Walter M. Hogue to Hello James (1 October 1962)

https://egrove.olemiss.edu/mercorr_pro/1570/thumbnail.jp

EFFECTS OF PARTICIPANT CONTROLLED VIDEO PROMPTING ON NOVEL TASKS IN A VOCATIONAL SETTING FOR ADULTS WITH AUTISM SPECTRUM DISORDER

The purpose of this study was to evaluate the effectiveness of teaching self-instructional skills to navigate to a mobile device to access video prompts to teach novel behaviors to two adults with autism spectrum disorder (ASD) in a vocational setting. This study used a multiple probe across conditions design to evaluate effectiveness. In baseline, the researcher directed the participants to complete a novel task and collected data on correct steps completed. In technology training the researcher used a system of least prompts procedure to teach participants to initiate the use of the mobile device, navigate to an app, navigate to the specific behavior schedule, watch video prompt, navigate to the next step, and complete the modeled behaviors. After mastery of technology training, researcher evaluated performance of novel tasks following self-instruction to access video prompts on the mobile device. Participant’s fidelity of navigation skills was assessed, however was not included in mastery criterion. Both participants learned to self-instruct to independently access video prompts on a mobile device. One participant self-instructed using the mobile device and video prompts to correctly complete novel tasks

Comparative Study on the Use of Coherent Radar-Derived Electric Fields vs. Statistical Electric Fields for the Initialization of a High-Latitude Ionospheric Model

The structure and time development of the magnetosphere-ionosphere system have significant impacts on the Air Force and its mission. Specifically, an accurate knowledge of ionospheric plasma densities is important for the operation of many Air Force systems. This research analyzes plasma density structure development through comparing two distinct electric field models. The two models compared here are a commonly used statistical model created by Heppner and Maynard 1987, and a more recently developed model using real-time coherent radar measurements from the SuperDARN radar network. Ionospheric simulations were run using Utah State University s Time-Dependent Ionospheric Model (TDIM) with the two electric field models as drivers, and density results from the simulations were compared with both a conceptual model and in-situ DMSP satellite measurements. While there are limitations to the comparison technique, results indicate that, in general, using the SuperDARN-derived electric fields to drive the TDIM has advantages over using the statistical fields. The higher spatial and temporal resolution of the input electric fields generally seem to produce more realistic morphological density structures, with smoothing due to statistical averaging and geomagnetic index-binning reduced. This research provides an essential first step in using high resolution, real-time SuperDARN-derived electric fields to drive a physical model of the ionosphere in order to create realistic ionospheric density results

Development of the health and economic consequences of smoking interactive model

Objective-To describe the health and economic consequences of smoking model, a user friendly, web based tool, designed to estimate the health and economic outcomes associated with smoking and the benefits of smoking cessation. Results-An overview of the development of the model equations and user interface is given, and data from the UK are presented as an example of the model outputs. These results show that a typical smoking cessation strategy costs approximately pound 1200 per life year saved and pound 22 000 per death averted. Conclusions-The model successfully captures the complexity required to model smoking behaviour and associated mortality, morbidity, and health care costs. Furthermore, the interface provides the results in a simple and flexible way so as to be useful to a variety of audiences and to simulate a variety of smoking cessation methods

Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures

Deep learning using neural networks is an effective technique for generating models of complex data. However, training such models can be expensive when networks have large model capacity resulting from a large number of layers and nodes. For training in such a computationally prohibitive regime, dimensionality reduction techniques ease the computational burden, and allow implementations of more robust networks. We propose a novel type of such dimensionality reduction via a new deep learning architecture based on fast matrix multiplication of a Kronecker product decomposition; in particular our network construction can be viewed as a Kronecker product-induced sparsification of an "extended" fully connected network. Analysis and practical examples show that this architecture allows a neural network to be trained and implemented with a significant reduction in computational time and resources, while achieving a similar error level compared to a traditional feedforward neural network.Comment: 24 pages, 29 figure

Parents\u27 Perceptions of Life Skills Development in the 4-H Cloverbud Program

Life skills are an important component of 4-H Youth Development programs. The study reported addresses life skill development of 4-H members who are 5 to 8 years old (also known as 4-H Cloverbuds). The focus was to explore parents\u27 perceptions of their child\u27s life skills development, program benefits, and activities. Parents interviewed in this study viewed the 4-H Cloverbud program as influential in life skill development, particularly in the areas of social skills, learning to learn, and personal development (self-confidence, self-care, and self-direction). Parents also identified health and diversity as important areas. Implications for practice and future research are discussed

Aboriginal ways of knowing and learning, 21st century learners, and STEM success

Open access article. Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (CC BY-NC-ND 3.0) appliesAboriginal people are alarmingly under-represented in science, technology, engineering, and mathematics (STEM)-related careers. This under-representation is a direct result of the lack of academic success in science and mathematics, an issue that begins early in elementary and middle school and often escalates in secondary school with the majority consequently doing poorly, not completing these courses and often dropping out. This makes them ineligible to pursue STEM-related paths at the post-secondary level. The greatest challenges to success in these courses are the lack of relevancy for Aboriginal learners and, as importantly, how they are taught; impediments that are also paramount to the increasing lack of success for many nonAboriginal students in STEM-related courses. This paper explores how Aboriginal ways of knowing and learning and those of the 21st century learners of today very closely parallel each other and illustrates how the creative multidisciplinary approach of a liberal education might be the way to enable early academic engagement, success and retention of Aboriginal learners in the sciences and mathematics.Ye

An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions