Background: This paper performs a detailed ordinal logistic regression study in an evaluation of a survey at a university in South Texas, USA. We show that, for categorical data in our case, ordinal logistic regression works well. Methods: The survey was designed according to the guidelines in diet and lifestyle from the American Heart Association and the United States Department of Agriculture and was sent out to all registered students at Texas A&M International University in Laredo, Texas. Data analysis included 601 students’ results from the survey. Data analysis was conducted in Rstudio. Results: The results showed that, compared with students who do not have enough whole grain food and exercise, those who have enough in both tend to have normal BMIs. As age increases, BMI tends to be out of the normal range. Conclusions: Because BMI in this research has three categories, applying an ordinal logistic regression model to describe the relationship between an ordered categorical response variable and more explanatory variables has several advantages compared with other models, such as the linear regression model.

The Association of College and University Educators (ACUE) course in effective teaching practices has been applied to an undergraduate mathematics class at Texas A and M International University (TAMIU) for the past two years (Spring 2022, Fall 2022 and Spring 2023). Practices such as ensuring equitable access to learning, embracing diversity in the classroom, providing useful feedback and teaching powerful note-taking skills were applied throughout the whole semester in class. This study focuses on the comparison between the same class in Spring 2022 when ACUE’s effective teaching practices were not present and the classes in which ACUE was present in Fall 2022 and Spring 2023; in total, 89 students from two classes were analyzed with identification data removed. Ordinal logistic regression is used to analyze how effective teaching practices are related to students' final grades by analyzing students' grades, assignment submissions and class participation in different classes. Fisher’s exact test is applied to data analysis to check how effective teaching practices affect class participation; results show that for students who were from the class with ACUE practices, the odds of being more likely (i.e., “very” with a value of 2 or “somewhat likely” with a value of 1 versus “unlikely” with a value of 0) to have a passing grade is 1.92 times that of students who were from the class without ACUE practices, holding constant all other variables such as homework assignments and class participation. The impact of effective teaching practices is statistically significant on class participation (p-value = 0.04); this study concurs with the previous studies that ACUE’s effective teaching practices have positively improved students’ performance and should be applied to college classrooms. Ordinal logistic regression was the first time applied in analyzing ACUE effectiveness in college classes.

Objectives To analyze the possible factors causing fatty liver in children based on ultrasound data of children in south Texas, and to establish machine learning models of fatty liver in children to provide ideas for the prevention and treatment of fatty liver in children. Methods The binary classification model of fatty liver problem in obese children in Texas was established under the multiple model. First, we selected important features using the CatBoost algorithm. Second, the best parameters of the algorithm were selected on the training set and the validation set by using the grid search method, and all six models were tested on the test set. The six models then were compared by area under the curve value, precision, accuracy, recall rate, and F1 score in a model evaluation. Then, two algorithms, logic regression and CatBoost, were selected to establish prediction models of fatty liver disease in children. Results We selected body mass index, height, liver size, kidney volume, glomerular filtration rate, and liver diameter as the features used in the machine learning model. The prediction models we chose showed that children with higher body mass index at the same age tended to have a greater probability of fatty liver. Conclusions Based on the analysis of the results of the two prediction models established by logistic regression and CatBoost, we determined that the mean probability of fatty liver in severely obese children was between 74.47% and 92.22%, 73.45% and 85.41% in obese children, and slightly higher in boys than in girls, with a mean difference of 3.00% to 3.95%.

Teaching effectively in college is not only key for student’ satisfaction in classroom but also for performance. This research, as a pilot study, has analyzed the survey and course evaluation results from an undergraduate Mathematics class, where The Association of College and University Educators’ (ACUE) effective teaching practices are applied. Results have shown the positive effects from ACUE practices. At the same time, differences in student performance in the same courses from different semesters have been discussed. With the same course content and learning outcomes, teaching with ACUE practices has resulted in better performance throughout the semester. The research compared course evaluations from the same courses in different semesters when including and not including ACUE practices. Results show that students are more satisfied with the courses using ACUE practices. Researcher has made suggestion that ACUE practices should be recommended for use by faculty members in universities and colleges to improve student performance and course satisfaction.

With the emergence and development of application requirements such as data analysis and publishing, it is particularly important to use differential privacy protection technology to provide more reliable, secure, and compliant datasets for research in the field of children’s health. This paper focuses on the differential privacy protection of the ultrasound examination health dataset of adolescents in southern Texas from three aspects: differential privacy protection with output perturbation on basic statistics, publication of differential privacy marginal histogram and synthesized data, and a machine learning differential privacy learning algorithm. Firstly, differential privacy protection results with output perturbation show that Laplace and Gaussian mechanisms for numerical data, as well as the exponential mechanism for non-numerical data, can achieve the goal of protecting privacy. The exponential mechanism provides higher privacy protection. Secondly, a differential privacy marginal histogram with four attributes can be obtained with an appropriate privacy budget that approximates the marginal histogram of the original data. In order to publish synthetic data, we construct a synthetic query to obtain the corresponding differential privacy histogram for two attributes. Further, a synthetic dataset can be constructed by following the data distribution of the original dataset and the quality of the synthetic data publication can also be evaluated by the mean square error and error rate. Finally, consider a differential privacy logistic regression model under machine learning to predict whether children have fatty liver in binary classification tasks. The experimental results show that the model combined with quadratic perturbation has better accuracy and privacy protection. This paper can provide differential privacy protection models under different demands, which provides important data release and analysis options for data managers and research organizations, in addition to enriching the research on child health data releasing and mining.