A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on thek-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process isk-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

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The occurrence of hydrophobic organic compounds (HOCs) in the soil has become a highly significant environmental issue. This problem has been exacerbated by the strong sorption of HOCs to the soils, which makes them unavailable for most remediation processes. More and more works show that surfactant-enhanced biological technologies offer a great potential to clear up HOCs-contaminated soils. This article is a critical review of HOCs removal from soils using Tween 80 (one of the mostly used nonionic surfactants) aided biological remediation technologies. The review begins with a discussion of the fundamentals of Tween 80-enhanced desorption of HOCs from contaminated soils, with special emphasis on the biotoxicity of Tween 80. Successful results obtained by Tween 80-enhanced microbial degradation and phytoremediation are documented and discussed in section 3 and section 4, respectively. Results show Tween 80-enhanced biotechnologies are promising for treating HOCs-contaminated soils. However, considering the fact that most of these scientific studies have only been conducted at the laboratory-scale, many improvements are required before these technologies can be scaled up to the full-scale level. Moreover, further research on mechanisms related to the interaction of Tween 80 with degrading microorganisms and the plants is in high demand.

Hunting for an effective medicine for brain stroke has been a medical task in neuroscience for decades. The present research showed that the lyophilized Powder of Catalpol and Puerarin (C-P) in all the tested doses (65.4 mg/kg, 32.7 mg/kg, 16.4 mg/kg) significantly reduced the neurological deficiency, infarct volume and apoptotic cells in ischemic/reperfusion (I/R) rats. It also promoted astrocyte processes and prolonged neuron axons in infarct area. Further, it decreased MDA, NO, NF-κB/p65, TNF-α, IL-1β and IL-6 and enhanced the EPOR and GAF-43. 65.4 mg/kg and 32.7 mg/kg C-P could up-regulated EPO and VEGF significantly. In vitro, 49 μg/mL and 24.5 μg/mL C-P decreased the leakage of sodium fluorescein and increased the activity of γ-GTP. Additionally, it increased SOD and decreased MDA, NO, and LDH and decreased NF-κB/p65, TNF-α, IL-1β and IL-6 and unregulated EPO, EPOR, VEGF, and GAP-43. Only the dose of 49 μg/mL increased TEER and Claudin-5 and turned the typically damaged morphologies of neurons, astrocytes and endothelium into a favorable trend. These data imply that C-P improved the recovery of neurological deficiency in motor, sense, balance and reflex, and protected the whole NVU by anti-oxidative stress, anti-inflammation and up-regulating some protective factors. This research provides a candidate medicine for brain stroke and, at the same time, a pattern for drug study targeting NVU in vitro.

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Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

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