A load-balancing mechanism for distributed SDN control plane using response time.

Software-Defined Networking (SDN) has become a popular paradigm for managing large-scale networks including cloud servers and data centers because of its advantages of centralized management and programmability. The issues of scalability and reliability that a single centralized controller suffers makes distributed controller architectures emerge. One key limitation of distributed controllers is the statically configured switch-controller mapping, easily causing uneven load distribution among controllers. Previous works have proposed load-balancing methods with switch migration to address this issue. However, the higher-load controller is always directly considered as the overloaded controller that need to shift its load to other controllers, even if it has no response time delay. The pursuit of absolute load-balancing effect can also result in frequent network delays and service interruptions. Additionally, if there are several overloaded controllers, just one controller with the maximum load can be addressed within a single load-balancing operation, reducing load-balancing efficiency. To address these problems, we propose SMCLBRT, a load-balancing strategy of multiple SDN controllers based on response time, considering the changing features of real-time response times versus controller loads. By selecting the appropriate response time threshold and dealing with multiple overloading controllers simultaneously, it can well solve load-balancing problem in SDN control plane with multiple overloaded controllers. Simulation experiments exhibit the effectiveness of our scheme.N/

Box-supervised Instance Segmentation with Level Set Evolution

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset.Comment: 17 page, 4figures, ECCV202

Hepatitis B virus–induced imbalance of inflammatory and antiviral signaling by differential phosphorylation of STAT1 in human monocytes

It is not clear how hepatitis B virus (HBV) modulates host immunity during chronic infection. In addition to the key mediators of inflammatory response in viral infection, monocytes also express a high-level IFN-stimulated gene, CH25H, upon response to IFN-a exerting an antiviral effect. In this study, the mechanism by which HBV manipulates IFN signaling in human monocytes was investigated. We observed that monocytes from chronic hepatitis B patients express lower levels of IFN signaling/stimulated genes and higher levels of inflammatory cytokines compared with healthy donors. HBV induces monocyte production of inflammatory cytokines via TLR2/MyD88/NF-kB signaling and STAT1-Ser727 phosphorylation and inhibits IFN-a–induced stat1, stat2, and ch25h expression through the inhibition of STAT1-Tyr701 phosphorylation and in an IL-10–dependent, partially autocrine manner. Further, we found that enhancement of STAT1 activity with a small molecule (2-NP) rescued HBV-mediated inhibition of IFN signaling and counteracted the induction of inflammatory cytokines. In conclusion, HBV contributes to the monocyte inflammatory response but inhibits their IFN-a/b responsiveness to impair antiviral innate immunity. These effects are mediated via differential phosphorylation of Tyr701 and Ser727 of STAT1

Comparative Analysis of the Complete Mitochondrial Genomes for Development Application

This present research work reports the comparative analysis of the entire nucleotide sequence of mitochondrial genomes of Serranochromis robustus and Buccochromis nototaenia and phylogenetic analyses of their protein-coding genes in order to establish their phylogenetic relationship within Cichlids. The mitochondrial genomes of S. robustus and B. nototaenia are 16,583 and 16,580 base pairs long, respectively, including 13 protein-coding genes (PCGs), 2 ribosomal RNA genes, 22 transfer RNA genes, and one control region (D-loop) which is 888 and 887 base pairs long, respectively, showing the same gene order and identical number of gene or regions with other well-elucidated mitogenomes of Cichlids. However, with exception of cytochrome-c oxidase subunit-1 (COX-1) gene, all the identified PCGs were initiated by ATG-codons. Structurally, 11 tRNA genes in B. nototaenia species and 9 tRNA genes in S. robustus species, folded into typical clover-leaf secondary structure created by the regions of self-complementarity within tRNA. All the 22 tRNA genes in both species lack variable loop. Moreover, 28 genes which include 12-protein-coding genes are encoded on the H-strand and the remaining 9 genes including one protein-coding gene are encoded on the L-strand. Thirteen sequences of concatenated mitochondrial protein-coding genes were aligned using MUSCLE, and the phylogenetic analyses performed using maximum likelihood and Bayesian inference showed that S. robustus and B. nototaenia had a broad phylogenetic relationship. These results may be a useful tool in resolving higher-level relationships in organisms and a useful dataset for studying the evolution of the Cichlidae mitochondrial genome, since Cichlids are well-known model species in the study of evolutionary biology, because of their extreme morphological, biogeographical, parental care behavior for eggs and larvae and phylogenetic diversities

Near-real-time monitoring of global CO₂ emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO₂) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO₂ emissions (−1551 Mt CO₂) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially

A three-dimensional numerical model of borehole heat exchanger heat transfer and fluid flow

Common approaches to the simulation of borehole heat exchangers assume heat transfer within the circulating fluid and grout to be in a quasi-steady state and ignore axial conduction heat transfer. This paper presents a numerical model that is three-dimensional, includes explicit representations of the circulating fluid and other borehole components, and so allows calculation of dynamic behaviours over short and long timescales. The model is formulated using a finite volume approach using multi-block meshes to represent the ground, pipes, fluid and grout in a geometrically correct manner. Validation and verification exercises are presented that use both short timescale data to identify transport delay effects, and long timescale data to examine the modelling of seasonal heat transfer and show the model is capable of predicting outlet temperatures and heat transfer rates accurately. At long timescales borehole heat transfer seems well characterized by the mean fluid and borehole wall temperature if the fluid circulating velocity is reasonably high but at lower flow rates this is not the case. Study of the short timescale dynamics has shown that nonlinearities in the temperature and heat flux profiles are noticeable over the whole velocity range of practical interest. The importance of representing the thermal mass of the grout and the dynamic variations in temperature gradient as well as the fluid transport within the borehole has been highlighted. Implications for simplified modelling approaches are also discussed