Including the siblings of youth substance abusers in a parent-focused intervention : a pilot test of the best plus program

Substance use is common among young people and can escalate into significant problems for affected individuals and their families. Family responses can influence the course of youth substance use and its consequences for family members, including parents and siblings. Family-level interventions developed to date have neglected the important role that siblings can play. This article describes a pilot test of an intervention designed to assist parents and siblings affected by youth substance use and related problems. The BEST Plus intervention consisting of professionally-led, multifamily groups sequenced over eight sessions is described with reference to the intended therapeutic processes. Professionally observed and self-reported changes for family participants including siblings suggested that the program had a beneficial therapeutic impact. This evaluation of early impacts suggests the BEST Plus programoffers a promising means of assisting families to respond to substance use problems in young people.<br /

A water rights transfer evaluation procedure with applications for western energy development

This report deals with questions of water supply for coal deve- lopment in the semiarid western United States. A method is developed to evaluate yields of water rights in "appropriation" or "permit" systems of water administration. Water rights are characterized in terms of location, priority, decreed maximum diversion, actual diver- sion in periods of low flow, and consumptive use. Transfers of water rights are evaluated in this method by using institutional procedures as a framework for analysis. A case study is performed on the North Fork of the Powder River, Wyoming, in which institutional considera- tions are discussed, and water rights are evaluated for a hypothetical facility. This procedure is not limited to energy facilities, but may be used in most cases of water rights trasnfers. The method is designed for use with easily obtained data in order to facilitate its use in practice.Sponsored by U.S. Dept. of Energy

Characterizing infection in anti-neutrophil cytoplasmic antibody-associated vasculitis:results from a longitudinal, matched-cohort data linkage study

We wish to thank Information Division Services Scotland for assisting with data linkage and data access in the National Safe Haven. Information presented in this article was previously presented as a poster at the American College of Rheumatology Annual Conference 2018, Chicago, IL, USA. The study was conceived by S.H.S., A.M., C.B. and N.B. All authors contributed to the study design and data collection. Data analysis and interpretation and drafting of the manuscript were conducted by all authors. C.B. and N.B. were joint senior authors. All authors critically reviewed the manuscript and approved the final version. Funding: S.H.S. and the study were funded by the Aberdeen Development Trust and the Farr Institute of Health Informatics Research. The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates) and the Wellcome Trust (Scotland MR/K007017/1). Disclosure statement: L.E. is a GlaxoSmithKline employee. The other authors have declared no conflicts of interest.Peer reviewedPublisher PD

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System