Objective To explore the impact of hospital staff’s risk perception on their emergency responses, and provide reference for future responses to public health emergencies. Methods Based on participatory observation and in-depth interviews, the staff of the First Affiliated Hospital of Guangzhou Medical University who participated in the prevention and control of the coronavirus disease 2019 from April to September 2020 were selected. The information on risk perception and emergency responses of hospital staff was collected. Results A total of 61 hospital staff were included. The positions of hospital staff were involved including hospital leading group, hospital office, medical department, logistics support department and outpatient isolation area. The interview results showed that both individual and organizational factors of hospital staff would affect the risk perception of hospital staff, thus affecting the emergency responses of hospital staff, mainly reflected in the psychological and behavioral aspects. Among them, their psychological reactions were manifested as more confidence, sensitivity, and sense of responsibility and mission; The behavior aspects was mainly reflected in the initiation time, execution ability, and standardization level of emergency responses actions. Conclusion Therefore, relevant departments should pay attention to the risk perception of hospital staff, improve the risk perception and emergency responses of hospital staff by influencing the individual and organizational factors of hospital staff, so as to respond more effectively to future public health emergencies and reduce the adverse impact of public health emergencies on the work of hospital staff.
Medical visual question answering (MVQA) plays a crucial role in the fields of computer-aided diagnosis and telemedicine. Due to the limited size and uneven annotation quality of the MVQA datasets, most existing methods rely on additional datasets for pre-training and use discriminant formulas to predict answers from a predefined set of labels. This approach makes the model prone to overfitting in low resource domains. To cope with the above problems, we propose an image-aware generative MVQA method based on image caption prompts. Firstly, we combine a dual visual feature extractor with a progressive bilinear attention interaction module to extract multi-level image features. Secondly, we propose an image caption prompt method to guide the model to better understand the image information. Finally, the image-aware generative model is used to generate answers. Experimental results show that our proposed method outperforms existing models on the MVQA task, realizing efficient visual feature extraction, as well as flexible and accurate answer outputs with small computational costs in low-resource domains. It is of great significance for achieving personalized precision medicine, reducing medical burden, and improving medical diagnosis efficiency.
ObjectiveTo investigate the differences in self-perception level of asthma control and the factors affecting the ability of self-perception in patients with bronchial asthma. MethodsA total of 322 patients who were diagnosed with bronchial asthma at the First Affiliated Hospital of Harbin Medical University from March 2013 to February 2015 were recruited in the study. The clinical data were collected, including the demographic characteristics of the patients, the Asthma Control Test (ACT) and results of routine blood test and pulmonary function test on the same day that they were required to fill out the ACT. Then they were followed up at the 1st, 3rd, 6th, 12th months, and required to fill out the ACT again, and underwent the blood routine test and lung function test. In addition, health education about asthma was offered regularly during these visits. ResultsA total of 226 patients met the inclusion criteria of the study. The patients with asthma had significant differences between self-perception control level and real symptoms control level (P<0.05). The patients who were 65 years old or older perceived their symptoms of bronchial asthma rather poorly (P=0.000). The patients who received senior high school or higher education had a higher ability of self-perceived asthma control (P=0.005). The patients with allergic rhinitis combined were less likely to correctly perceive their illness compared with those who did not suffered from allergic rhinitis, and the difference was statistically significant (P=0.001). In addition, for those diagnosed with allergic rhinitis combined with bronchial asthma, regular treatment also made difference--longer treatment for rhinitis means a higher ability of self-perceived asthma control (P=0.000). The health education did play a constructive role in helping patients correctly perceive their illness (P=0.000). There was no correlation between the absolute value of peripheral blood eosinophils and the accuracy of self-perceived asthma control. Nevertheless,there was a noticeable correlation between the ability of peripheral blood eosinophils of patients with asthma and acute attack of bronchial asthma (P=0.003),which was a meaningful finding in assessing the risk of future acute attack of bronchial asthma (P=0.469). ConclusionsThere is a significant difference between self-perception control level and symptom control level in patients with asthma. The self-perception control level of asthma patients who are elderly, the low degree of educational level, merged allergic rhinitis, and lack of health education are associated with lower accuracy of self-perception control level. The absolute value of peripheral blood eosinophils of the patients with asthma can be used to assess the risk of asthma acute attack in the future, but has no significant correlation with the accuracy of self-perception control level.
As the most efficient perception system in nature, the perception mechanism of the insect (such as honeybee) antennae is the key to imitating the high-performance sensor technology. An automated experimental device suitable for collecting electrical signals (including antenna reaction time information) of antennae was developed, in response to the problems of the non-standardized experimental process, interference of manual operation, and low efficiency in the study of antenna perception mechanism. Firstly, aiming at the automatic identification and location of insect heads in experiments, the image templates of insect head contour features were established. Insect heads were template-matched based on the Hausdorff method. Then, for the angle deviation of the insect heads relative to the standard detection position, a method that calculates the angle of the insect head mid-axis based on the minimum external rectangle of the long axis was proposed. Eventually, the electrical signals generated by the antennae in contact with the reagents were collected by the electrical signal acquisition device. Honeybees were used as the research object in this study. The experimental results showed that the accuracy of template matching could reach 95.3% to locate the bee head quickly, and the deviation angle of the bee head was less than 1°. The distance between antennae and experimental reagents could meet the requirements of antennae perception experiments. The parameters, such as the contact reaction time of honeybee antennae to sucrose solution, were consistent with the results of the manual experiment. The system collects effectively antenna contact signals in an undisturbed state and realizes the standardization of experiments on antenna perception mechanisms, which provides an experimental method and device for studying and analyzing the reaction time of the antenna involved in biological antenna perception mechanisms.
Emotion can reflect the psychological and physiological health of human beings, and the main expression of human emotion is voice and facial expression. How to extract and effectively integrate the two modes of emotion information is one of the main challenges faced by emotion recognition. In this paper, a multi-branch bidirectional multi-scale time perception model is proposed, which can detect the forward and reverse speech Mel-frequency spectrum coefficients in the time dimension. At the same time, the model uses causal convolution to obtain temporal correlation information between different scale features, and assigns attention maps to them according to the information, so as to obtain multi-scale fusion of speech emotion features. Secondly, this paper proposes a two-modal feature dynamic fusion algorithm, which combines the advantages of AlexNet and uses overlapping maximum pooling layers to obtain richer fusion features from different modal feature mosaic matrices. Experimental results show that the accuracy of the multi-branch bidirectional multi-scale time sensing dual-modal emotion recognition model proposed in this paper reaches 97.67% and 90.14% respectively on the two public audio and video emotion data sets, which is superior to other common methods, indicating that the proposed emotion recognition model can effectively capture emotion feature information and improve the accuracy of emotion recognition.
Ultrasound diffraction tomography (UDT) possesses the characteristics of high resolution, sensitive to dense tissue, and has high application value in clinics. To suppress the artifact and improve the quality of reconstructed image, classical interpolation method needs to be improved by increasing the number of projections and channels, which will increase the scanning time and the complexity of the imaging system. In this study, we tried to accurately reconstruct the object from limited projection based on compressed sensing. Firstly, we illuminated the object from random angles with limited number of projections. Then we obtained spatial frequency samples through Fourier diffraction theory. Secondly, we formulated the inverse problem of UDT by exploring the sparsity of the object. Thirdly, we solved the inverse problem by conjugate gradient method to reconstruct the object. We accurately reconstructed the object using the proposed method. Not only can the proposed method save scanning time to reduce the distortion by respiratory movement, but also can reduce cost and complexity of the system. Compared to the interpolation method, our method can reduce the reconstruction error and improve the structural similarity.
Taking advantages of the sparsity or compressibility inherent in real world signals, compressed sensing (CS) can collect compressed data at the sampling rate much lower than that needed in Shannon’s theorem. The combination of CS and low rank modeling is used to medical imaging techniques to increase the scanning speed of cardiac magnetic resonance (CMR), alleviate the patients’ suffering and improve the images quality. The alternating direction method of multipliers (ADMM) algorithm is proposed for multiscale low rank matrix decomposition of CMR images. The algorithm performance is evaluated quantitatively by the peak signal to noise ratio (PSNR) and relative l2 norm error (RLNE), with the human visual system and the local region magnification as the qualitative comparison. Compared to L + S, kt FOCUSS, k-t SPARSE SENSE algorithms, experimental results demonstrate that the proposed algorithm can achieve the best performance indices, and maintain the most detail features and edge contours. The proposed algorithm can encourage the development of fast imaging techniques, and improve the diagnoses values of CMR in clinical applications.
In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.
The method of directly using speed information and angle information to drive attractors model of grid cells to encode environment has poor anti-interference ability and is not bionic. In response to the problem, this paper proposes a grid field calculation model based on perceived speed and perceived angle. The model has the following characteristics. Firstly, visual stream is decoded to obtain visual speed, and speed cell is modeled and decoded to obtain body speed. Visual speed and body speed are integrated to obtain perceived speed information. Secondly, a one-dimensional circularly connected cell model with excitatory connection is used to simulate the firing mechanism of head direction cells, so that the robot obtains current perception angle information in a biomimetic manner. Finally, the two kinds of perceptual information of speed and angle are combined to realize the driving of grid cell attractors model. The proposed model was experimentally verified. The results showed that this model could realize periodic hexagonal firing field mode of grid cells and precise path integration function. The proposed algorithm may provide a foundation for the research on construction method of robot cognitive map based on hippocampal cognition mechanism.
Electric and electronic products are required to pass through the certification on electrical safety performance before entering into the market in order to reduce electrical shock and electrical fire so as to protect the safety of people and property. The leakage current is the most important factor in testing the electrical safety performance and the test theory is based on the perception current effect and threshold. The traditional method testing the current threshold for perception only depends on the sensing of the human body and is affected by psychological factors. Some authors filter the effect of subjective sensation by using physiological and psychological statistical algorithm in recent years and the reliability and consistency of the experiment data are improved. We established an experiment system of testing the human body's current threshold for perception based on EEG feature analysis, and obtained 967 groups of data. We used wavelet packet analysis to detect α wave from EEG, and used FFT to do spectral analysis on α wave before and after the current flew through the human body. The study has shown that about 97.72% α wave energy changes significantly when electrical stimulation occurs. It is well proved that when the EEG feature identification is applied to test the human body current threshold for perception, and meanwhile α wave energy change and human body sensing are used together to confirm if the current flowing through the human body reaches the perception threshold, the measurement of the human body current threshold for perception could be carried out objectively and accurately.