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Abstract-Multigrid methods are widely used to accelerate the convergence of iterative solvers for linear systems used in a number of different application areas. In this paper, we explore optimization techniques for geometric multigrid on existing and emerging multicore systems including the Opteronbased Cray XE6, Intel R Xeon R E5-2670 and X5550 processorbased Infiniband clusters, as well as the new Intel R Xeon Phi TM coprocessor (Knights Corner). Our work examines a variety of novel techniques including communication-aggregation, threaded wavefront-based DRAM communication-avoiding, dynamic threading decisions, SIMDization, and fusion of operators. We quantify performance through each phase of the V-cycle for both single-node and distributed-memory experiments and provide detailed analysis for each class of optimization. Results show our optimizations yield significant speedups across a variety of subdomain sizes while simultaneously demonstrating the potential of multi-and manycore processors to dramatically accelerate single-node performance. However, our analysis also indicates that improvements in networks and communication will be essential to reap the potential of manycore processors in largescale multigrid calculations.

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that-(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

[1] Interannual variability of the Wyrtki jets is studied in the context of Indian Ocean Dipole (IOD) and El Niño and Southern Oscillation (ENSO) wind-forcing using a three dimensional numerical ocean model and observations. The boreal fall (October-November) Wyrtki jet is more significantly affected than the boreal spring (April-May) Wyrtki jet since both the IOD and ENSO tend to peak toward the end of the calendar year. Various statistical methods are used in an attempt to separate the impacts of the IOD and ENSO on these jets, with emphasis on the fall jet. The first two modes of an Empirical Orthogonal Function (EOF) decomposition account for about 90% and 85% of variability in zonal currents and wind stress respectively along the equator in the Indian Ocean, but EOF analysis does not cleanly separate out IOD and ENSO forcing and response. Partial correlation analysis reveals that IOD wind-forcing and zonal equatorial current response are stronger on average than for ENSO and extend further west across the basin. Composite analysis of IOD only, ENSO only, and combined IOD and ENSO years provides a complementary definition of the relative contributions of these two phenomena on Wyrtki jet variability and in general is consistent with the results of the partial correlation analysis.

Particulate air pollution is becoming a serious public health concern in urban cities in India due to air pollution-related health effects associated with disability-adjusted life years (DALYs) and economic loss. To obtain the quantitative result of health impact of particulate matter (PM) in most populated Mumbai City and most polluted Delhi City in India, an epidemiology-based exposure-response function has been used to calculate the attributable number of mortality and morbidity cases from 1991 to 2015 in a 5-year interval and the subsequent DALYs, and economic cost is estimated of the health damage based on unit values of the health outcomes. Here, we report the attributable number of mortality due to PM in Mumbai and Delhi increased to 32,014 and 48,651 in 2015 compared with 19,291 and 19,716 in year 1995. And annual average mortality due to PM in Mumbai and Delhi was 10,880 and 10,900. Premature cerebrovascular disease (CEV), ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD) causes are about 35.3, 33.3, and 22.9% of PM-attributable mortalities. Total DALYs due to PM10 increased from 0.34 million to 0.51 million in Mumbai and 0.34 million to 0.75 million in Delhi from average year 1995 to 2015. Among all health outcomes, mortality and chronic bronchitis shared about 95% of the total DALYs. Due to PM, the estimated total economic cost at constant price year 2005 US$ increased from 2680.87 million to 4269.60 million for Mumbai City and 2714.10 million to 6394.74 million for Delhi City, from 1995 to 2015, and the total amount accounting about 1.01% of India's gross domestic product (GDP). A crucial presumption is that in 2030, PM levels would have to decline by 44% (Mumbai) and 67% (Delhi) absolutely to maintain the same health outcomes in year 2015 levels. The results will help policy makers from pollution control board for further cost-benefit analyses of air pollution management programs in Mumbai and Delhi.

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You and a friend walk outside on an April morning. You announce that the weather is "mild". Your friend declares that it is "cold". Who is wrong? Or are you both right?People recognize that language can be imprecise and that concepts such as cold, hot, or mild do not have well-defined boundaries. In 1965, Lotfi Zadeh introduced fuzzy logic, a means of processing data by extending classical set theory to handle partial membership (1). Classical set theory deals with sets that are "crisp" in the sense that members are either in or out according to rules of binary logic. For example, binary logic dictates that apples in a basket are red OR not red. With fuzzy logic, some of the apples could be categorized as red AND not red.In everyday life and in fields such as environmental health, people deal with concepts that involve factors that defy classification into crisp sets-"safe", "harmful", "acceptable", "unacceptable", and so on. A classic example is a regulator carefully explaining the result of a detailed quantitative risk assessment to a community group, only to be asked