Hurstmcgarry8625

The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally. Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data. In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events. As the results, we estimate the mean of TG at 4.0 days (95%CI 3.3-4.6), and the mean of pre-symptomatic transmission period at 2.2 days (95%CI 1.3-4.7). We approximate the mean latent period of 3.3 days, and 32.2% (95%CI 10.3-73.7) of the secondary infections may be due to pre-symptomatic transmission. #link# The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics.The combination of medical field and big data has led to an explosive growth in the volume of electronic medical records (EMRs), in which the information contained has guiding significance for diagnosis. And how to extract these information from EMRs has become a hot research topic. In this paper, we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records (CEMRs). Firstly, a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs. Then we use the encoder from Transformer (ET) as our model's encoder to alleviate the long context dependency problem, and the CRF is utilized as the decoder. At last, we compare the BiLSTM-CRF and ET-CRF model with word2vec and ELMo embeddings to CEMRs respectively to validate the effectiveness of ELMo-ET-CRF model. With the same training data and test data, the ELMo-ET-CRF outperforms all the other mentioned model architectures in this paper with 85.59% F1-score, which indicates the effectiveness of the proposed model architecture, and the performance is also competitive on the CCKS2019 leaderboard.Anomaly detection has been widely researched in financial, biomedical and other areas. However, most existing algorithms have high time complexity. Another important problem is how to efficiently detect anomalies while protecting data privacy. In this paper, we propose a fast anomaly detection algorithm based on local density estimation (LDEM). The key insight of LDEM is a fast local density estimator, which estimates the local density of instances by the average density of all features. The local density of each feature can be estimated by the defined mapping function. Furthermore, we propose an efficient scheme named PPLDEM based on the proposed scheme and homomorphic encryption to detect anomaly instances in the case of multi-party participation. Compared with existing schemes with privacy preserving, our scheme needs less communication cost and less calculation cost. link2 From security analysis, our scheme will not leak privacy information of participants. And experiments results show that our proposed scheme PPLDEM can detect anomaly instances effectively and efficiently, for example, the recognition of activities in clinical environments for healthy older people aged 66 to 86 years old using the wearable sensors.In the field of remote sensing image processing, the classification of hyperspectral image (HSI) is a hot topic. There are two main problems lead to the classification accuracy unsatisfactory. One problem is that the recent research on HSI classification is based on spectral features, the relationship between different pixels has been ignored; the other is that the HSI data does not contain or only contain a small amount of labeled data, so it is impossible to build a well classification model. To solve these problems, a dual-channel CNN model has been proposed to boost its discriminative capability for HSI classification. The proposed dual-channel CNN model has several distinct advantages. Firstly, the model consists of spectral feature extraction channel and spatial feature extraction channel; the 1-D CNN and 3-D CNN are used to extract the spectral and spatial features, respectively. Secondly, the dual-channel CNN have been used for fusing the spatial-spectral features, the fusion feature is input into the classifier, which effectively improves the classification accuracy. Finally, due to considering the spatial and spectral features, the model can effectively solve the problem of lack of training samples. The experiments on benchmark data sets have demonstrated that the proposed dual-channel CNN model considerably outperforms other state-of-the-art method.This paper studies a reaction-diffusion-advection system describing a directed movement of immune cells toward chemokines during the immune process. We investigate the global solvability of the model based on the bootstrap argument for minimal chemotaxis models. We also examine the stability of nonconstant steady states and the existence of periodic orbits from theoretical aspects of bifurcation analysis. Through numerical simulations, we observe the occurrence of steady or time-periodic pattern formations.Human behavior has a major impact on the spread of the disease during an epidemic. At the same time, the spread of disease has an impact on human behavior. In this paper, we propose a coupled model of human behavior and disease transmission, take into account both individual-based risk assessment and neighbor-based replicator dynamics. The transmission threshold of epidemic disease and the stability of disease-free equilibrium point are analyzed. Some numerical simulations are carried out for the system. Three kinds of return matrices are considered and analyzed one by one. The simulation results show that the change of human behavior can effectively inhibit the spread of the disease, individual-based risk assessments had a stronger effect on disease suppression, but also more hitchhikers. This work contributes to the study of the relationship between human behavior and disease epidemics.In recent years, there are many research cases for the diagnosis of Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by utilizing the traditional unsupervised machine learning methods and the supervised deep learning models. However, unsupervised learning methods are not good at extracting accurate features among MRIs and it is difficult to collect enough data in the field of PD to satisfy the need of training deep learning models. Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology (SMC). The model firstly introduces the sliding window method to grasp different features, and then uses the dimensionality reduction algorithms of Linear Discriminant Analysis (LDA) to process the data with different features. Finally, the traditional single-view clustering and multi-view clustering methods are employed on multiple feature views to obtain the results. Experiments show that our proposed method is superior to the state-of-art unsupervised learning models on the clustering effect. As a result, it may be noted that, our work could contribute to improving the effectiveness of identifying PD by previous labeled and subsequent unlabeled medical MRI data in the realistic medical environment.Additive manufacturing is becoming increasingly popular because of its unique advantages, especially fused deposition modelling (FDM) which has been widely used due to its simplicity and comparatively low price. All the process parameters of FDM can be changed to achieve different goals. For example, lower print speed may lead to higher strength of the fabricated parts. While changing these parameters (e.g. print speed, layer height, filament extrusion speed and path distance in a layer), the connection between paths (lines) in a layer will be changed. To achieve the best connection among paths in a real printing process, how these parameters may result in what kind of connection should be studied. In this paper, a machine learning (deep neural network) model is proposed to predict the connection between paths in different process parameters. Four hundred experiments were conducted on an FDM machine to obtain the corresponding connection status data. Among them, there are 280 groups of data that were used to train the machine learning model, while the rest 120 groups of data were used for testing. The results show that this machine learning model can predict the connection status with the accuracy of around 83%. In the future, this model can be used to select the best process parameters in additive manufacturing processes with corresponding objectives.Fatigue driving is one of the main factors which affect the safety of drivers and passengers in mountain freeway. To improve the driving safety, the application of fatigue driving detection system is a crucial measure. Accuracy, speed and robustness are key performances of fatigue detection system. However, most researches pay attention to one of them, instead of taking care of them all. It has limitation in practical application. This paper proposes a novel three-layered framework, named Real-time and Robust Detection System. Specifically, the framework includes three modules, called facial feature extraction, eyes regions extraction and fatigue detection. In the facial feature extraction module, the paper designs a deep cascaded convolutional neural network to detect the face and locate eye key points. Then, a face tracking sub-module is constructed to increase the speed of the algorithm, and a face validation submodule is applied to improve the stability of detection. Furthermore, to ensure the orderly operation of each sub-module, we designed a recognition loop based on the finite state machine. It can extract facial feature of the driver. compound library inhibitor In the second module, eyes regions of the driver were captured according to the geometric feature of face and eyes. In the fatigue detection module, the ellipse fitting method is applied to obtain the shape of driver's pupils. According to the relationship between the long and short axes of the ellipse, eyes state (opening or closed) can be decided. Lastly, the PERCLOS, which is defined by calculating the number of closed eyes in a period, is used to determine whether fatigue driving or not. The experimental results show that the comprehensive accuracy of fatigue detection is 95.87%. The average algorithm rate is 32.29 ms/f in an image of 640×480 pixels. The research results can serve the design of a new generation of driver fatigue detection system to mountain freeway.This study aims to investigate the force transmission characteristics of a multiple-fulcrum supporting platform, and the significance of this paper is to creatively put forward the law of uniform force characteristics of multiple-fulcrum supporting platform with heavy loads and layout reconfiguration methods to realize the law. In this paper, firstly, a force transmission model for the multiple-fulcrum supporting platform has been constructed, the relationship between force transmission characteristics and layout parameters for all fulcrums has been discussed in detail, and the layout law for all fulcrums with the same supporting force has been discovered. Then, layout reconfiguration methods have been proposed to realize the uniform force characteristics of all fulcrums under different types of constraint surfaces (rectangular or square constrained surface, circular constrained surface, and unconstrained surface). Finally, layout reconfiguration methods have been applied to some engineering problems by ADAMS simulation.