The Arab Community Center for Economic and Social Services (ACCESS) is a human services organization committed to the development of the Arab American community. ACCESS helps low-income families, as well as newly arrived immigrants adapt to life in the United States. Its goal is to foster a greater understanding of Arab culture in the U.S. and in the Arab world. ACCESS provides social, mental health, educational, artistic, employment, legal, and medical services..
The aim of the study was to clarify how ethnic identity may impact poor mental health outcomes related to discrimination among Arab American adults in Southeast Michigan, USA. 286 respondents completed a health attitudes and behaviors survey. We used structural equation modeling with path and multi-group analyses to examine moderation effects of ethnic identity on the relationship between discrimination and depression and anxiety, and further moderation based on gender. Ethnic identity positively buffered against depression and anxiety associated with discrimination. In the subgroup analysis, ethnic identity was protective for female participants, though not male participants. These findings provide evidence for ethnic identity as a buffer between discrimination and poor mental health among Arab American adults. Mechanisms may include feelings of belonging and social support. The stronger effects for women may be due to their role of transmitting cultural and religious traditions. Future interventions should incorporate ethnic identity as a protective feature for mental health.
Fast prediction of microstructural responses based on realistic material topology is vital for linking process, structure, and properties. This work presents a digital framework for metallic materials using microscale features. We explore deep learning for two primary goals: (1) segmenting experimental images to extract microstructural topology, translated into spatial property distributions; and (2) learning mappings from digital microstructures to mechanical fields using physics-informed operator learning. Loss functions are formulated using discretized weak or strong forms, and boundary conditions-Dirichlet and periodic-are embedded in the network. Input space is reduced to focus on key features of 2D and 3D materials, and generalization to varying loads and input topologies are demonstrated. Compared to FEM and FFT solvers, our models yield errors under 1–5% for averaged quantities and are over 1000× faster during 3D inference.
Laser and wire-based processes have shown strong potential for cladding and additive manufacturing of aerospace components. This study develops a process map for coaxial laser metal deposition using cold wire (LMD-W) through single-track experiments on Inconel 718. The effects of process parameters on track cross-sectional dimensions are examined, and multi-track walls are produced under various conditions without internal defects. The influence of wire feed rate and energy density on build rate is analyzed, revealing the wire feed rate as the dominant factor; however, at constant feed rate, energy density becomes the key parameter, directly affecting both build rate and microstructure size. For a fixed wire diameter, comparisons with other technologies are made, and strategies for build-rate optimization via parameter tuning are explored. Cooling rates, estimated from primary dendritic arm spacing and verified through thermal imaging, range from 10^3 ^∘C/s to 10^4 ^∘C/s , depending on input energy. These findings position LMD-W as an intermediate process in terms of cooling rate and build rate within the additive manufacturing spectrum.
In this work, two different variants of image segmentation are compared to evaluate the use of generalized machine learning models against the accuracy of bespoke models to further their use for the analysis of microstructure images with multiple phases. The results from the analysis are then used to evaluate the effect of different iron contents and the presence or absence of convection on the formation of the microstructure with emphasis on the development of the intermetallic phases in technical aluminum alloys. To this end, the study focuses on aluminum-silicon base cast alloys with high iron content directionally solidified under microgravity conditions, with additional controlled convection created by a rotating magnetic field. Optical microscopy images from the different processing zones are then used to train the different chosen models, which are afterwards used to segment and analyze the microstructures. Key results include the evaluation of the effects of convection and iron content on several parameters describing the different intermetallic phases as well as the comparison of the models.
Nursing handover education is critical for ensuring patient safety; however, persistent gaps remain between educational approaches and clinical handover practice. A sequential mixed-methods study was conducted with 52 nursing students and 53 registered nurses. Qualitative interviews identified key educational needs, which informed the development of a survey. The survey results were analyzed using descriptive statistics and Importance–Performance Analysis. Both groups identified structured handover methods, standardized frameworks such as SBAR, and authentic patient data as essential components for effective handover. Nevertheless, satisfaction was low regarding small-group learning, situational overview materials, and performance-based feedback. Nursing students rated the importance of detailed patient information and interactive learning environments significantly higher than nurses did ( P < .05). These findings suggest that nursing curricula should integrate standardized communication tools, simulation-based training with authentic cases, and learner-centered strategies. Enhancing situational overviews, interactive environments, and formative feedback could contribute to improved handover competency and ultimately improve patient safety outcomes.
