Statistical properties of microcracking in polyurethane foams under tensile test, influence of temperature and density

We report tensile failure experiments on polyurethane (PU) foams. Experiments have been performed by imposing a constant strain rate. We work on heterogeneous materials for whom the failure does not occur suddenly and can develop as a multistep process through a succession of microcracks that end at pores. The acoustic energy and the waiting times between acoustic events follow power-law distributions. This remains true while the foam density is varied. However, experiments at low temperatures (PU foams more brittle) have not yielded power-laws for the waiting times. The cumulative acoustic energy has no power law divergence at the proximity of the failure point which is qualitatively in agreement with other experiments done at imposed strain. We notice a plateau in cumulative acoustic energy that seems to occur when a single crack starts to propagate

Validation of a multifactorial risk factor model used for predicting future caries risk with nevada adolescents

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting.</p> <p>Methods</p> <p>This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008). The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural), tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP), negative predictive value (PVN), and prevalence across all six years of screening to assess the validity of the Risk Factor Model.</p> <p>Results</p> <p>Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%.</p> <p>Conclusions</p> <p>Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.</p

Impact of Diabetes on Postinfarction Heart Failure and Left Ventricular Remodeling

Diabetes mellitus, the metabolic syndrome, and the underlying insulin resistance are increasingly associated with diastolic dysfunction and reduced stress tolerance. The poor prognosis associated with heart failure in patients with diabetes after myocardial infarction is likely attributable to many factors, important among which is the metabolic impact from insulin resistance and hyperglycemia on the regulation of microvascular perfusion and energy generation in the cardiac myocyte. This review summarizes epidemiologic, pathophysiologic, diagnostic, and therapeutic data related to diabetes and heart failure in acute myocardial infarction and discusses novel perceptions and strategies that hold promise for the future and deserve further investigation

Diastolic dysfunction in diabetes and the metabolic syndrome: promising potential for diagnosis and prognosis

Cardiac disease in diabetes mellitus and in the metabolic syndrome consists of both vascular and myocardial abnormalities. The latter are characterised predominantly by diastolic dysfunction, which has been difficult to evaluate in spite of its prevalence. While traditional Doppler echocardiographic parameters enable only semiquantitative assessment of diastolic function and cannot reliably distinguish perturbations in loading conditions from altered diastolic functions, new technologies enable detailed quantification of global and regional diastolic function. The most readily available technique for the quantification of subclinical diastolic dysfunction is tissue Doppler imaging, which has been integrated into routine contemporary clinical practice, whereas cine magnetic resonance imaging (CMR) remains a promising complementary research tool for investigating the molecular mechanisms of the disease. Diastolic function is reported to vary linearly with age in normal persons, decreasing by 0.16 cm/s each year. Diastolic function in diabetes and the metabolic syndrome is determined by cardiovascular risk factors that alter myocardial stiffness and myocardial energy availability/bioenergetics. The latter is corroborated by the improvement in diastolic function with improvement in metabolic control of diabetes by specific medical therapy or lifestyle modification. Accordingly, diastolic dysfunction reflects the structural and metabolic milieu in the myocardium, and may allow targeted therapeutic interventions to modulate cardiac metabolism to prevent heart failure in insulin resistance and diabetes

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

A fuzzy rule-based approach to achieve visual comfort conditions

In this paper, rule based controller is used to achieve visual comfort in buildings. The objective of this paper is to investigate the fuzzification process and develop a working tuning strategy. The performance of the resultant self-tuning rule base controller is tested against input perturbations and set point changes. The advantages of the proposed system are clarified and compared by the classical control systems. © 1995 Taylor &amp; Francis Group, LLC

Knowledge-based versus classical control for solar-building designs

The present paper compares classical control systems with knowledge-based systems in the control of building designs to achieved comfort conditions. Initially the goal has been the minimization of energy usage. For this target, thermostats and PID controllers have been employed. Adaptive and ad hoc first-generation controller implemented for the improvement of specific problems are described next. The achievement of thermal and visual comfort conditions within living and working space fits the application of fuzzy logic expert systems. The structure of a fuzzy control system is described. This paper also discusses the capabilities of the fuzzy logic expert system in the achievement of optimal resource management in passive-building designs. © 1995