A multi-criteria decision-making method based on single-valued trapezoidal neutrosophic preference relations with complete weight information

Single-valued trapezoidal neutrosophic numbers (SVTNNs) have a strong capacity to depict uncertain, inconsistent, and incomplete information about decisionmaking problems. Preference relations represent a practical tool for presenting decision makers’ preference information regarding various alternatives

2015美国内分泌学会关于肥胖的药物治疗指南解读

With the improvement of living standards, the incidence of obesity in China is increasing year by year. A series of diseases caused by obesity, such as hypertension, coronary heart disease, diabetes, nonalcoholic fatty liver disease and other diseases, is gradually increasing, which poses serious harm to people's health. Meanwhile, the treatment of obesity has attracted much attention. Diet, exercise and behavior intervention are the main methods of controlling obesity. However, by the influence of various factors, some patients have difficulties to achieve the purpose of reducing weight through the methods mentioned above. The American endocrine society proposes drug treatment of obesity and makes a certain requirement for the standard treatment. It provides a thought for the treatment of obesity due to nonalcoholic fatty liver disease.随着生活水平的提高，肥胖症在中国的发病率逐年升高。由肥胖引发的一系列疾病，如高血压、冠心病、糖尿病、非酒精性脂肪肝病等疾病的发病率也在逐渐增加，严重危害人们的身体健康，因此对于肥胖的治疗备受关注。饮食、运动及行为干预治疗是主要的控制肥胖的方法。但受各种因素的影响，有些患者很难通过饮食、运动及行为干预治疗达到减轻体重的目的，对于此类患者，可以适当的进行药物治疗。2015年美国内分泌学会针对肥胖患者的药物治疗提出了新的建议，对规范化治疗肥胖作出了一定要求。对肥胖相关的非酒精性脂肪肝病的治疗提供了新思路

Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning

Transfer learning has become a common practice for training deep learning models with limited labeled data in a target domain. On the other hand, deep models are vulnerable to adversarial attacks. Though transfer learning has been widely applied, its effect on model robustness is unclear. To figure out this problem, we conduct extensive empirical evaluations to show that fine-tuning effectively enhances model robustness under white-box FGSM attacks. We also propose a black-box attack method for transfer learning models which attacks the target model with the adversarial examples produced by its source model. To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model. Empirical results show that the adversarial examples are more transferable when fine-tuning is used than they are when the two networks are trained independently

A linguistic Neutrosophic Multi-Criteria Group Decision-Making Method to University Human Resource Management

Competition among different universities depends largely on the competition for talent. Talent evaluation and selection is one of the main activities in human resource management (HRM) which is critical for university development. Firstly, linguistic neutrosophic sets (LNSs) are introduced to better express multiple uncertain information during the evaluation procedure. We further merge the power averaging operator with LNSs for information aggregation and propose a LN-power weighted averaging (LNPWA) operator and a LN-power weighted geometric (LNPWG) operator. Then, an extended technique for order preference by similarity to ideal solution (TOPSIS) method is developed to solve a case of university HRM evaluation problem. The main contribution and novelty of the proposed method rely on that it allows the information provided by different decision makers (DMs) to support and reinforce each other which is more consistent with the actual situation of university HRM evaluation. In addition, its effectiveness and advantages over existing methods are verified through sensitivity and comparative analysis. The results show that the proposal is capable in the domain of university HRM evaluation and may contribute to the talent introduction in universities

Interior sound quality evaluation model of heavy commercial vehicles

Based on back-propagation (BP) artificial neural network (ANN) technique, interior sound quality evaluation model of heavy commercial vehicles(HCV) was designed in this paper. According to the standard named GB/T18697-2002, firstly, the interior noises of five different types of HCV under different working conditions were measured and collected. Secondly, the subjective evaluation of sound quality was estimated by jury tests following the magnitude estimation. Meanwhile, seven objective psycho-acoustical parameters of these samples were calculated. Using the objective and subjective evaluation results, furthermore, the sound quality prediction model of HCV was developed based on BP ANN. Thirdly, this model was proved by some verification tests. The results suggest that the proposed model has ability of high precision and good generalization. And lastly, the sound quality prediction model of HCV could be used to determine the impact weight of measuring objective evaluation parameters contributing to the results of subjective evaluation. The results played a significant guiding role in both HCV and other areas for sound quality evaluation and analysis