policies on the sustainable development of the social economy. The purpose of this paper is to propose an assessment method to accurately reflect the sustainability of regional water resources in various areas. The method is based on the relative entropy of the information entropy theory. The steps are as follows. Firstly, the pretreatment of the evaluation sample data is required, before the relative entropy of each standard evaluation sample and evaluation grade (SEG) is calculated to obtain the entropy weight of each evaluation index. After this, the entropy weighted comprehensive index (WCI) of the standard evaluation grade sample is obtained. The function relation between WCI and SEG can be fitted by the cubic polynomial to construct the evaluation function. Using the above steps, a generalized entropy method (GEM) for the sustainable assessment of regional water resources is established and it is used to evaluate the sustainability of water resources in the Pingba and Huai River areas in China. The results show that the proposed GEM model can accurately reflect the sustainable water resources in the two regions. Compared with the other evaluation models, such as the Shepherd method, Artificial Neural Network and Fuzzy comprehensive evaluation, the GEM model has larger differences in its evaluation results, which are more reasonable. Thus, the proposed GEM model can provide scientific data support for coordinating the relationship between the sustainable development and utilization of regional water resources in order to improve the development of regional population, society and economy.
The ecological compensation scheme of water pollution in the basin is a result of the interplay between upstream and downstream cities, which is of great significance to the guidance of regional economic development. The purpose of this paper is to propose a multi-attribute scheme decision algorithm, which is expressed in the form of interval number, to reduce the uncertainty of decision results and improve the reliability of decision results. This method first uses the Monte Carlo simulation technique to produce a large number of random samples in the various attributes of the decision matrix to construct the random decision-making matrix (DMM). Then, according to the overall dispersion and local concentration of the random DMM, the clustering method of the projection pursuit is adopted. By accelerating the genetic algorithm, the weight and the best projection eigenvalues of each scheme are optimized, and the sorting results of the decision-making cases are obtained based on the projected eigenvalues. The results of the case study show that the uncertainty of the decision results is greater when the number of random simulations is very low; as the number of random simulations increases, the result of the decision becomes more and more stable and clear, and the uncertainty decreases. The results of the Duncan test show that, scheme 2, which is composed of financial compensation and remote development, is better than other schemes, and the decision making is more reasonable. The result of this decision has certain values for the ecological compensation scheme in Suzhou and Jiaxing cities, and the proposed method can be applied in similar range multi-attribute scheme decision-making issues.
Load shedding is an efficient solution to improve power system reliability when a contingency occurs. Considering loads difference has great significance in the process of load shedding. This paper proposes a new minimum load shedding calculation approach based on multi objective optimization theory. The load shedding model proposed in this paper considers the loads difference and introduces the importance factor to describe the difference. This approach will first cut those less important loads and try to get the minimum total load shedding amount in the process of load shedding. The case results shows the rightness and validity of the approach proposed in this paper.
This study aimed to investigate the effects of customers’ motivations (specifically young consumers) on online purchase intentions as mediated by commitment toward online fashion retailers. The survey method was used to collect data from Chinese respondents using a questionnaire. The convenience sampling technique was used to collect data from 275 respondents. Collected data were analyzed on smart-PLS using the structural equation modeling technique. Results of the study show a significant and positive impact of social empowerment and remuneration motivations on consumer commitment online purchase intention. Further results show that consumer commitment partially mediates the relationship between social empowerment, remuneration, and online purchase intention. This study contributes to the literature in the domain of consumer commitment by focusing on the underlying needs and motivations of consumers. The researchers have demonstrated a strong need to understand the dynamics of commitment due to its importance in affecting purchase intention. This study also has several implications that guide online retailers how to motivate consumers with social, remuneration and empowerment incentives to develop their intention to purchase online. Fashion retailers are suggested to gratify certain consumer motives to increase commitment. Specifically, among the three motives, empowerment motivation emerged as the strongest predictor of consumer commitment in social media environment. This study will help to the online brands to attract more customers by providing the motivation such financial, empowerment and socialization.
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