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Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.

Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process. [ABSTRACT FROM AUTHOR]

Objective: To identify the casual mutation of a Chinese pedigree with hypertrophic cardiomyopathy (HCM), and to analyze the genotype-phenotype relationship., Methods: The coding exons of 26 reported disease genes were sequenced by targeted resequencing in the proband and the identified mutation were detected with bi-directional Sanger sequencing in all family members and 307 healthy controls. The genotype-phenotype correlation was analyzed in the family., Results: A missense mutation (c.2191C > T, p. Pro731Ser) in the 20th exon of MYH7 gene was identified. This mutation was absent in 307 healthy controls and predicted to be pathogenic by PolyPhen-HCM. Totally 13 family members carried this mutation, including 10 patients with HCM and 3 asymptomatic mutation carriers. The proband manifested severe congestive heart failure and 8 patients expressed various clinical manifestations of heart failure, including dyspnea, palpitations, chest pain, amaurosis or syncope. Five patients were diagnosed as HCM at the age of 16 or younger. One family member suffered sudden cardiac death., Conclusions: The Pro731Ser of MYH7 gene mutation is a causal and malignant mutation linked with familiar HCM.