Virtual machine-based task scheduling algorithm in a cloud computing environment

Virtualization technology has been widely used to virtualize single server into multiple servers, which not only creates an operating environment for a virtual machine-based cloud computing platform but also potentially improves its efficiency. Currently, most task scheduling-based algorithms used in cloud computing environments are slow to convergence or easily fall into a local optimum. This paper introduces a Greedy Particle Swarm Optimization (G&PSO) based algorithm to solve the task scheduling problem. It uses a greedy algorithm to quickly solve the initial particle value of a particle swarm optimization algorithm derived from a virtual machine-based cloud platform. The archived experimental results show that the algorithm exhibits better performance such as a faster convergence rate, stronger local and global search capabilities, and a more balanced workload on each virtual machine. Therefore, the G&PSO algorithm demonstrates improved virtual machine efficiency and resource utilization compared with the traditional particle swarm optimization algorithm

Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives

Idealizing Tauc Plot for Accurate Bandgap Determination of Semiconductor with UV-Vis: A Case Study for Cubic Boron Arsenide

The Tauc plot method is widely used to determine the bandgap of semiconductors via UV-visible optical spectroscopy due to its simplicity and perceived accuracy. However, the actual Tauc plot often exhibits significant baseline absorption below the expected bandgap, leading to discrepancies in the calculated bandgap depending on whether the linear fit is extrapolated to zero or non-zero baseline. In this study, we show that both extrapolation methods can produce significant errors by simulating Tauc plots with varying levels of baseline absorption. To address this issue, we propose a new method that involves idealizing the absorption spectrum by removing its baseline before constructing the Tauc plot. Experimental verification of this method using a gallium phosphide (GaP) wafer with intentionally introduced baseline absorptions shows promising results. Furthermore, we apply this new method to cubic boron arsenide (c-BAs) and resolve discrepancies in c-BAs bandgap values reported by different groups, obtaining a converging bandgap of 1.835 eV based on both previous and new transmission spectra. The method is applicable to both indirect and direct bandgap semiconductors, regardless of whether the absorption spectrum is measured via transmission or diffuse reflectance, will become essential to obtain accurate values of their bandgaps

A 130nm 1Mb Embedded Phase Change Memory with 500kb/s Single Channel Write Throughput

Abstract A 130 nm 1Mb embedded phase change memory (PCM) has been achieved, requiring only three additional masks for phase change storage element, featuring 500 kb/s single channel write throughput and > 10 8 endurance. The prepare process has been optimized to reduce the cost and power. An 80 nm heat electrode has been prepared with 130 nm process. The optimal Read/Write circuit module is designed to realize the load/store function for PCM. The critical operation parameter is Reset/70 ns/2.5 mA and Set/1500 ns/1 mA, which means that the signal channel write throughput arrives to 500 kb/s

RESET Current Reduction for Phase Change Memory Based on Standard 0.13-μm CMOS Technology

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Cloning, distribution, and effects of growth regulation of MC3R and MC4R in red crucian carp (Carassius auratus red var.)

BackgroundMelanocortin-3 and -4 receptors (MC3R and MC4R), G protein-coupled receptors, play vital roles in the regulation of energy homeostasis. To understand the functions of mc3r and mc4r in the energy homeostasis of red crucian carp (Carassius auratus red var., RCC), we cloned mc3r and mc4r, analyzed the tissue expression and localization of the genes, and investigated the effects of knockout of mc3r (mc3r+/-) and mc4r (mc4r+/-) in RCC. ResultsThe full-length cDNAs of RCC mc3r and mc4r were 1459 base pairs (bp) and 1894 bp, respectively. qRT-PCR indicated that mc3r and mc4r were profusely expressed in the brain, but lower expressed in the periphery tissues. ISH revealed that mc3r and mc4r were located in NPP, NPO, NAPv, NSC, NAT, NRL, NLTl, and NLTp of the brain, suggesting that mc3r and mc4r might regulate many physiological and behavioral aspects in RCC. To further verify the roles of mc3r and mc4r in energy homeostasis, the mc3r+/- and mc4r+/- fish were obtained by the CRISPR/Cas9 system. The average body weights, total lengths, body depths, and food intake of mc4r+/- fish were significantly higher than those of mc3r+/- and the normal wild-type (WT) fish, but there was no difference between the mc3r+/- and WT fish, indicating that the RCC phenotype and food intake were mainly influenced by mc4r but not mc3r. Interestingly, mc4r+/- fish displayed more visceral fat mass than mc3r+/- and WT fish, and mc3r+/- fish also exhibited slightly more visceral fat mass compared to WT. RNA-seq of the liver and muscle revealed that a large number of differentially expressed genes (DEGs) differed in WT vs. mc3r+/-, WT vs. mc4r+/-, and mc3r+/- vs. mc4r+/-, mainly related to lipid, glucose, and energy metabolism. The KEGG enrichment analysis revealed that DEGs were mainly enriched in pathways such as steroid biosynthesis, fatty acid metabolism, fatty acid biosynthesis, glycolysis/gluconeogenesis, wnt signaling pathway, PPAR signaling pathway, and MAPK signaling pathway, thereby affecting lipid accumulation and growth. ConclusionIn conclusion, these results will assist in the further investigation of the molecular mechanisms in which MC3R and MC4R were involved in the regulation of energy homeostasis in fish

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S