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Objective Analyze the current situation, hotspots and development trends of sarcopenia animal model to provide research direction and basic information for sarcopenia animal model research.Methods English literature of research on animal models of sarcopenia was retrieved from the Web of Science core data (WOS) set from 1900-01-01 to 2022-12-31. Chinese literature related to animal models of sarcopenia was retrieved from CNKI database between 1915 and 2022. The bibliometric analysis software VOSviewer was used to explore the countries, orgonizations, authors, hotspots and frontier directions in these studies.Results A total of 2 819 articles on animal models of sarcopenia were retrieved from WOS core database. The first paper was published in 1995. The United States has the largest number of animal model studies of sarcopenia with 1 105 articles. The institution with the most published articles is the University of Florida in the United States, with 69 articles. The University of Hong Kong has the highest number of publications in China, with 20 articles. American author Van Remmen H, with 50 publications, is the author of the most articles. The journal with the largest number of articles published on animal models of sarcopenia is the American journal called FASEB Journal, with 196 articles. In total, 423 articles on animal models of sarcopenia were retrieved from the CNKI database. Author LI Zhuyi has published 19 articles, and is the author of the most articles in China. The keyword co-occurrence clustering analysis of WOS literature search found that the research focus on animal model of sarcopenia can be summarized as the correlation between sarcopenia and metabolism, cytology and regenerative medicine of sarcopenia animal models, the study of sarcopenia animal models in bone, muscle, nerve and exercise therapy. The retrieval results of CNKI database revealed that the most extensive research was about on the model of denervated sarcopenia, and researches on the effects of Chinese medicine on sarcopenia were also widely reported. Through reading the full articles or abstracts of the literature, the animal models of sacopenia mainly include natural aging model, genetic modification model, high-fat diet induction model, disuse model, hormone induction model and complex sarcopenia models of other diseases.Conclusion In recent years, the study on animal model of sarcopenia has become a hotspot at home and abroad.The bibliometric analysis provides a basis for the research of animal models of sarcopenia in terms of research direction, hotspots, model animal selection, animal model making, and domestic and international communication and cooperation.

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles' characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network's ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements. [ABSTRACT FROM AUTHOR]

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups. [ABSTRACT FROM AUTHOR]

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Objective: This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs). Background: Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse. Method: 34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers' physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters. Result: Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%. Conclusion: The features investigated and the RF model developed can identify trust-change directions during TTs accurately. Application: Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers' overtrust and undertrust in conditionally AVs. [ABSTRACT FROM AUTHOR]

Hierarchical structures have attracted considerable interest due to their super-oleophobic/super-hydrophobic behavior. However, it is rare to present a novel additive manufacturing (AM) approach to fabricate hierarchical metal structures (HMSs). A micro/nano mixture ink was deposited on a substrate and a laser was used to selectively scan the ink layer. A new layer of ink was deposited on the previous consolidation layer during manufacturing. The surfaces of the as-sintered HMSs exhibit inherently super-hydrophilic and super-oleophobic behavior with a 155° contact angle (CA) with oil. Furthermore, the HMSs were successfully turned into super-hydrophobic and super-oleophilic mode (with a 152° CA) after surface modification with a solvent-free, electrostatic polytetrafluoroethylene particle deposition. They can be used as oil/water separation media by the functional surfaces existing in the pore channels. The experimental study of HMSs shows an effective removal of oil contaminants from water. The developed process also possesses an advantage of AM of HMSs with complex shapes in ambient air under the protection of an organic ink. Importantly, the present approach could be extended to a vast number of HMSs, for the preparation of highly durable functional materials for various applications.