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The three control strategies are shown to be important preventive measures to lower disease transmission rate. The model is further extended to its stochastic counterpart to encapsulate the variation or uncertainty observed in the disease transmissibility. We observe the variability in the infective population and found their distribution at certain fixed time, which shows that for small populations, the stochasticity will play an important role.Vehicle drivers usually perceive a higher risk when driving on snow covered roads. The city cleaning efficiency would directly influence the risk and mitigation of wintertime events, especially for snow covered roads. Under the risk-informed approach background, more attention is paid to the capacitated arc routing problem (CARP) of urban snow plowing operations. Current algorithms mainly relies on the topology of road network without considering snow covered pavement’s negative effect on road capacity and traffic flow. This paper proposes a vulnerability-based parallel heuristic algorithms applied for the CARP by implementing risk-informed approach. First, a method is proposed to set service priorities based on the vulnerability evaluation by considering the added cost of travel demands. Second, a sub-process path-scanning approach is developed to avoid redundant path scans. Then verification and comparison between this newly proposed constructive heuristic and existing algorithms of whole-process path-scanning and sequential processing are conducted. Results show that the sub-process path-scanning approach obviously costs less service completion time than the existing algorithms for solving the CARP. However, this improved algorithm would also cause an increase of deadhead time upon dispatch. The balance between service completion time and deadhead time for more routing problems would be discussed in the near future.The new type of coronavirus pneumonia is caused by the new type of coronavirus which appeared at the end of 2019. Because of its strong contagiousness, rapid spread and great harm, it has already given countries around the world serious effects. So far there is no clear specific drug. Scientifically grasping the development law of epidemics is extremely important for preventing and controlling epidemics. Since the latent of this epidemic are also highly contagious, traditional infectious disease models cannot accurately describe the regularity of this epidemic transmission. 6-OHDA in vitro Based on the traditional infectious disease model, an infectious disease model with a time delay is proposed. The time difference is used to characterize the cycle of viral infection and treatment time. Using the epidemic data released in real time, firstly, through the numerical simulation parameter inversion, the minimum error is obtained; then we simulate the development trend of the epidemic according to the dynamics system; finally, we compare and analyze the effectiveness of isolation measures. This article has simulated COVID-19 and analyzed the development of the epidemic in Beijing and Wuhan. By comparing the severity of the epidemic in the two regions, early detection and isolation are still the top priority of epidemic prevention and control.Gesture recognition is critical in the field of Human-Computer Interaction, especially in healthcare, rehabilitation, sign language translation, etc. Conventionally, the gesture recognition data collected by the inertial measurement unit (IMU) sensors is relayed to the cloud or a remote device with higher computing power to train models. However, it is not convenient for remote follow-up treatment of movement rehabilitation training. In this paper, based on a field-programmable gate array (FPGA) accelerator and the Cortex-M0 IP core, we propose a wearable deep learning system that is capable of locally processing data on the end device. With a pre-stage processing module and serial-parallel hybrid method, the device is of low-power and low-latency at the micro control unit (MCU) level, however, it meets or exceeds the performance of single board computers (SBC). For example, its performance is more than twice as much of Cortex-A53 (which is usually used in Raspberry Pi). Moreover, a convolutional neural network (CNN) and a multilayer perceptron neural network (NN) is used in the recognition model to extract features and classify gestures, which helps achieve a high recognition accuracy at 97%. Finally, this paper offers a software-hardware co-design method that is worth referencing for the design of edge devices in other scenarios.The signaling axis from the primary tumor to the tumor-draining lymph node (TDLN) has emerged as a crucial mediator for the efficacy of immunotherapies in neoadjuvant settings, challenging the primary use of immunotherapy in adjuvant settings. TDLNs are regarded as highly opportunistic sites for cancer cell dissemination and promote further spread via several primary tumor-dependent mechanisms. Lesion-level mixed responses to antibody immunotherapy have been traced to local immune signatures present in the TDLN and the organ-specific primary tumors that they drain. However, the pharmacokinetics (PK) and biodistribution gradients of antibodies in primary tumors and TDLNs have not been systemically evaluated. These concentration gradients are critical in ensuring adequate antibody pharmacodynamic (PD) T-cell activation and/or anti-tumor response. The current work reviews the knowledge for developing physiologically-based PK and pharmacodynamic (PBPK/PD) models to quantify antibody biodistribution gradients in anatomically distinct primary tumors and TDLNs as a means to characterize the clinically observed heterogeneous responses to antibody therapies. Several clinical and pathophysiological considerations in modeling the primary tumor-TDLN axis, as well as a summary of both preclinical and clinical PK/PD lymphatic antibody disposition studies, will be provided.Asymptomatic transmission of infectious diseases has been recognized recently in several epidemics or pandemics. There is a great need to incorporate asymptomatic transmissions into traditional modeling of infectious diseases and to study how asymptomatic transmissions shift epidemic dynamics. In this work, we propose a compartmental model with asymptomatic transmissions for waterborne infectious diseases. We conduct a detailed analysis and numerical study with shigellosis data. Two parameters, the proportion $p$ of asymptomatic infected individuals and the proportion $k$ of asymptomatic infectious individuals who can asymptomatically transmit diseases, play major rules in the epidemic dynamics. The basic reproduction number $\mathscrR_0$ is a decreasing function of parameter $p$ when parameter $k$ is smaller than a critical value while $\mathscrR_0$ is an increasing function of $p$ when $k$ is greater than the critical value. $\mathscrR_0$ is an increasing function of $k$ for any value of $p$. When $\mathscrR_0$ passes through 1 as $p$ or $k$ varies, the dynamics of epidemics is shifted.