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Background A broad spectrum of skin diseases, including hair and nails, can be directly or indirectly triggered by COVID‐19. It is aimed to examine the type and frequency of hair and nail disorders after COVID‐19 infection. Methods This is a multicenter study conducted on consecutive 2171 post‐COVID‐19 patients. Patients who developed hair and nail disorders and did not develop hair and nail disorders were recruited as subject and control groups. The type and frequency of hair and nail disorders were examined. Results The rate of the previous admission in hospital due to COVID‐19 was statistically significantly more common in patients who developed hair loss after getting infected with COVID‐19 ( P < 0.001). Telogen effluvium (85%) was the most common hair loss type followed by worsening of androgenetic alopecia (7%) after COVID‐19 infection. The mean stress scores during and after getting infected with COVID‐19 were 6.88 ± 2.77 and 3.64 ± 3.04, respectively, in the hair loss group and were 5.77 ± 3.18 and 2.81 ± 2.84, respectively, in the control group ( P < 0.001, P < 0.001). The frequency of recurrent COVID‐19 was statistically significantly higher in men with severe androgenetic alopecia (Grades 4–7 HNS) ( P = 0.012; Odds ratio: 2.931 [1.222–7.027]). The most common nail disorders were leukonychia, onycholysis, Beau's lines, onychomadesis, and onychoschisis, respectively. The symptoms of COVID‐19 were statistically significantly more common in patients having nail disorders after getting infected with COVID‐19 when compared to the control group ( P < 0.05). Conclusion The development of both nail and hair disorders after COVID‐19 seems to be related to a history of severe COVID‐19.

A greater need to enhance comfort characteristics during vehicle design process has recently forced the manufacturers to develop simulation-based approaches. In this study, a simulation-based model of a full-car suspension system is proposed to predict the ride comfort. A simulation model was created for calculating ride comfort effectively. This simulation uses seat-back, seat-surface, and feet acceleration values collected from four different road vehicles which were run on six different roads. Parameters which effect ride comfort were also investigated. Using these parameters, a simulation-based model of a full-car suspension system including engine and seat is created for predicting the ride comfort. The correlation between the results of physical tests and the simulation is very promising. It was found that the effect of an engine has a substantial influence on the ride comfort. To find the optimum values of each parameter, an optimization process was executed properly and added in the model. Using this model, the best ride comfort values were computed without the need of physical prototypes. The developed algorithm can be very helpful as an assistant tool for engineers during vehicle design and manufacturing process.