Research on static seating comfort of the Chinese population under different seat angle design parameters

IntroductionThis study aimed to deepen the understanding of seating comfort for the Chinese population by developing a human finite element (FE) model.MethodsThis model was integrated with a specific vehicle seat FE model to construct a comprehensive human-seat FE model, and the mechanical responses of the human body were analyzed under varying seat angles. Body pressure distribution, intervertebral disc stress and strain, and vertebral body stress were examined to study the relationship between the internal reactions of the human body and surface contact conditions.ResultsThe results indicate that when the seat is flipped, the trends of disc stress, average pressure, and contact area are consistent, and the maximum strain closely aligns with the maximum pressure. When the backrest is adjusted, lumbar spine stress and surface pressure exhibit similar trends, while disc stress, strain, and the 1-SPD value show consistent patterns.DiscussionThe study concludes that increasing the backrest angle does not necessarily enhance comfort. Moreover, the stress variations in the thoracic and lumbar spines correlate with spinal angle alterations, suggesting that spinal angle can serve as a reliable indicator of stress conditions. Finally, the study highlights the correlation between spinal force and body pressure distribution, underscoring the utility of body pressure distribution metrics as a valuable proxy for understanding spinal responses

A review of the progress in machine vision-based crack detection and identification technology for asphalt pavements

With the aging of transportation infrastructure and the increasing frequency of use, the detection and identification of cracks in asphalt pavements are crucial for ensuring road safety and maintenance efficiency. Traditional manual inspection methods are not only inefficient and limited in accuracy but also susceptible to subjective factors and environmental conditions. In contrast, machine vision-based crack detection technology enhances the efficiency and reliability of detection through automated image acquisition and analysis processes. This article reviews the latest advancements in machine vision-based crack detection technology for asphalt pavements, with a particular focus on the applications of digital image processing and deep learning. Although image processing-based methods perform well in detecting cracks against simple backgrounds, they exhibit poor robustness under complex lighting and background conditions. On the other hand, deep learning-based methods, while effectively handling complex image data, rely on large amounts of annotated data and significant computational resources. Through critical analysis, the article evaluates the strengths and weaknesses of existing technologies and looks forward to future research directions that integrate multiple sensing data and automated data annotation tools, aiming to further advance and innovate road maintenance technology

Improved isolation of good-quality total RNA from the optic stalk of Mud crab, Scylla paramamosain

Abstract An improved and efficient protocol was developed based on the TaKaRa RNAiso Plus Kit (Code: D9108A) for isolating good-quality total RNA from the optic stalk of mud crab, Scylla paramamosain . The protocol was based on the Trizol method with modifications. The carapace overlapping the optic stalk was retained with RNA in regular protocol. In order to remove the abundant deposition correlative with the carapace which makes the isolation of RNA particularly difficult, 5M potassium acetate solution (pH = 6.0) was added before the precipitation of RNA, and the temperature of RNA deposition was also decreased to -70\ubaC to ensure the stabilization of RNA. Good-quality total RNA from the optic stalk of S. paramamosain could be easily isolated with this modified protocol and three conventional methods were also employed to confirm the quality of RNA. This improved method would be helpful in facilitating molecular research of crabs involving RNA from the optic stalk

Translational medical bioengineering research of traumatic brain injury among Chinese and American pedestrians caused by vehicle collision based on human body finite element modeling

Based on the average human body size in China and the THUMS AM50 finite element model of the human body, the Kriging interpolation algorithm was used to model the Chinese 50th percentile human body, and the biological fidelity of the model was verified. We built three different types of passenger vehicle models, namely, sedan, sports utility vehicle (SUV), and multi-purpose vehicle (MPV), and used mechanical response analysis and finite element simulation to compare and analyze the dynamic differences and head injury differences between the Chinese 50th percentile human body and the THUMS AM50 model during passenger vehicle collisions. The results showed that there are obvious differences between the Chinese mannequin and THUMS in terms of collision time, collision position, invasion speed, and angle. When a sedan collided with the mannequins, the skull damage to the Chinese human body model was more severe, and when a sedan or SUV collided, the brain damage to the Chinese human body was more severe. The abovementioned results suggest that the existing C-NCAP pedestrian protection testing regulations may not provide the best protection for Chinese human bodies, and that the regulations need to be improved by combining collision damage mechanisms and the physical characteristics of Chinese pedestrians. This thorough investigation is positioned to shed light on the fundamental biomechanics and injury mechanisms at play. Furthermore, the amalgamation of clinically rooted translational and engineering research in the realm of traumatic brain injury has the potential to establish a solid foundation for discerning preventive methodologies. Ultimately, this endeavor holds the potential to introduce effective strategies aimed at preventing and safeguarding against traumatic brain injuries

Wrinkle-Enabled Highly Stretchable Strain Sensors for Wide-Range Health Monitoring with a Big Data Cloud Platform

Flexible and stretchable strain sensors are vital for emerging fields of wearable and personal electronics, but it is a huge challenge for them to possess both wide-range measurement capability and good sensitivity. In this study, a highly stretchable strain sensor with a wide strain range and a good sensitivity is fabricated based on smart composites of carbon black (CB)/wrinkled Ecoflex. The sensor exhibits a maximum recoverable strain of up to 500% and a high gauge factor of 67.7. It has a low hysteresis, a fast signal response (as short as 120 ms), and a high reproducibility (up to 5000 cycles with a strain of 150%). The sensor is capable of detecting and capturing wide-range human activities, from speech recognition and pulse monitoring to vigorous motions. It is also applicable for real-time monitoring of robot movements and vehicle security crash in an anthropomorphic field. More importantly, the sensor is successfully used to send signals of a volunteer’s breathing data to a local hospital in real time through a big data cloud platform. This research provides the feasibility of using a strain sensor for wearable Internet of things and demonstrates its exciting prospect for healthcare applications