Gender, Family, Socioeconomic Status, and Work Values in China

Abstract This study examines gender differences in work values i

The Correlation between Logistics Industry and Other Industries: An Evaluation of the Empirical Evidence from China

As an emerging producer services industry, the logistics industry is the third-largest industry in China. The rapid development of the logistics industry accelerates the optimization and adjustment of the industrial structure, advances the transformation of economic growth, and thus promotes a rapid economic development. It is essential to understand the correlation between the logistics industry and other industries, for the purpose of facilitating other industries by logistics industry. Based on the current status of the logistics industry and the general industrial structure, several concerns about the development of the logistics industry and other industries have been addressed in this paper. A qualitative analysis between the logistics industry and industrial structure has been presented. Furthermore, the development of the logistics industry was quantified with the regional entropy coefficient. A linear regression model of the regional entropy coefficient and other industries data was established to analyze the correlation between logistics and the industrial structure. Then, the gray correlation was analyzed to further verify the correlation degree. The correlation degrees between logistics and industries in Pizhou were (sorted from high to low): primary industry, tertiary industry, and secondary industry. Combining development issues and the obtained correlation, several feasible suggestions are presented that promote the logistics industry on a county scale. Keywords: Logistics Industry, Industrial Structure, Linear Regression, Gray Correlatio

B. Besharati Multi-Objective Single Product Robust Optimization: An Integrated Design and Marketing Approach

We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i

Effect of salinity on the growth performance, osmolarity and metabolism-related gene expression in white shrimp Litopenaeus vannamei

An 8-week feeding trial was conducted to study the effect of long-term low-salinity stress on the growth performance, and expression of osmolarity and metabolism-related genes (Na+-K+-ATPase α-subunit and in gills, trypsin and chymotypsin in hepatopancreas) in white shrimp (L. vannamei). Four groups of the white shrimp (mean initial weight, 0.31 ± 0.02 g) were cultivated at salinity of 2, 10, 20 and 30 psu for 8-week. All treatments were conducted in triplicate of 40 each. The results indicated that shrimps reared at salinity 20 psu and 30 psu were significantly higher in final weight, weight gain and specific growth ratio than the other treatments (p < 0.05 when compared to 2 and 10), and those reared at salinity 2 psu were significantly lower than the other treatments in growth performance and survival (p < 0.05 when compared to other treatments). Quantitative real-time PCR (qRT-PCR) results indicated that Na+-K+-ATPase α-subunit and carbonic anhydrase mRNA levels at salinity 2 psu and 10 psu were increased significantly 1.79-, 1.65-fold and 3.22-, 2.31-fold respectively according to salinity 20 psu, chymotrypsin and trypsin mRNA level at salinity 10 psu and 2 psu decreased significantly 15%, 36% and 72%, 45% respectively according to salinity 30 psu. In conclusion, low salinity could, to some extent, reduce growth performance and survival significantly, and influence transcript levels of Na+-K+-ATPase α-subunit, carbonic anhydrase in gills and chymotrypsin, trypsin in hepatopancreas

B. Besharati Multi-Objective Single Product Robust Optimization: An Integrated Design and Marketing Approach

We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i