Taking the Beijing-Tianjin-Hebei region as a case study, the development of coupling mechanism among tourism, urbanization and ecological environment of the city are analyzed. The coupling coordinative development and types among tourism, urbanization and ecological system were studied by building the tourism-urbanization-ecological evaluation index system and using the coupled coordination function and quantitative model. The result shows that coupling between tourism, urbanization and ecological environment system generally promoted from the imbalance to coordination from 2004 to 2013. Tourism industry is developing rapidly within the city, and it become more volatile. The urbanization level maintains steady growth. With the development of tourism and urbanization, ecological environmental problems are continuously emerging. The type of the coupling development is given priority to urbanization more than tourism in Beijing-Tianjin-Hebei region.

To reflect the hospital scale and capacity of service more accurately, hospital scale is determined by size of residents, and the search radius is determined dynamically. The spatial accessibility of the main areas in Xi′an city is analyzed by using the original and the enhanced method. The results show the accessibility of main areas of Xi′an city is unequal, midwest is superior to the east, and the spatial accessibility of facilities decreases gradually from the northwest to the southeast. Accessibility is related to the density of street population around the hospital.With the similar hospital, the streets which have fiercer competition of the resource get low accessibility. Accessibility is also related to the hospital scale, under the similar resource competition, the streets with high level hospitals and convenient transportation get high accessibility. The enhanced method can present a more reasonable evaluation of the accessibility so that the differences of same level hospital could be judged efficiently.

To investigate the effect of different sleep deprivations (SD) on the mood,learning and memory of mice, both multiple platform method and sleep deprivation tank method were used to observe the effects of the relative factors on the anxiety-like behavior,learning and memory.The following results were obtained.12 h SD and 24 h SD did not affect anxiety-like behavior, learning and memory in either of the above paradigms. The anxiety-like behavior was observed in mice subjected to 48 h SD via multiple platform method and the spatial memory was not affected，but sleep deprivation tank did not make mice appear anxiety-like behavior and spatial memory ability after 48 h SD. In the sleep deprivation tank experiment, a long-time SD could induce anxiety-like behavior, whereas the learning process of the mice slowed down and spatial memory was impaired after 3 weeks SD. Thus, deprivation tank is advantageous as an SD model for studies on human long-term sleep insufficiency.

Using Google Earth to locate precisely the geographical location of 591 provincial traditional villages in Henan Province, the spatial correlation analysis of the social and natural factors, such as topography, population, economy, transportation network, etc. in Henan Province was conducted combining with ArcGIS 10.2 and SPSS 19.0. The results show that the traditional villages of Henan Province present the characteristics of the whole decentralized and local settlement in the spatial distribution, and there are two high density cores located in Pingdingshan and Anyang City. River system, traffic and population are the main factors affecting the distribution of traditional villages which are built along river, and distributed in the area of where the road traffic developed and the population is relatively sparse. The elevation, economy and urbanization rate are also the important factors affecting the development traditional villages, and the traditional villages are mostly distributed in the areas with low topography, medium level of economic development and urbanization rate. The climate is the reference factor of the traditional village′s foothold, whereas the precipitation and the temperature are not obvious with the traditional villages′ distribution.

To research the improving effects of Tai Chi exercise on the glucose Metabolism to abnormal glycoMetabolism patients, PubMed, WOS, EBSCO, Embase, Cochrane, CNKI, WanFang and SinoMed electronic databases were retrieved, and the Metaanalysis was conducted through RevMan 5.3 and StataSE 12.0 software. Tai Chi exercise can significantly decrease FBG (SMD=-0.85，95%CI is-1.17～-0.52，P＜0.000 1), 2 h PBG(MD=-1.03，95%CI is -1.34～-0.73，P＜0.000 01) ,HOMA(MD= -0.69，95%CI is-0.06～-1.31，P=0.03) and HbA1c（MD= -0.76，95%CI is-1.38～-0.14，P=0.02）, while can not on TG and TC(P＞0.05).Meta regression analysis indicated that the Tai Chi exercise type might be the main source of FBG combined effect heterogeneity.So Tai Chi exercise could be significantly improve glucose Metabolism function, insulin sensitivity compared to control group, and Tai Chi exercise type could be influence the improve effect of improving FBG.

Taking 156 healthy tourism destinations in ChangshaZhuzhouXiangtan urban agglomeration as research samples, applying spatial analysis methods such as nearest neighbor index, Ripley′s K(d) function, kernel density estimation and buffer analysis, the spatial structure and the spatial difference of different types of healthy tourism destinations were discussed, and the influencing factors were studied. The study found that healthy tourism resources in ChangZhuTan are rich, which can be divided into five basic types, that is forest health, water health, traditional Chinese medicine and so on. Healthy tourism destinations have significant agglomeration characteristics in ChangZhuTan. The healthy tourism destinations of the north is more than that of the south in ChangZhuTan, it is in the form of “V” shape circle structure. There is a high degree of gathering and a large number of oldage health care and rural healthy tourist destinations, the distribution of forest health and traditional Chinese medicine tourism is discrete, and the scale is large. The difference of natural resources is the internal driving force that affecting the spatial structure of the healthy tourism destinations, and traffic location, economic foundation and population density are important external thrust.

Object detection is one of the core tasks in the field of computer vision. In recent years, with the rapid development of deep learning, the object detection technology based on deep learning has become the very popular mainstream algorithm. It has been widely used in many fields, such as face detection, vehicle detection, pedestrian detection, and unmanned driving, etc.. This paper systematically summarizes the current research progress of deep learning-based object detection algorithms, and thoroughly analyzes the advantages and disadvantages of each algorithm and its results on the datasets VOC2007 and COCO. Finally, the future development of object detection based on deep learning is also discussed in this paper.

Based on the data of A-grade scenic spots of China in 2016, the number, grade structure and spatial distribution of A-grade scenic spots were studied by the means of GIS spatial analysis and some quantitative analysis methods including the nearest neighbor index, Gini coefficient and Anselin Local Moran I. The results showed that the development of A-grade scenic spots in China has entered into the stage of quality enhancement instead of the stage of quantity enlargement. The spatial distribution of A-grade scenic spots and population in China are overlapped spatially, 87% of A-grade scenic spots located in the east of Hu Huanyong line and formed three concentrated areas, including Beijing-Tianjin-Hebei region, Yangtze River Delta and Shandong Peninsula. Tourist receipts and tourism revenue are significantly and positively related to the number of A-grade scenic spots at provincial scale, especially related to the number of 5A and 4A scenic spots. Based on the above conclusions, five proposals for the developing of A-grade scenic spots in China are put forward, that is, quality improvement of scenic spots, priority development of A grade scenic spots in western regions, regional tourism cooperation, priority development of transportation and tourist products innovation of scenic spots.

Aiming at severe weather conditions, traffic flow predition modd based on random forest was proposed.Based on taxi data and weather conditions in New York city in 2016, screening the original GPS data layer by layer, the data that meet the definition of severe weather conditions are screened out. Based on the random forest regression method, the traffic flow prediction model under severe weather is studied, and the performance of the model is improved by adjusting the super parameters of the model. At the same time, the performance of random forest model is compared with that of BP neural network model and decision tree model, and the experimental results of random forest prediction model are better.

The outbreak of COVID-19 at the end of 2019 is spreading around the world, causing serious impact on socio-economic development and human health and safety. Clarification of the spatial-temporal spread of COVID-19 at individual and population levels is necessary for epidemic prevention and control as well as resource allocation. This study targeted on Shenzhen city which locates far away from the COVID-19 origin city of Wuhan but experienced high incidence of COVID-19. Based on the officially published and confirmed cases, manually collected case details and the related POI data of Shenzhen, the epidemiological characteristics, spatio-temporal evolution and prevention and control measures regarding COVID-19 in Shenzhen were analyzed through text analysis, mathematical statistics, spatial analysis and other methods. The results showed that: (1)the confirmed COVID-19 cases covered all age levels, mainly concentrated in the young adults. Most of them were the second generation of infection originating from the places out of Shenzhen, and they were mainly from family cluster transmission with “linear” migration and diffusion mode between cities. (2)The time evolution of epidemic experienced 4 stages: low incidence period, growing period, deceleration period and ending period. The number of cured cases presented the evolution process from slow growth to rapid growth. The time interval from onset to admission was mostly within 0~2 days, and the time interval from admission to diagnosis was about 1~3 days. (3)In terms of spatial distribution, the confirmed cases moved out mainly through two types: “directly from Hubei” and “stop by Hubei”, with the main purpose of visiting relatives during the Spring Festival. The spatial distribution of COVID-19 affected communities demonstrated “northeast to southwest” trend, mainly gathered in the southwest part around the “two cores”.(4)The overall risk distribution of the city was “two cores and three areas”. The high-risk areas were located in the south of Nanshan District and in the center of Futian District. Finally, combined with the situation of COVID-19, requirement of work recovery and the new risk from overseas, this study put forward an accurate prevention and control measure system based on the dimensions of “real-time, daily, normal”, “community, district, city” and “individual,family,society” from the perspective of “time-space-human” mutual feedback and integration.

The data of confirmed cases, death cases and cured cases of coronavirus disease 2019 (COVID-19) in seven epidemic seriously affected countries (Italy, South Korea, the United Kingdom, the United States, France, Spain, Germany) were obtained from the System Science and Engineering Center at Johns Hopkins University. Data were collected between March 4, 2020 and April 4, 2020. Traditional SIR model was used and improved by adding a new parameter to characterize the infection coefficient changes over time, and regression analysis was introduced to estimate the parameters. Based on the improved SIR model, the development of COVID-19 in 7 countries were simulated and predicted, and the effects of contact rate control measures of different countries on epidemic development were analyzed. Results showed that the improved SIR model was reliable for analyzing the epidemic trend of COVID-19. Apart from the United Kingdom and the United States, the other five countries have had their pandemic under initial control, while the United Kingdom and the United States need to do more to reduce the pandemic damage. Our finding confirmed the importance of reducing population contact rate in COVID-19 prevention and control, such as reducing assembly, avoiding contact and centralized admission, so as to provide reference for the subsequent epidemic prevention and control.

Taking 6 819 traditional village lists published by the government as the research object, the spatial distribution characteristics of Chinese traditional villages and its influencing factors were studied using ArcGIS spatial analysis techniques and methods. The results show that Chinese traditional villages present the distribution characteristics of “core-edge” in space, and formed four agglomeration areas. Chinese traditional villages have strong imbalance in the spatial distribution of each provinces (autonomous regions, municipalities),cities and counties, and which are mostly distributed in the provincial border areas. The spatial distribution of Chinese traditional villages is affected by both natural environment and social environment, which mainly distributed in the valley, basin, low mountain, hilly and middle mountain far away from the main grain producing areas, and in the sunny slope areas or riverside areas far away from the central city and the economic and transportation less developed. The main factors influencing the spatial distribution of Chinese traditional villages from strong to weak are: rural per capita disposable income, slope direction, population density, river network density, distance from central city, elevation, agricultural land, traffic density.