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Sexual Health, Alcohol Use, Childhood Sexual Abuse, and Mental Health Outcomes Among Spanish-speaking Latino MSM in the Northeastern United States
No full textBackground: Little is known about the health status of predominantly Spanish-speaking Latino men who have sex with men (MSM). Methods: Between January and March of 2014 a cohort of Latino MSM (N = 176) was recruited to participate in Latinos en Pareja, an HIV/STI prevention intervention adaptation study. A multinomial logistic regression model predicting problematic alcohol consumption was carried out; demographic characteristics, sexual risk factors, childhood sexual abuse experiences, and mental health outcomes were included in the model. Results: Prevalence estimates of problematic alcohol consumption in the past 30 days and clinically significant depressive symptoms (CES-D score ≥ 10) were 47% and 68%, respectively. Internal consistency reliability coefficients of the CES-D scale were satisfactory (Cronbach α = 0.86). Among participants who reported sexual activity before the age of 17 (n = 130, 74%), 39 participants (30%) reported childhood sexual abuse. In univariable analyses, characteristics and covariates associated with problematic alcohol consumption included having more than one sexual partner in the past 3 months, engaging in risky sexual behavior (operationalized as condom nonuse in the past 3 months), being in a relationship, reporting intimate partner violence, screening for clinically significant depressive symptoms, and having experienced childhood sexual abuse. In the multinomial logistic regression model, problematic alcohol consumption was predicted by having more than one sexual partner in the past 3 months, engaging in risky sexual behavior, being in a relationship, and reporting intimate partner violence. Conclusions: Further work is needed to develop effective prevention intervention approaches for problematic alcohol consumption among Latino MSM. Given the gap in research on Latino MSM and the high prevalence estimate of childhood sexual abuse among this subpopulation, there is a need to steer effective preventive and treatment interventions to meet the particular needs of this community
Networks of attention in children with the 22q11 deletion syndrome
No full textThe 22q11 chromosomal deletion syndrome (22q11 DS) is associated with learning disabilities and a complex neuropsychological profile. Previous findings have suggested that executive attention deficits might underlie other neurocognitive anomalies. We administered the childAttentionNetworkTest (ANT) to 52 children ages 5.0 to 11.5, 32 22q11 DS children (19 girls) and 20 controls (13 girls) and assessed the efficiency of segregated executive, orienting, and alerting networks. We hypothesized that 22q11 DS children have impaired executive network efficiency as compared to control siblings. The internal validity of the child ANT was confirmed for this population. Analysis of variance results showed significant main effects for flanker and cue types and no interaction effect in either 22q11 DS children or control siblings. Compared to control siblings, 22q11 DS children had significantly larger (less efficient) executive network scores, significantly increased errors on only incongruent trials, and a significant correlation between executive network scores and accuracy. The implications of these findings for future neurocognitive studies of 22q11 DS children are considered
2025 Calendar: El Paso Goes Hollywood
No full textThe 2025 calendar for UTEP Special Collections features El Paso people and places with movie themes.https://scholarworks.utep.edu/calendars/1010/thumbnail.jp
A Numerical Analysis Tool To Evaluate Pavements Integrated With Dynamic Wireless Power Transfer Technology
No full textAs electric vehicles (EVs) continue to gain market share in the U.S., the integration of dynamic wireless power transfer (DWPT) systems into pavements has emerged as a promising solution to address range anxiety and infrastructure limitations. However, the structural implications of embedding DWPT technology into roadways remain insufficiently explored. This dissertation presents the development and application of a numerical analysis tool to evaluate the mechanical performance of flexible pavements containing embedded charging units (CUs). A series of finite element models, validated with full-scale experimental data, were used to assess the influence of axle loading, material properties, layer configurations, bonding conditions, temperature, and vehicle speed on pavement responses. Parametric studies were performed to identify critical design factors impacting fatigue and rutting life. Results show that CU inclusion can improve or degrade pavement performance depending on bonding quality and design conditions, with CU-HMA interface stiffness emerging as a dominant factor. The findings support the advancement of design strategies for electrified road infrastructure and provide a framework for evaluating structural responses prior to large-scale deployment
Line Outage Impact Factors: A New Approach To Line Outage Detection With Machine Learning
No full textElectric power systems have become one of our most critical infrastructures as we\u27ve grown dependent on electricity for everyday tasks. Ensuring power systems provide reliable service is a priority that can be affected by disturbance events. A common event is transmission line outages, where a line in the system becomes disconnected due to varying forms of physical damage. If an outage isn\u27t detected in time, other lines in the system may overload, causing cascading failures that leave many customers without power. Therefore, having a power system that can automatically detect outages is crucial for reliability, as it promotes real-time response and recovery. Various methods for line outage detection have been developed over the years, focusing on issues such as minimum deployment of Phasor Measurement Units (PMU) and detecting outages with partial data. Furthermore, machine learning has gained traction for its improvements in line outage detection. In this thesis, we developed two machine learning-based methods for detecting transmission line outages using K-Nearest Neighbors. Our first method, we showed the potential of Line Outage Distribution Factors (LODF) as a feature or data observation point selection tool. Identifying critical observation points through LODF enables the detection of outages with limited data by monitoring power flow from one transmission line while accounting for load uncertainty to estimate the status of another line. Our second method introduces a new set of factors called Line Outage Impact Factors (LOIF), a modified version of the LODF, which solves some concerns we have when using LODF for feature selection. Instead of showing the distribution of power from an outage line, LOIF shows the change in power flow of a line due to the outage of another. We develop a feature-selection method that focuses on determining the outages that provide significant and distinct changes in power flow
Teaching Bilingual Features To A Young Augmentative And Alternative Communication User With Autism
No full textThis study examines how a minimally verbal, young Spanish-English bilingual child with Autism Spectrum Disorder (ASD) uses translanguaging toggle features on an Augmentative and Alternative Communication (AAC) device to produce multi-word picture descriptions. The study employed a Rapidly Alternating Treatments Design (RATD) to analyze AAC performance under language-controlled Spanish-only and English-only conditions. A quantitative analysis examined the frequency and patterns of bilingual AAC feature use during aided modeling and instruction, while qualitative methods explored the related challenges and benefits. This study highlights the potential for bilingual AAC systems to enhance communication effectiveness and underscores the importance of incorporating user and caregiver perspectives in developing culturally and linguistically responsive AAC interventions
Environmental Drivers Of Vegetation Dynamics In Constructed And Natural Wetlands Of The Chihuahuan Desert
No full textDesert wetlands are critical ecosystems that provide water, food, and habitat for many different species at different trophic levels. Additionally, they have the capacity to recharge aquifers, control floods, and purify water, among others. Unfortunately, they have been facing multiple environmental challenges due to different human activities, sometimes with disastrous consequences. There is an urgent need to better understand the structure and function of wetlands of different types and in different biomes in order to conserve, restore, and even create them so we can continue to obtain all the ecological services they provide. In this dissertation, I investigated how environmental factors and hydrologic variability affect the vegetation dynamics in two different desert wetlands of the southwest U.S: constructed and natural wetlands. The first data chapter (Chapter 2) examines how changes in water delivery in two constructed wetland cells influenced the development of plant communities over more than a decade of restoration activities (2005-2017). Here, the frequency of water delivery increased in Rio Bosque Wetlands Park (RB) in 2016, which gave us the unique opportunity to study the response of vegetation to water availability in the two ponds of RB prior to summer water delivery (2005, 2009, 2014) and after (2016, 2017). I found three significant changes in vegetation with water level increases: the relative frequency and cover of aquatic plants increased, a transition from upland to aquatic plants, and plant diversity increased. The second data chapter (Chapter 3) focused on elucidating the environmental factors that influence plant community structure and productivity in playa wetlands of the northern Chihuahuan Desert. I used 5 years of satellite imagery (2019 to 2024) to classify the playas according to their annual and inter-annual patterns in greenness (NDVI) and to select six representative playas from each group for direct sampling. The most significant results of this chapter were: plant productivity varied across the playas and years, the most productive vegetation was found in playas correlated with clay texture, abundant soil nitrogen, and situated in depressions or connected with arroyos. Conversely, the least productive playas were associated with gypsiferous sediments, and notably Red Lake (the least productive playa) showed high soil salinity. A reduction in vegetation productivity was also observed, coinciding with the decrease in rainfall during the year surveyed. This decrease in precipitation also coincides with the megadrought reported in this region. This dissertation highlights the importance of conducting long-term studies to observe the behavior of wetlands over time and how they can change depending on the availability of environmental factors. Also, how useful it can be to combine fieldwork, remote sensing, and GIS to interpret and visualize changes in vegetation dynamics over time
Racial And Ethnic Differences In Knowledge Of Gender-Affirming Care Among Community And Student Participants In The Bloom Transgender Health Education Program In The Paso Del Norte Region, 2022-2024
No full textSUMMARY OF BACKGROUND AND SIGNIFICANCE: There is a noticeable lack of training and knowledge regarding gender-affirming care (GAC) among providers and within the general community. OBJECTIVE: Assess racial and ethnic disparities in knowledge of GAC among participants in BLOOM, a quasi-experimental transgender health education program in the Paso Del Norte region. HYPOTHESES: Among community and student participants at pre-survey, assess racial and ethnic differences in sociodemographic characteristics, preparedness, attitudes, and basic knowledge regarding LGBT clients/patients and GAC. METHODS: Secondary data among community (pre: 53; post: 14) and student (pre: 29; post: 17) BLOOM participants in 2022-2024 were used. Descriptive statistics for all measures and racial and ethnic differences were determined using IBM SPSS Version 29.0.2. RESULTS: For student participants, knowledge of how to navigate the healthcare system (100.0% vs. 31.3%; p=0.022) and knowing how to use their personal and social resources to gain control over their healthcare (100.0% vs. 40.0%; p=0.047) was higher for those of white race and knowledge of how to navigate the legal system (6.3% vs. 100%; p=0.013)was lower for those of Latinx ethnicity. CONCLUSION: Though few findings were significant by race and ethnicity, we observed increased knowledge in the post-survey among student participants compared to the pre-survey. RECOMMENDATIONS: BLOOM can continue promoting and expanding the understanding of GAC of the TGD community of the Paso Del Norte region in the future, addressing the limitations, emphasizing increasing the knowledge of self-advocacy skills, and continuing to address barriers to care
Testing Revenge Pornography Using The General Strain Theory and Online Disinhibition Effect
No full textThe current study tests whether perceptions of general strain and online disinhibition effect could explain the intent to commit Revenge Pornography (RP). The data was collected using an online survey that was launched on a research platform called Connect Cloud Research. After gathering the data, bivariate correlation and multiple regression analysis were necessary to establish the relationship and causality. Results were mixed, showing that there is a positive correlation with general strain and online disinhibition effect factors with the intent to commit RP, while multiple regression indicated that the independent variables did not predict the intent to commit RP. Unexpectedly, findings demonstrated that the theoretical control variables (peer cybercrime, revenge behavior, & opportunity cybercriminal behavior) predict the intent to commit RP. In conclusion, strain and online disinhibition effect could not explain the intent to commit RP; however, the theoretical control variables explain the intent to commit RP