Measuring the robustness of resource allocations for distributed domputer systems in a stochastic dynamic environment

Heterogeneous distributed computing systems often must function in an environment where system parameters are subject to variations during operation. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times vary within predictable ranges and tasks arrive randomly. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of the stochastically modeled dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function

A 32 kb Critical Region Excluding Y402H in CFH Mediates Risk for Age-Related Macular Degeneration

Complement factor H shows very strong association with Age-related Macular Degeneration (AMD), and recent data suggest that multiple causal variants are associated with disease. To refine the location of the disease associated variants, we characterized in detail the structural variation at CFH and its paralogs, including two copy number polymorphisms (CNP), CNP147 and CNP148, and several rare deletions and duplications. Examination of 34 AMD-enriched extended families (N = 293) and AMD cases (White N = 4210 Indian = 134; Malay = 140) and controls (White N = 3229; Indian = 117; Malay = 2390) demonstrated that deletion CNP148 was protective against AMD, independent of SNPs at CFH. Regression analysis of seven common haplotypes showed three haplotypes, H1, H6 and H7, as conferring risk for AMD development. Being the most common haplotype H1 confers the greatest risk by increasing the odds of AMD by 2.75-fold (95% CI = [2.51, 3.01]; p = 8.31×10−109); Caucasian (H6) and Indian-specific (H7) recombinant haplotypes increase the odds of AMD by 1.85-fold (p = 3.52×10−9) and by 15.57-fold (P = 0.007), respectively. We identified a 32-kb region downstream of Y402H (rs1061170), shared by all three risk haplotypes, suggesting that this region may be critical for AMD development. Further analysis showed that two SNPs within the 32 kb block, rs1329428 and rs203687, optimally explain disease association. rs1329428 resides in 20 kb unique sequence block, but rs203687 resides in a 12 kb block that is 89% similar to a noncoding region contained in ΔCNP148. We conclude that causal variation in this region potentially encompasses both regulatory effects at single markers and copy number

Synthesis, Bioactivity Evaluation and Application of Plant-Based Nanoparticles

Environmental and biomedical fields have various potential applications for the green synthesis of nanoparticles [...

Measuring the robustness of resource allocations in a stochastic dynamic environment

Heterogeneous distributed computing systems often must operate in an environment where system parameters are subject to uncertainty. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times are stochastic. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of a stochastic dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function. 1

Measuring the robustness of resource allocations in a stochastic dynamic environment

Includes bibliographical references.Heterogeneous distributed computing systems often must operate in an environment where system parameters are subject to uncertainty. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times are stochastic. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of a stochastic dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function.This research was supported by the NSF under Contract No: CNS-0615170, by the Colorado State University Center for Robustness in Computer Systems (funded by the Colorado Commission on Higher Education Technology Advancement Group through the Colorado Institute of Technology), and by the Colorado State University George T. Abell Endowment

Stochastic-based Robust Dynamic Resource Allocation for Independent Tasks in a Heterogeneous Computing System

Heterogeneous parallel and distributed computing systems frequently must operate in environments where there is uncertainty in system parameters. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. In such an environment, the execution time of any given task may fluctuate substantially due to factors such as the content of data to be processed. Determining a resource allocation that is robust against this uncertainty is an important area of research. In this study, we define a stochastic robustness measure to facilitate resource allocation decisions in a dynamic environment where tasks are subject to individual hard deadlines and each task requires some input data to start execution. In this environment, the tasks that cannot meet their deadlines are dropped (i.e., discarded). We define methods to determine the stochastic completion times of tasks in the presence of the task dropping. The stochastic task completion time is used in the definition of the stochastic robustness measure. Based on this stochastic robustness measure, we design novel resource allocation techniques that work in immediate and batch modes, with the goal of maximizing the number of tasks that meet their individual deadlines. We compare the performance of our technique against several well-known approaches taken from the literature and adapted to our environment. Simulation results of this study demonstrate the suitability of our new technique in a dynamic heterogeneous computing system

Effectiveness of the ChAdOx1 nCoV-19 Coronavirus Vaccine (CovishieldTM) in Preventing SARS-CoV2 Infection, Chennai, Tamil Nadu, India, 2021

We estimated the effectiveness of two doses of the ChAdOx1 nCoV-19 (Covishield) vaccine against any COVID-19 infection among individuals ≥45 years in Chennai, Tamil Nadu, India. A community-based cohort study was conducted from May to September 2021 in a selected geographic area in Chennai. The estimated sample size was 10,232. We enrolled 69,435 individuals, of which 21,793 were above 45 years. Two-dose coverage of Covishield in the 18+ and 45+ age group was 18% and 31%, respectively. Genomic analysis of 74 out of the 90 aliquots collected from the 303 COVID-19-positive individuals in the 45+ age group showed delta variants and their sub-lineages. The vaccine’s effectiveness against COVID-19 disease in the ≥45 age group was 61.3% (95% CI: 43.6–73.4) at least 2 weeks after receiving the second dose of Covishield. We demonstrated the effectiveness of two doses of the ChAdOx1 vaccine against the delta variant in the general population of Chennai. We recommend similar future studies considering emerging variants and newer vaccines. Two-dose vaccine coverage could be ensured to protect against COVID-19 infection