Energy efficiency in cloud computing based on mixture power spectral density prediction

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.

     

Deep neural network-based fusion model for emotion recognition using visual data

The Journal of Supercomputing - In this study, we present a fusion model for emotion recognition based on visual data. The proposed model uses video information as its input and generates emotion...     

Noise-adaptive synthetic oversampling technique

Applied Intelligence - In the field of supervised learning, the problem of class imbalance is one of the most difficult problems, and has attracted a great deal of research attention in recent...     

A self-learning particle swarm optimizer for global optimization problems

Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.     

Environmental-aware virtual data center network

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.     

Bead-based polymerase chain reaction on a microchip

We present a bead-based approach to microfluidic polymerase chain reaction (PCR), enabling fluorescent detection and sample conditioning in a single microchamber. Bead-based PCR, while not extensively investigated in microchip format, has been used in a variety of bioanalytical applications in recent years. We leverage the ability of bead-based PCR to accumulate fluorescent labels following DNA amplification to explore a novel DNA detection scheme on a microchip. The microchip uses an integrated microheater and temperature sensor for rapid control of thermal cycling temperatures, while the sample is held in a microchamber fabricated from (poly)dimethylsiloxane and coated with Parylene. The effects of key bead-based PCR parameters, including annealing temperature and concentration of microbeads in the reaction mixture, are studied to achieve optimized device sensitivity and detection time. The device is capable of detecting a synthetically prepared section of the Bordetella pertussis genome in as few as 10 temperature cycles with times as short as 15?min. We then demonstrate the use of the procedure in an integrated device; capturing, amplifying, detecting, and purifying template DNA in a single microfluidic chamber. These results show that this method is an effective method of DNA detection which is easily integrated in a microfluidic device to perform additional steps such as sample pre-conditioning.     

Modelling of the mist formation in a segmented grinding wheel system

It has been found that using a segmented grinding wheel with a fluid chamber can significantly minimise the quantity of coolant while improving the ground surface integrity. The present investigation aims to explore the fluid flow mechanism in such a wheel system. To this end, the Weber theory for Newtonian jet instability was applied to quantitatively determine the contribution of coolant flow rate to mist and ligament modes. A semi-analytical model was then developed to predict the mist flow rate by taking into account both the grinding parameters and fluid properties. It was shown that the model prediction was in good agreement with experimental measurements. Because of the comprehensive integration of variables in the formulation, the model provides a good fundamental understanding of the mist formation and offers a practical guideline for the selection and use coolant in minimising the mist flow rate.     

Fluctuating NMDA Receptor Subunit Levels in Perirhinal Cortex Relate to Their Dynamic Roles in Object Memory Destabilization and Reconsolidation

Reminder cues can destabilize consolidated memories, rendering them modifiable before they return to a stable state through the process of reconsolidation. Older and stronger memories resist this process and require the presentation of reminders along with salient novel information in order to destabilize. Previously, we demonstrated in rats that novelty-induced object memory destabilization requires acetylcholine (ACh) activity at M₁ muscarinic receptors. Other research predominantly has focused on glutamate, which modulates fear memory destabilization and reconsolidation through GluN2B- and GluN2A-containing NMDARs, respectively. In the current study, we demonstrate the same dissociable roles of GluN2B- and N2A-containing NMDARs in perirhinal cortex (PRh) for object memory destabilization and reconsolidation when boundary conditions are absent. However, neither GluN2 receptor subtype was required for novelty-induced destabilization of remote, resistant memories. Furthermore, GluN2B and GluN2A subunit proteins were upregulated selectively in PRh 24 h after learning, but returned to baseline by 48 h, suggesting that NMDARs, unlike muscarinic receptors, have only a temporary role in object memory destabilization. Indeed, activation of M₁ receptors in PRh at the time of reactivation effectively destabilized remote memories despite inhibition of GluN2B-containing NMDARs. These findings suggest that cholinergic activity at M₁ receptors overrides boundary conditions to destabilize resistant memories when other established mechanisms are insufficient.     

Choline Acetyltransferase Induces the Functional Regeneration of the Salivary Gland in Aging SAMP1/Kl -/- Mice

Salivary gland dysfunction induces salivary flow reduction and a dry mouth, and commonly involves oral dysfunction, tooth structure deterioration, and infection through reduced salivation. This study aimed to investigate the impact of aging on the salivary gland by a metabolomics approach in an extensive aging mouse model, SAMP1/Klotho -/- mice. We found that the salivary secretion of SAMP1/Klotho -/- mice was dramatically decreased compared with that of SAMP1/Klotho WT (+/+) mice. Metabolomics profiling analysis showed that the level of acetylcholine was significantly decreased in SAMP1/Klotho -/- mice, although the corresponding levels of acetylcholine precursors, acetyl-CoA and choline, increased. Interestingly, the mRNA and protein expression of choline acetyltransferase (ChAT), which is responsible for catalyzing acetylcholine synthesis, was significantly decreased in SAMP1/Klotho -/- mice. The overexpression of ChAT induced the expression of salivary gland functional markers (α–amylase, ZO-1, and Aqua5) in primary cultured salivary gland cells from SAMP1/Klotho +/+ and -/- mice. In an in vivo study, adeno-associated virus (AAV)-ChAT transduction significantly increased saliva secretion compared with the control in SAMP1/Klotho -/- mice. These results suggest that the dysfunction in acetylcholine biosynthesis induced by ChAT reduction may cause impaired salivary gland function     

One-Step Generation of Multiple Gene-Edited Pigs by Electroporation of the CRISPR/Cas9 System into Zygotes to Reduce Xenoantigen Biosynthesis

Xenoantigens cause hyperacute rejection and limit the success of interspecific xenografts. Therefore, genes involved in xenoantigen biosynthesis, such as GGTA1, CMAH, and B4GALNT2, are key targets to improve the outcomes of xenotransplantation. In this study, we introduced a CRISPR/Cas9 system simultaneously targeting GGTA1, CMAH, and B4GALNT2 into in vitro-fertilized zygotes using electroporation for the one-step generation of multiple gene-edited pigs without xenoantigens. First, we optimized the combination of guide RNAs (gRNAs) targeting GGTA1 and CMAH with respect to gene editing efficiency in zygotes, and transferred electroporated embryos with the optimized gRNAs and Cas9 into recipient gilts. Next, we optimized the Cas9 protein concentration with respect to the gene editing efficiency when GGTA1, CMAH, and B4GALNT2 were targeted simultaneously, and generated gene-edited pigs using the optimized conditions. We achieved the one-step generation of GGTA1/CMAH double-edited pigs and GGTA1/CMAH/B4GALNT2 triple-edited pigs. Immunohistological analyses demonstrated the downregulation of xenoantigens; however, these multiple gene-edited pigs were genetic mosaics that failed to knock out some xenoantigens. Although mosaicism should be resolved, the electroporation technique could become a primary method for the one-step generation of multiple gene modifications in pigs aimed at improving pig-to-human xenotransplantation.