Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme

Summary:Throughout the duration of the New Cooperative Medical Scheme(NCMS),it was found that an increasing number of rural patients were seeking out-of^county medical treatment,which posed a great burden on the NCMS fund.Our study was conducted to examine the prevalence of out-of^county hospitalizations and its related factors,and to provide a scientific basis for follow?up health insurance policies.A total of 215 counties in central and western China from 2008 to 2016 were selected.The total out-of-county hospitalization rate in nine years was 16.95%,which increased from 12.37%in 2008 to 19.21%in 2016 with an average annual growth rate of 5.66%.Its related expenses and compensations were shown to increase each year,with those in the central region being higher than those in the western region.Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region(XI),rural population(X2),per capita per year net income(X3),per capita gross domestic product(GDP)(X4),per capita funding amount of NCMS(X5),compensation ratio of out-of^county hospitalization cost(X6),per time average in-county(X7)and out-of-county hospitalization cost(X8).According to Bayesian network(BN),the marginal probability of high out-of^county hospitalization rate was as high as 81.7%.Out-of^county hospitalizations were directly related to X8,X3,X4 and X6.The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors,economy factors,regional characteristics and NCMS policy factors was 95.7%,91.1%,93.0% and 88.8%,respectively.And how these factors affect out-of-county hospitalization and their interrelationships were found out.Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations,and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.

A New Method of Semantic Feature Extraction for Medical Images Data

In order to overcome the disadvantages of color, shape and texture-based features definition for medical images, this paper defines a new kind of semantic feature and its extraction algorithm. We firstly use kernel density estimation statistical model to describe the complicated medical image data, secondly, define some typical representative pixels of images as feature and finally, take hill-climbing strategy of Artificial Intelligence to extract those semantic features. Results of a content-based medial image retrieve system show that our semantic features have better distinguishing ability than those color, shape and texture-based features and can improve the ratios of recall and precision of this system smartly.

Research on the shear model and optimal algorithm of long products

Incoming materials sometimes must be slit according to the customers’ order requirements.A reasonable shear model can reduce residual materials and improve the rate of finished products.In this paper, a shear model is established, and several commonly used optimization algorithms are compared, with the minimum residual materials, running time, and CPU ratio as the evaluation index.Results show that the genetic algorithm is the best algorithm that can be used in online automatic shear control to achieve the highest yield and highest computational efficiency.

Operation Strategy Analysis and Configuration Optimization of Solar CCHP System

This paper proposed a new type of combined cooling heating and power(CCHP)system,including the parabolic trough solar thermal(PTST)power generation and gas turbine power generation.The thermal energy storage subsystem in the PTST unit provides both thermal energy and electrical energy.Based on the life cycle method,the configuration optimization under eight operation strategies is studied with the economy,energy,and environment indicators.The eight operation strategies include FEL,FEL-EC,FEL-TES,FEL-TES&EC,FTL,FTL-EC,FTL-TES,and FTL-TES&EC.The feasibility of each strategy is verified by taking a residential building cluster as an example.The indicators under the optimal configuration of each strategy are compared with that of the separate production(SP)system.The results showed that the PTST-CCHP system improves the environment and energy performance by changing the ratio of thermal energy and electric energy.The environment and energy indicators of FEL-TES&EC are superior to those of FTL-TES&EC in summer,and the results are just the opposite in winter.The initial annual investment of the PTST-CCHP system is higher than that of the SP system,but its economic performance is better than that of the SP system with the increase of life-cycle.

Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket

In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution.Addressing at this issue,we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket.We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm.Greedy algorithm is applied to initialize the population,and then hill-climbing algorithm is used to optimize individuals in each generation after selection,crossover and mutation.Our approach is evaluated on the dataset of BBG Supermarket which is one of the top 10 supermarkets in China.Experimental results show that our method outperforms some other methods in the field.

Enhancement of the polynomial functions response surface model for real-time analyzing ozone sensitivity

Quantification of the nonlinearities between ambient ozone(O3)and the emissions of nitrogen oxides(NOx)and volatile organic compound(VOC)is a prerequisite for an effective O3 control strategy.An Enhanced polynomial functions Response Surface Model(Epf-RSM)with the capability to analyze O3-NOx-VOC sensitivities in real time was developed by integrating the hill-climbing adaptive method into the optimized Extended Response Surface Model(ERSM)system.The Epf-RSM could single out the best suited polynomial function for each grid cell to quantify the responses of O3 concentrations to precursor emission changes.Several comparisons between Epf-RSM and pf-ERSM(polynomial functions based ERSM)were performed using out-of-sample validation,together with comparisons of the spatial distribution and the Empirical Kinetic Modeling Approach diagrams.The comparison results showed that Epf-RSM effectively addressed the drawbacks of pf-ERSM with respect to overfitting in the margin areas and high biases in the transition areas.The O3 concentrations predicted by Epf-RSM agreed well with Community Multi-scale Air Quality simulation results.The case study results in the Pearl River Delta and the north-western area of the Shandong province indicated that the O3 formations in the central areas of both the regions were more sensitive to anthropogenic VOC in January,April,and October,while more NOx-sensitive in July.