Water Carrying Capacity Evaluation Method Based on Cloud Model Theory and an Evidential Reasoning Approach

The scientific and reasonable evaluation of the carrying capacity of water resources is of guiding significance for solving the issues of water resource shortages and pollution control. It is also an important method for realizing the sustainable development of water resources. Aiming at an evaluation of the carrying capacity of water resources, an evaluation model based on the cloud model theory and evidential reasoning approach is studied. First, based on the existing indicators, a water resources evaluation index system based on the pressure-state-response (PSR) model is constructed, and a classification method of carrying capacity grade is designed. The cloud model theory is used to realize the transformation between the measured value of indicators and the degree of correlation. Second, to obtain the weight of the evaluation index, the weight method of the index weights model based on the entropy weight method and evidential reasoning approach is proposed. Then, the reliability distribution function of the evaluation index and the graded probability distribution of the carrying capacity of water resources are obtained by an evidential reasoning approach. Finally, the evaluation method of the carrying capacity of water resources is constructed, and specific steps are provided. The proposed method is applied to the evaluation of water resources carrying capacity for Hunan Province, which verifies the feasibility and effectiveness of the method proposed in the present study. This paper applies this method of the evaluation of the water resources carrying capacity of Hunan Province from 2010 to 2019. It is concluded that the water resources carrying capacity of Hunan Province belongs to III~V, which is between the critical state and the strong carrying capacity state. The carrying capacity of the province’s water resources is basically on the rise. This shows that the carrying capacity of water resources in Hunan Province is in good condition, and corresponding protective measures should be taken to continue the current state.

Subgroup analysis reveals higher reliability of the new comprehensive evaluation of Global Initiative for Chronic Obstructive Lung Disease 2019

To estimate the severity of the disease in outpatients with chronic obstructive pulmonary disease (COPD) in Hunan Province, China and use the subgroup analysis to evaluate the reliability of the new comprehensive evaluation of Global Initiative for Chronic Obstructive Lung Disease (GOLD). COPD outpatients from 12 medical centers in Hunan Province, China were stratified into groups A–D, and group D patients were further stratified into subgroups D1–D3 according to the GOLD 2016 and 2019 comprehensive assessment. Demography, clinical characteristics and medications were compared among groups. In 1017 COPD outpatients, the distribution from group A to D and subgroup D1 to D3 was 41 (4.0%), 249 (24.5%), 17 (1.7%), 710 (69.8%) and 214 (30.2%), 204 (28.7%), 292 (41.1%), according to GOLD 2016. In terms of demographic and clinical characteristics related to A–D groups, there was a significant difference in COPD assessment test (CAT), modified Medical British Research Council (mMRC), the clinical COPD questionnaire(CCQ), age, BMI, education level, smoking history, comorbidities, the course of chronic bronchitis/emphysema, number of exacerbations/hospitalisations in the previous year, treatment protocols, forced expiratory volume in one second (FEV1) % predicted, and FEV1/forced vital capacity (FVC) (p < 0.01). Furthermore, some patients in groups C–D regrouped to groups A–B were all C1 and D1 subgroups according to GOLD 2019. Comparing subgroup D1 with group B, subgroup D2 and subgroup D3, it was found that the demography, clinical characteristics and medications of subgroup D1 were the closest to group B, according to GOLD 2016 (p < 0.01). The disease severity of outpatients with COPD in Hunan Province was more pronounced in group B and D and patients in groups A–D had different demography, clinical characteristics and medications. Subgroup analysis can explain to a certain extent that GOLD2019’s new comprehensive assessment is more reliable than GOLD 2016.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Estimating the Spatial Heterogeneity and Seasonal Differences of the Contribution of Tourism Industry Activities to Night Light Index by POI

The spatial distribution of tourism has a profound impact on its operational efficiency and geographical relevance. Point of interest (POI), as a kind of spatial data shared by subject and object, can reflect the spatial distribution form and function of tourism geographical objects under the all-for-one tourism policy. Continuous satellite observation and in-depth study of night lights pave the way to clarify human activities and socio-economic dynamics. The purpose of this paper is to investigate the seasonal changes of night light images and their correlation with tourism in 122 counties (cities, districts) of Hunan Province. We obtained night earth observation data (seasonality) and POI in 2019 and processed them by Geographic Information System and statistical analysis (ordinary least squares (OLS) and geographically weighted regression (GWR)). The results show that the luminous radiation intensity is highly correlated with the POI of tourism activities. The POI of different tourism activities in different regions shows obvious spatial heterogeneity and seasonal differences, which is the result of the comprehensive effect of tourism resource distribution and social environment in Hunan Province. GWR has proved to be a more effective tool. It provides a new method and perspective for tourism research and especially reveals the geographical spatial differences of tourism activities, which is helpful to study the spatial distribution and seasonality of tourism at the county level. In addition, the spatial evaluation of the contribution of tourism and luminous radiation can provide reference and suggestions for relevant departments to formulate tourism night protection measures.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Factors influencing medication adherence among patients with severe mental disorders from the perspective of mental health professionals

Background Medication adherence is a common issue influenced by various factors among patients with severe mental disorders worldwide. However, most literature to date has been primarily quantitative and has focused on medication adherence issue from the perspective of patients or their caregivers. Moreover, research focused on medication adherence issue in China is scarce. Present study aims to explore the influential factors of medication adherence among patients with severe mental disorders form the perspective of mental health professionals in Hunan Province, China. Methods A qualitative study was performed in Hunan Province, China with 31 mental health professionals recruited from October to November 2017. And semi-structured interviews or focus group interviews were conducted along with audio recordings of all interviews. Interview transcripts were then coded and analyzed in Nvivo software with standard qualitative approaches. Results Three major themes influencing medication adherence among patients with severe mental disorders were identified as: (1) attitudes towards mental disorder/treatment; (2) inadequate aftercare; (3) resource shortages. Conclusions This qualitative study identified the factors influencing medication adherence among patients with severe mental disorders in China. As a locally driven research study, it provides practical advice on medication adherence promotion for mental health workers and suggests culturally tailored models that improve the management of patients with severe mental disorders in order to reduce economic burden on individual and societal level.

The Mediational Role of Self-Support Personality in the Association of Family Function and School Belonging in Adolescents

Some previous studies have explored the impact of family function on school belonging. However, little is known about the parallel mediating relationship underlying them. This study aims to investigate the formation mechanism of school beginning in a sample of Chinese adolescents and examined the parallel mediating role of interpersonal self-support and individual self-support in the link between family function and school belonging. A cross-sectional study was conducted in four schools of the district of Hunan province in China, and 741 students were surveyed using cluster sampling. Family cohesion and adaptability scale (FACES), Adolescent students self-supporting personality scale (SSPS-AS), School belonging scale were applied. The results indicated that interpersonal self-support and individual self-support, together, and uniquely, parallel mediated the relationship between family function and school belonging. It can be concluded that family function not only has direct effects on school belonging but also has indirect effects through interpersonal self-support and individual self-support.