The obesity epidemic is a global health concern. In the United States alone, 68.5% of adults are categorized as overweight or obese; of these, 35.1% are considered obese. Obesity is a leading cause of morbidity and mortality from diabetes and cardiovascular disease, two diseases adversely affecting minority groups such as Mexican Americans. Yet, a modest 5% decrease in weight, through changes in diet and physical activity, can help control type 2 diabetes. The current study extracted the dietary data and selected outcome variables from Beyond Sabor, a 12 week intervention conducted in the Lower Rio Grande Valley, Texas, a predominantly Mexican American disadvantaged community. Social Cognitive Theory, guided the design of this culturally tailored intervention. Community resources and natural helpers emerged through the utilization of community based participatory research methods. Study participants (n= 1,273) were recruited from local food bank sites and randomized into treatment and control groups. The treatment group received 12 weekly sessions focusing on healthier eating habits, cooking methods, and physical activity. The control group received 6 nutrition education sessions on similar topics. The study measured changes in several food groups including consumption of soda, fruit juice, and fruit and vegetables. A repeated measures Analysis of Variance was employed to determine changes in treatment and control groups from baseline, post intervention and 40 week follow up. The results showed a significant decrease in soda (F= 8.48, p< .001) and fruit juice (F= 3.12, p= .045) consumption for both groups, with a particular decrease in soda for the treatment group. In addition, there was a significant increase in fruit (F=15.32, p< .001) and vegetable (F=3.16, p= .04) consumption in both groups. The outcome variables selected were weight, body mass index (BMI), and fasting plasma glucose (FPG). There were significant changes for all three variables over time. The intervention resulted in changes in dietary behaviors that ultimately led to changes in weight, BMI, and FPG. It is evident from the current study, that the use of community based helpers facilitated changes in food habits. This study serves as a prognosticator for future interventions.

Abstract Fatal coronary heart disease among African Americans is associated with a disproportionate burden of cardiovascular disease (CVD) risk factors. Research has indicated that CVD risk factor knowledge and the prevalence of ideal CVH both persist at suboptimal levels. However, few researchers have investigated the relationship between culturally-tailored community-based heart health sessions, short-term knowledge acquisition of CVD risk factors, and the awareness of the American Heart Association's (AHA's) CVH construct. The purpose of this cross-sectional, secondary analysis study was to examine the interplay between these variables in an urban African American sample. Guided by social cognitive theory, the study analyzed de-identified data (data sets of demographic characteristics and Heart Disease Facts Questionnaire) from participant responses collected at multiple community sites to assist in the planning of future health programs. Multiple community sites were randomized into an intervention (n = 50) or comparison group (n = 57). Pearson's correlation and multiple regression were used to analyze data. Knowledge was higher for intervention group participants (β =.44, p = .001) and tended to be higher for those with more education (β = .20, p = .06) and those with less income (β = -.22, p = .07). Notably, most participants (73%) reported awareness of the AHA construct, CVH. The results support culturally-tailored interventions as a useful strategy for CVD risk reduction. The implication for social change is that initiatives at the community-level may positively impact CVH in minority/ethnic communities and subsequently impact CVD disparities.

The recent release of AI tools for public use allows for the development of novel teaching approaches for goals that often present challenges in the classroom, such as the need for personalized learning materials. The current study enlists a four-week ChatGPT-based personalized learning intervention in tandem with a teacher questionnaire and interviews in two upper-secondary schools in Southern Sweden to investigate English teacher perceptions of the benefits and challenges of using AI for personalized language learning. In addition, the intervention investigates the potential effectiveness of personalized learning assignments using ChatGPT on the development of students’ grammar abilities in a specific, local classroom context to both address a local need at the school in question and to serve as a proof of concept for more broad-based, future research on the use of these tools for this purpose. The questionnaire revealed that teachers initially had some concerns regarding the accuracy, reliability, and practical implementation of such tools. However, the intervention was found to significantly reduce grammar errors in student writing, and in follow-up interviews, teachers reported feeling more receptive to such approaches after interacting with the tools and seeing the beneficial results. These findings demonstrate that teachers may be hesitant to implement AI tools, which underscores the importance of training and first-hand use for promoting their successful adoption into pedagogical practices. In addition, the findings suggest that AI-based tools for personalized language learning may also be successful in a broader educational context. Finally, certain limitations, such as the small sample size, are acknowledged which emphasizes that further research is necessary to acquire a more comprehensive understanding of personalized learning using AI-based tools like ChatGPT.

No description available.

The rise of additive manufacturing has enabled new degrees of freedom in terms of design and functionality. In this context, this contribution addresses the design and characterization of structural unit cells that are intended as building blocks of highly porous lattice structures with tailored properties. While typical lattice structures are often composed of gyroid or diamond lattices, this study presents stackable unit cells of different sizes created by a generative design approach tomeet boundary conditions such as printability and homogeneous stress distributions under a given mechanical load. Suitable laser powder bed fusion (LPBF) parameterswere determined forAlSi10Mg to ensure high resolution and process reproducibility for all considered unit cells. Stacks of unit cells were integrated into tensile and pressure test specimens for which the mechanical performance of the cells was evaluated. Experimentally measured material properties, applied process parameters, and mechanical test results were employed for calibration and validation of finite element (FE) simulations of both the LPBF process as well as the subsequent mechanical characterization. The obtained data therefore provides the basis to combine the different unit cells into tailored lattice structures and to numerically investigate the local variation of properties in the resulting structures. / Durch die Einführung der Additiven Fertigung können neue Freiheitsgrade in Bezug auf Gestaltungsfreiheit und Funktionalität erreicht werden. In diesem Zusammenhang adressiert dieser Beitrag das Design und die Charakterisierung struktureller Einheitszellen als Bausteine für hochgradig poröse Gitterstrukturen mit maßgeschneiderten Eigenschaften. Während typische Gitterstrukturen oft auf Gyroid- oder Diamantstrukturen basieren, präsentiert dieser Beitrag stapelbare Einheitszellen unterschiedlicher Größe, die durch einen generativen Designansatz erstellt wurden. Hierdurch sollen verschiedene Randbedingungen wie eine gute Druckbarkeit und homogene Spannungsverteilung unter gegebenen mechanischen Lasten erreicht werden. Um eine hohe Auflösung und Reproduzierbarkeit der Einheitszellen zu erreichen, wurden für den verwendeten Werkstoff AlSi10Mg geeignete Druckparameter für das Laserstrahlschmelzen (LPBF) ermittelt. Stapel von Einheitszellen wurden in Zug- und Druckproben integriert, anhand derer die mechanische Stabilität der Zellen ermittelt wurde. Experimentell bestimmte Materialeigenschaften, die verwendeten Prozessparameter und die Ergebnisse der mechanischen Untersuchungen wurden anschließend für die Kalibrierung und Validierung Finiter Elemente (FE) Simulationen herangezogen, wobei simulationsseitig sowohl der Prozess des Laserstrahlschmelzens als auch die nachgelagerte mechanische Charakterisierung berücksichtigt wurden. Die hier präsentierten Ergebnisse sollen als Basis sowohl für eine gezielte Anordnung der Einheitszellen zu maßgeschneiderten Gitterstrukturen dienen als auch für die numerische Auswertung der lokal variierenden Eigenschaften der somit resultierenden Strukturen.

Focusing on personalized grammar tasks, this study dives into the integration of Generative Artificial Intelligence into English as a Second Language education. By utilizing a mixed methods approach, incorporating both qualitative and quantitative analyses the study explores how personalized learning can be improved by employing ChatGPT. Results from the study indicate that GAI-driven personalization significantly enhances student engagement and motivation. This offers a promising path for tailoring education to individual learner needs toward a more inclusive classroom. A central outcome of this study is the proposal of a new theoretical framework the Personalization-Motivation Integration Framework (PMIF). This framework clarifies the synergistic effects of integrating content and topic personalization to significantly boost student motivation and reach a more inclusive learning environment. This adds to the growing research about AI's potential in education as it indicates that these technologies can significantly enhance teaching and offer a more tailored and inclusive learning environment.

[pt] Esta pesquisa de tese teve como objetivo explorar a análise de mídias sociais, especialmente as disponíveis em comunidades online de sites de redes sociais, a fim de realizar estudos sociais sobre questões de saúde. Com base em uma abordagem prática foi definido um processo para realizar esses estudos. Este processo contou com ferramentas computacionais adaptados para fornecer apoio em tarefas específicas, tais como recuperação de conteúdo, seleção e análise. Duas ferramentas que se destacam são apresentadas por causa de sua utilidade e a complexidade do processo em que a sua construção se baseou. Para o benefício da análise de comunidades online, o Mapa de Associação de Comunidades é um processo desenvolvido para apoiar especialistas em compreender os interesses dos usuários com base em suas associações dentro de suas comunidades. A outra ferramenta visa auxiliar analistas a selecionar discussões de fóruns online a serem analisados manualmente com técnicas de pesquisa qualitativa, por exemplo, análise de conteúdo e do discurso. Esta ferramenta, TorchSR, foi criada baseada em aprendizado de máquina não supervisionado, usando agrupamento hierárquico, para dar suporte na resolução do problema de seleção de conteúdo. Um estudo de caso exploratório mostra que esta ferramenta ajuda na resolução do problema. O processo proposto foi utilizado em dois estudos sobre questões relevantes de saúde (hepatite C e o abuso de drogas), que resultou em descobertas relevantes sobre saúde pública. Em conclusão, este trabalho apresenta a aplicação prática de ciência social computacional no campo da saúde, através do desenvolvimento de um processo e ferramentas utilizadas para apoiar os analistas e melhorar a sua aplicação. / [en] This research thesis is aiming to exploit valuable social media, especially those available in online communities of social network sites, in order to perform social studies about healthcare issues. Based on a practical approach, a process was defined to conduct such studies. This process relied on tailored computational tools to provide support for specific tasks such as contente retrieval, selection, and analysis. Two tools that stand out are presented because of their utility and the complexity of the process in which their development was based on. The first tool, for the benefit of online community analysis, is the Community Association Map, a process developed to support experts in understanding users’ interests based on their associations within their communities. Our second tool (TorchSR) aims to aid analysts in the selection of discussions from online forums to be manually analyzed by (qualitative) research techniques (e.g. content and discourse analysis). This task, which was defined as solving the content selection problem, was tackled with a tool based on unsupervised machine learning techniques, such as hierarchical clustering. An exploratory study case shows that TorchSR helps analysts in dealing with the problem. The proposed process was employed in two studies about relevant healthcare issues (i.e. hepatitis C and drug abuse) which resulted in interesting findings in the field of public health. In conclusion, this thesis presents a practical application of computational social science to the field of health, through development of a process and tools used to support analysts and improve its application.