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Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts' knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system.

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A self-powered biosensor for monitoring the maximal lactate steady state (MLSS) during exercise has been developed for intelligently assisting training system. It has been presented to create poly (vinylidene fluoride) (PVDF)/Tetrapod-shaped ZnO (T-ZnO)/enzyme-modified nanocomposite film through an efficient and cost-effective fabrication process. This sensor can be readily attached to the skin surface of the tester. Due to the piezoelectric surface coupling effect, this biosensor can monitor/sense and analyze physical information in real-time under the non-invasive condition and work independently without any battery. By actively outputting piezoelectric signals, it can quickly and sensitively detect body movements (changes of joint angle, frequency relative humidity during exercise) and physiological information (changes of lactate concentration in sweat). A practical application has been demonstrated by an excellent professional speed skater (male). The purpose of this study is to increase the efficiency of MLSS evaluation, promote the development of piezoelectric surface coupling effect and motion monitoring application, develop an intelligently assisting training system, which has opened up a new direction for human motion monitoring.

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Neonicotinoid insecticides are one of the most important commercial insecticides used worldwide. The potential toxicity of the residues present in environment to humans has received considerable attention. In this study, a novel Ochrobactrum sp. strain D-12 capable of using acetamiprid as the sole carbon source as well as energy, nitrogen source for growth was isolated and identified from polluted agricultural soil. Strain D-12 was able to completely degrade acetamiprid with initial concentrations of 0–3000 mg·L−1 within 48 h. Haldane inhibition model was used to fit the special degradation rate at different initial concentrations, and the parameters q max, K s and K i were determined to be 0.6394 (6 h)−1, 50.96 mg·L−1 and 1879 mg·L−1, respectively. The strain was found highly effective in degrading acetamiprid over a wide range of temperatures (25–35°C) and pH (6–8). The effects of co-substrates on the degradation efficiency of acetamiprid were investigated. The results indicated that exogenously supplied glucose and ammonium chloride could slightly enhance the biodegradation efficiency, but even more addition of glucose or ammonium chloride delayed the biodegradation. In addition, one metabolic intermediate identified as N-methyl-(6-chloro-3-pyridyl)methylamine formed during the degradation of acetamiprid mediated by strain D-12 was captured by LC-MS, allowing a degradation pathway for acetamiprid to be proposed. This study suggests the bacterium could be a promising candidate for remediation of environments affected by acetamiprid.