National Cheng Kung University

About: National Cheng Kung University is a education organization based out in Tainan City, Taiwan. It is known for research contribution in the topics: Population & Thin film. The organization has 49723 authors who have published 69799 publications receiving 1437420 citations. The organization is also known as: NCKU.

Effect of light supply and carbon source on cell growth and cellular composition of a newly isolated microalga Chlorella vulgaris ESP-31

Abstract: Light supply is one of the most important factors affecting autotrophic growth of microalgae. This study investigated the effect of the type and light intensity of artificial light sources on the cell growth of an indigenous microalga Chlorella vulgaris ESP-31 obtained from southern Taiwan. In addition, a dissolved inorganic carbon source (i.e. sodium bicarbonate) was used to improve the biomass production of strain ESP-31. The results show that a new fluorescent light source (TL5) was effective in indoor cultivation of microalgae. Better overall productivity of 0.029 g dry cell weight/L-d was obtained when using TL5 lamps as the light source with a light intensity of 9 W/m2. A carbon source (sodium bicarbonate) concentration of 1000 mg/L was found to be optimal for the growth of strain ESP-31 in terms of both biomass production and carbon source utilization. Under the optimal growth conditions, the resulting microalgal biomass consisted of 25–30% protein, 6–10% carbohydrate, and 30–40% lipid.

A Hyper-Heuristic Scheduling Algorithm for Cloud

Abstract: Rule-based scheduling algorithms have been widely used on many cloud computing systems because they are simple and easy to implement. However, there is plenty of room to improve the performance of these algorithms, especially by using heuristic scheduling. As such, this paper presents a novel heuristic scheduling algorithm, called hyper-heuristic scheduling algorithm (HHSA), to find better scheduling solutions for cloud computing systems. The diversity detection and improvement detection operators are employed by the proposed algorithm to dynamically determine which low-level heuristic is to be used in finding better candidate solutions. To evaluate the performance of the proposed method, this study compares the proposed method with several state-of-the-art scheduling algorithms, by having all of them implemented on CloudSim (a simulator) and Hadoop (a real system). The results show that HHSA can significantly reduce the makespan of task scheduling compared with the other scheduling algorithms evaluated in this paper, on both CloudSim and Hadoop.