Jimei University

About: Jimei University is a education organization based out in Amoy, China. It is known for research contribution in the topics: Computer science & Population. The organization has 4645 authors who have published 4293 publications receiving 42239 citations. The organization is also known as: Jíměi Dàxué.

Partial replacement of fish meal with peanut meal in practical diets for the Pacific white shrimp, Litopenaeus vannamei

Abstract: In this study, we replaced fish meal with peanut meal (PM) in isonitrogenous and isolipidic diets for Pacific white shrimp at inclusion levels of 0, 70, 140, 210, 280 and 350 g kg−1. The diets were hand‐fed to three independent groups of shrimp three times a day over a 6‐week period. Shrimp fed PM diets at a level of 280 g kg−1 or higher had lower per cent weight gain compared with those fed the basal diet, whereas shrimp fed PM diets at 140 g kg−1 or higher had a lower feed utilization and protein efficiency ratio compared with shrimp fed the basal diet. The feeding rate in shrimp fed PM diets at 350 g kg−1 and the survival and protease activity in shrimp fed PM diets at 210 g kg−1 or higher were lower than that in shrimp fed the basal diet. Diets containing 280 g kg−1 or higher of PM caused an increase in the whole‐body moisture content of the shrimp, but decreased whole‐body protein and ash contents compared with the basal diet. Nutrient digestibility was lower or tended to be lower in shrimp fed a PM diet compared with those fed the basal diet. The activities of peroxidase and acid and alkaline phosphatases in plasma decreased with increasing levels of PM inclusion up to 210 g kg−1. Superoxide dismutase activity decreased at dietary PM levels of 280 g kg−1 or higher. Aflatoxin B1 residue in the muscle was not affected by any of the treatments and remained low. The data suggest that up to 140 g kg−1 of PM could be included in practical diets for Pacific white shrimp.

Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies

Abstract: Cloud computing is an efficient technology to serve the requirement of big data applications. Minimizing the makespan of the cloud system while increasing resource utilization is important to reduce costs. In this case, task scheduling is a challenging task to meet the requirement because it requires both effectiveness and efficiency. This article proposes a task scheduler with several discrete variants of the particle swarm optimization (PSO) algorithm for task scheduling in cloud computing. In order to evaluate the performance, these approaches were compared with three well-known heuristic algorithms on task scheduling problems. Experiment results demonstrate the efficiency and effectiveness of the proposed approaches. For the proposed PSO-based scheduler, an appropriate choice is to use the logarithm decreasing strategy to provide an optimal scheduling scheme. The average makespan of the proposed PSO-based scheduler that adopts logarithm decreasing strategy is reduced by 19.12%, 21.42% and 15.14% relative to the compared gravitational search algorithm, artificial bee colony algorithm and dragonfly algorithm respectively.