Experimental Design and Comparative Testing of a Hybrid-Cooled Computer Cluster

With water cooling becoming an affordable option both at home and at scale, it is important to consider the possible benefits over air cooling. There are several methods of liquid cooling, notables include: immersion, cold water cooling, and warm water cooling. The total cost of ownership is difficult to determine with these options as each has a different impact on the data center. Considering retrofit, over a new data center, introduces unforeseen variables that make cost analysis a challenge. Besides the added costs of additional infrastructure, and the cost to remove old, the upfront costs could be daunting. Therefore a cost analysis would be a study of its won. This study however hopes to reveal the resulting tradeoffs in temperature, performance, and power usage presented in the case between classical airflow based heat sink mechanisms to water provided directly at the heatsink. Having control over a discrete chiller will provide answers to the CPU temperatures, power usage, and performance at various inlet water temperatures. To water or to air

A Dynamic Programming Model of Retirement Behavior

This paper formulates a model of retirement behavior based on the solution to a stochastic dynamic programming problem. The workers objective is to maximize expected discounted utility over his remaining lifetime. At each time period the worker chooses how much to consume and whether to work full-time, part-time, or exit the labor force. The model accounts for the sequential nature f the retirement decision problem, and the role of expectations of uncertain future variables such as the worker's future lifespan, health status, marital and family status, employment status, as well as earnings from employment, assets, and social security retirement, disability and medicare payments. This paper applies a "nested fixed point" algorithm that converts the dynamic programming problem into the problem of repeatedly recomputing the fixed point to a contraction mapping operator as a subroutine of a standard nonlinear maximum likelihood program. The goal of the paper is to demonstrate that a fairly complex and realistic formulation of the retirement problem can be estimated using this algorithm and a current generation supercomputer, the Cray-2.

The Mobile Health Revolution?

Rarely does a class of technologies excite physicians, patients, financeers, gadgeteers, and policymakers alike. But mobile health — the use of mobile devices like smartphones and tablets for health or medical purposes — has captured our collective imagination. Observers predict that mobile health, also referred to as “mHealth” or “medical apps,” can save millions of lives, billions in spending, and democratize access to health care. Proponents argue that mobile health technologies will transform the ways in which we deliver, consume, measure, and pay for care; disrupting our sclerotic health care system. This Article evaluates mobile health and its many ambitions. Given the significant hype surrounding mobile health, I try to provide a relatively sober, dispassionate review of the many claims here. I begin by surveying the universe of mobile health technologies, offering a typology of products based on their functions, many of which have regulatory significance. The Article then considers the federal government’s posture towards mobile health. To date, Congress and over half a dozen federal agencies have addressed mobile health. Contrary to the prevailing wisdom, federal regulators are sympathetic, not hostile, to these technologies. However, I demonstrate how one agency, the U.S. Food and Drug Administration (FDA), is repeating the same mistakes that it made when it first confronted medical device software 25 years ago, relying on nonbinding guidance documents that are largely weak and unenforceable. I argue that, somewhat counter-intuitively, mobile health will only reach its immense potential if regulators like the FDA provide meaningful oversight. Otherwise, users will be flooded with mobile technologies that are ineffective, or worse, unsafe

Exploring Spatial Patterns of Tropical Peatland Subsidence in Selangor, Malaysia Using the APSIS-DInSAR Technique

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts

Exploring spatial patterns of tropical peatland subsidence in Selangor, Malaysia using the APSIS-DInSAR technique

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts

Concurrent Probabilistic Simulation of High Temperature Composite Structural Response

A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership

Oral manifestations of menopause: an interprofessional intervention for dental hygiene and physician assistant students

Purpose: Interprofessional education (IPE) is a means of fostering integration and collaboration between health care professions. This study evaluated the effect of an IPE educational module on the oral manifestations of menopause. Methods: This mixed-method study used a convenience sample of dental hygiene (DH) and physician assistants (PA) students. Pre- and posttests collected quantitative data using a modified Readiness for Interprofessional Learning Survey (RIPLS), and a PI-designed knowledge of menopause test to determine students attitudes and learning. Students participated in a one-time workshop that included an educational lecture and a case study exercise using a pseudo-standardized patient. Students worked in preselected groups, representing both disciplines, to create a patient care plan addressing oral manifestations of menopause. Qualitative data was collected from student comments. Results: Study results indicate an increase in participants\u27 knowledge of oral manifestations of menopause. Additionally, results suggest improved attitudes toward interprofessional teamwork and collaboration, professional identity, roles and responsibilities and interprofessional communication. Finally, data shows facilitation of gained confidence in applying new skills related to oral manifestations of menopause. Conclusion: Implementation of an IPE intervention demonstrated correlation between an IPE experience and participants\u27 attitudes, learning, and confidence. Patients experiencing menopause are prone to oral manifestation. Therefore, preparing students to meet the needs of menopausal women may ultimately decrease oral discomfort and improve quality of life --Leaf x

EJP-CONCERT. D3.7 Second joint roadmap for radiation protection research

EJP-CONCERT Work Package 3, Deliverable 3.7

Research in Structures and Dynamics, 1984

A symposium on advanced and trends in structures and dynamics was held to communicate new insights into physical behavior and to identify trends in the solution procedures for structures and dynamics problems. Pertinent areas of concern were (1) multiprocessors, parallel computation, and database management systems, (2) advances in finite element technology, (3) interactive computing and optimization, (4) mechanics of materials, (5) structural stability, (6) dynamic response of structures, and (7) advanced computer applications

Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in australia

Australia is an agricultural nation characterised by one of the most naturally diverse climates in the world, which translates into significant sources of risk for agricultural production and subsequent farm revenues. Extreme climatic events have been significantly affecting large parts of Australia in recent decades, contributing to an increase in the vulnerability of crops, and leading to subsequent higher risk to a large number of agricultural producers. However, attempts at better managing climate related risks in the agricultural sector have confronted many challenges. First, crop insurance products, including classical claim-based and index-based insurance, are among the financial implements that allow exposed individuals to pool resources to spread their risk. The classical claim-based insurance indemnifies according to a claim of crop loss from the insured customer, and so can easily manage idiosyncratic risk, which is the case where the loss occurs independently.Nevertheless, the existence of systemic weather risk (covariate risk), which is the spread of extreme events over locations and times (e.g., droughts and floods), has been identified as the main reason for the failure of private insurance markets, such as the classical multi-peril crop insurance, for agricultural crops. The index-based insurance is appropriate to handle systemic but not idiosyncratic risk. The indemnity payments of the index-based insurance are triggered by a predefined threshold of an index (e.g., rainfall), which is related to such losses. Since the covariate nature of a climatic event, it sanctions the insurers to predict losses and ascertain indemnifications for a huge number of insured customers across a wide geographical area. However, basis risk, which is related to the strength of the relationship between the predefined indices used to estimate the average loss by the insured community and the actual loss of insured assets by an individual, is a major barrier that hinders uptake of the index-based insurance. Clearly, the high basis risk, which is a weak relationship between the index and loss, destroys the willingness of potential customers to purchase this insurance product. Second, the impact of multiple synoptic-scale climate mode indices (e.g., Southern Oscillation Index (SOI) and Indian Ocean Index (IOD)) on precipitation and crop yield is not identical in different spatial locations and at different times or seasons across the Australian continent since the influence of large-scale climate heterogeneous over the different regions. The occurrence, role, and amplitude of synoptic-scale climate modes contributing to the variability of seasonal crop production have shifted in recent decades. These variables generally complicate the climate and crop yield relationship that cannot be captured by traditional modelling and analysis approaches commonly found in published agronomic literature such as linear regression. In addition, the traditional linear analysis is not able to model the nonlinear and asymmetric interdependence between extreme insurance losses, which may occur in the case of systemic risk. Relying on the linear method may lead to the problem that different behaviour may be observed from joint distributions, particularly in the upper and lower regions, with the same correlation coefficient. As a result, the likelihood of extreme insurance losses can be underestimated or overestimated that lead to inaccuracies in the pricing of insurance policies. Another alternative is the use of the multivariate normal distribution, where the joint distribution is uniquely defined using the marginal distributions of variables and their correlation matrix. However, phenomena are not always normally distributed in practice. It is therefore important to develop new, scientifically verified, strategic measures to solve the challenges as mentioned above in order to support mitigating the influences of the climate-related risk in the agricultural sector. Copulas provide an advanced statistical approach to model the joint distribution of multivariate random variables. This technique allows estimating the marginal distributions of individual variables independently with their dependence structures. It is clear that the copula method is superior to the conventional linear regression since it does not require variables have to be normally distributed and their correlation can be either linear or non-linear. This doctoral thesis therefore adopts the advanced copula technique within a statistical modelling framework that aims to model: (1) The compound influence of synoptic-scale climate indices (i.e., SOI and IOD) and climate variables (i.e., precipitation) to develop a probabilistic precipitation forecasting system where the integrated role of different factors that govern precipitation dynamics are considered; (2) The compound influence of synoptic-scale climate indices on wheat yield; (3) The scholastic interdependencies of systemic weather risks where potential adaptation strategies are evaluated accordingly; and (4) The risk-reduction efficiencies of geographical diversifications in wheat farming portfolio optimisation. The study areas are Australia’s agro-ecological (i.e., wheat belt) zones where major seasonal wheat and other cereal crops are grown. The results from the first and second objectives can be used for not only forecasting purposes but also understanding the basis risk in the case of pricing climate index-based insurance products. The third and fourth objectives assess the interactions of drought events across different locations and in different seasons and feasible adaptation tools. The findings of these studies can provide useful information for decision-makers in the agricultural sector. The first study found the significant relationship between SOI, IOD, and precipitation. The results suggest that spring precipitation in Australia, except for the western part, can be probabilistically forecasted three months ahead. It is more interesting that the combination of SOI and IOD as the predictors will improve the performance of the forecast model. Similarly, the second study indicated that the largescale climate indices could provide knowledge of wheat crops up to six months in advance. However, it is noted that the influence of different climate indices varies over locations and times. Furthermore, the findings derived from the third study demonstrated the spatio-temporally stochastic dependence of the drought events. The results also prove that time diversification is potentially more effective in reducing the systemic weather risk compared to spatially diversifying strategy. Finally, the fourth objective revealed that wheat-farming portfolio could be effectively optimised through the geographical diversification. The outcomes of this study will lead to the new application of advanced statistical tools that provide a better understanding of the compound influence of synoptic-scale climatic conditions on seasonal precipitation, and therefore on wheat crops in key regions over the Australian continent. Furthermore, a comprehensive analysis of systemic weather risks performed through advanced copula-statistical models can help improve and develop novel agricultural adaptation strategies in not only the selected study region but also globally, where climate extreme events pose a serious threat to the sustainability and survival of the agricultural industry. Finally, the evaluation of the effectiveness of diversification strategies implemented in this study reveals new evidence on whether the risk pooling methods could potentially mitigate climate risks for the agricultural sector and subsequently, help farmers in prior preparation for uncertain climatic events