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In providing physical assistance to elderly people, ensuring cooperative behavior from the elderly persons is a critical requirement. In sit-to-stand assistance, for example, an older adult must lean forward, so that the body mass can shift towards the feet before a caregiver starts lifting the body. An experienced caregiver guides the older adult through verbal communications and physical interactions, so that the older adult may be cooperative throughout the process. This guidance is of paramount importance and is a major challenge in introducing a robotic aid to the eldercare environment. The wide-scope goal of the current work is to develop an in-telligent eldercare robot that can a) monitor the mental state of an older adult, and b) guide the older adult through an assisting procedure so that he/she can be cooperative in being assisted. The current work presents a basic modeling framework for describing a human's physical behaviors reflecting an internal mental state, and an algorithm for estimating the mental state through interactive observations. The sit-to-stand assistance problem is considered for the initial study. A simple Kalman Filter is constructed for estimating the level of cooperativeness in response to applied cues, with a thresholding scheme being used to make judgments on the cooperativeness state. 

Abstract We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine-learning tool, namedSMDET, applied to time-series images from the Wide-field Infrared Survey Explorer. We gathered photometric and astrometric data to estimate each candidate’s spectral type, distance, and tangential velocity. This sample has a photometrically estimated spectral class distribution of 28 M dwarfs, 64 L dwarfs, and 18 T dwarfs. We also identify a T-subdwarf candidate, two extreme T-subdwarf candidates, and two candidate young ultracool dwarfs. Five objects did not have enough photometric data for any estimations to be made. To validate our estimated spectral types, spectra were collected for two objects, yielding confirmed spectral types of T5 (estimated T5) and T3 (estimated T4). Demonstrating the effectiveness of machine-learning tools as a new large-scale discovery technique. 

Abstract This article reviews extant multidisciplinary literature to uncover existing themes and directions in the knowledge of the overlap between natural resource scarcity and illicit supply chain activity. In doing so, the authors present a novel review of this nascent, complex, and multidisciplinary research area. This review has uncovered 127 articles that have not been synthesized or organized in a meaningful way with the supply chain literature. It extracts insights and develops a comprehensive process framework encompassing the following: (a) antecedents associated with natural resource extraction, which foments the opportunity for illicit activity to thrive; (b) resulting economic, social, and environmental outcomes from illicit activity as it relates to natural resource extraction; and (c) potential moderating processes, which either enable or inhibit illicit activity to occur, including firm‐level tactics that businesses can employ to counteract illicit activity throughout the supply chain and to promote sustainable long‐term operations. An extensive agenda is presented suggesting future research paths, methodologies, theories, and potential contributions. 

Spatial reasoning skills contribute to performance in many STEM fields. For example, drawing sectional views of three-dimensional objects is an essential skill for engineering students. There is considerable variation in the spatial reasoning skills of prospective engineering students, putting some at risk for compromised performance in their classes. This study takes place in a first-year engineering Spatial Visualization course to integrate recent practices in engineering design education with cognitive psychology research on the nature of spatial learning. We employed three main pedagogical strategies in the course - 1) in class instruction on sketching; 2) spatial visualization training; and 3) manipulation of physical objects (CAD/3D print creations). This course endeavors to use current technology, online accessibility, and implementation of the three pedagogical strategies to bring about student growth in spatial reasoning. This study is designed to determine the effect of adding two different spatial reasoning training apps to this environment. Over 230 students (three sections) participated in our study. In two of the three sections, students received interactive spatial visualization training using either a spatial visualization mobile touchscreen app in one section or an Augmented Reality (AR) app in the other section. Research suggests that there are benefits to using the Spatial Vis Classroom mobile app for college students.The app has been shown to increase student persistence resulting in large learning gains as measured by the Purdue assessment of spatial visualization (PSVT-R), especially for students starting with poor spatial visualization skills. The Spatial Vis Classroom app can be used in the classroom or assigned as homework. The AR app is designed to help users develop their mental rotation abilities. It is designed to support a holistic understanding of 3-dimensional objects, and research has shown that, in combination with a traditional curriculum, it increases students’ abilities also measured by the PSVT-R. Of particular interest, the data suggest that the app overcomes the advantage found by males over females in a traditional class alone focused on spatial reasoning. Both of the course sections were required to use the apps for approximately the same time in class and outside of class. Students in the control section were required to do hand sketching activities in class and outside of class, with roughly the same completion time as for the sections with the apps. Students grades were not affected by using the three different approaches as grading was based on completion only. Based on current literature, we hypothesize that overall benefits (PSVT-R gains) will be comparable across the 3 treatments but there will be different effects on attitude and engagement (confidence,enjoyment, and self-efficacy). Lastly, we hypothesize that the treatments will have different effects on male/female and ethnic categories of the study participants. The final paper will include an analysis of results and a report of the findings.