Texas A&M University-Commerce

We measured the Coulomb dissociation of ¹⁶O into ⁴He and ¹²C within the FAIR Phase-0 program at GSI Helmholtzzentrum für Schwerionenforschung Darmstadt, Germany. From this we will extract the photon dissociation cross section ¹⁶O(α,γ)¹²C, which is the time reversed reaction to 12C(α,γ)¹⁶O. With this indirect method, we aim to improve on the accuracy of the experimental data at lower energies than measured so far. The expected low cross section for the Coulomb dissociation reaction and close magnetic rigidity of beam and fragments demand a high precision measurement. Hence, new detector systems were built and radical changes to the R³B setup were necessary to cope with the high-intensity ¹⁶O beam. All tracking detectors were designed to let the unreacted ¹⁶O ions pass, while detecting the ¹²C and ⁴He.

When individuals engage in social contexts such as attending a college basketball game, opportunities to enrich and broaden social connections with others often transpire. The University of Notre Dame Women’s Basketball (NDWB) program has a comparably larger and more loyal fan base than most universities in the country, of which older women comprise a big portion of season ticket holders. The purpose of this study was to investigate the motivations and meanings of being a NDWB season ticket holder among older women. Nineteen participants ages 55–85 years old completed individual interviews. The findings indicated that being season ticket holders fostered a strong sense of identification with the team, allowing them to have a sense of pride of being part of the program. Additionally, being season ticket holders enabled the older women to enhance their social contacts and friendships with other women that fostered a sense of community.

Much effort is devoted to measuring the nuclear symmetry energy through neutron star (NS) and nuclear observables. Since matter in the NS core may be nonhadronic, observables like radii and tidal deformability may not provide reliable constraints on properties of nucleonic matter. By performing the first consistent inference using ensembles of core and crust equations of state from astrophysical and nuclear data, we demonstrate that coincident timing of a resonant shattering flare (RSF) and gravitational wave signal during binary NS inspiral probes the crust-core transition region and provides constraints on the symmetry energy comparable to terrestrial nuclear experiments. We show that nuclear masses, RSFs, and measurements of NS radii and tidal deformabilities constrain different density ranges of the equation of state, providing complementary probes.

Importance Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. Objective To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data. Design, setting, and participants We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest’s feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods. Main outcomes and measures Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation. Results This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html . Conclusions and relevance In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19.

Objective To investigate the prevalence, correlates, and sources of women's health information-seeking behaviors in the United States using the Andersen Behavioral Model. Methods The 2012–2019 Health Information National Trends Survey data were used to analyze how and where women seek health theoretically. Weighted prevalence, descriptive analysis, and separate multivariable logistic regression models were computed to test the argument. Results The overall prevalence of seeking health information from any source was (83%, 95% CI: 0.82–0.84). Between 2012 and 2019, the analysis revealed a downward trend in health information seeking from any source (85.2–82.4%), health care provider (19.0–14.8%), family/friends (10.4–6.6%), and traditional channels (5.4–4.8%). Interestingly, there was an increase in Internet usage from 65.4% to 73.8%. Conclusions We found statistically significant relationships between the predisposing, enabling, and need factors of the Andersen Behavioral Model. Specifically, age, race/ethnicity, income levels, educational status, perceived health status, having a regular provider, and smoking status predicted women’s health information-seeking behaviors. Practice implications Our study concludes that several factors influence health information-seeking behaviors, and disparities exist in the channels through which women seek care. The implications for health communication strategies, practitioners, and policymakers are also discussed.

In this study, we construct a compartmental model that tracks the different states and their respective hazards for typical mortgage loans. We consider that an active mortgage loan could become delinquent in light of either common systemic risks or idiosyncratic risks in the job market. These two groups of employment-related perils jeopardize the sources of income underlying the mortgage monthly payments to lenders and could hurt the ability of mortgage loan borrowers to retire their debt. We also contemplate ongoing risks of a collapse in the housing market, which might transform the mortgage loan to be “underwater” and consequently diminish borrowers’ incentives to service the outstanding balance. We develop the necessary derivations, illustrate the functionality of the model over several hypothetical simulations and sensitivity analyses, suggest variable estimation specific guidelines, conclude, and discuss potential extensions for the proposed model.

Smith et al. (2019) found standing resulted in better performance than sitting in three different cognitive control paradigms: a Stroop task, a task-switching, and a visual search paradigm. Here, we conducted close replications of the authors' three experiments using larger sample sizes than the original work. Our sample sizes had essentially perfect power to detect the key postural effects reported by Smith et al. The results from our experiments revealed that, in contrast to Smith et al., the postural interactions were quite limited in magnitude in addition to being only a fraction of the size of the original effects. Moreover, our results from Experiment 1 are consistent with two recent replications (Caron et al., 2020; Straub et al., 2022), which reported no meaningful influences of posture on the Stroop effect. In all, the current research provides further converging evidence that postural influences on cognition do not appear to be as robust, as was initially reported in prior work.

Recent research indicates that mock jurors place too much weight on eyewitness confidence expressed in the courtroom rather than confidence expressed immediately after an identification, though eyewitness identification research clearly shows that only the latter is indicative of guilt. Researchers rarely present mock jurors with photo arrays, which could help them to better understand the eyewitness’ point of view. Across three experiments, potential jurors viewed photo arrays and hypothetical eyewitness confidence statements described as coming either immediately after the identification or much later in the courtroom. In Experiment 1, suspect guilt was rated as more likely when immediate or courtroom confidence was high. Experiment 2 reduced suspect guilt estimates associated with high courtroom confidence by providing partial Henderson instructions. Experiment 3 replicated this effect and found that simple directives from an eyewitness identification expert were even more beneficial in helping potential jurors correctly evaluate confidence based on timing. We recommend that eyewitness experts be allowed to instruct jurors not to trust confidence expressed at trial.

This study examined how academic and faculty mentoring experience in public and private colleges make a long-term impact on people’s intellectual lives, careers, interpersonal relationships, and personal development. This study employed a qualitative case study research methodology with twenty-four alumni who graduated from either a private liberal arts college or a public R1 university. For data collection, individual semi-structured interviews were conducted and telephone interviews were employed for this purpose. Collected interview data were transcribed for analysis. The findings illuminate ways in which college experience affects the graduates’ lives many years after they graduated. Based on the findings, implications for research and practice are discussed.

As bilingual scholarship increasingly examines who counts in bilingual education, we join the conversation by exploring how two DLBE teachers’ languaging is often marginalized, even in bilingual education contexts. In this comparative case study, we draw from the testimonios of two differently racialized DLBE teachers (one of Mexican and one Persian heritage) in the U.S. to answer the research questions: (a) Which forms of languaging do the DLBE teachers employ across their lives? and (b) How is their languaging privileged or marginalized within official bilingual education spaces? Taking a raciolinguistic perspective, findings reveal both participants share a variety of complex, sophisticated languaging over various spaces of their lives, with only a fraction of one participant’s languaging being sanctioned in school. The other participant’s languaging is invisibilized significantly more. In turn, the researchers call for more expansive bilingual policies that affirm and extend whose languaging and what languaging counts in schools.

The Big Bang Nucleosynthesis (BBN) model is a cornerstone for the understanding of the evolution of the early universe, making seminal predictions that are in outstanding agreement with the present observation of light element abundances in the universe. Perhaps, the only remaining issue to be solved by theory is the so-called “lithium abundance problem". Dedicated experimental efforts to measure the relevant nuclear cross sections used as input of the model have lead to an increased level of accuracy in the prediction of the light element primordial abundances. The rise of indirect experimental techniques during the preceding few decades has permitted the access of reaction information beyond the limitations of direct measurements. New theoreticaldevelopments have also opened a fertile ground for tests of physics beyond the standard model of atomic,nuclear, statistics, and particle physics. We review the latest contributions of our group for possible solutions of the lithium problem.

Participation in experimental studies can be conceptualized as Goffmanian frames, i.e. a set of rules which include the fact the experimenter will be observing participant behavior through (the recording of) the experiment. This study is focused on frame breaches in 16 video- and audio-recorded dyadic conversations taking place in an experimental setting. Our main conclusion is that the experimental frame is conceptualized by participants as including constraints that go beyond non-experimental interactions, and in particular the need to mitigate frame breaches, which are seen as face-threatening. Analyses revealed that participants only broke the research frame after they completed the task they were assigned by the researcher, and that breaches did not necessarily correspond to changes in key. Insights gained in relation to face and mitigation are discussed, as well as the participants’ need to determine their next steps once the research purpose has been perceived complete.

Prediction of changes in biomedical signals, such as vital signs, is useful for many clinical applications. Several signal prediction (forecasting) tools were developed, but their evaluation and applicability to a specific clinical use is context dependent. In this work, we propose a novel method to tackle the problem of evaluation and comparison of vital sign predictors for intervention based clinical studies. The proposed prediction quality measures are particularly well-suited for forecasting rare events scenarios. Specifically, using the novel metrics, we measure the prediction statistics and compare nine deep learning and autoregressive forecasting models for multi-step prediction of rare bradycardia events in preterm infants, however the new concepts allow applications to other biomedical signals. We validated the novel metrics with experimental results on testing sets with several days of vital sign recordings. Our results show that simple statistical predictors could outperform state-of-the-art deep learning architectures for low-dimensional signals.

Colloids of natural river water is a key intermediate carrier of lead (Pb). It is important to monitor the transport–transformation behavior of Pb in the colloidal phase of seaward water because this behavior is related to the levels of pollution input and environmental risks posed to the sea, especially in coastal delta areas. In this study, the fractionation behavior and distribution of toxic Pb from the truly dissolved phase and the different colloidal phases in seven seaward rivers in the Yellow River Delta were investigated. The concentrations of total dissolved Pb, truly dissolved Pb, and colloidal Pb were 0.99–40.09 μg L–1, 0.40–8.10 μg L–1, and 0.60–35.88 μg L–1, respectively. In freshwater rivers, the main component of total dissolved Pb (about > 50%) is truly dissolved Pb but the main component of total dissolved Pb in the seawater environment is colloidal Pb (> 80%). A dramatic increase in salinity causes the deposition (about ≈94%) of all forms of Pb to sediment from estuarine water in winter. However, this sedimentation behavior of colloidal Pb gradually decreases (in the Shenxiangou River) when the river salinity approaches seawater salinity (S = ≈29). In the industrial port (Xiaoqinghe River) and mariculture (Yongfenghe River) estuarine areas, which have extensive seawater, the deposition behavior of colloidal Pb (<15%) is less affected by the change in salinity. This suggests that human activity contributes to the spread of Pb in the offshore environment. The concentration of 100 kDa–0.22 μm Pb has a postive correlation with total colloidal Pb. Its variation is minimally affected by salinity compared with other colloidal components. In addition, the correlation between the molecular weight and aromaticity of chromophoric dissolved organic matter (CDOM) and colloidal Pb suggests that macromolecules in seawater will be important transport carriers of Pb. In all, truly dissolved Pb is the main transport form of dissolved Pb in river freshwater; however, in brackish water in estuaries, colloidal matter gradually becomes the main transport carrier. Surging salinity immobilizes truly dissolved Pb in the estuarine region, but colloidal matter inhibits this deposition. Colloidal phase is the important conversion for land–sea transport of Pb by seaward rivers.

Texas–Mexico border region is a unique place where two countries and culture connected. We sought to investigate border school district students’ academic performance as measured by Texas standardized test: the State of Texas Assessments of Academic Readiness (STAAR). To do so, we first used propensity score matching (PSM) techniques to analyze data collected from a public database: Texas Assessment Management System (TAMS). Specifically, we provided a PSM analysis of non-border and border school districts regarding their demographic characteristics [i.e., identified as a rural district, percentage of economically disadvantaged (ED) students, percentage of English learners (ELs), mobility rate, instructional hours, principal experience, teacher experience, teacher–student ratio, and teacher turnover rate]. Then, multiple regression analyses were conducted to compare Texas border and non-border school students’ reading, math, and science achievements, respectively, based on a matching sample with control for demographic variables. The results of the current study indicate that no significant difference was found between border and non-border school districts, regarding students’ academic performance in reading, math, and science, when districts were matched and demographic characteristics were controlled. We further found that demographic variables, such as percent of ED students, principal experience, and teacher turnover rate, significantly impact students’ academic achievement. Such findings have suggested that the achievement gap between border and non-border districts can be closed if extra support can be provided to ED students, and funding could be allocated in border districts to maintain experienced principals and teachers.

Federal funding agencies have invested significant resources supporting evidence-based, innovative approaches that address education problems and incorporate rigorous design and evaluation, particularly through randomized controlled trials (RCTs), the gold standard for yielding causal inference in scientific research. In this study, we introduced factors (i.e., evaluation design, attrition, outcome measures, analytic approach, and fidelity of implementation) that are often times required in the Federal Notice for application by the U.S. Department of Education, with an emphasis on What Works Clearinghouse (WWC) standards. We further presented a federally funded research protocol with a multi-year, clustered RCT design to determine the impact of an instructional intervention on students' academic performance in high-needs schools. In the protocol, we elaborated on how our research design, evaluation plan, power analysis, as well as confirmatory research questions and analytical approaches were aligned with the grant requirement and WWC standards. We intend to provide a road map to meeting WWC standards and to increase the likelihood of successful grant applications.

Owing to the remarkable development of deep learning technology, there have been a series of efforts to build deep learning-based climate models. Whereas most of them utilize recurrent neural networks and/or graph neural networks, we design a novel climate model based on two concepts, the neural ordinary differential equation (NODE) and the advection–diffusion equation. The advection–diffusion equation is widely used for climate modeling because it describes many physical processes involving Brownian and bulk motions in climate systems. On the other hand, NODEs are to learn a latent governing equation of ODE from data. In our presented method, we combine them into a single framework and propose a concept, called neural advection–diffusion equation (NADE). Our NADE, equipped with the advection–diffusion equation and one more additional neural network to model inherent uncertainty, can learn an appropriate latent governing equation that best describes a given climate dataset. In our experiments with three real-world and two synthetic datasets and fourteen baselines, our method consistently outperforms existing baselines by non-trivial margins.