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Fifth Street, TX, United States

Huston–Tillotson University is a historically black university in Austin, Texas, United States. The school is affiliated with the United Methodist Church, the United Church of Christ, and the United Negro College Fund. Huston–Tillotson University awards four-year degrees in business, education, the humanities, natural science, social science, science and technology. The University also offers alternative teacher certification and academic programs for undergraduates interested in pursuing post-graduate degrees in Law and Medicine. Wikipedia.


Wong M.S.,Hong Kong Polytechnic University | Peng F.,Hong Kong Polytechnic University | Zou B.,Central South University | Shi W.Z.,Hong Kong Polytechnic University | Wilson G.J.,Huston-Tillotson University
International Journal of Environmental Research and Public Health | Year: 2016

Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity "hotspots" were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect. © 2016 by the authors; licensee MDPI, Basel, Switzerland. Source


Zou B.,Central South University | Zou B.,Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention LAP3 | Wang M.,Central South University | Wan N.,University of Utah | And 3 more authors.
Environmental Science and Pollution Research | Year: 2015

Accurate measurements of PM2.5 concentration over time and space are especially critical for reducing adverse health outcomes. However, sparsely stationary monitoring sites considerably hinder the ability to effectively characterize observed concentrations. Utilizing data on meteorological and land-related factors, this study introduces a radial basis function (RBF) neural network method for estimating PM2.5 concentrations based on sparse observed inputs. The state of Texas in the USA was selected as the study area. Performance of the RBF models was evaluated by statistic indices including mean square error, mean absolute error, mean relative deviation, and the correlation coefficient. Results show that the annual PM2.5 concentrations estimated by the RBF models with meteorological factors and/or land-related factors were markedly closer to the observed concentrations. RBF models with combined meteorological and land-related factors achieved best performance relative to ones with either type of these factors only. It can be concluded that meteorological factors and land-related factors are useful for articulating the variation of PM2.5 concentration in a given study area. With these covariate factors, the RBF neural network can effectively estimate PM2.5 concentrations with acceptable accuracy under the condition of sparse monitoring stations. The improved accuracy of air concentration estimation would greatly benefit epidemiological and environmental studies in characterizing local air pollution and in helping reduce population exposures for areas with limited availability of air quality data. © 2015 Springer-Verlag Berlin Heidelberg Source


Rose R.,Huston-Tillotson University | Waks L.,Temple University
E-Learning and Digital Media | Year: 2012

The members of the working group on National Educational Technology Policy continue to base their formulations around entrenched conceptions of education, retaining the language of teachers, students, curriculum standards, specified objectives and the like. Several of those participating in the panel examining the policy report in an earlier issue (Volume 8 Number 2 2011) of E-Learning and Digital Media worried that these conceptions hamper imagination about new educational possibilities already bubbling up around the edges of conventional educational practice. The working group authors, in their rejoinder, have defended choosing and building on the older framework as a more practicable avenue for rapid change. Here Raymond Rose and Leonard Waks respond by (a) teasing out deep conflicts about this choice within the working group report, and (b) demonstrating the heavy costs of sticking with what Rose and Waks see as an outmoded approach to learning and education in the age of the Internet. Source


Zou B.,Central South University | Peng F.,Central South University | Peng F.,Hong Kong Polytechnic University | Wan N.,University of Utah | And 2 more authors.
Atmospheric Pollution Research | Year: 2014

Recent studies examining racial and ethnic inequities in exposure to urban air pollution have led to advances in understanding the nature and extent of overall concentration exposures by pollutant, demarcated by disadvantaged groups. However, the stability of inequities at various spatial units and the exposure by air pollution sources are often neglected. In this case study from the Dallas-Fort Worth (Texas, USA) area, we used Geographic Information Systems (GIS) and an air dispersion model to estimate environmental justice impacts at different spatial scales (i.e., zip code, census tract, block group) and by source (i.e., industrial pollution sources, vehicle pollution sources, industry and vehicle pollution sources combined). Using whites as a reference, blacks and other races were more likely to be exposed to higher sulfur dioxide (SO2) concentrations although the Odds Ratio (OR) varied substantially by pollution source type [e.g., industrial pollution source based: (OR=1.80; 95% CI (Confidence Interval): 1.79-1.80) vs. vehicle pollution source based: (OR=2.70; 95% CI: 2.68-2.71)] and varied less between spatial scales [for vehicle pollution sources, (OR=2.70; 95% CI: 2.68-2.71) at the census tract level but was (OR=2.54; 95% CI: 2.53-2.55) at the block group scale]. Similar to the pattern of racial inequities, people with less education (i.e., less than 12 years of education) and low income (i.e., per capital income below $20 000) were more likely to be exposed to higher SO2 concentrations, and those ORs also varied greatly with the pollution sources and slightly with spatial scales. It is concluded that the type of pollution source plays an important role in SO2 pollution exposure inequity assessment, while spatial scale variations have limited influence. Future studies should incorporate source-specific exposure assessments when conducting studies on environmental justice. © Author(s) 2014. Source


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 398.33K | Year: 2015

The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Targeted Infusion Projects supports the development, implementation, and study of evidence-based innovative models and approaches for improving the preparation and success of HBCU undergraduate students so that they may pursue STEM graduate programs and/or careers. The project at Huston-Tillotson University (HT) seeks to implement inquiry-based courses and undergraduate research to improve and sustain STEM student engagement, training, and transition to STEM careers and graduate study. The curriculum will emphasize real-world research experiences, reconfigure course objectives to emphasize inquiry and problem-based learning, and challenge students to use the critical-thinking skills central to the practice of science. Project activities will advance STEM students knowledge and understanding, enhancing the number and preparedness of the institutions STEM graduates. Thus, this project will have a societal impact on STEM representation from underserved groups.

The goal of Huston-Tillotsons Attaining and Sustaining STEM Excellence with Research Training (HT-ASSERT) program is to improve and sustain the retention, engagement, and scientific training of Natural Science students at HT by emphasizing undergraduate research. The project will enhance undergraduate research through two linked project activities: 1) integrate the science curriculum to highlight inquiry and research, beginning with scientific skill building in introductory courses and culminating in a capstone independent student research project for every student; and 2) develop the material and intellectual infrastructure (lab spaces, faculty expertise, and research collaborations) needed to enact deep and lasting transformation of STEM education at HT. Project activities will integrate with existing STEM programs at HT, including HTs STEM education and campus academic enrichment programs. This project will involve collaborating faculty at the University of Texas at Austin.

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