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Recently, the novel SNP rs12704290 in GRM3 was identified in a genome-wide association study on schizophrenia susceptibility. Our study was to investigate the association of 29 selected SNPs (including rs12704290) with schizophrenia and to evaluate any possible relationship between them and cognition related to schizophrenia. The SNPs were analyzed in 1115 unrelated schizophrenic patients and 2289 healthy controls. The results showed significant associations between these SNPs and schizophrenia as well as with changes in cognition. [ABSTRACT FROM AUTHOR]

Abstract: Enhanced index tracking is a popular strategy in portfolio management that focuses on adding reliable value relative to the index on the basis of mimicking the behavior of the benchmark index. In this paper, we propose a multi-objective optimization scheme for the enhanced index tracking problem, which provides the framework of defining the objectives as both maximizing the degree of beating the benchmark index and minimizing the accumulated error of underperforming the benchmark. Transaction costs are limited in the constraints. An immunity-based multi-objective optimization algorithm is presented to search for the solution of the enhanced index tracking problem. Treatment of infeasibility and solution selection are also presented. Our proposed approach is implemented to five data sets drawn from major world markets. The computational results compared with other published results show that our method has superior performance. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a hybrid methodology that exploits strengths of wavelet analysis and support vector machine model in forecasting time series, and deals with the application of proposed methodology in manufacturing time series forecasting. This method is characteristic of the preprocessing of sample data using wavelet transformation for forecast, i.e., the data sequence of evolvement of share of some sectors in manufacturing is first mapped into several time-frequency domains, and then a support vector machine is established for each domain. The final forecasting results are the algebraic sums of all the forecasted components obtained by respective support vector machine models corresponding to different time-frequency domains. Nevertheless, one of disadvantages of the method is dilemma of selection of values of parameters in support vector machine because the way of selecting values for the parameters will affect the generalization performance remarkably. In this paper, chaos optimization is applied to accomplish selection of values of parameters. Results of experiments based on gross values of textile product in Japan suggest that this hybrid method can both achieve higher accuracy in manufacturing forecasting. [Copyright &y& Elsevier]

Abstract: In this paper, matched synchronous daily trading data, the cointegration theory and the Permanent-transitory (PT) model are employed to analyze the price discovery mechanism and evolvement of the A and the B shares in the Shanghai Stock Exchange and the Shenzhen Stock Exchange markets. The results show that the process of enlarging the scope of investor for the B shares have much improved informational flow between the A shares and the B shares in the Shanghai Stock Exchange than that of the Shenzhen Stock Exchange. At the same time, the results also show that the A shares play a dominant role in price discovery in China''s emerging stock markets. Seven hypotheses were formulated and analyzed. Evidence supporting the domestic investors'' information advantage hypothesis, market segmentation hypothesis, trading cost hypothesis, trading volume ratio hypothesis and negotiable volume ratio hypothesis is found in Shanghai Stock Exchange market. Evidence supporting the investor structure hypothesis, domestic investors'' information advantage hypothesis and trading cost hypothesis is found in the Shenzhen Stock Exchange market. [Copyright &y& Elsevier]

Abstract: This paper addresses a single machine scheduling problem in which the actual job processing times are determined by resource allocation function, its position in a sequence and a rate-modifying activity simultaneously. We discuss two objective functions with two resource allocation functions under the consideration of a rate-modifying activity. We show that the problems are solvable in time for a linear resource allocation function and are solvable in time for a convex resource allocation function. [Copyright &y& Elsevier]

Abstract: In this paper we consider the single machine scheduling problems with exponential sum-of-logarithm-processing-times based learning effect. By the exponential sum-of-logarithm-processing-times based learning effect, we mean that the processing time of a job is defined by an exponent function of the sum of the logarithm of the processing times of the jobs already processed. We consider the following objective functions: the makespan, the total completion time, the sum of the quadratic job completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions. [Copyright &y& Elsevier]

Abstract: This paper introduces a new problem called the capacitated plant location problem with customer and supplier matching (CLCSM). The product distribution from plants to customers and the material supply from suppliers to plants are considered together. We merge a distribution trip and a supply trip into one triangular trip for saving allocation cost. Vehicles from plants visit a customer and a supplier for each trip. We provide a heuristic solution procedure based on Lagrangian relaxation. Computational results indicate that the proposed heuristic solution procedure is shown to be efficient yielding optimal or near-optimal solutions for randomly generated instances. [Copyright &y& Elsevier]