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The experimental and numerical investigation of convective heat transfer in a rectangular cavity of aspect ratio (L/H) = 5 has been presented in this study. The comparison of experimental results is done with a two-dimensional numerical model considered with steady and turbulent flow conditions. Six different arrangements (bottom/bottom/bottom, top/top/top, right/top/left, bottom/top/right, bottom/top/left, and bottom/right/left) were considered. The lowest and highest hotspot temperatures were observed in bottom/bottom/bottom and bottom/right/left arrangements, respectively. For similar configurations, the bottom/bottom/bottom arrangement displays better heat transfer performance, while heat transfer performance is inferior for the bottom/right/left arrangement than all other configurations. [ABSTRACT FROM PUBLISHER]

Objective: In the evolving landscape of medical research and radiology, effective communication of intricate ideas is imperative, with visualizations playing a crucial role. This study explores the transformative potential of ChatGPT4, a powerful Large Language Model (LLM), in automating the creation of schematics and figures for radiology research papers, specifically focusing on its implications for musculoskeletal studies. Materials and methods: Deploying ChatGPT4, the study aimed to assess the model's ability to generate anatomical images of six large joints—shoulder, elbow, wrist, hip, knee, and ankle. Four variations of a text prompt were utilized, to generate a coronal illustration with annotations for each joint. Evaluation parameters included anatomical correctness, correctness of annotations, aesthetic nature of illustrations, usability of figures in research papers, and cost-effectiveness. Four panellists performed the assessment using a 5-point Likert Scale. Results: Overall analysis of the 24 illustrations encompassing the six joints of interest (4 of each) revealed significant limitations in ChatGPT4's performance. The anatomical design ranged from poor to good, all of the illustrations received a below-average rating for annotation, with the majority assessed as poor. All of them ranked below average for usability in research papers. There was good agreement between raters across all domains (ICC = 0.61). Conclusion: While LLMs like ChatGPT4 present promising prospects for rapid figure generation, their current capabilities fall short of meeting the rigorous standards demanded by musculoskeletal radiology research. Future developments should focus on iterative refinement processes to enhance the realism of LLM-generated musculoskeletal schematics. [ABSTRACT FROM AUTHOR]

Doped n- and p-type hydrogenated silicon (Si:H) thin films prepared by plasma enhanced chemical vapor deposition have been investigated for uncooled microbolometer applications. The material microstructure has been studied by in situ real time spectroscopic ellipsometry collected during thin film deposition or ex situ spectroscopic ellipsometry measurements on a static sample with a multiple sample analysis technique. The key electrical properties of interest, including film resistivity (ρ), temperature coefficient of resistance (TCR), and 1/f noise, have been measured as a function of deposition conditions for p-type amorphous hydrogenated silicon (a-Si:H) films and microcrystalline content for n-type amorphous (a), microcrystalline (µc), and mixed-phase amorphous + microcrystalline (a + µc) Si:H films. The TCR and 1/f noise values were compared for p- and n-type a-Si:H samples in the resistivity range of 100 < ρ < 3000 Ω cm and show that for a given resistivity, amorphous p-type films exhibit a lower 1/f noise, which might be expected due to a larger density of majority carriers. [ABSTRACT FROM AUTHOR]