A New Duplication Task Scheduling Algorithm in Heterogeneous Distributed Computing Systems

The efficient scheduling algorithm is critical to achieve high performance in parallel and distributed systems. The main objective of task scheduling is to assign the tasks onto the available processors with the aim of producing minimum schedule length and without violating the precedence constraints. So we developed new algorithm called Mean Communication Node with Duplication MCND algorithm to achieve high performance task scheduling. The MCND algorithm has two phases namely, task priority and processor selection. Our algorithm takes into account the average of parents' communication costs for each task to reduce the overhead communication. The algorithm uses new task duplication algorithm. We build a simulation to compare the MCND algorithm with CPOP with duplication algorithm. The algorithms are applied on real application. From results, the MCND algorithm shows the best result

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Observation of top quark pairs produced in association with a vector boson in pp collisions at s=8 √s=8TeV

Measurements of the cross sections for top quark pairs produced in association with a W or Z boson are presented, using 8 TeV pp collision data corresponding to an integrated luminosity of 19.5 fb −1 , collected by the CMS experiment at the LHC. Final states are selected in which the associated W boson decays to a charged lepton and a neutrino or the Z boson decays to two charged leptons. Signal events are identified by matching reconstructed objects in the detector to specific final state particles from t t ¯ W tt¯W or t t ¯ Z tt¯Z decays. The t t ¯ W tt¯W cross section is measured to be 382 − 102 + 117 fb with a significance of 4.8 standard deviations from the background-only hypothesis. The t t ¯ Z tt¯Z cross section is measured to be 242 − 55 + 65 fb with a significance of 6.4 standard deviations from the background-only hypothesis. These measurements are used to set bounds on five anomalous dimension-six operators that would affect the t t ¯ W tt¯W and t t ¯ Z tt¯Z cross sections

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Search for associated production of a Z boson with a single top quark and for tZ flavour-changing interactions in pp collisions at root s=8 TeV

A search for the production of a single top quark in association with a Z boson is presented, both to identify the expected standard model process and to search for flavour-changing neutral current interactions. The data sample corresponds to an integrated luminosity of 19.7 fb−1 recorded by the CMS experiment at the LHC in proton-proton collisions at s√=8s=8 TeV. Final states with three leptons (electrons or muons) and at least one jet are investigated. An events yield compatible with tZq standard model production is observed, and the corresponding cross section is measured to be σ(pp → tZq → ℓνbℓ+ℓ−q) = 10− 7+ 8 fb with a significance of 2.4 standard deviations. No presence of flavour-changing neutral current production of tZq is observed. Exclusion limits at 95% confidence level on the branching fractions of a top quark decaying to a Z boson and an up or a charm quark are found to be ℬ(t → Zu) < 0.022% and ℬ(t → Zc) < 0.049%

A new online scheduling approach for enhancing QOS in cloud

Quality-of-Services (QoS) is one of the most important requirements of cloud users. So, cloud providers continuously try to enhance cloud management tools to guarantee the required QoS and provide users the services with high quality. One of the most important management tools which play a vital role in enhancing QoS is scheduling. Scheduling is the process of assigning users’ tasks into available Virtual Machines (VMs). This paper presents a new task scheduling approach, called Online Potential Finish Time (OPFT), to enhance the cloud data-center broker, which is responsible for the scheduling process, and solve the QoS issue. The main idea of the new approach is inspired from the idea of passing vehicles through the highways. Whenever the width of the road increases, the number of passing vehicles increases. We apply this idea to assign different users’ tasks into the available VMs. The number of tasks that are allocated to a VM is in proportion to the processing power of this VM. Whenever the VM capacity increases, the number of tasks that are assigned into this VM increases. The proposed OPFT approach is evaluated using the CloudSim simulator considering real tasks and real cost model. The experimental results indicate that the proposed OPFT algorithm is more efficient than the FCFS, RR, Min-Min, and MCT algorithms in terms of schedule length, cost, balance degree, response time and resource utilization

Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art

Recognizing Beehives’ Health Abnormalities Based on Mobile Net Deep Learning Model

Abstract Monitoring beehive health is a major area of interest within the field of honeybee economy. Ensuring beehives are free of problems such as Varroa destructors and hive beetles, ant problems, and missing queen represents an important challenge in the honeybee industry. Therefore, it is mandatory to have untraditional ways to diagnose these types of honeybee attacks. Artificial Intelligence (AI), computer vision, and the Internet of Things (IoT) can be integrated to develop smart systems for developing warning, prediction, and recognition systems to analyze beehives' health impacts, and conditions as well as monitor bees' behaviors and the environmental conditions inside/outside beehives. In this paper, a deep learning methodology is proposed to recognize the beehives' health abnormalities, Varroa destructors, hive beetles, ant problems, and missing queens. A novel version of the MobileNet model is developed by modifying the front layers of the mobile net model for performing the features selection phase. Three optimization algorithms are utilized and tested on a benchmark dataset of beehives, Adam optimizer, Nesterov-accelerated Adam (Nadam) optimizer, and Stochastic gradient descent (SGD) for selecting the most important features to recognize the three beehive health abnormalities. The implementation and validation results proved the efficiency of the Mobile Net using Adam optimizer in classifying beehives according to the three beehive health abnormalities (Varroa destructor and hive beetles, ant problems, and missing queen) where the model achieved testing accuracy of 95% and testing loss of 35%. In addition, the validation and comparison results confirmed the superiority of Mobile Net using ADAM optimizer in recognizing beehive health abnormalities compared to four deep learning models, Shuffle Net, Resent 50, VGG-19, and Google Net