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Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that was discovered in China in 2010. The causative agent has been identified as a new member of the Phlebovirus genus in the family Bunyaviridae and has been designated severe fever with thrombocytopenia virus (SFTSV). SFTSV infection can be transmitted person-to-person, and the average case fatality rate is approximately 10% in humans. There is a high seroprevalence of SFTSV infection in a wide range of domesticated animals, including sheep, goats, cattle, pigs, dogs and chickens. Ticks are suspected to be the vector that transmits the virus to humans. Currently, the SFTS endemic area is expanding. Therefore, SFTSV infection is an increasingly important public health threat.

Because the majority of information in the industrial Internet of things (IIoT) is transmitted over an open and insecure channel, it is indispensable to design practical and secure authentication and key agreement protocols. Considering the weak computational power of sensors, many scholars have designed lightweight authentication protocols that achieve limited security properties. Moreover, these existing protocols are mostly implemented in a single-gateway scenario, whereas the multigateway scenario is not considered. To deal with these problems, this paper presents a novel three-factor authentication and key agreement protocol based on elliptic curve cryptography for IIoT environments. Based on the elliptic curve Diffie–Hellman problem, we present a protocol achieving desirable forward and backward secrecy. The proposed protocol applies to single-gateway and is also extended to multigateway simultaneously. A formal security analysis is described to prove the security of the proposed scheme. Finally, the comparison results demonstrate that our protocol provides more security attributes at a relatively lower computational cost.

Two-dimensional (2D) Stiefel-Whitney insulator (SWI), which is characterized by the second Stiefel-Whitney class, is a class of topological phases with zero Berry curvature. As an intriguing topological state, it has been well studied in theory but seldom realized in realistic materials. Here we propose that a large class of liganded Xenes, i.e., hydrogenated and halogenated 2D group-IV honeycomb lattices, are 2D SWIs. The nontrivial topology of liganded Xenes is identified by the bulk topological invariant and the existence of protected corner states. Moreover, the large and tunable bandgap (up to 3.5 eV) of liganded Xenes will facilitate the experimental characterization of the 2D SWI phase. Our findings not only provide abundant realistic material candidates that are experimentally feasible but also draw more fundamental research interest towards the topological physics associated with Stiefel-Whitney class in the absence of Berry curvature.

Coronaviruses represent a significant zoonotic threat, with host adaptation serving as a pivotal determinant of cross-species transmission. The bat-derived β-coronavirus HKU5 utilizes its spike (S) protein for receptor recognition and viral entry. Here, we report the cryo-electron microscopy (cryo-EM) structure of the HKU5 S protein in a closed conformation. Two fatty acids are found in each protomer of the HKU5 S protein, which stabilize the S protein in the closed conformation. Furthermore, we solve the structure of the HKU5 receptor-binding domain (RBD) in complex with the peptidase domain (PD) of Pipistrellus abramus angiotensin-converting enzyme 2 (ACE2), uncovering a unique binding mode distinct from other coronaviruses that use ACE2 as their receptor. Evolutionary and functional analyses indicate that mutations in the RBD can modulate receptor-binding, while conservation and structural modeling suggest that HKU5 has the potential to cross the species barrier. Notably, we identify ACE2 orthologs in avian species, such as Pitta sordida , that support stable HKU5 RBD binding and interaction. Our functional assays, including pseudovirus entry and cell–cell fusion experiments, demonstrate that HKU5 can exploit ACE2 orthologs across species, providing molecular insights into its host adaptation and underscoring the importance of surveillance for this virus and its zoonotic risk. This study reveals how the bat coronavirus HKU5 recognizes ACE2 receptors across mammals and birds, uncovering a cross-class receptor usage in coronaviruses and highlighting its potential for interspecies and zoonotic transmission.

As one of the core technologies for autonomous mobile robots, Visual Simultaneous Localization and Mapping (VSLAM) has been widely researched in recent years. However, most state-of-the-art VSLAM adopts a strong scene rigidity assumption for analytical convenience, which limits the utility of these algorithms for real-world environments with independent dynamic objects. Hence, this paper presents a semantic and geometric constraints VSLAM (SGC-VSLAM), which is built on the RGB-D mode of ORB-SLAM2 with the addition of dynamic detection and static point cloud map construction modules. In detail, a novel improved quadtree-based method was adopted for SGC-VSLAM to enhance the performance of the feature extractor in ORB-SLAM (Oriented FAST and Rotated BRIEF-SLAM). Moreover, a new dynamic feature detection method called semantic and geometric constraints was proposed, which provided a robust and fast way to filter dynamic features. The semantic bounding box generated by YOLO v3 (You Only Look Once, v3) was used to calculate a more accurate fundamental matrix between adjacent frames, which was then used to filter all of the truly dynamic features. Finally, a static point cloud was estimated by using a new drawing key frame selection strategy. Experiments on the public TUM RGB-D (Red-Green-Blue Depth) dataset were conducted to evaluate the proposed approach. This evaluation revealed that the proposed SGC-VSLAM can effectively improve the positioning accuracy of the ORB-SLAM2 system in high-dynamic scenarios and was also able to build a map with the static parts of the real environment, which has long-term application value for autonomous mobile robots.

The purpose of human object detection is to obtain the number of people and their position in images, which is one of the core problems in the field of machine vision. However, the high missing detection rate from small- and medium-sized human bodies due to the large variety of human scale in human object detection tasks still influences the performance of human object detection. To solve the above problem, this paper proposed an improved ASPP_BiFPN_YOLOv4 (ABYOLOv4) method to detect human object detection. In detail, Atrous Spatial Pyramid Pooling (ASPP) module was used to replace the original Spatial Pyramid Pooling module to increase the receptive field level of the network and improve the perception ability of multi-scale targets. Then, the original Path Aggregation Network (PANet) multi-scale fusion module was replaced by the self-built bi-layer bidirectional feature pyramid network (Bi-FPN). Meanwhile, a new feature was imported into the proposed model to reuse the mid- and low-level features, which could enhance the ability of the network to express the characteristics of small- and medium-sized targets. Finally, the standard convolution in Bi-FPN was replaced by depth-separable convolution to make the network achieve the balance of accuracy and the number of parameters. To identify the performance of the proposed ABYOLOv4 model, the human object detection experiment is carried out by using the public data set of VOC2007 and VOC2012, the improved YOLOv4 algorithm is 0.5% higher than the original AP algorithm, and the weight file size of the model is reduced by 45.3 M. The experimental results demonstrated that the proposed ABYOLOv4 network has higher accuracy and lower computational cost for human target detection.

Objectives The psychological distress caused by COVID-19 may be pronounced among the parents of children with autism spectrum disorder (ASD). This study aimed to investigate psychological distress among parents of children with ASD during the COVID-19 pandemic. Methods A total of 1764 parents of children with ASD and 4962 parents of typically developing (TD) children were recruited. The participants completed an online survey which contained demographic information, the impact due to COVID-19 crisis, resilience, coping styles, anxiety and depression. Hierarchical linear regression was used to assess the contributions of these variables to anxiety and depression. Results After adjusting for demographic variables, the following factors were associated with parents’ anxiety and depression symptoms: (i) Whether or not the participants had a child with ASD; (ii) resilience; (iii) coping strategies, and; (iv) the impact due to COVID-19. Among these, the psychological stress caused by COVID-19 played the most important role in parental anxiety ( β  = 0.353) and depression ( β  = 0.242) symptoms. Parents of children with ASD had lower levels of resilience and positive coping, and used more negative coping strategies than parents of TD children. Among all participants, 8.0 and 24.2% of parents had symptoms of anxiety and depression, respectively. Compared to parents of TD children, more parents of children with ASD exhibited symptoms of anxiety and depression (12.2% vs. 6.6%; 31.0% vs. 21.7%, respectively). Conclusions During the COVID-19 pandemic, parents experienced varying levels of anxiety and depression, particularly, parents of children with ASD. More specific attention should be paid to parental mental health and long-term effective intervention programs, that are targeted towards parents of children with ASD, and such programs should be promoted around China in the wake of the COVID-19 crisis.

Overtaking on a dual-lane road with the presence of oncoming vehicles poses a considerable challenge in the field of autonomous driving. With the assistance of high-definition maps, autonomous vehicles can plan a relatively safe trajectory for executing overtaking maneuvers. However, the creation of high-definition maps requires extensive preparation, and in rural areas where dual two-lane roads are common, there is little pre-mapping to provide high-definition maps. This paper proposes an end-to-end model called OG-Net (Overtaking Guide Net), which accomplishes overtaking tasks without map generation or communication with other vehicles. OG-Net initially evaluates the likelihood of a successful overtaking maneuver before executing the necessary actions. It incorporates the derived probability value with a set of simple parameters and utilizes a Gaussian differential controller to determine the subsequent vehicle movements. The Gaussian differential controller effectively adapts a fixed geometric curve to various driving scenarios. Unlike conventional autonomous driving models, this approach employs uncomplicated parameters rather than RNN-series networks to integrate contextual information for overtaking guidance. Furthermore, this research curated a new end-to-end overtaking dataset, CarlaLanePass, comprising first-view image sequences, overtaking success rates, and real-time vehicle status during the overtaking process. Extensive experiments conducted on diverse road scenes using the Carla platform support the validity of our model in achieving successful overtaking maneuvers.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging viral disease transmitted mainly through the bite of Haemaphysalis longicornis, and can cause clusters through contact transmission, and its incidence shows a rising and spreading trend in China. This study aimed to analyze the occurrence process of human-to-human transmission clusters and provide evidence for effective implementation of interventions. Data about SFTS human-to-human transmission clusters were extracted from 42 published articles. R 4.4.1 and Microsoft Excel software were used to process and analyze the epidemiological and clinical data extracted. 37 clusters occurring from 1996 to 2023 were retrieved, of which 2 (5.41%) involved third-generation transmission, 7 (18.92%) involved 16 asymptomatic infections, and 9 (24.32%) involved 17 medical personnel. There were 37 index cases with a case fatality rate of 97.30%, 135 secondary cases with a case fatality rate of 12.40%, and an overall of 31.33%. The first treatment of the index case was mainly in primary medical institutions (24, 64.86%) and the most common symptoms were fever, fatigue and gastrointestinal symptoms. The index cases were distributed from March to October each year, the peak was from April to July, and the incubation period was 5-21 days, mostly in middle-aged and elderly farmers. Clusters were mainly distributed in Jiangsu Province (9 clusters), followed by Henan, Shandong and Zhejiang Provinces (6 clusters each). The clusters occurred mostly in the progress of care (72.97%), funeral (64.86%) and treatment of patients (24.32%), involving relatives (75.76%), medical workers (12.12%), villagers (9.85%) and morticians (2.27%). Almost all clusters were spread by contact with patients' blood and bloody secretions (97.30%). SFTS human-to-human transmission clusters sometimes occur in China, with a high case fatality rate. It is necessary to strengthen public health education, and improve the early diagnosis and treatment ability of medical workers, to avoid nosocomial infection or family (community) transmission.

Hand pose estimation is the basis of dynamic gesture recognition. In vision-based hand pose estimation, the performance of hand pose estimation is affected due to the high flexibility of hand joints, local similarity and severe occlusion among hand joints. In this paper, the structural relations between hand joints are established, and the improved nonparametric structure regularization machine (NSRM) is used to achieve more accurate estimation of hand pose. Based on the NSRM network, the backbone network is replaced by the new high-resolution net proposed in this paper to improve the network performance, and then the number of parameters is decreased by reducing the input and output channels of some convolutional layers. The experiment of hand pose estimation is carried out by using public dataset, the experimental results show that the improved NSRM network has higher accuracy and faster inference speed for hand pose estimation.