Prediction of force coefficients for labyrinth seals

The development of a linear model for the prediction of labyrinth seal forces and on its comparison to available stiffness data is presented. A discussion of the relevance of fluid damping forces and the preliminary stages of a program to obtain data on these forces are examined. Fluid-dynamic forces arising from nonuniform pressure patterns in labyrinth seal glands are known to be potentially destablizing in high power turbomachinery. A well documented case in point is that of the space Shuttle Main Engine turbopumps. Seal forces are also an important factor for the stability of shrouded turbines, acting in that case in conjunction with the effects of blade-tip clearance variations

Are false positives in suicide classification models a risk group? Evidence for “true alarms” in a population-representative longitudinal study of Norwegian adolescents

IntroductionFalse positives in retrospective binary suicide attempt classification models are commonly attributed to sheer classification error. However, when machine learning suicide attempt classification models are trained with a multitude of psycho-socio-environmental factors and achieve high accuracy in suicide risk assessment, false positives may turn out to be at high risk of developing suicidal behavior or attempting suicide in the future. Thus, they may be better viewed as “true alarms,” relevant for a suicide prevention program. In this study, using large population-based longitudinal dataset, we examine three hypotheses: (1) false positives, compared to the true negatives, are at higher risk of suicide attempt in future, (2) the suicide attempts risk for the false positives increase as a function of increase in specificity threshold; and (3) as specificity increases, the severity of risk factors between false positives and true positives becomes more similar.MethodsUtilizing the Gradient Boosting algorithm, we used a sample of 11,369 Norwegian adolescents, assessed at two timepoints (1992 and 1994), to classify suicide attempters at the first time point. We then assessed the relative risk of suicide attempt at the second time point for false positives in comparison to true negatives, and in relation to the level of specificity.ResultsWe found that false positives were at significantly higher risk of attempting suicide compared to true negatives. When selecting a higher classification risk threshold by gradually increasing the specificity cutoff from 60% to 97.5%, the relative suicide attempt risk of the false positive group increased, ranging from minimum of 2.96 to 7.22 times. As the risk threshold increased, the severity of various mental health indicators became significantly more comparable between false positives and true positives.ConclusionWe argue that the performance evaluation of machine learning suicide classification models should take the clinical relevance into account, rather than focusing solely on classification error metrics. As shown here, the so-called false positives represent a truly at-risk group that should be included in suicide prevention programs. Hence, these findings should be taken into consideration when interpreting machine learning suicide classification models as well as planning future suicide prevention interventions for adolescents

Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach

IntroductionResearch on the classification models of suicide attempts has predominantly depended on the collection of sensitive data related to suicide. Gathering this type of information at the population level can be challenging, especially when it pertains to adolescents. We addressed two main objectives: (1) the feasibility of classifying adolescents at high risk of attempting suicide without relying on specific suicide-related survey items such as history of suicide attempts, suicide plan, or suicide ideation, and (2) identifying the most important predictors of suicide attempts among adolescents.MethodsNationwide survey data from 173,664 Norwegian adolescents (ages 13–18) were utilized to train a binary classification model, using 169 questionnaire items. The Extreme Gradient Boosting (XGBoost) algorithm was fine-tuned to classify adolescent suicide attempts, and the most important predictors were identified.ResultsXGBoost achieved a sensitivity of 77% with a specificity of 90%, and an AUC of 92.1% and an AUPRC of 47.1%. A coherent set of predictors in the domains of internalizing problems, substance use, interpersonal relationships, and victimization were pinpointed as the most important items related to recent suicide attempts.ConclusionThis study underscores the potential of machine learning for screening adolescent suicide attempts on a population scale without requiring sensitive suicide-related survey items. Future research investigating the etiology of suicidal behavior may direct particular attention to internalizing problems, interpersonal relationships, victimization, and substance use

Making Communities More Flood Resilient: The Role of Cost Benefit Analysis and Other Decision-support Tools in Disaster Risk Reduction

Given the series of large-scale flood disasters that have occurred in recent years, there is a growing recognition among community leaders, businesses, insurers, governments and international donors of the need to invest in risk reduction measures before such events happen. Due to the costs of risk reduction measures, these actions need to be justified and as a result there is an increasing need to utilize decision-support tools, which can help to make the case for action to reduce disaster risks and build flood resilience when faced with limited resources. Across stakeholders, the specific objectives from the use of decision-support tools include (i) demonstrating the efficiency of the action ex-ante (before the flood); (ii) aiding in the selection of a particular intervention in enhancing community flood resilience from a suite of possible options; (iii) helping communities make the right choice when faced with limited investments; (iv) demonstrating the benefits of donor funding of community flood resilience projects; and (v) monitoring the successes and weaknesses of past interventions to generate lessons learned for future work. Typically, discussion on decision-support for disaster risk reduction (DRR) in floods (as well as for other hazards) has focused on cost-benefit analysis (CBA), however there are a number of other tools available to support decision-making. These include cost-effectiveness analysis (CEA), multi-criteria analysis (MCA) and robust-decision-making approaches (RDMA), which have been applied to similar problems, and can also be used to aid decision-making regarding flooding. This white paper provides an overview of the opportunities and challenges of applying these different tools, and guides the reader to select among them. Selection depends on the desired objective, circumstances, data available, timeframe to perform analyses, level of detail, and other considerations. We first focus on the CBA decision-tool, as this has been the mainstay of research and implementation. We then go beyond CBA to consider the other techniques for prioritising DRR investments. While our analysis is specific to flood DRR actions, the conclusion are also applicable to other hazards. The key findings arising from this white paper with relevance to research, policy and implementation of flood DRR decision-support tools, are: (1) Following a comprehensive review of the quantitative CBA flood DRR evidence, we find that flood DRR investments largely pay off, with an average of five dollars saved for every dollar spent through avoided and reduced losses; (2) Using CBA for flood risk reduction assessment should properly account for low-frequency, high-impact flood events, and also tackle key challenges such as intangible impacts; (3) Decision-making can be improved by using various decision support tools tailored to the desired outcomes and contexts. This white paper is the foundation upon which the Zurich flood resilience alliance work on integration of a decision toolbox will proceed "on the ground," with established community-based risk assessment tools, in particular Vulnerability Capacity Assessments (VCA) or Participatory Capacity and Vulnerability Assessments (PCVA). Based on these findings we propose a way forward over the next several years on informing risk-based decision making as part of the alliance program

Summary of failure analysis activities at Brookhaven National Laboratory

Brookhaven National Laboratory has for many years conducted examinations related to the failures of nuclear materials and components. These examinations included the confirmation of root cause analyses, the determination of the causes of failure, identification of the species that accelerate corrosion, and comparison of the results of nondestructive examinations with those obtained by destructive examination. The results of those examinations, which had previously appeared in various formats (formal and informal reports, journal articles, etc.), have been collected together and summarized in the present report. The report is divided into sections according to the general subject matter (for example, corrosion, fatigue, etc.). Each section presents summaries of the information contained in specific reports and publications, all of which are fully identified as to title, authors, report number or journal reference, date of publication, and FIN number under which the work was performed

A framework for analyzing both linkage and association: An analysis of Genetic Analysis Workshop 16 simulated data

We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We conducted linkage only, linkage and association, and association only tests under this framework. We also compared these results with variance-component linkage analysis and regression analyses. The results indicate that the method shows some promise, but finding genes that have very small (<0.1%) contributions to trait variance may require additional sources of information. All methods examined fared poorly for the smallest in the simulated "polygene" range (h(2 )of 0.0015 to 0.0002)

Preliminary Validation of the AFWA-NASA Blended Snowcover Product Over the Lower Great Lakes region

A new snow product created using the standard Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow cover and snow-water equivalent products has been evaluated for the Lower Great Lakes region during the winter of 2002- 03. National Weather Service Co-Operative Observing Network stations and student-acquired snow data were used as ground truth. An interpolation scheme was used to map snow cover on the ground from the station measurements for each day of the study period. It is concluded that this technique does not represent the actual ground conditions adequately to permit evaluation of the new snow product in an absolute sense. However, use of the new product was found to improve the mapping of snow cover as compared to using either the MODIS or AMSR-E product, alone. Plans for further analysis are discussed

Psychological and Neural Contributions to Appetite Self-Regulation

Objective: This paper reviews the state of the science on psychological and neural contributions to appetite self-regulation in the context of obesity. Methods: Three content areas (neural systems and cognitive functions; parenting and early childhood development; and goal setting and goal striving) served to illustrate different perspectives on the psychological and neural factors that contribute to appetite dysregulation in the context of obesity. Talks were initially delivered at an NIH workshop consisting of experts in these three content areas, and then content areas were further developed through a review of the literature. Results: Self-regulation of appetite involves a complex interaction between multiple domains, including cognitive, neural, social, and goal-directed behaviors and decision-making. Self-regulation failures can arise from any of these factors, and the resulting implications for obesity should be considered in light of each domain. In some cases, self-regulation is amenable to intervention; however, this does not appear to be universally true, which has implications for both prevention and intervention efforts. Conclusions: Appetite regulation is a complex, multifactorial construct. When considering its role in the obesity epidemic, it is advisable to consider its various dimensions together to best inform prevention and treatment efforts

On the discovery of doubly-magic 48^{48}Ni

The paper reports on the first observation of doubly-magic Nickel-48 in an experimental at the SISSI/LISE3 facility of GANIL. Four Nickel-48 isotopes were identified. In addition, roughly 100 Nickel-49, 50 Iron-45, and 290 Chromium-42 isotopes were observed. This opens the possibility to search for two-proton emission from these nuclei.Comment: 4 pages, 3 figures, accepted for publication in Phys. Rev. Let

Measurement of nuclide cross-sections of spallation residues in 1 A GeV 238U + proton collisions

The production of heavy nuclides from the spallation-evaporation reaction of 238U induced by 1 GeV protons was studied in inverse kinematics. The evaporation residues from tungsten to uranium were identified in-flight in mass and atomic number. Their production cross-sections and their momentum distributions were determined. The data are compared with empirical systematics. A comparison with previous results from the spallation of 208Pb and 197Au reveals the strong influence of fission in the spallation of 238U.Comment: 20 pages, 10 figures, background information at http://www-wnt.gsi.de/kschmidt