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Accurate numerical values of aerodynamic parameters are important in aircraft design. The knowledge of stability and control aerodynamic parameters is essential to postulate high-fidelity control laws. The aerodynamic forces and moments are strong functions of the angle of attack (AOA), Reynolds number, and control surface deflections. Typically, conventional estimation techniques such as maximum likelihood (MLE) and least-squares (LS) principles facilitate the determination of these parameters. Unsteady aerodynamics may complicate the estimation of aerodynamic parameters at high AOA. Data-driven techniques employing neural networks provide an alternative for modeling the system behavior based on its observed state and control input variables. Nonlinearity increases because of flow separation at high AOA, which is close to stall. This paper explores the feasibility of employing a machine learning approach using neural networks to predict aircraft dynamics in a limited sense to identify aerodynamic characteristics. Integrating a neural network with the artificial bee colony (ABC) method facilitated the optimization of unknowns of the proposed aerodynamic model (AM). The proposed neural artificial bee colony (NABC) optimization approach estimated the longitudinal dynamics and stall properties for two experimental aircraft. Comparison of the estimates provided by the NABC approach with those of the standard MLE and neural Gauss–Newton (NGN) techniques established its efficacy. Furthermore, robust statistical analysis indicated that the proposed method provides a viable alternative for parameter estimation in nonlinear applications. [ABSTRACT FROM AUTHOR]

This article discusses a study conducted in an urban slum in Bengaluru, India, which examined the use of neck circumference (NC) as a screening tool for overweight and obesity. The study involved 180 subjects between the ages of 18 and 40 and found that NC measurements above certain thresholds indicated a need for further evaluation to address the risks of obesity-related diseases. The study suggests that NC can be a simple and inexpensive measure to identify overweight and obese individuals, particularly in resource-poor healthcare settings. However, the study had limitations and further research is needed to establish proper cutoff points. [Extracted from the article]

A study conducted on female undergraduate medical students in Bengaluru found a high prevalence of body image dissatisfaction (BID) among the participants. The study revealed that factors such as gender disparities, body dysmorphic disorder, and societal pressure to be thin contributed to the high rates of BID. The findings suggest a need for increased awareness and interventions to address body image issues among medical students, as their attitudes towards their own bodies can impact their patients' health and behaviors. However, it is important to note that the study primarily focused on female students from a single college, so the results may not be applicable to all medical students. [Extracted from the article]

This article discusses a study conducted in Bengaluru, India, which examined the prevalence of text neck syndrome among medical college students. The study found that a majority of students experienced moderate to severe text neck syndrome, and a significant percentage also had severe nomophobia (fear of being without a mobile phone). The study suggests that students should limit their smartphone use and practice good posture to prevent these conditions. It emphasizes the importance of raising awareness about proper posture and ergonomics while using electronic devices, particularly among students who are at a higher risk of developing text neck syndrome. [Extracted from the article]