A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Voltage quality improvement in distribution networks containing DERs using UPQC

Single phase distributed energy resources (DERs) can cause voltage rise along distribution feeder and power imbalance among the phases. Usually transformer tap setting are used to mitigate voltage drop along feeders. However this can aggravate the voltage rise problem when DERs are connected. Moreover if the power generation in a phase is more than its load demand, the excess power in that phase will be fed back to the transmission network. In this paper, a unified power quality compensator (UPQC) has been utilized to alleviate the voltage quality excess power circulation problems. Through analysis and simulation results, the mode of operation of UPQC is highlighted. The proposals are validated through extensive digital computer simulation studies using PSCAD and MATLAB.

QUT Library advances the use of open public tools to navigate genetic inventions and builds research networks

Many emerging economies are dangling the patent system to stimulate bio-technological innovations with the ultimate premise that these will improve their economic and social growth. The patent system mandates full disclosure of the patented invention in exchange of a temporary exclusive patent right. Recently, however, patent offices have fallen short of complying with such a mandate, especially for genetic inventions. Most patent offices provide only static information about disclosed patent sequences and even some do not keep track of the sequence listing data in their own database. The successful partnership of QUT Library and Cambia exemplifies advocacy in Open Access, Open Innovation and User Participation. The library extends its services to various departments within the university, builds and encourages research networks to complement skills needed to make a contribution in the real world.

Improved molecular tools for sugar cane biotechnology

Sugar cane is a major source of food and fuel worldwide. Biotechnology has the potential to improve economically-important traits in sugar cane as well as diversify sugar cane beyond traditional applications such as sucrose production. High levels of transgene expression are key to the success of improving crops through biotechnology. Here we describe new molecular tools that both expand and improve gene expression capabilities in sugar cane. We have identified promoters that can be used to drive high levels of gene expression in the leaf and stem of transgenic sugar cane. One of these promoters, derived from the Cestrum yellow leaf curling virus, drives levels of constitutive transgene expression that are significantly higher than those achieved by the historical benchmark maize polyubiquitin-1 (Zm-Ubi1) promoter. A second promoter, the maize phosphonenolpyruvate carboxylate promoter, was found to be a strong, leaf-preferred promoter that enables levels of expression comparable to Zm-Ubi1 in this organ. Transgene expression was increased approximately 50-fold by gene modification, which included optimising the codon usage of the coding sequence to better suit sugar cane. We also describe a novel dual transcriptional enhancer that increased gene expression from different promoters, boosting expression from Zm-Ubi1 over eightfold. These molecular tools will be extremely valuable for the improvement of sugar cane through biotechnology.

Genetic analysis of GRIA2 and GRIA4 genes in migraine

Background Migraine is a brain disorder affecting ∼12% of the Caucasian population. Genes involved in neurological, vascular, and hormonal pathways have all been implicated in predisposing individuals to developing migraine. The migraineur presents with disabling head pain and varying symptoms of nausea, emesis, photophobia, phonophobia, and occasionally visual sensory disturbances. Biochemical and genetic studies have demonstrated dysfunction of neurotransmitters: serotonin, dopamine, and glutamate in migraine susceptibility. Glutamate mediates the transmission of excitatory signals in the mammalian central nervous system that affect normal brain function including cognition, memory and learning. The aim of this study was to investigate polymorphisms in the GRIA2 and GRIA4 genes, which encode subunits of the ionotropic AMPA receptor for association in an Australian Caucasian population. Methods Genotypes for each polymorphism were determined using high resolution melt analysis and the RFLP method. Results Statistical analysis showed no association between migraine and the GRIA2 and GRIA4 polymorphisms investigated. Conclusions Although the results of this study showed no significant association between the tested GRIA gene variants and migraine in our Australian Caucasian population further investigation of other components of the glutamatergic system may help to elucidate if there is a relationship between glutamatergic dysfunction and migraine.

Genetic polymorphisms in miRNAs targeting the estrogen receptor and their effect on breast cancer risk

Breast cancer is the cancer that most commonly affects women worldwide. This type of cancer is genetically complex, but is strongly linked to steroid hormone signalling systems. Because microRNAs act as translational regulators of multiple genes, including the steroid nuclear receptors, single nucleotide polymorphisms (SNPs) in microRNAs genes can have potentially wide-ranging influences on breast cancer development. Thus, this study was conducted to investigate the relationships between six SNPs (rs6977848, rs199981120, rs185641358, rs113054794, rs66461782, and rs12940701) located in four miRNA genes predicted to target the estrogen receptor (miR-148a, miR-221, miR-186, and miR-152) and breast cancer risk in Caucasian Australian women. By using high resolution melt analysis (HRM) and polymerase chain reaction- restriction fragment length polymorphism (PCR-RFLP), 487 samples including 225 controls and 262 cases were genotyped. Analysis of their genotype and allele frequencies indicated that the differences between case and control populations was not significant for rs6977848, rs66461782, and rs12940701 because their p-values are 0.81, 0.93, 0.1 which are all above the threshold value (p=0.05). Our data thus suggests that these SNPs do not affect breast cancer risk in the tested population. In addition, rs199981120, rs185641358, and rs113054794 could not be found in this population, suggesting that these SNPs do not occur in Caucasian Australians.

Elements of Better Assessment for the Improvement of Learning

Building a community of shared practice at the classroom level calls for clarity about the important assessment capabilities and dispositions of teachers, especially when teachers are expected to take a direct focus on learning. In this chapter, we present new ways of thinking about teachers’ assessment literacies, offering a formulation of better assessment for the improvement of learning, including three elements, namely (i) assessment criteria and standards; (ii) the teacher’s professional judgment; and (iii) social moderation. The potential of the first element lies in teachers’ classroom practices that deliberately embed assessment criteria and standards in pedagogy in productive ways. The second element involves the engagement of teachers and students in judgment practice, that develops the understanding that judgment involves more than the application of explicit or stated criteria. More fundamental is the matter of how teachers bring to bear stated features of quality and other intellectual and experiential resources in arriving at judgment. That is to say, they range across and orient to explicit (stated), tacit (unstated) and meta-criteria in judgment making. These insights have direct relevance to teachers’ efforts to develop students’ own evaluative experience, especially as this involves students working with stated features of quality for self-assessment and peer-assessment purposes. Further, practices for social moderation are discussed, giving examples of good practice in moderation, how teachers experience moderation and the potential benefits of various types.

High penetration of rooftop photovoltaic cells in low voltage distribution networks : voltage imbalance and improvement

Installation of domestic rooftop photovoltaic cells (PVs) is increasing due to feed–in tariff and motivation driven by environmental concerns. Even though the increase in the PV installation is gradual, their locations and ratings are often random. Therefore, such single–phase bi–directional power flow caused by the residential customers can have adverse effect on the voltage imbalance of a three–phase distribution network. In this chapter, a voltage imbalance sensitivity analysis and stochastic evaluation are carried out based on the ratings and locations of single–phase grid–connected rooftop PVs in a residential low voltage distribution network. The stochastic evaluation, based on Monte Carlo method, predicts a failure index of non–standard voltage imbalance in the network in presence of PVs. Later, the application of series and parallel custom power devices are investigated to improve voltage imbalance problem in these feeders. In this regard, first, the effectiveness of these two custom power devices is demonstrated vis–à–vis the voltage imbalance reduction in feeders containing rooftop PVs. Their effectiveness is investigated from the installation location and rating points of view. Later, a Monte Carlo based stochastic analysis is utilized to investigate their efficacy for different uncertainties of load and PV rating and location in the network. This is followed by demonstrating the dynamic feasibility and stability issues of applying these devices in the network.

Improving the performance of nutrition screening through a series of quality improvement initiatives

Background Nutrition screening identifies patients at risk of malnutrition to facilitate early nutritional intervention. Studies have reported incompletion and error rates of 30-90% for a range of commonly used screening tools. This study aims to investigate the incompletion and error rates of 3-Minute Nutrition Screening (3-MinNS) and the effect of quality improvement initiatives in improving the overall performance of the screening tool and the referral process for at risk patients. Methods Annual audits were carried out from 2008-2013 on 4467 patients. Value Stream Mapping, Plan-Do-Check-Act cycle and Root Cause Analysis were used in this study to identify gaps and determine the best intervention. The intervention included 1) implementing a nutrition screening protocol, 2) nutrition screening training, 3) nurse empowerment for online dietetics referral of at-risk cases, 4) closed-loop feedback system and 5) removing a component of 3-MinNS that caused the most error without compromising its sensitivity and specificity. Results Nutrition screening error rates were 33% and 31%, with 5% and 8% blank or missing forms, in 2008 and 2009 respectively. For patients at risk of malnutrition, referral to dietetics took up to 7.5 days, with 10% not referred at all. After intervention, the latter decreased to 7% (2010), 4% (2011) and 3% (2012 and 2013), and the mean turnaround time from screening to referral was reduced significantly from 4.3 ± 1.8 days to 0.3 ± 0.4 days (p < 0.001). Error rates were reduced to 25% (2010), 15% (2011), 7% (2012) and 5% (2013) and percentage of blank or missing forms reduced to and remained at 1%. Conclusion Quality improvement initiatives are effective in reducing the incompletion and error rates of nutrition screening, and led to sustainable improvements in the referral process of patients at nutritional risk.

Quantitative genetic parameters for body traits at different ages in a cultured stock of giant freshwater prawn (Macrobrachium rosenbergii) selected for fast growth

The aim of the current study was to estimate heritabilities and correlations for body traits at different ages (Weeks 10 and 18 after stocking) in a giant freshwater prawn (Macrobrachium rosenbergii) population selected for fast growth rate in Vietnam. The dataset consisted of 4650 body records (2432 and 2218 records collected at Weeks 10 and 18, respectively) in the full pedigree comprising a total of 18 387 records. Variance and covariance components were estimated using restricted maximum likelihood fitting a multi-trait animal model. Estimates of heritability for body traits (bodyweight, body length, cephalothorax length, abdominal length, cephalothorax width and abdominal width) were moderate and ranged from 0.06 to 0.11 and from 0.11 to 0.22 at Weeks 10 and 18, respectively. Body-trait heritabilities estimated at Week 10 were not significantly lower than at Week 18. Genetic correlations between body traits within age and genetic correlations for body traits between ages were generally high. Our results suggested that selection for high growth rate in GFP can be undertaken successfully before full market size has been reached.

JPEG image steganalysis improvement via image-to-image variation minimization

In this research, we introduce an approach to enhance the discriminative capability of features by employing image-to-image variation minimization. In order to minimize image-to-image variation, we will estimate the cover image from the stego image by decompressing the stego image, transforming the decompressed image and recompressing back. Since the effect of the embedding operation in an image steganography is actually a noise adding process to the image, applying these three processes will smooth out the noise and hence the estimated cover image can be obtained.

Hybrid genetic algorithm for elimination of severe stress concentration in railhead ends

This paper presents a computational method for eliminating severe stress concentration at the unsupported railhead ends in rail joints through innovative shape optimization of the contact zone, which is complex due to near field nonlinear contact. With a view to minimizing the computational efforts, hybrid genetic algorithm method coupled with parametric finite element has been developed and compared with the traditional genetic algorithm (GA). The shape of railhead top surface where the wheel contacts nonlinearly was optimized using the hybridized GA method. Comparative study of the optimal result and the search efficiency between the traditional and hybrid GA methods has shown that the hybridized GA provides the optimal shape in fewer computational cycles without losing accuracy. The method will be beneficial to solving complex engineering problems involving contact nonlinearity.

A new grouping genetic algorithm for the MapReduce placement problem in cloud computing

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NPcomplete. Thus, in this paper we propose a new grouping genetic algorithm for the mappers/reducers placement problem in cloud computing. Compared with the original one, our grouping genetic algorithm uses an innovative coding scheme and also eliminates the inversion operator which is an essential operator in the original grouping genetic algorithm. The new grouping genetic algorithm is evaluated by experiments and the experimental results show that it is much more efficient than four popular algorithms for the problem, including the original grouping genetic algorithm.