Epidemiological and genomic analyses of human isolates of Streptococcus suis between 2005 and 2021 in Shenzhen, China

Streptococcus suis (S. suis) is an important food-borne zoonotic pathogen that causes swine streptococcosis, which threatens human health and brings economic loss to the swine industry. Three-quarters of human S. suis infections are caused by serotype 2. A retrospective analysis of human S. suis cases in Shenzhen, a megacity in China, with high pork consumption, between 2005 and 2021 was conducted to understand its genomic epidemiology, pathogen virulence, and drug resistance characteristics. The epidemiological investigation showed that human cases of S. suis in Shenzhen were mainly associated with people who had been in close contact with raw pork or other swine products. Whole-genome sequence analysis showed that 33 human isolates in Shenzhen were dominated by serotype 2 (75.76%), followed by serotype 14 (24.24%), and the most prevalent sequence types (STs) were ST7 (48.48%) and ST1 (39.40%). ST242 (9.09%) and ST25 (3.03%), which were rarely reported, were also found. Phylogenetic analysis showed that the Shenzhen human isolates had close genetic relatedness to isolates from Guangxi (China), Sichuan (China), and Vietnam. We found a new 82 KB pathogenicity island (PAI) in the serotype 2 isolate that may play a role in sepsis. Similarly, a serotype 14 isolate, containing 78 KB PAI, was isolated from a patient presenting with streptococcal toxic shock syndrome (STSLS) who subsequently died. Multi-drug resistance (MDR) was high in human isolates of S. suis from Shenzhen. Most human isolates were resistant to tetracycline, streptomycin, erythromycin, and clindamycin, and 13 isolates had intermediate resistance to penicillin. In conclusion, swine importation from Guangxi, Sichuan, and Vietnam should be more closely monitored, and the use of antibiotics limited to reduce the potential for antimicrobial resistance (AMR)

Effect of Fitness Qigong-Wuqinxi exercise on some physiological indexes of female college students

To know the reaction and adaption of human body after taking Fitness Qigong-Wuqinxi exercise and promote the popularity of Fitness Qigong-Wuqinxi exercise in universities,especially in female college students who do not major in sports,we observed their gas metabolism indexes and heart rates and contrast body shape and some physical quality indexes before and after the regular exercise for 16 weeks.The results showed that the indexes of waist,BMI,back force,grip force,and proneness when sitting improved obviously.Although height,weight,abdominal skinfold thickness,body fate percentage didn′t have significant change,the development trend is toward the direction of health.After exercise for 16 weeks,the three indexes of lung ventilation (VE、VO2、VCO2) showed wave shape.It is obvious that Fitness Qigong-Wuqinxi exercise can obviously improve the body shape,physical quality,and the function of heart and lung.Also,the wave feature of the indexes of lung ventilation can adjust the cardiopulmonary function,so Fitness Qigong-Wuqinxi exercise is a new safe and reliable fitness program

Formation Stabilization with Cone-like Sensing Field

While most existing literature assumes agents in formation control are with no sensing limitations or with 360-degree sensing fields, we consider each agent in formation stabilization problem is with a cone-like sensing filed for the leader-follower formation in three-dimensional space. We discuss the strategies for the agent with cone-like sensing filed to follow one leader, two leaders and three leaders, respectively. And correspondingly the related control laws are proposed to achieve the formation. Finally, simulation results, including an animation, about four-agent formation with cone-like sensing field demonstrate the effectiveness of the strategies and the proposed control laws

An autonomous control scheme of global smooth transitions for bidirectional DC-DC converter in DC microgrid

In dc microgrid, energy storage system (ESS) plays a crucial role to provide short-or-long term and high-quality electric energy. The different control strategies for bidirectional dc-dc converter (BDC) of ESS in grid-tied and islanded modes pose challenges to the coordination control of the dc microgrid. This paper proposes an autonomous control scheme for the BDC in dc microgrid. The proposed control scheme is based on V2-P droop control and unifies bus voltage regulation and power regulation in a single control structure. Thus, global smooth transition between various operation modes can be achieved without any control strategy changes, which avoids various mode switch detection mechanisms and improves system stability. Furthermore, the proposed control scheme is fully decentralized which reduces the reliance on communication, and enhances the reliability of the microgrid. On the other hand, V2-P droop approach eliminates the negative effect of widespread constant power loads (CPLs) in dc microgrid. The stability of the proposed control method is illustrated, and the design guideline of some critical control gains is addressed. Finally, the effectiveness of the proposed control scheme is validated by the real-time hardware-in-loop (HIL) platform.This work was supported in part by Scholarship from the China Scholarship Council under Grant 201806290189 and in part by Energy Research Institute at NTU (ERI@N)

Hierarchical Task Assignment for Multi-UAV System in Large-Scale Group-to-Group Interception Scenarios

The multi-UAV task assignment problem in large-scale group-to-group interception scenarios presents challenges in terms of large computational complexity and the lack of accurate evaluation models. This paper proposes an effective evaluation model and hierarchical task assignment framework to address these challenges. The evaluation model incorporates the dynamics constraints specific to fixed-wing UAVs and improves the Apollonius circle model to accurately describe the cooperative interception effectiveness of multiple UAVs. By evaluating the interception effectiveness during the interception process, the assignment scheme of the multiple UAVs could be given based on the model. To optimize the configuration of UAVs and targets, a hierarchical framework based on the network flow algorithm is employed. This framework utilizes a clustering method based on feature similarity and interception advantage to decompose the large-scale task assignment problem into smaller, complete submodels. Following the assignment, Dubins curves are planned to the optimal interception points, ensuring the effectiveness of the interception task. Simulation results demonstrate the feasibility and effectiveness of the proposed scheme. With the increase in the model scale, the proposed scheme has a greater descending rate of runtime. In a large-scale scenario involving 200 UAVs and 100 targets, the runtime is reduced by 84.86%

An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method

Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal coding. The simple improved leaky integrate-and-fire (LIF) model was used to describe the behavior of a spike neuron. Then the actor-critic network structure and the update formulas using temporally encoded information were provided. The current model was finally examined in the decision-making task, the gridworld task, the UAV flying through a window task and the avoiding a flying basketball task. In the 5 × 5 grid map, the value function learned was close to the ideal situation and the quickest way from one state to another was found. A UAV trained by this method was able to fly through the window quickly in simulation. An actual flight test of a UAV avoiding a flying basketball was conducted. With this model, the success rate of the test was 96% and the average decision time was 41.3 ms. The results show the effectiveness and accuracy of the temporal coded spiking neural network RL method. In conclusion, an attempt was made to provide insights into developing spiking neural network reinforcement learning methods for decision-making and autonomous control of unmanned systems

An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method

Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal coding. The simple improved leaky integrate-and-fire (LIF) model was used to describe the behavior of a spike neuron. Then the actor-critic network structure and the update formulas using temporally encoded information were provided. The current model was finally examined in the decision-making task, the gridworld task, the UAV flying through a window task and the avoiding a flying basketball task. In the 5 × 5 grid map, the value function learned was close to the ideal situation and the quickest way from one state to another was found. A UAV trained by this method was able to fly through the window quickly in simulation. An actual flight test of a UAV avoiding a flying basketball was conducted. With this model, the success rate of the test was 96% and the average decision time was 41.3 ms. The results show the effectiveness and accuracy of the temporal coded spiking neural network RL method. In conclusion, an attempt was made to provide insights into developing spiking neural network reinforcement learning methods for decision-making and autonomous control of unmanned systems