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Neighborhood externalities influence residential land prices and housing prices simultaneously, contributing to a nexus relationship between them. Most existing studies have focused on the causal relationship between the two, with limited attention to their nexus. This research matched land parcel transactions from 2003 to 2015 and housing transaction records with purchasers' housing purchase records before 2019 to construct a spatial-temporal land-housing dataset in Shanghai. Both spatial statistical tools and regression tree models were employed to explore the underlying mechanism, focusing on proximity to industries. According to the modeling results, residential land prices and housing prices are highly sensitive to polluting industries and producer service industries. The impacts of polluting industries on residential land prices have intensified, but the impacts on housing prices have not. The regression tree models further showed that living service industries were the primary positive contributor to residential land and housing prices. Regarding the social environment, non-local people are taking over communities with high-priced housing on high-value residential land parcels. These research outcomes encourage more efforts to explore the determinants of the nexus between residential land prices and housing prices, rather than discussion of the causality relationship. • Analyzing the nexus between land and housing price using matched land and housing transaction records. • Construct a joint model to estimate the determinants of the nexus between land and housing prices. • Living service related industries could increase both land and housing price. • Non-local's communities are usually located on the high-value land parcels with higher housing price. [ABSTRACT FROM AUTHOR]

Objective To construct the prediction index of anxiety outcome of nurses in operating room by using classified regression tree model. Methods A total of 284 operating room nurses in 971 th Hospital of Navy of Chinese People's Liberation Army from March 2017 to June 2020 were selected to evaluate the baseline set data by using the classification regression tree model to determine the risk factors inducing anxiety of operating room nurses in military hospitals, and calculate the contribution rate of different risk factors. The prediction set was used as the evaluation method of prediction results to determine the risk factors that induced nurses' self rating Anxiety Scale ( SAS * score to reach the anxiety standard. Results The results of single factor analysis confirmed that the weekly working time, marital status, having children, CD-RISC score, age educational background were statistically significant ( all P < 0. 05 *. The SAS score split regression tree analysis of nurses showed that the weekly working time, marital status, children's situation, CD-RISC score, age entry model, formed 6 nodes, among which there were divorce, weekly working time >45 h and the highest score. Conclusion The risk factors of nurses anxiety in operating room were composed of five items: working hours, marital status, children's condition, psychological elasticity and age. Nursing managers can use the risk factors of nurses anxiety in operating room to predict the anxiety outcome in advance and carry out timely and reasonable intervention, which can help to reduce the anxiety degree of nursing staff. [ABSTRACT FROM AUTHOR]

We propose a modeling framework that uses only cross-sectional data to disentangle labor supply and demand choices simultaneously. This modeling framework extends the labor-market analytical toolkits to adapt to environments where data are limited, flexibility in working hours is lacking, or structural changes are present, as is the case in most emerging and low-income countries. We showcase our model by using the 2011 China Household Finance Survey to decipher labor market choices in urban China. We find that the main discrepancies in labor supply between males and females are driven by the number and age of children, the lower utility of working rather than fewer job opportunities for females, and larger impacts of education and work experience on females' job opportunities. Household wealth in the form of 'cash inflow' incentivizes individuals not to work, while wealth in the form of 'stock' induces higher utility to work for both males and females. The interpretation of empirical findings hinges on particular assumptions that might be disputed. • A modeling framework that uses only cross-sectional data to disentangle labor supply and demand choices simultaneously. • This modeling framework extends the labor-market analytical toolkits to adapt to emerging and low-income countries' environments. • We showcase our model by using the 2011 China Household Finance Survey to decipher labor market choices in urban China. [ABSTRACT FROM AUTHOR]

Accumulations of heavy metals in urban soils are highly spatial heterogeneity and affected by multiple factors including soil properties, land use and pattern, population and climatic conditions. We studied accumulation risks of Cd, Cu, Pb and Zn in unban soils of Beijing and their influencing based on the regression tree analysis and a GIS-based overlay model. Result shows that Zinc causes the most extensive soil pollution and Cu result in the most acute soil pollution. The soil's organic carbon content and CEC and population growth are the most significant factors affecting heavy metal accumulation. Other influence factors in land use pattern, urban landscape, and wind speed also contributed, but less pronounced. The soils in areas with higher degree of urbanization and surrounded by intense vehicular traffics have higher accumulation risk of Cd, Cu, Pb, and Zn. [ABSTRACT FROM AUTHOR]

Abstract: We examined changes in soil properties, crop biomass, and weed communities in the Horqin Sandy Land of China to elucidate cropland degradation. We studied three local cropland types having periods of cultivation of up to 20 years: maize cropland on lowlands without irrigation (nonirrigated lowland), maize cropland on flat sandy lands with irrigation (irrigated flatland), and bean-centered cropland on sand dunes without irrigation (nonirrigated dunes). Soil properties and crop biomass were more degraded in nonirrigated lowland and nonirrigated dunes than in irrigated flatland. Weed communities in the nonirrigated croplands were the type that become established in drier conditions, whereas wetland weeds were more abundant on irrigated flatland. Trends of change in each indicator did not always occur in parallel and differed statistically among the cropland types. Monitoring these indicators within the context of local land-use systems can provide scientific evidence on which to base local management practices or recommendations for change. [Copyright &y& Elsevier]