Release of biogenic volatile organic compounds and physiological responses of two sub-tropical tree species to smoke derived from forest fire.

Forests emit a large amount of biogenic volatile organic compounds (BVOCs) in response to biotic and abiotic stress. Despite frequent occurrence of large forest fires in recent years, the impact of smoke stress derived from these forest fires on the emission of BVOCs is largely unexplored. Thus, the aims of the study were to quantify the amount and composition of BVOCs released by two sub-tropical tree species, Cunninghamia lanceolata and Schima superba, in response to exposure to smoke. Physiological responses and their relationship with BVOCs were also investigated. The results showed that smoke treatments significantly (p < 0.001) promoted short-term release of BVOCs by C. lanceolata leaves than S. superba; and alkanes, olefins and benzene homologs were identified as major classes of BVOCs. Both C. lanceolata and S. superba seedlings showed significant (p < 0.005) physiological responses after being smoke-stressed where photosynthetic rate remained unaffected, chlorophyll content greatly reduced and Activities of anti-oxidant enzymes and the malondialdehyde content generally increased with the increase in smoke concentration. Activities of anti-oxidant enzymes showed mainly positive correlations with the major BVOCs. In conclusion, the release of BVOCs following smoke stress is species-specific and there exists a link between activities of antioxidant enzymes and BVOCs released. The findings provide insight about management of forest fires in order to control excessive emission of smoke that would trigger increased release of BVOCs.

Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision.

With the changes in human work and lifestyle, the incidence of cervical spondylosis is increasing substantially, especially for adolescents. Cervical spine exercises are an important means to prevent and rehabilitate cervical spine diseases, but no mature unmanned evaluating and monitoring system for cervical spine rehabilitation training has been proposed. Patients often lack the guidance of a physician and are at risk of injury during the exercise process. In this paper, we first propose a cervical spine exercise assessment method based on a multi-task computer vision algorithm, which can replace physicians to guide patients to perform rehabilitation exercises and evaluations. The model based on the Mediapipe framework is set up to construct a face mesh and extract features to calculate the head pose angles in 3-DOF (three degrees of freedom). Then, the sequential angular velocity in 3-DOF is calculated based on the angle data acquired by the computer vision algorithm mentioned above. After that, the cervical vertebra rehabilitation evaluation system and index parameters are analyzed by data acquisition and experimental analysis of cervical vertebra exercises. A privacy encryption algorithm combining YOLOv5 and mosaic noise mixing with head posture information is proposed to protect the privacy of the patient's face. The results show that our algorithm has good repeatability and can effectively reflect the health status of the patient's cervical spine.

Safety assessment for autonomous vehicles: A reference driver model for highway merging scenarios.

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because of their role as reference models. Specifically, an AV is expected to achieve at least the same level of safety performance as a careful and competent driver model. To make this comparison possible, quantitative modeling of careful and competent driver models is essential. Thus, the UNECE Regulation No. 157 proposes two driver models as benchmarks for AVs, enabling safety assessment of AV longitudinal behaviors. However, these two driver models are unable to be applied in non-car-following scenarios, limiting their applications in scenarios such as highway merging. To this end, we propose a careful and competent driver model for highway merging (CCDM2) scenarios using interpretable reinforcement learning-based decision-making and safety constraint control. We compare our model's safe driving capabilities with human drivers in challenging merging scenarios and demonstrate the "careful" and "competent" characteristics of our model while ensuring its interpretability. The results indicate the model's capability to handle merging scenarios with even better safety performance than human drivers. This model is of great value for AV safety assessment in merging scenarios and contributes to future reference driver models to be included in AV safety regulations.

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems.

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learning algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learning algorithms in AIS's domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learning algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field.