5G-V2X Based Traffic Safety Warning System through Mobile Sensor and Wireless Communication

This project designs a traffic safety early warning system based on 5G-V2X for the current situation of increasing traffic accidents in China, which concentrates on two modules of V2V (vehicle-to-vehicle), V2I (vehicle-to-road) for early warning system design, with OBU (vehicle communication unit) and RSU (roadside communication unit) based on 5G-V2X communication technology to establish vehicle-to-vehicle and vehicle-to-road interactive communication, and realize V2V collision warning and V2I traffic light emergency event warning at intersections through collision risk warning algorithm and intersection passage assistance algorithm, thus alerting drivers to avoid dangerous situations and reducing the incidence of traffic accidents.

Embedded Microprocessor Wireless Communication Data Collection Aids in Early Warning of Default Risk for Internet Finance Bank Customers

Thanks to the maturity and innovation of embedded technology, products based on embedded platforms continue to penetrate people’s lives and play a pivotal role in various fields of society, while the development of data acquisition systems based on wireless communication of embedded microprocessors is one of the frontier directions of embedded development. Modern commercial bank risk management system includes risk control organization, risk measurement techniques (including risk warning), risk avoidance techniques, and total risk management model. In this paper, a multichannel data acquisition system combined with a wireless sensor platform is designed for early warning of default risk of Internet financial bank customers, which can realize real-time monitoring, acquisition, display, and data storage of DC signals and indoor environment information output from sensor platforms or electronic devices, and for frequent transactions, fund splitting, and mortgage pledging related to complex customers in today’s e-commerce platform, which triggers when the associated enterprise credit risk presents complexity, continuity, wholeness, multiplicity, volatility, severity, etc., it is used to improve risk judgment and risk warning ability, enhance investment risk response ability, and reduce the related losses caused by marketing risk.

Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

In order to ensure the benign operation of the social security fund system, it is necessary to understand the social security fund facing all aspects of the risk, more importantly to know the relationship between different risks. Based on RBF, the interpretative structure model is applied to draw the risk correlation hierarchy diagram, which provides a scientific risk management method for the social security fund. RBF neural network is used to build the risk warning model of social security fund operation. Then, put forward the corresponding risk treatment scheme to the warning signal. Finally, the RBF neural network is used for comprehensive risk warning. In this paper, the risk warning of social security fund operation is the research object, and the corresponding risk treatment scheme is put forward for the warning signal. This paper uses an improved ant colony algorithm to optimize the parameters of the RBF neural network, which overcomes the shortcomings of the traditional RBF neural network such as slow convergence, ease of falling into local extremes, and low accuracy, and improves the generalization ability of the RBF neural network. It has the characteristics of good output stability and fast convergence speed. On this basis, the prediction model based on the improved ANT colony-RBF neural network is established, and the MATLAB software calculation tool is used for accurate calculation, which makes the prediction results of coal mine safety risk more accurate and provides more reliable decision basis for decision makers. The results show that the network has small calculation error, fast convergence, and good generalization ability.

Modeling salinity risk response to irrigation practices for cotton production under film mulched drip irrigation in Xinjiang

Predicting the impacts of the irrigation amount (IA), water salinity (WS), and antecedent soil salinity (AS) on soil salinization, the crop yield, and water productivities (WPs) are important for precision agriculture. We used a calibrated HYDRUS − 2D model coupled with a validated crop water production function to quantitatively determine the response of a soil − cotton system to three factors (IA, WS, and AS) in 30 scenarios under film mulched drip irrigation. These scenarios included five IAs, two ASs, and three WSs. Under the same IA and WS, the transpiration, evapotranspiration, yield, and WPs were lower, whereas the evaporation, drainage, soil water storage, and leached salt were higher under higher AS (over the salt tolerance threshold of cotton) scenarios. Under lower AS scenarios, desalination processes (20.2 to 166.8 g m−2) occurred in freshwater (0.38 dS m−1) irrigation scenarios and salt accumulated (425.8 to 1,442.4 g m−2) in saline water (3.10 and 7.42 dS m−1) irrigation scenarios. Desalination processes (2,273.4 to 4,692 g m−2) occurred in the higher AS scenarios. Salinity risk warning should be the focus for cotton fields with lower AS and saline water irrigation. Our results may help to identify the salinity risk to support sustainable cotton production in Xinjiang.

Survey of Nursing Staff’s Training on Early Warning Ability for Inpatients with “Three Infarcts and One Hemorrhage”

Background and Objective. Nursing staff’s cognition and training willingness on early warning ability of inpatients is an important measure to reduce the occurrence of adverse events and severe disease. In this article, we aim to understand the cognition and training needs of nursing staff in a tertiary referral center in Changsha City, Hunan Province, on the early warning ability of inpatients with myocardial infarction, pulmonary embolism, cerebral infarction, and dangerous hemorrhage (referred to as “three infarcts and one hemorrhage”). Methods. A total of 787 nursing staff in a tertiary referral center in Changsha City, Hunan Province, were selected using a convenient sampling method. We used an online questionnaire designed by ourselves to survey them. The content of the questionnaire primary included basic information, related knowledge of the nursing staff on the potential risk prediction and precontrol of inpatients with “three infarcts and one hemorrhage,” relevant information on improving early warning scores, management of clinical early warning, training needs, and training methods. Results. Over 50% of the nursing staff had little understanding about the risk warning knowledge of inpatients with “three infracts and one hemorrhage,” and the degree of understanding was related to education, job title, and working years. The nursing staff with higher education level or professional title or longer working experience have a better understanding of the risk warning knowledge of inpatients with “three infracts and one hemorrhage.” Conclusion. The cognitive competence of nursing staff in a tertiary referral center in Changsha City, Hunan Province, on the early warning ability of inpatients with “three infarcts and one hemorrhage” needs to be improved. Medical institutions should actively train nursing staff on early warning ability for inpatients with “three infarcts and one hemorrhage” to improve the nursing staff’s awareness and patients’ safety and efficiency.

Prediction of the financial risk of Special Treatment of a Listed Pharmaceutical Company in China: A Principal Component Analysis (Preprint)

BACKGROUND The listed pharmaceutical industry of China is growing swiftly by about 10% interest per year. However, risk always keeps pace with improvements. In China, listed companies with significant financial distress or irregularities will be assigned a special treatment (ST) label indicating a risk warning. Recently, eight listed pharmaceutical companies with a ST sign are surviving with but present serious investment risks. OBJECTIVE This paper aimed to discover the most significant factors that cause conversion of a listed pharmaceutical company into an ST firm. Tailored approaches for protecting China’s listed pharmaceutical companies from financial risks are being developed in order to help this domain in profit. Besides, we also aimed to offer suggestions for investors for investigating Chinese listed pharmaceuticals with the goal of assisting the investors in making successful investments. METHODS After collecting data from online databases, a principal component analysis (PCA) model was applied for descending data dimensions. After selecting the components with highest contribution, a logistic regression (LR) model was conducted for simplifying the outcome and calculating the intercept, component coefficients, standard error (SE), and Z and p-values. RESULTS Nine principal components were crucial from the principal component analysis (PCA) model, and two components (components 1 and 5) remaining as the most important factors after the LR model. The estimated intercept was 4.866 (SE 1.096, Z-value 4.442, p < 0.001). The estimated coefficient for components 1 (SE 0.332, Z-value 3.067, p = 0.002) and 5 (SE 0.643, Z-value −2.6, p = 0.009) were 1.017 and −1.672, respectively. CONCLUSIONS Investors are supposed to supervise the accounting conditions in three sectors: (1) solvency, profitability, and research and development (R&D) investment; (2) running the firm properly; and/or (3) investing successfully. Firms are supposed to hire professional partitioners as leaders. The major shareholders should not plan any questionable investments for personal income, and they should ensure the firm works under conditions with low liability, high profitability, and R&D costs that match the perfect growth opportunity after the Coronavirus 2019 (COVID-19) pandemic and the strong growth of China’s economy in 2020.

Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.