Finite element (FE) method is a proven powerful and efficient tool to study the biomechanics of the human lumbar spine. However, due to the large inter-subject variability of geometries and material properties in human lumbar spines, concerns existed on the accuracy and predictive power of one single deterministic FE model with one set of spinal geometry and material properties. It was confirmed that the combined predictions (median or mean value) of several distinct FE models can be used as an improved prediction of behavior of human lumbar spine under identical loading and boundary conditions. In light of this improved prediction, five FE models (L1-L5 spinal levels) of the human lumbar spine were developed based on five healthy living subjects with identical modeling method. The five models were extensively validated through experimental and computational results in the literature. Mesh convergence and material sensitivity analysis were also conducted. We have shown that the results from the five FE models developed in this paper were consistent with the experimental data and simulation results from the existing literature. The validated modeling method introduced in this study can be used in modeling dysfunctional lumber spines such as disc degeneration and scoliosis in future work.

Driver behavior models have been used as input to self-coaching, accident prevention studies, and developing driver-assisting systems. In recent years, driver behavior recognition has revolutionized autonomous vehicles (AVs) and traffic management studies. This comprehensive survey provides an up-to-date review of the different driver behavior models and modeling approaches. In heterogeneous streets where humans and autonomous vehicles operate simultaneously, predicting the intent and action of human drivers is crucial for AVs with the help of wireless communication and artificial intelligence (AI) technologies. Therefore, the review also summarizes the applications of driver behavior modeling (DBM) for effective behavior recognition and human-like AV driving. Moreover, the review also covers the application of DBM in capturing behaviors of complex dynamic driving tasks. In this review, we solely cover car-following (CF) and lane-changing (LC) maneuvers.

This paper presents a computational fluid dynamics (CFD) simulation approach for the prediction of leakage between an N95 filtering facepiece respirator (FFR) and a headform and an infrared camera (IRC) method for validating the CFD approach. The CFD method was used to calculate leak location(s) and ‘filter-to-faceseal leakage’ (FTFL) ratio for 10 headforms and 6 FFRs.The computational geometry and leak gaps were determined from analysis of the contact simulation results between each headform-N95 FFR combination. The volumetric mesh was formed using a mesh generation method developed by the authors. The breathing cycle was described as a time-dependent profile of the air velocity through the nostril. Breathing air passes through both the FFR filter medium and the leak gaps. These leak gaps are the areas failing to achieve a seal around the circumference of the FFR. The CFD approach was validated by comparing facial temperatures and leak sites from IRC measurements with eight human subjects. Most leaks appear at the regions of the nose (40%) and right (26%) and left cheek (26%) sites. The results also showed that, with N95 FFR (no exhalation valves) use, there was an increase in the skin temperature at the region near the lip, which may be related to thermal discomfort. The breathing velocity and the viscous resistance coefficient of the FFR filter medium directly impacted the FTFL ratio, while the freestream flow did not show any impact on the FTFL ratio. The proposed CFD approach is a promising alternative method to study FFR leakage if limitations can be overcome.