Proximal humeral epiphysiolysis (Little League shoulder)

This issue of eMedRef provides information to clinicians on the pathophysiology, diagnosis, and therapeutics of proximal humeral epiphysiolysis

Remarital Chances, Choices, and Economic Consequences: Issues of Social and Personal Welfare

Many divorced women experience a significant decline in financial, social, physical, and psychological well-being following a divorce. Using data from the NLSY79 (n= 2,520) we compare welfare recipients, mothers, and impoverished women to less marginalized divorcees on remarriage chances. Furthermore, we look at the kinds of men these women marry by focusing on the employment and education of new spouses. Finally, we address how remarriage and spousal quality (as defined by education and employment) impact economic well-being after divorce. Our results show that remarriage has positive economic effects, but that is dependent upon spousal quality. However, such matches are rare among divorced women with children and in poverty. The implications of our results for social welfare issues are discussed

Effects of Native American Geographical Location and Marital Status on Poverty

This study examined the association between geographic location (urban, rural, and tribal) and marital status on poverty among the Native American community. A sample of 5,110 Native Americans in the 2008-2010 American Community Survey were used for analyses. Results indicated that Native Americans were similar with the general population in their geographic location, marital status, and poverty. We found that the protective characteristics of marriage in the Native American community varied according to geographic location. We also discuss the impact this may have on the Native American community and what practitioners and policy makers should consider when working with the important but often overlooked population

Quit Methods Used by US Adult Cigarette Smokers, 2014–2016

To quantify the prevalence of 10 quit methods commonly used by adult cigarette smokers, we used data from a nationally representative longitudinal (2014-2016) online survey of US adult cigarette smokers (n = 15,943). Overall, 74.7% of adult current cigarette smokers used multiple quit methods during their most recent quit attempt. Giving up cigarettes all at once (65.3%) and reducing the number of cigarettes smoked (62.0%) were the most prevalent methods. Substituting some cigarettes with e-cigarettes was used by a greater percentage of smokers than the nicotine patch, nicotine gum, or other cessation aids approved by the US Food and Drug Administration. Further research into the effectiveness of e-cigarettes as a cessation aid is warranted

Reasons for current E -cigarette use among U.S. adults

E-cigarette use has increased rapidly among U.S. adults. However, reasons for use among adults are unclear. We assessed reasons for e-cigarette use among a national sample of U.S. adults. Data were collected via online surveys among U.S. adults aged 18 or older from April through June 2014. Descriptive and multivariate regression analyses were conducted to assess reasons for e-cigarette use among 2448 current e-cigarette users, by sociodemographic characteristics and product type. Assessed reasons included cessation/health, consideration of others, convenience, cost, curiosity, flavoring, and simulation of conventional cigarettes. Among current e-cigarette users, 93% were also current cigarette smokers. The most common reasons for e-cigarette use were cessation/health (84.5%), consideration of others (71.5%), and convenience (56.7%). The prevalence of citing convenience (adjusted prevalence ratio [aPR] = 1.49) and curiosity (aPR = 1.54) as reasons for e-cigarette use were greater among current cigarette smokers than nonsmokers (P < 0.05). The prevalence of citing flavoring as a reason for use was greater among adults aged 18 to 24 (aPR = 2.02) than 55 or older (P < 0.05). Tank use was associated with greater prevalence of citing every assessed reason except convenience and curiosity. Cessation- and health-related factors are primary reasons cited for e-cigarette use among adults, and flavorings are more commonly cited by younger adults. Efforts are warranted to provide consumers with accurate information on the health effects of e-cigarettes and to ensure that flavoring and other unregulated features do not promote nicotine addiction, particularly among young adults

Silo Storage Preconceptual Design

The National Nuclear Security Administration (NNSA) has a need to develop and field a low-cost option for the long-term storage of a variety of radiological material. The storage option’s primary requirement is to provide both environmental and physical protection of the materials. Design criteria for this effort require a low initial cost and minimum maintenance over a 50-year design life. In 1999, Argonne National Laboratory-West was tasked with developing a dry silo storage option for the BN-350 Spent Fuel in Aktau Kazakhstan. Argon’s design consisted of a carbon steel cylinder approximately 16 ft long, 18 in. outside diameter and 0.375 in. wall thickness. The carbon steel silo was protected from corrosion by a duplex coating system consisting of zinc and epoxy. Although the study indicated that the duplex coating design would provide a design life well in excess of the required 50 years, the review board was concerned because of the novelty of the design and the lack of historical use. In 2012, NNSA tasked Idaho National Laboratory (INL) with reinvestigating the silo storage concept and development of alternative corrosion protection strategies. The 2012 study, “Silo Storage Concepts, Cathodic Protection Options Study” (INL/EST-12-26627), concludes that the option which best fits the design criterion is a passive cathotic protection scheme, consisting of a carbon steel tube coated with zinc or a zinc-aluminum alloy encapsulated in either concrete or a cement grout. The hot dipped zinc coating option was considered most efficient, but the flame-sprayed option could be used if a thicker zinc coating was determined to be necessary

Fathers stepping up? A cross-national comparison of fathers’ domestic labour and parents’ satisfaction with the division of domestic labour during the COVID-19 pandemic

The COVID-19 pandemic disrupted work and family life around the world. For parents, this upending meant a potential re-negotiation of the ‘status quo’ in the gendered division of labour. A comparative lens provides extended understandings of changes in fathers’ domestic work based in socio-cultural context–in assessing the size and consequences of change in domestic labour in relation to the type of work-care regime. Using novel harmonized data from four countries (the United States, Canada, the United Kingdom, and the Netherlands) and a work-care regime framework, this study examines cross-national changes in fathers’ shares of domestic labour during the early months of the pandemic and whether these changes are associated with parents’ satisfaction with the division of labour. Results indicate that fathers’ shares of housework and childcare increased early in the pandemic in all countries, with fathers’ increased shares of housework being particularly pronounced in the US. Results also show an association between fathers’ increased shares of domestic labour and mothers’ increased satisfaction with the division of domestic labour in the US, Canada, and the UK. Such comparative work promises to be generative for understanding the pandemic’s imprint on gender relations far into the future

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work

Arizona\u27s Vulnerable Populations

Arizona’s vulnerable populations are struggling on a daily basis but usually do so in silence, undetected by traditional radar and rankings, often unaware themselves of their high risk for being pushed or pulled into a full crisis. Ineligible for financial assistance under strict eligibility guidelines, they don’t qualify as poor because vulnerable populations are not yet in full crisis. To be clear, this report is not about the “poor,” at least not in the limited sense of the word. It is about our underemployed wage earners, our single-parent households, our deployed or returning military members, our under-educated and unskilled workforce, our debt-ridden neighbors, our uninsured friends, our family members with no savings for an emergency, much less retirement

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures