Evaluation of Environmental Carrying Capacity of Water Resources in Jiangxi Province

In order to quantitatively evaluate the sustainable development status of water resources in Jiangxi Province, the dynamic changes in ecological footprint, carrying capacity and load index of water resources in Jiangxi Province during 2009-2013 were analyzed according to the primary principle and calculation model of ecological footprint. The results showed that in Jiangxi Province during 2009- 2013, the water resources ecological footprint per capita was relatively low; the wa- ter resources utilization level was relatively low; the overall development potential of water resources was great; the water resources ecological carrying capacity per capita and ecological footprint per capita were trended to be increased overall. The changes in water resources ecological footprint are closely related to the social and economic development. Therefore, the industrial structure should be fully adjusted, and the water resources should be scheduled and utilized reasonably so as to pro- mote the protection of water resources and sustainable development of society and economic in Jiangxi Province.

Microscopic colitis

Microscopic colitis may be defined as a clinical syndrome, of unknown etiology, consisting of chronic watery diarrhea, with no alterations in the large bowel at the endoscopic and radiologic evaluation. Therefore, a definitive diagnosis is only possible by histological analysis. The epidemiological impact of this disease has become increasingly clear in the last years, with most data coming from Western countries. Microscopic colitis includes two histological subtypes [collagenous colitis (CC) and lymphocytic colitis (LC)] with no differences in clinical presentation and management. Collagenous colitis is characterized by a thickening of the subepithelial collagen layer that is absent in LC. The main feature of LC is an increase of the density of intra-epithelial lymphocytes in the surface epithelium. A number of pathogenetic theories have been proposed over the years, involving the role of luminal agents, autoimmunity, eosinophils, genetics (human leukocyte antigen), biliary acids, infections, alterations of pericryptal fibroblasts, and drug intake; drugs like ticlopidine, carbamazepine or ranitidine are especially associated with the development of LC, while CC is more frequently linked to cimetidine, non-steroidal antiinflammatory drugs and lansoprazole. Microscopic colitis typically presents as chronic or intermittent watery diarrhea, that may be accompanied by symptoms such as abdominal pain, weight loss and incontinence. Recent evidence has added new pharmacological options for the treatment of microscopic colitis:the role of steroidal therapy, especially oral budesonide, has gained relevance, as well as immunosuppressive agents such as azathioprine and 6-mercaptopurine. The use of anti-tumor necrosis factoragents, infliximab and adalimumab, constitutes a new, interesting tool for the treatment of microscopic colitis, but larger, adequately designed studies are needed to confirm existing data.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.