Speech recognition: The interpretation of training and using speech recognition software from the perspectives of postsecondary students with learning challenges

This study examined the utilization of speech recognition programs by five college students with learning difficulties. A qualitative approach was implemented over the course of two semesters, Fall 2003 and Spring 2004, to: (a) ascertain students\u27 perspectives on speech recognition, and (b) understand how students used speech recognition programs as a tool to meet the academic demands of postsecondary education. Data collection for this study included interviews, participant observations, field notes, videotape, and course assignments. This study provided qualitative data regarding an assistive technology tool that has traditionally been studied within a quantitative paradigm. Environment, individual characteristics, and life demands were found to impact student use of their speech recognition program. Implications for users, educational professionals, and researchers are presented in the final chapter of this paper

Relations of Water-quality Constituent Concentrations to Surrogate Measurements in the Lower Platte River Corridor, Nebraska, 2007 through 2011

The lower Platte River, Nebraska, provides drinking water, irrigation water, and in-stream flows for recreation, wildlife habitat, and vital habitats for several threatened and endangered species. The United States Geological Survey (USGS), in cooperation with the Lower Platte River Corridor Alliance (LPRCA) developed site-specific regression models for water-quality constituents at four sites (Shell Creek near Columbus, Nebraska [USGS site 06795500]; Elkhorn River at Waterloo, Nebraska [USGS site 06800500]; Salt Creek near Ashland, Nebraska [USGS site 06805000]; and Platte River at Louisville, Nebraska [USGS site 06805500]) in the lower Platte River corridor. The models were developed by relating continuously monitored water-quality properties (surrogate measurements) to discrete water-quality samples. These models enable existing web-based software to provide near-real-time estimates of stream-specific constituent concentrations to support natural resources management decisions.Since 2007, USGS, in cooperation with the LPRCA, has continuously monitored four water-quality properties seasonally within the lower Platte River corridor: specific conductance, water temperature, dissolved oxygen, and turbidity. During 2007 through 2011, the USGS and the Nebraska Department of Environmental Quality collected and analyzed discrete water-quality samples for nutrients, major ions, pesticides, suspended sediment, and bacteria. These datasets were used to develop the regression models. This report documents the collection of these various water-quality datasets and the development of the site-specific regression models.Regression models were developed for all four monitored sites. Constituent models for Shell Creek included nitrate plus nitrite, total phosphorus, orthophosphate, atrazine, acetochlor, suspended sediment, and Escherichia coli (E. coli) bacteria. Regression models that were developed for the Elkhorn River included nitrate plus nitrite, total Kjeldahl nitrogen, total phosphorus, orthophosphate, chloride, atrazine, acetochlor, suspended sediment, and E. coli. Models developed for Salt Creek included nitrate plus nitrite, total Kjeldahl nitrogen, suspended sediment, and E. coli. Lastly, models developed for the Platte River site included total Kjeldahl nitrogen, total phosphorus, sodium, metolachlor, atrazine, acetochlor, suspended sediment, and E. coli

Ethnicity as a moderator of motivational interviewing for incarcerated adolescents after release

Motivational interviewing (MI) has been found to be an effective treatment for substance using populations, including incarcerated adolescents. Although some studies suggest MI is more successful with individuals from minority backgrounds, the research remains mixed. The current study investigated the impact of ethnicity on treatment in reducing alcohol and marijuana use among incarcerated adolescents. Adolescents (14–19 years of age) were recruited from a state juvenile correctional facility and randomly assigned to receive MI or relaxation therapy (RT) (N = 147; 48 White, 51 Hispanic, and 48 African American; 126 male; 21 female). Interviews were conducted at admission to the facility and 3 months after release. Results suggest that the effects of MI on treatment outcomes are moderated by ethnicity. Hispanic adolescents who received MI significantly decreased total number of drinks on heavy drinking days (NDHD) and percentage of heavy drinking days (PHDD) as compared to Hispanic adolescents who received RT. These findings suggest that MI is an efficacious treatment for an ethnic minority juvenile justice-involved population in need of evidence-based treatments

Cannabis Withdrawal Among Detained Adolscents: Exploring the Impact of Nicotine and Race

Rates of marijuana use among detained youths are exceptionally high. Research suggests a cannabis withdrawal syndrome is valid and clinically significant; however, these studies have mostly been conducted in highly controlled laboratory settings with treatment-seeking, White adults. The present study analyzed archival data to explore the magnitude of cannabis withdrawal symptoms within a diverse sample of detained adolescents while controlling for tobacco use and investigating the impact of race on symptom reports. Adolescents recruited from a juvenile correctional facility (N=93) completed a background questionnaire and the Marijuana Withdrawal Checklist. Analyses revealed a significant main effect for level of tobacco use on severity of irritability, and for level of marijuana use on severity of craving to smoke marijuana and strange/wild dreams. Furthermore, a significant main effect for race was found with Black adolescents reporting lower withdrawal discomfort scores and experiencing less severe depressed mood, difficulty sleeping, nervousness/anxiety, and strange/wild dreams. Although exploratory, these findings may have significant clinical implications for providers in juvenile detention facilities, allowing the execution of proper medical and/or behavioral interventions to assist adolescents presenting with problematic cannabis and/or tobacco withdrawal

Closed-loop feedback control for microfluidic systems through automated capacitive fluid height sensing

Precise fluid height sensing in open-channel microfluidics has long been a desirable feature for a wide range of applications. However, performing accurate measurements of the fluid level in small-scale reservoirs (<1 mL) has proven to be an elusive goal, especially if direct fluid-sensor contact needs to be avoided. In particular, gravity-driven systems used in several microfluidic applications to establish pressure gradients and impose flow remain open-loop and largely unmonitored due to these sensing limitations. Here we present an optimized self-shielded coplanar capacitive sensor design and automated control system to provide submillimeter fluid-height resolution (∼250 μm) and control of small-scale open reservoirs without the need for direct fluid contact. Results from testing and validation of our optimized sensor and system also suggest that accurate fluid height information can be used to robustly characterize, calibrate and dynamically control a range of microfluidic systems with complex pumping mechanisms, even in cell culture conditions. Capacitive sensing technology provides a scalable and cost-effective way to enable continuous monitoring and closed-loop feedback control of fluid volumes in small-scale gravity-dominated wells in a variety of microfluidic applications.United States. Defense Advanced Research Projects Agency (Award W911NF-12-2-0039

TabText: A Flexible and Contextual Approach to Tabular Data Representation

Tabular data is essential for applying machine learning tasks across various industries. However, traditional data processing methods do not fully utilize all the information available in the tables, ignoring important contextual information such as column header descriptions. In addition, pre-processing data into a tabular format can remain a labor-intensive bottleneck in model development. This work introduces TabText, a processing and feature extraction framework that extracts contextual information from tabular data structures. TabText addresses processing difficulties by converting the content into language and utilizing pre-trained large language models (LLMs). We evaluate our framework on nine healthcare prediction tasks ranging from patient discharge, ICU admission, and mortality. We show that 1) applying our TabText framework enables the generation of high-performing and simple machine learning baseline models with minimal data pre-processing, and 2) augmenting pre-processed tabular data with TabText representations improves the average and worst-case AUC performance of standard machine learning models by as much as 6%