Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by Hui-Jun Yang
Total Records ( 2 ) for Hui-Jun Yang
  Jin Niu , Yong-Jian Liu , Li-Xia Tian , Kang-Sen Mai , Qi-Cun Zhou , Hui-Jun Yang and Chao-Xia Ye
  This experiment was conducted to evaluate the effects of dietary lipid sources on growth, survival and body composition of 40 day post hatch larval grouper, Epinephelus coioides. Fish were fed fish meal and protein hydrolysate based diets for 32 days with either 100% maize oil or 100% fish oil in triplicate from 40-day after hatching to slaughter size (fish weight: 0.32 g to 11 g). Final body wet weight (FBW: 11.8±0.7 and 11.1±0.1, respectively), weight gain (WG: 3556±251 and 3360±189, respectively), specific growth rate (SGR: 11.2±0.2 and 11.1±0.2, respectively) and survival rate (80±5 and 79±4, respectively) were not significantly affected by dietary lipid sources (P>0.05). The effect of different oil sources on the composition of tissues was significant only for dorsal muscle lipid. In dorsal muscle, lipid content was significantly higher in fish oil group. The fatty acids composition of the muscle lipids well reflected the fatty acids composition of the experimental diets. The growth performance showed that a balance is required between growth-promoting essential fatty acids (EFA) qualities of dietary n-3 highly unsaturated fatty acid (n-3 HUFA) and their potentially growth-inhibiting (pro-oxidant) qualities. Results indicated when EFA of the diet are sufficient for the development of postlarvae, there is no difference whether use fish or maize oil in the formulated diets, 2.87% n-3 HUFA is sufficient for grouper postlarvae development and more n-3 HUFA are not necessarily beneficial to fish performance.
  Hui-jun Yang , Dong-jian He , Shao-Hua Jiang and Hua Wang
  Expensive computation, low efficiency and inaccuracy remain a long-standing challenge in extraction of fruit shape from massive background points. Towards these objectives, an improved K-neighborhood adaptive subdivision algorithm is proposed to simplify point clouds for reduction of calculation; an MLS-based local surface fitting is employed for more accurate geometric attributes and a Geometric Similarity-based Augmenter (GSA) recursive process is used for rapid extraction of apple shape. As a result, the optimal subdivision parameters help to reduce the number of apple cells to 3.0% of the original points, a 25% down than the K (4%) neighborhood, the GSA extraction algorithm reduce the number of extracted pear cells by 49.5%.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility