Application of AHP – Entropy–TOPSIS methodology for soil heavy metal classification: Analysis of soil environment status in Jingshan as a case study

Given the complexity of the soil environment, the evaluation of soil pollution should consider the comprehensive weight of multiple evaluation factors to obtain highly objective and scientific conclusions. In this paper, two main ways are proposed to comprehensively analyze the degree of heavy metal pollution in the region: the combination of subjective weight (the analytic hierarchy process method) and objective weight (the entropy method) to determine the combination weight, and the use of the TOPSIS method to quantify the relative relationship between samples and the soil background values in the study area and analyze the spatial and geographical distribution of heavy metal elements in the samples.Analysis results show that the weight ranking of 31 out of 56 samples in the study area is higher than that based on the soil background value of Hubei Province, indicating that 55.36% of the samples had a comprehensive pollution degree lower than the soil background value of Hubei Province. According to the spatial distribution of heavy metal pollution, the soil pollution status in the study area is poor, and some parts are polluted by heavy metals to a certain extent.

The Relationship between Body Image Dissatisfaction and New Anthropometric Indices in Women Referred to the Nutrition Clinic in Northwest of Iran, Ardabil City, 2019

Introduction: Recent studies have shown a high prevalence of body image dissatisfaction in women. This study purposed to examine the relationship between body image dissatisfaction and new anthropometric indices in women. Methods: A cross-sectional study was done among 384 women referred to the nutrition clinic in Ardabil city in 2019, using convenience sampling method. Data were collected through multidimensional body self-relations questionnaires and anthropometric measurements. This questionnaire is an attitudinal assessment of body image, using a 5-point disagrees–agree Likert scale to collect responses. Data was analyzed using SPSS software (version 21). The Pearson correlation coefficient was used to investigate the relation between body image dissatisfaction and anthropometric indices. The significance level was less than 0.05. Results: The mean age and body mass index (BMI) of the women under study were 30.01±7.20 years and 30.21±5.17kg/m2, respectively. Correlation analysis presented that a significant positive relationship was between all anthropometric indices and the subjective weight and overweight preoccupation subscales. The highest relationship of subjective weight subscale was found with BMI, waist circumference, and waist-to-height ratio (WHtR) (r=0.85, p<0.001) followed by abdominal volume index (AVI) (r=0.82, p<0.001). The highest relationship of the overweight preoccupation subscale was obtained with waist circumference (r=0.44, p<0.001) followed by AVI and weight (r=0.42, p<0.001) and WHtR (r=0.41, p<0.001). Conclusion: Body image dissatisfaction was associated with anthropometric indices. The findings indicated the need for interventions designed to improve anthropometric indices and, consequently, body image dissatisfaction.

Interval TOPSIS with a novel interval number comprehensive weight for threat evaluation on uncertain information

Threat evaluation (TE) is essential in battlefield situation awareness and military decision-making. The current processing methods for uncertain information are not effective enough for their excessive subjectivity and difficulty to obtain detailed information about enemy weapons. In order to optimize TE on uncertain information, an approach based on interval Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the interval SD-G1 (SD standard deviation) method is proposed in this article. By interval SD-G1 method, interval number comprehensive weights can be calculated by combining subjective and objective weights. Specifically, the subjective weight is calculated by interval G1 method, which is an extension of G1 method into interval numbers. And the objective weight is calculated by interval SD method, which is an extension of SD method with the mean and SD of the interval array defined in this paper. Sample evaluation results show that with the interval SD-G1 method, weights of target threat attributes can be better calculated, and the approach combining interval TOPSIS and interval SD-G1 can lead to more reasonable results. Additionally, the mean and SD of interval arrays can provide a reference for other fields such as interval analysis and decision-making.

Probabilistic Linguistic-Based Group DEMATEL Method with Both Positive and Negative Influences

Decision-making trial and evaluation laboratory (DEMATEL) is a widely accepted factor analysis algorithm for complex systems. The rationality of the evaluation scale is the basis of sound DEMATEL decision-making. Unfortunately, the existing evaluation scales of DEMATEL failed to reasonably distinguish and describe the positive and negative influences between factors. Generally, the positive and negative influences between factors should be considered at the same time. In other words, negative influence between factors should not be directly ignored, which is improper and unrealistic. To better address this issue, we extend the evaluation scale of DEMATEL. We also integrate the scale-based group DEMATEL method with probabilistic linguistic term sets (PLTSs) to increase its effectiveness, which allows experts to express incomplete and uncertain linguistic preferences in DEMATEL decision-making. An experts’ subjective weight adjustment method based on the similarity degree between PLTSs is introduced to determine experts’ weights. Finally, an algorithm of probabilistic linguistic-based group DEMATEL method with both positive and negative influences is summarized, and an example is used to illustrate the proposed method and demonstrate its superiority. Our results demonstrate that the method proposed in this paper deals reasonably with realistic problems.

Study on Green Construction Evaluation of Highway in Seasonal Frozen Zone

In recent years, research on highway development in the seasonal frozen region has been progressing. However, research on the evaluation system of green construction for the roads in the seasonal frozen region is still under exploration. China's seasonal frozen region accounts for more than half of its land area, and many roads traverse through it. Given the mutual relationship between highway construction and environmental impacts, it is necessary to set up an evaluation system of green construction for the roads in the seasonally frozen region to avoid more energy waste and environmental damages caused by highway construction. The authors used a hierarchical clustering method to determine the index weight, used SPSS to do clustering analysis, established a gray clustering model, and determined the evaluation rating of green construction for the roads according to the principle of maximum membership level. Hereby, this paper evaluated the highway construction from Yichun to Wudalianchi, and the result is "gold level." The result shows that hierarchical cluster analysis combines the index weight and expert weight, which can avoid shortcomings of subjective weight. Therefore, reasonable and scientific weight was obtained. Gray clustering model is suitable for the grade evaluation of green construction for seasonal frozen region's roads. It makes the development of the construction for the green road in the seasonal frozen region more accurate and to gain more practical significance.

An extended framework to evaluate sustainable suppliers in manufacturing companies using a new Pythagorean fuzzy entropy-SWARA-WASPAS decision-making approach

PurposeThe purpose of this study to introduce a new extended framework to evaluate and rank the sustainable suppliers based on the different sustainable criteria in the manufacturing companies using a new fuzzy decision-making approach.Design/methodology/approachThis paper introduces a new approach using decision-making and Pythagorean fuzzy sets (PFSs) to assess the best sustainable supplier. To doing so, this study integrated the entropy, stepwise weight assessment ratio analysis (SWARA) and weighted aggregates sum product assessment (WASPAS) methods under PFSs. To calculate the criteria weights, the combined entropy-SWARA method is used to compute the objective weight and subjective weight, respectively. Furthermore, the WASPAS model is utilized to rank sustainable supplier alternatives.FindingsThe results of the analysis found that occupational health and safety systems had the highest rank among other criteria, followed by green product and eco-design, green R&D and innovation and green technology. In addition, the findings of the paper demonstrated that the extended approach was efficient and useful for selecting and evaluating the best sustainable supplier in the manufacturing companies.Originality/valueRecent years have witnessed a number of studies aimed at incorporating the sustainability standards into the supplier selection problem; however, only a little research has been conducted on developing a fuzzy method for decision-making in a manner to assess and choose suppliers with high sustainability in the insurance market, encompassing the three above-mentioned sustainability criteria.

BSS: a brokering model for service selection using integrated weighting approach in cloud environment

Cloud providers shares their resources and services through collaboration in order to increase resource utilization, profit and quality of services. The offered services with different access patterns, similar characteristics, varied performance levels and cost models create a heterogeneous service environment. It becomes a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service management to help both cloud providers and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users’ feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon’s Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.

Drought grade assessment method based on grey cloud incidence clustering model

PurposeThe purpose of this paper is to establish a grey cloud incidence clustering model to assess the drought disaster degree under 15 indexes in 18 cities of Henan province.Design/methodology/approachThe grey incidence degree between each index and ideal index is used to determine the index weight and combined with the subjective weight, the comprehensive weight is given; the traditional possibility function is transformed into grey cloud possibility function by using the principle of maximum deviation and maximum entropy, which fully reflects the coexistence of multiple decision-making conclusions and constructs the grey cloud incidence clustering model.FindingsThe drought disaster degree of Henan province is divided into four grades under the selected 15 indexes. The drought grades show obvious regional differences. The risk levels of the east and southwest are higher, and the risk levels of the north and southeast are lower. This result is consistent with the study of drought disaster grades in Henan province, which shows the practicability and usefulness of the model.Practical implicationsIt provides an effective method for the assessment of drought disaster grade and the basis for formulating disaster prevention and mitigation plan.Originality/valueBy studying the method of multiattribute and multistage decision-making with interval grey number information. The objective weight model of index value is designed, and the subjective weight is given by experts. On the basis of the two, the comprehensive weight of subjective and objective combination is proposed, which effectively weakens the randomness of subjective weight and reasonably reflects the practicality of index decision-making. The time weight reflects the dynamic of the index. The traditional possibility function is transformed into the grey cloud possibility function, which effectively takes advantage of the grey cloud model in dealing with the coexistence of fuzzy information, grey information and random information. Thus, the conflict between the decision-making results and the objective reality is effectively solved. The interval grey number can make full use of the effective information and improve the accuracy of the actual information.

BSS: A Brokering Model for Service Selection using Integrated Weighting approach in Federated Cloud

Federated Cloud is a multi-cloud platform that integrates various cloud providers either through standardization or an agreement. The services offered by different federation members possess different access patterns, similar characteristics, varied performance levels, different costs, etc. This heterogeneity creates a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service and federation management to help both service provider and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users' feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon's Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.