Nowadays, with the rapid development of intelligent technology, it is urgent to effectively prevent infectious diseases and ensure people's privacy. The present work constructs the intelligent prevention system of infectious diseases based on edge computing by using the edge computing algorithm, and further deploys and optimizes the privacy information security defense strategy of users in the system, controls the cost, constructs the optimal conditions of the system security defense, and finally analyzes the performance of the model. The results show that the system delay decreases with the increase of power in the downlink. In the analysis of the security performance of personal privacy information, it is found that six different nodes can maintain the optimal strategy when the cost is minimized in the finite time domain and infinite time domain. In comparison with other classical algorithms in the communication field, when the intelligent prevention system of infectious diseases constructed adopts the best defense strategy, it can effectively reduce the consumption of computing resources of edge network equipment, and the prediction accuracy is obviously better than that of other algorithms, reaching 83%. Hence, the results demonstrate that the model constructed can ensure the safety performance and forecast accuracy, and achieve the best defense strategy at low cost, which provides experimental reference for the prevention and detection of infectious diseases in the later period.
Here, we aimed to retrospectively analyze the clinical characteristics of 27 patients with severe pneumonia caused by Chlamydia psittaci between January 2019 and April 2021 in southwest China. To this end, we collected data on the exposure history, clinical symptoms, laboratory examination, imaging characteristics, evolution, etiology, treatment, and outcomes to suggest a better diagnosis and prevention system. Our results showed that a metagenomic next-generation sequencing test could provide early diagnosis. All patients were sensitive to quinolones and tetracyclines, and the recovery rate was relatively high. Overall, all patients were in critical condition with moderate to severe acute respiratory distress syndrome and shock. In conclusion, early diagnosis of pneumonia caused by C. psittaci depends on effective molecular testing, and most patients recover after treatment.
The word ‘automatic’ is unavoidable in this modern technical era. Automation facilitates not only technical advancement and time reduction to several processes, but also provides protection in various aspects. Delivery scam is a commonly occurring crime and it has to be reduced. Product delivery is a long process which involves various people to ensure correct delivery to the customer, providing chances for scam to occur. This paper discusses on an automatic delivery-scam prevention system with the help of Raspberry-Pi controller. This system provides safety to the ordered goods by limiting the authorisation of opening the packages to company and the customer only. It assures the safe and correct delivery of the ordered product.
Cloud computing is a form of technological progress that has developed along with the times, this has spurred the increasing use of the internet. By usingtechnology internet that is able to implement server a virtual, which has the aim of building a cloud computing server at the District Communications and Information Office. Padang Pariaman uses the Operating System (OS) Proxmox VE (Virtual Environment) 6.4. Cloud computing is able to provide storage services that can be used simultaneously. The results of this study produce a cloud computing server that implements a security system with themethods ids (intrusion detection system) and ips (intrusion prevention system)that are able to process data(storagestorage), use software simultaneously in the network, and use infrastructure within the scope of this research.network cloud computing at the District Communications and Information Office. Padang Pariaman using aservice model private cloud
The analysis and assessment of regional characteristics of the preventive healthcare organization for children population in the Republic of Tatarstan were carried out within this research. It has revealed the major issues of prevention system in children healthcare in the Republic. Our goal was to develop and implement a set of measures on improvement of medical prevention in the system of children healthcare, and to evaluate their efficacy. The guidelines developed for preventive care in children have allowed us to create three-level model of preventive care for children in five main areas. The provision of medical, social, legal, psychological, and pedagogical care for children and adolescents (especially for children in difficult circumstances and/or socially dangerous situations) is one of the major issues for preserving the health of future generations in contrast to all socio-economic changes. This necessitates the creation of new forms, approaches, and mechanisms, as well as the development of measures on improvement of existing preventive technologies at the individual, group, and population levels via using information technologies.
Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.