Performance Assessment of a Novel Pyramid Photobioreactor for Cultivation of Microalgae Using External and Internal Light Sources

Novi piramidalni fotobioreaktor, modificirana verzija pločastog fotobioreaktora, koji se sastoji od četiri zasebne komore jednakih volumena, upotrijebljen je za uzgoj alge Chlorella sp., nazastupljenije mikroalge u Perzijskom zaljevu. U istraživanju su korištena dva izvora svjetlosti: vanjsko, bijelo svjetlo gustoće toka fotona od 108 osvjetljavalo je sve četiri komore, dok je unutarnje svjetlo iste gustoće toka fotona te različitih valnih duljina (crveno, bijelo i plavo) osvjetljavalo tri od četiri komore. Praćeni su slijedeći parametri: utjecaj izvora svjetlosti na proizvodnju klorofila a, maksimalna specifična brzina rasta (gmax), brzina proizvodnje biomase (rp) i fotosintetska učinkovitost. Rezultati pokazuju da je alga proizvela najviše klorofila a nakon izlaganja crvenom LED svjetlu. Najveće vrijednosti rp i te najveća fotosintetska učinkovitost postignute su pod utjecajem bijelog svjetla.The cultivation of Chlorella sp., the most abundant microalga in the Persian Gulf, took place in a novel pyramid photobioreactor (PBR), a modified version of plate PBR, consisting of four completely separate equal-volume chambers. In this study we used two light sources incident in each chamber: light-emitting diode (LED) at various wavelengths (red, white and blue) of 108 μmol/(m2·s) photosynthetic photon flux density as internal lighting, and the same photon flux density for external white lighting. PBR served to study the effects of light sources on chlorophyll a production, maximum specific growth rate (μmax), biomass productivity rate (rp) and photon performance. The results showed that the highest chlorophyll a production was obtained under red LED illumination. The highest values for rp, μmax and photon performance were obtained under white light

Resource discovery for distributed computing systems: A comprehensive survey

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category

Partial Offloading with Stable Equilibrium in Fog-cloud Environments using Replicator Dynamics of Evolutionary Game Theory

In this research, we propose a solution for partial offloading based on the replicator equation of evolutionary game theory. The ultimate goal is for users to be in a stable equilibrium based on their priorities in terms of energy consumption, latency, and computational cost so that no user is willing to change his/her strategy. The results of solving the replicator equation followed by statistical analysis show that the proposed method has a remarkable performance improvement compared to the state-of-the-art methods. Our method not only shows a better average based on the weighted combination of energy consumption, latency, and cost but also has a lower variance compared to the full local execution and the full offloading methods. Our results also show that when the number of users is very large, the system can reach equilibrium with the least amount of information exchange among players

Partial Offloading with Stable Equilibrium in Fog-cloud Environments using Replicator Dynamics of Evolutionary Game Theory

In this research, we propose a solution for partial offloading based on the replicator equation of evolutionary game theory. The ultimate goal is for users to be in a stable equilibrium based on their priorities in terms of energy consumption, latency, and computational cost so that no user is willing to change his/her strategy. The results of solving the replicator equation followed by statistical analysis show that the proposed method has a remarkable performance improvement compared to the state-of-the-art methods. Our method not only shows a better average based on the weighted combination of energy consumption, latency, and cost but also has a lower variance compared to the full local execution and the full offloading methods. Our results also show that when the number of users is very large, the system can reach equilibrium with the least amount of information exchange among players