The logistics serving as Tianjin' pillar industry, to a certain degree, has developed very well, but it still needs to be strengthened. This article not only provides the evaluation standard of green logistics, but also analyzes the Tianjin's present situation in this aspect. At the end of the article, some suggestions are put forward in hope of being helpful to the green logistics develop better and quicker. Keywords: Green logistics, Standard, Path analysis, Present situationWith the rapid development of world economy and modern science, the modern logistics has become a strong growth point of economic development, as well as a nearly established pillar industry in the national economy, namely the development of the green logistics represents both a city's modernization level and its comprehensive strength. Although the logistics promotes economic development, meanwhile, it causes many side effects on city environment, such as noise, gas pollution, traffic jam and improper waste disposal in production and life. Therefore, in the 21 st century, we need to meet new requirements of logistic development, namely green logistics. Concept of green logisticsThe green logistics refers to plan, control, management and implementation the logistics system through the advanced logistics technology and environmental management, aiming to reduce the pollutant emission. Evaluation standard of the green logisticsAccording to the logistics link, the evaluation criterions of enterprise green logistics are as follows:(1) Green transportation. The green transportation refers to use a kind of fuel with the least pollution as the power to try to implement the multi-transportation and allocation mode. Correctly arrange the transportation can we reduce the pollution, lower the cost and raise the allocation level.(2) Green storage. The green storage refers to adoption the mechanized operation in the process of goods-storing to save the manpower cost, adoption the environmentally-friendly products to sterilize the storage goods, adoption the method of centralized-stock to reduce the radialization to the surroundings and reduce the adverse effect of the warehousing on the environment.(3) Green packing. The green package refers to a kind of commodity package that will not cause the environmental pollution. The packing materials should save the resources and reduce the packing waste, moreover, it is supposed to be recycled and regenerated after using, as well as occupies little land while burying in order to be decomposed easily.(4) Reverse logistics. The reverse logistics is contrary to the traditional supply chain, it devotes to reasonably disposal or recovering the value by planning, managing and controlling the raw materials, middle stock, final products and relative information from consumer place to start point.(5) Green technology. The green technology refers to adoption the information and communication technology, biological technology, monitoring technology and a variety of specific technologies in the process of logistic...
To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively.
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