To improve the concealment of the dummy location generated by the dummy location generation algorithm in location privacy and enhance the ability to resist the attacker to deduce the user's real location based on background knowledge, this paper proposes a dummy location generation method based on spatiotemporal correlation. Firstly, the historical query probability is used to preliminarily screen and select the appropriate dummy location set, and then the spurious location set is filtered and optimized by adding the relevant definition of spatial sensitivity metric, and the dummy location with higher concealment is filtered and generated. Then, through theoretical analysis and demonstration, it is verified that the algorithm can effectively resist the attacker's background knowledge inference attack, to improve the concealment of the dummy location. Finally, the future research directions of the dummy location generation method are summarized and prospected.

Spectrum is an important but very scarce resource in broadband wireless networks. With the rapid growth of the wireless network scale, the limited spectrum resources have been unable to meet the increasing mobile traffic. In order to relieve the network pressure and improve the utilization efficiency of spectrum resources, the spectrum resource management mechanism has received wide attention. This paper first describes the basic situation of spectrum resource shortage in wireless communication, and presents the problem of heterogeneous cellular network; then, classify the traditional interference coordination method, summarizes its advantages and disadvantages, and provide reference and help for researchers in related fields.

Wireless sensor network is a self-organizing network composed of a large number of nodes with sensing, computing and communication capabilities. It collaboratively senses, collects and processes the information of the sensing objects in the coverage area of the network, and sends the information through self-organizing to replace some manual sensing and processing work, and finally realizes the monitoring of the sensing area. Node deployment is an important research topic in wireless sensor networks. This paper first analyzes the characteristics and difficulties of the node deployment optimization problem, and then elaborates the application of intelligent algorithm in the node deployment optimization of wireless sensor networks.

Studying protein folding can not only drive the great development of life sciences, but also provide tremendous help for human disease prevention and treatment, and has great application value in the fields of medicine and bioengineering. This article uses the BP neural network model to predict the rate of protein folding, and provides an effective way to find the key factors of protein folding kinetics. The main research contents are as follows: (1) Optimization of the neural network model. This article selected 4 types of optimizers and 36 types of activation function combinations to assess the performance of the neural network model in predicting the rate of protein folding. From the results, it is more accurate and fast to predict the rate of protein folding when using the Adam optimizer and Sigmoid and Tanh function combinations as the parameters of the neural network model. (2) The influence of chain length in primary structure information on prediction accuracy is studied and compared. From the prediction results, it was found that the prediction accuracy was higher when using the effective chain length of the protein than using the protein chain length. (3) Study the effect of different separation cutoffs on protein folding rates. The results show that when the separation cutoff value lcut of the contact order is 3, the most accurate prediction value can be obtained.

This paper discusses the key technologies and existing issues in fault diagnosis and prediction of power equipment in smart grids, and proposes corresponding optimization strategies. In terms of data processing technology, solutions are proposed for data quality issues, including data cleaning and missing value imputation, data augmentation and smoothing, as well as efficient data transmission and storage schemes. In terms of algorithm model optimization, the accuracy and robustness of fault diagnosis and prediction are improved through the design of lightweight and efficient algorithms, model fusion and ensemble learning, as well as adaptive and online learning methods. In terms of system integration and application optimization, the compatibility, real-time performance, and security of the system are enhanced through standardized and modular design, establishment of real-time monitoring and response systems, and implementation of safety protection and privacy mechanisms, ensuring the safe and stable operation of smart grids.

This paper expounds the related technologies of the development and application of VS development environment, .NET framework and skills promotion platform, analyzes the practical significance of this platform research and development, discusses the development and function description of courseware module, experiment module, design module, document module, Information Query module, etc., and forecasts and analyzes the application effect and prospect of the platform. The difference of operation between the selection method and the bubble method is fully compared and analyzed. Among 8 groups of data, the time consumption of 6 groups of data using the selection method is lower than that using the bubble method. Through the data analysis, from the cycle number or stability of the optimization, the use of bubble method is better; from the time-consuming optimization, the use of selection method is better than the use of bubble method to carry out this study. This platform has been applied in practice and its functions are optimized and upgraded in professional course teaching.

is the most extensive global pandemic affecting mankind in the past century, and it is a serious crisis and a severe test for the whole world. Human life security and health are facing a major threat. The aim of this study was to analyze the epidemic situation in Chinese mainland in November 2022. Based on the data given on the official website of the National Health and Wellness Commission of China, the data on the outbreak of infected persons during the period from 0:00 a.m. on November 1, 2022, to 24:00 a.m. on November 30, 2022, were quantitatively analyzed by means of statistical analyses. The overall epidemic trend graph we plotted through EXCEL, and descriptive statistics were analyzed according to the data on the spread of the epidemic, deaths and cures. The results showed that the data for the whole month of November showed a slow and almost linear increase in the number of deaths, and the number of cures continued to increase at a higher rate than the number of deaths. The average mortality rate was 1.85%, with a minimum of 1.61% on November 30, while the cure rate had a maximum of 96.13% and a minimum of 86.84%. The stabilization of the cure rate and the low fluctuation of the mortality rate indicate that the Government's emergency response has been effective. China established a national emergency management system after the SARS outbreak in 2003, emphasizing hierarchical responsibility and territorialbased public health emergency management. In order to improve public health services, it is necessary to strengthen the planning of public health emergencies, improve the capacity of medical institutions and health administrative departments, increase resource investment and personnel training, and establish an incentive system for medical personnel. It is also crucial to strengthen the rule of law in public health, raise public health awareness, and enhance international exchanges and cooperation. These measures will help improve society's ability to respond to public health incidents and safeguard the health and safety of citizens.

Statistical analysis of coal mine accidents plays an important role in reducing the probability of accidents and promoting coal mine safety management. However, the current research on the year, region, accident type and so on has some emphasis and many cross. In order to comprehensively and systematically master the characteristics and laws of coal mine accidents in China in recent years, this paper analyzes and discusses the number of coal mine accidents and the number of deaths in China based on the statistical data of coal mine accidents from 2012 to 2023, from the six aspects of year, month, period, region, accident grade and accident type. Based on this, the grey Markov model is used to predict and analyze the future trend of coal mine accidents in China. The research shows that the number of coal mine accidents and the number of deaths in China have shown a downward trend in recent years, but with tortuosity and recurrence, and more attention should be paid to general accidents. The high-incidence period of accidents is March, August, November and 10:00-13:59 every day. The incidence and mortality of gas and roof accidents are higher. In the large and above accidents, flood, fire, poisoning, and suffocation accidents account for a large proportion. It is predicted that the number of accidents in China in 2024 and 2025 will be 95 and 64, and the number of deaths will be 133 and 99.

With the advent of the information age, computer communication technology has been widely applied, promoting the development of various industries. However, the accompanying information security issues have attracted widespread attention. This article explored the current application status of computer communication technology in different fields, analyzed its potential threats in information security, and explored protective measures using CNN (Convolutional Neural Networks) models, aiming to provide guidance and suggestions for industry practitioners. The research structure showed that the average accuracy of the CNN model was 94.8%, significantly better than the 89.5% of the SVM (Support Vector Machine) model. The average response time of the CNN model was only 20.5 milliseconds. The average false alarm rate of the CNN model on the false alarm rate indicator was 7.2%. In the final system overhead experiment, the CNN model required a significant amount of system resources in high traffic environments. From the above data conclusions, it can be seen that the CNN model exhibited higher efficiency and accuracy in network communication security, despite the high resource demand under high load conditions.

In recent years, data governance has emerged as a critical pillar of enterprise digital transformation, playing a key role in driving business growth and optimizing decision-making processes. Tencent's ola Platform, as a one-stop data governance solution, is built on the principles of DataOps and Tencent's unique data governance methodology, providing comprehensive capabilities across the data lifecycle. Among these, full-link metadata capabilities serve as a cornerstone for data discovery, asset management, metric construction, and governance execution. This paper uses Tencent's ola Platform as a case study to explore the concept, architectural design, implementation pathways, and practical value of full-link metadata capabilities. Furthermore, it summarizes key insights and best practices to provide a reference for enterprises aiming to build efficient and trustworthy data governance systems.