The apnea-hypopnea index (AHI) is a widely accepted measure for the severity of obstructive sleep apnea (OSA). Current methods to determine AHI fail to provide anatomic information for treatment decisions. In this report, we studied three-dimensional models of upper airways acquired by computed tomographic scanning with geometric measurements and computational fluid dynamics (CFD) analysis and evaluated the correlations with AHI.Participants had CT scans of their upper airways after standard polysomnography studies. Three-dimensional surface models of upper airways were generated for cross-sectional area measurements of the choanae (ACH) and the smallest cross-sectional area (Amin). Computational fluid dynamic analysis was then performed by using this three-dimensional model. Pressure differences required to set tidal volume during inspiration (ΔPmin-INSP) and expiration (ΔPmax-EXP) and minimum negative pressure produced in the level of ACH (Pmin-INSP at ACH) and Amin (Pmin-INSP at Amin) were calculated. Correlations of these parameters and the body mass index with AHI were analyzed. Statistical differences between groups of different AHI ranges were also compared.The pressure distribution simulated by CFD demonstrated abrupt pressure drops in Amin level, and this phenomenon was more significant in severe OSA. All parameters except ACH and Pmin-INSP at Amin significantly correlated with the AHI, and there were significant statistical differences between the OSA groups and the normal group. The results indicate that, in our study group, the geometry of pharyngeal airway and its CFD simulation correlate well with AHI. This model may be further applied for clinical evaluation.

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This study proposes an image recognition method to assist swine farm managers in collecting health data related to pig feeding and excretion. Analyzing the correlation between sow health data and indoor air quality in the pigsties revealed significant influences of air quality indicators on sow health. Increased levels of TVOC, CO2, and temperature were found to negatively affect feeding health, while increased temperature, humidity, and PM10 were found to negatively impact excretion health. These findings provide a basis for evaluating variables when constructing future sow disease prediction models. Analyzing the correlation between pig health status and air quality can help swine farm managers improve sow production environments, reduce disease risks, enhance production efficiency, and provide valuable insights for future research on disease prediction models.

Heat stress poses a significant challenge to egg production in layer hens. High temperatures can disrupt the physiological functions of these birds, leading to reduced egg production and lower egg quality. This study evaluated the microclimate of laying hen houses using different management systems to determine the impact of heat stress on productivity and hen health. The results showed that the ALPS system, which manages the hen feeding environment, effectively improved productivity and decreased the daily death rate. In the traditional layer house, the daily death rate decreased by 0.045%, ranging from 0.086% to 0.041%, while the daily production rate increased by 3.51%, ranging from 69.73% to 73.24%. On the other hand, in a water-pad layer house, the daily death rate decreased by 0.033%, ranging from 0.082% to 0.049%, while the daily production rate increased by 21.3%, ranging from 70.8% to 92.1%. The simplified hen model helped design the indoor microclimate of commercial layer houses. The average difference in the model was about 4.4%. The study also demonstrated that fan models lowered the house’s average temperature and reduced the impact of heat stress on hen health and egg production. Findings indicate the need to control the humidity of inlet air to regulate temperature and humidity, and suggest that Model 3 is an energy-saving and intelligent solution for small-scale agriculture. The humidity of the inlet air affects the temperature experienced by the hens. The THI drops to the alert zone (70–75) when humidity is below 70%. In subtropical regions, we consider it necessary to control the humidity of the inlet air.