Variations in Internet Access Across Kansas

With social distancing, reduced health care services and school building closings during the COVID-19 pandemic, there has been an increasing need for adequate internet access, which is required for telehealth, education, business and social activities. While information is available on areas with broadband coverage, households still might not have adequate internet access due to technical and infrastructure issues, or prohibitive costs.This brief examines variations in adequate internet access by geography, population characteristics, insurance coverage and other factors to better understand how each one impacts Kansans

Stylistic analysis and recognition of piano sonatas of four composers -- Mozart, Chopin, Debussy, Anton Webern

This thesis describes a system that incorporates techniques developed by musicologists to do stylistic analysis of music, an important applied field in music theory analysis. To do the analysis requires the knowledge of many musicological analysis methods and pattern recognition algorithms that are central issues to this project. In addition, AI techniques of learning were used to improve the whole system\u27s skills. The conclusions reached as a result of this project were that computers can perform musical tasks usually associated exclusively with naturally intelligent musicologists, and that learning techniques can expand and enrich the behavior of musically intelligent systems

Graduate Student Recital: Emily Jo Shua Lin, Piano; April 27, 1973

Centennial East Recital HallFriday EveningApril 27, 19738:15 p.m

Intersectional Discrimination Is Associated with Housing Instability among Trans Women Living in the San Francisco Bay Area.

Trans women face numerous structural barriers to health due to discrimination. Housing instability is an important structural determinant of poor health outcomes among trans women. The purpose of this study was to determine if experiences of intersectional anti-trans and racial discrimination are associated with poor housing outcomes among trans women in the San Francisco Bay Area. A secondary analysis of baseline data from the Trans *National study (n = 629) at the San Francisco Department of Public Health (2016-2018) was conducted. Multivariable logistic regression was used to analyze the association between discrimination as an ordered categorical variable (zero, one to two, or three or more experiences) and housing status adjusting for age, years lived in the Bay Area, and gender identity. We found that the odds of housing instability increased by 1.25 for every categorical unit increase (1-2, or 3+) in reported experiences of intersectional (both anti-trans and racial) discrimination for trans women (95% CI = 1.01-1.54, p-value < 0.05). Intersectional anti-trans and racial discrimination is associated with increased housing instability among trans women, giving some insight that policies and programs are needed to identify and address racism and anti-trans stigma towards trans women. Efforts to address intersectional discrimination may positively impact housing stability, with potential for ancillary effects on increasing the health and wellness of trans women who face multiple disparities

User acceptance of observation and response charts with a track and trigger system: A multisite staff survey

Aims and objectives: To examine user acceptance with a new format of charts for recording observations and as a prompt for responding to episodes of clinical deterioration in adult medical–surgical patients. Background: Improving recognition and response to clinical deterioration remains a challenge for acute healthcare institutions globally. Five chart templates were developed in Australia, combining human factors design principles with a track and trigger system for escalation of care. Two chart templates were previously tested in simulations, but none had been evaluated in clinical practice. Design: Prospective multisite survey of user acceptance of the charts in practice. Methods: New observation and response charts were trialled in parallel with existing charts for 24 hours across 36 adult acute medical–surgical wards, covering 108 shifts, in five Australian states. Surveys were completed by 477 staff respondents, with open-ended comments and narrative from short informal feedback groups providing elaboration and context of user experiences. Results: Respondents were broadly supportive of the chart format and content for monitoring patients, and as a prompt for escalating care. Some concerns were noted for chart size and style, use of ranges to graph vital signs and with specific human factors design features. Information and training issues were identified to improve usability and adherence to chart guidelines and to support improved detection and response for patients with clinical deterioration. Conclusions: This initial evaluation demonstrated that the charts were perceived as appropriate for documenting observations and as a prompt to detect clinical deterioration. Further evaluation after some minor modifications to the chart is recommended. Relevance to clinical practice: Explicit training on the principles and rationale of human factors chart design, use of embedded change management strategies and addressing practical issues will improve authentic engagement, staff acceptance and adoption by all clinical users when implementing a similar observation and response chart into practice

Data Quality Matters: Suicide Intention Detection on Social Media Posts Using a RoBERTa-CNN Model

Suicide remains a global health concern for the field of health, which urgently needs innovative approaches for early detection and intervention. In this paper, we focus on identifying suicidal intentions in SuicideWatch Reddit posts and present a novel approach to suicide detection using the cutting-edge RoBERTa-CNN model, a variant of RoBERTa (Robustly optimized BERT approach). RoBERTa is used for various Natural Language Processing (NLP) tasks, including text classification and sentiment analysis. The effectiveness of the RoBERTa lies in its ability to capture textual information and form semantic relationships within texts. By adding the Convolution Neural Network (CNN) layer to the original model, the RoBERTa enhances its ability to capture important patterns from heavy datasets. To evaluate the RoBERTa-CNN, we experimented on the Suicide and Depression Detection dataset and obtained solid results. For example, RoBERTa-CNN achieves 98% mean accuracy with the standard deviation (STD) of 0.0009. It also reaches over 97.5% mean AUC value with an STD of 0.0013. In the meanwhile, RoBERTa-CNN outperforms competitive methods, demonstrating the robustness and ability to capture nuanced linguistic patterns for suicidal intentions. Therefore, RoBERTa-CNN can detect suicide intention on text data very well.Comment: 4 pages, 1 figure, 4 table

Integration of Membrane Bioreactor and Reverse Osmosis for Textile Wastewater Treatment and Reclamation: A Pilot-Scale Study

Membrane bioreactor (MBR) technology, a combination of traditional activated sludge and membrane filtration, has been widely used for industrial wastewater treatment and reclamation. This paper highlights a pilot-scale MBR system treating textile wastewater from a textile factory in Taiwan. Over 7 months of continuous operation, the average MBR influent chemical oxygen demand (COD) is 332 mg/L, and the average effluent COD is 38 mg/L, which results in approximately 88% COD removal. A reverse osmosis (RO) module is installed after 2 months of MBR operation and uses the MBR permeate as its influent. The RO produces pure water with average COD, conductivity, and color of 7 mg/L, 16 μS/cm, and 7 Pt-Co, respectively. The RO permeate is suitable for reuse in manufacturing processes, and the RO membrane shows stable performance with TMP, which is less than or equal to 0.5 kg/cm2 during the test. The study demonstrates the great feasibility of MBR combined with RO for treating and reclaiming textile wastewater