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We investigate and characterize the effect of compressibility and rarefaction on vortex structures in the benchmark lid-driven cavity flow. Direct numerical simulations are performed, employing the unified gas kinetic scheme to examine the changes in vortex generation mechanisms and the resulting flow structures at different Mach and Knudsen numbers. At high degrees of rarefaction, where inter-molecular interactions are minimal, the molecules mainly collide with the walls. Consequently, the dominant flow structure is a single vortex in the shape of the cavity. It is shown that increasing compressibility or decreasing rarefaction lead to higher molecular density in the cavity corners, due to more frequent inter-molecular collisions. This results in lower flow velocities, creating conditions conducive to the development of secondary and corner vortices. The physical processes underlying vortex formations at different Knudsen numbers, Mach numbers, and cavity shapes are explicated. A parametric map that classifies different regimes of vortex structures as a function of compressibility, rarefaction, and cavity shape is developed. [ABSTRACT FROM AUTHOR]

Introduction: Public speaking skills are crucial skills for medical students, enabling effective communication in their academic and future professional roles. However, public speaking anxiety (PSA) can hinder their full potential and warrant early intervention. This study aims to determine the prevalence of PSA and associated factors among medical undergraduates at a Malaysian public university. Materials and methods: A cross-sectional study was conducted using a validated and reliable online self-administered questionnaire. The Personal Report of Public Speaking Anxiety (PRPSA) questionnaire was used to assess PSA level and other factors such as sociodemographic, English proficiency level, public speaking training and experience, and the CompACT questionnaire for psychological flexibility. Descriptive, bivariate, and multivariate analyses were performed. Results: The analysis included a total of 362 respondents. The prevalence of moderate and high PSA was 49.3%. There was a significant association between race, psychological flexibility, public speaking experience, public speaking training, and PSA (p<0.05). Multiple logistic regression analysis showed that having high to moderate PSA was significantly linked to being 19-21 years old (AOR: 1.70, 95% CI: 1.081-2.678) and never having had public speaking training (AOR: 1.865, 95% CI: 1.067-3.261). On the other hand, being more psychologically flexible was a protective factor (AOR: 0.944, 95% CI: 0.925-0.964). Conclusion: The high prevalence of PSA among medical students highlights the need for early recognition, screening, and intervention within the education system. Incorporating public speaking training and interventions to enhance psychological flexibility within the curriculum can be beneficial to prevent PSA for better-quality graduates. [ABSTRACT FROM AUTHOR]

The objective of the present study was to characterize the liver oil extracted from the deep sea shark, Echinorhinus brucus, caught from Central Indian Ocean and to evaluate its cytotoxic effect on neuroblastoma cell line (SHSY-5Y). Characterization of liver oil of Echinorhinus brucus revealed the presence of palmitic acid (15%), oleic acid (12%), stearic acid (8%), docosahexaenoic acid (DHA) (18%), and eicosapentaenoic acid (EPA) (16%). It was also found to be a good source of squalene (38.5%) and fat soluble vitamins such as A, D, and K (vitamin A: 17.08 mg/100 g of oil, vitamin D: 15.04 mg/100 g oil, and vitamin K: 11.45 mg/100 g oil). Since it was found to be rich in essential fatty acids, fat soluble vitamins, and squalene, it can be considered as better dietary supplement. The oil of Echinorhinus brucus also showed high in vitro cytotoxic effect against the human neuroblastoma cell line (SHSY-5Y) and the IC50 value laid between 35 and 45 ng. [ABSTRACT FROM AUTHOR]

Lead zirconate titanate actuators and sensors have been the widely used in Lamb wave–based damage detection applications. The excitation frequency, waveform, and wave propagation characteristics should be comprehensively considered to effectively conduct diagnosis of incipient forms of damage. In this article, we investigate Lamb wave propagation in a beam under lead zirconate titanate actuation/sensing, in which the lead zirconate titanate effects are included. First, mathematical models are developed to account for both unimorph (i.e. sensor mode) and bimorph (i.e. actuator mode) configurations. The Timoshenko beam theory is adopted for both base beam and lead zirconate titanate layers to accommodate high-frequency responses. Second, the fully coupled electromechanical governing equations are determined and solved in an analytical form to formulate the spectral finite element model. Finally, parametric studies are carried out to determine the optimal actuation frequency, sensor size, actuator, and sensor placement. Our spectral finite element model predictions are validated by the experimental data and results in the literature. The application of spectral finite element model in the Lamb wave–based damage detection is demonstrated as well. In summary, the newly developed spectral finite element model provides an analytical framework in which to predict Lamb wave propagation under lead zirconate titanate actuation and sensing, as well as to develop new interrogation schemes. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI) in healthcare can enhance clinical workflows and diagnoses, particularly in large-scale operations like COVID-19 mass testing. This study presents a deep Convolutional Neural Network (CNN) model for automated COVID-19 RATD image classification., Methods: To address the absence of a RATD image dataset, we crowdsourced 900 real-world images focusing on positive and negative cases. Rigorous data augmentation and StyleGAN2-ADA generated simulated images to overcome dataset limitations and class imbalances., Results: The best CNN model achieved a 93% validation accuracy. Test accuracies were 88% for simulated datasets and 82% for real datasets. Augmenting simulated images during training did not significantly improve real-world test image performance but enhanced simulated test image performance., Discussion: The findings of this study highlight the potential of the developed model in expediting COVID-19 testing processes and facilitating large-scale testing and tracking systems. The study also underscores the challenges in designing and developing such models, emphasizing the importance of addressing dataset limitations and class imbalances., Conclusion: This research contributes to the deployment of large-scale testing and tracking systems, offering insights into the potential applications of AI in mitigating outbreaks similar to COVID-19. Future work could focus on refining the model and exploring its adaptability to other healthcare scenarios., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Pannipulath Venugopal, Babu Saheer and Maktabdar Oghaz.)