抽水引起岩溶塌陷的机理及非线性预测研究

Karst collapse is one of the most important engineering geology hazards in Karst district, which seriously endangers the living of humankind and the environment around us, as well as the natural resources. Generally speaking, there exist three processes of overburden karst collapse:the formation of soil cavity, the expansion of soil cavity and the fall of the cavity roof. During these processes, groundwater is always the most active factor and plays a key role. Pumping will bring into the great change of groundwater in flow state, flowrate, frequency of fluctuation as well as hydraulic gradient and will speed the fall. Statistics shows that most of the man-made karst collapse are induced by pumping, so studying the mechanism of Karst collapse induced by pumping will provide theoretical base for the prediction and precaution of collapse. By theoretically studying the initial condition for the forming and expanding of a soil cavity, Spalling step by step the essential mechanism of Karst collapse induced by pumping is put forward. The catastrophe model for the collapse induced by pumping is set up to predict the fall probability of a cavity roof, and the criterion for the collapse is determined. Simultaneously, Karst collapse induced by pumping is predicted with manmade neural network theory. Finally, the appropriate precaution measurements for the collapse induced by pumping are provided. The creative opinions of the paper is following: The initial condition of forming a soil cavity is put forwarded as formula (4-1-5), (4-1-24),(4-1-25) and (4-1-27); which provide theoretical base for foreclosing the formation of a soil cavity and defending collapse. Spaliing step by step as the essential mechanism of Karst collapse induced by pumping is put forward. The spaliing force is defined as formula (4-2-15). The condition for the expanding of a soil cavity is that spaliing force is greater than tensile strength of soil. The stability of a soil cavity is first studied with catastrophe theory. It is concluded that the process of development up to ground collapse of a small cavity is continuous, however, the process of a big cavity is catastrophic. It is feasibility that the Karst collapse be predicted with manmade neural network theory as a new way.

同事关系质量及其作用机制

Although the research into coworker relationship quality has been recognized one of key factors related to organization performance, and has been thought a new trend in organization behavior research with the flatting of organization structure and complication of task assignment, there is relatively little empirical research on the mechanism between coworkers’ interaction, contraring to the fruitful results on member exchange research based on social network theory, say nothing of the influence of cultural differences such as GUANXI. This research developed the scale for the assessment of Coworker Relationship Quality by literature review, deep interview, and questionnaires, compared the predictable ability of Coworker Relationship Quality (CRQ) scale and Coworker Exchange (CWX) scale on employees’ work attitudes and behaviors. Finally, the mediating effect of Coworker Relationship Quality between employees’ similarities on personality and their work attitudes and behaviors was investigated. Following are main results. Firstly, we found that the interpersonal communication, trust, and mutual support are the key factors of coworker relationship quality, which is similar to the result getting from western samples. But Chinese people are more GUANXI ORIENTATION, means they want to build longtime relationship with others, not only when they are coworkers, but also when one of them left the organization. Secondly, though the core meaning of CRQ and CWX are same, their predictable ability on organization outcomes is different. CRQ is more powerful than CWX, especially on turnover intention. The result showed that after controlling the effect of demographic variables and CRQ, CWX cannot predict turnover intention significantly, but CRQ can still predict turnover intention significantly after controlling demographic variables and CWX. Thirdly, the partial mediating effect of CRQ between positive affectivity similarity and organizational citizenship behavior, coworker satisfaction, organizational commitment and turnover intention are validated, but we did not find the mediating effect of CRQ between demographic variable similarity and workers’ attitudes and behaviors. The Similarity Attraction Paradigm, Social Identity Theory, and Self Category Theory were supported.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.