The use of pulsar X-ray signals for spacecraft navigation is a hot research field in deep space exploration. In X-ray Pulsarbased Navigation (XNAV), identifying X-ray pulsar signals is extremely important. This article proposes the Time Cycle Kurtosis (TCK) as an identification algorithm for extracting pulsar signal features, and combines it with ResNet34 for training and verifica‐ tion. The criterion for successful identification is based on an accuracy of over 90%, with the shortest observation times for B0531+ 21, B0540-69, and B1509-58 pulsars to complete the identification task being 0.5 s, 2.5 s, and 15 s, respectively. This article demon‐ strates through experiments that TCK can quickly and accurately complete the identification task of X-ray pulsar signals.

In today's rapidly advancing technological era, intelligent parking lot recognition, positioning, and navigation systems have become an indispensable part of large public venues such as commercial complexes and hospitals. Traditional parking lots suf‐ fer from various issues including weak signals, low navigation accuracy, and difficulty in finding parking spaces. To enhance parking efficiency, we have designed a machine vision-based parking space detection and path planning system. This system utilizes ultra‐ sonic sensors to gather parking space information and employs YoloV5 image recognition technology to analyze the license plate in‐ formation of incoming vehicles. All data is transmitted to a backend server and then presented to customers through a WeChat miniprogram. Using advanced path planning algorithms, the system can identify the nearest available parking space to the customer and provide navigation services.

Based on the basic principles of the four-step method (1×1) weaving technology, a detailed analysis is conducted on the periodic motion of the yarn carrier and the trajectory of the yarn in space during the weaving process. Utilizing Matlab software, two binary variables are defined to track the regular movement of the yarn carrier in a programmatic form. Based on a series of hypotheti‐ cal conditions for the yarn, the internal structure of three-dimensional woven composite materials is represented using an inner cell characterization, and a three-dimensional four-directional structure unit cell model is established. Meanwhile, by providing the spa‐ tial coordinates of the yarn, the modeling of the three-dimensional woven composite material preform is completed using Texgen software.

To fully implement the student-centered teaching philosophy, the survey questionnaire is designed with binary branching logic, dividing the subjects into two groups: those familiar with AIGC (Artificial Intelligence Generated Content) products and those unfamiliar. Starting from the usage needs of on-campus university students towards AIGC products, a questionnaire survey is con‐ ducted among 1,250 undergraduates from diverse backgrounds to gain an in-depth understanding of students' intentions and specific scenarios of using AIGC products. Based on the perspective of university students, the survey results elucidates the potential positive impacts of integrating AIGC products into the educational structure, including promoting personalized learning and improving the accessibility of educational resources. At the same time, potential integrity risks are analyzed, such as an increase in academic mis‐ conduct and potential threats to intellectual property rights. In response to issues revealed by the survey, strategies are proposed to strengthen digital literacy education, construct multimodal intelligent learning environments, and enrich the educational process. These strategies aim not only to maximize the positive effects of AIGC products in education but also to effectively prevent and miti‐ gate potential negative impacts.

In the context of Blended Learning reform in universities, intelligent technology has become a key factor affecting teach‐ ing reform. Based on constructivist theory and connectionism theory, this article constructs Blended Learning model supported by in‐ telligent technology, and applies this model to Information Technology Curriculum and Teaching for instructional design and imple‐ mentation. The research results indicate that this model is beneficial for improving learner participation in the classroom, enhancing their learning interest, and providing valuable experience for boring theoretical teaching.

Under the background of "Internet+", technological progress, industrial form and production organization mode change have brought about the continuous evolution of occupational form and the change of post's demand for talent quality. As the core pro‐ fessional course of computer network technology, Windows Server Operating System Management is highly practical. In order to solve the problems such as the content of teaching materials cannot match the project-based content constructed by teachers, and the supporting online curriculum resources are not updated in time, the teaching team has formed a "three action cycle" higher voca‐ tional curriculum management model based on "Internet +", the dynamic update of curriculum content as the basis, the demand for industrial development as the guide, the dynamic construction of curriculum resources as the starting point, and the implementation of curriculum teaching as the guarantee. Practical research has shown that the implementation of the new management model has stimulated students' interest in learning, improved the teaching ability of the teacher team, and enhanced the teaching implementa‐ tion effect of Windows Server Operating System Management.

This study aims to explore the teaching challenges of Database Principles and Applications in private applied undergradu‐ ate universities in the context of information technology innovation, and to implement the "Triple Optimization and Innovation" teaching innovation and practice. The project aims to improve the training quality of information and innovation talents, enhance the teaching and research capabilities of the main teaching team, and provide new ideas and plans for the reform of new engineering edu‐ cation by optimizing course content, reconstructing new forms, optimizing teaching methods, deepening new teaching reforms, and optimizing evaluation models to enhance new quality. Research has found that optimized course content can better align with stu‐ dents' actual needs and market dynamics, forming a new form of interdisciplinary and practical orientation.

In the context of rapid development of Artificial Intelligence, video investigation technology is playing an increasingly im‐ portant role in the cultivation of students in public security universities. In response to the problem that the current content of Video Investigation Technology and curriculum groups cannot meet the needs of the cultivation of New Engineering talents, this article con‐ ducts an in-depth research on the content construction of Video Investigation Technology, explores the integration of Artificial Intelli‐ gence technology and elements into course construction and professional cultivation, and cultivates public security technical talents from the aspects of knowledge, ability, and values, promoting the construction of New Engineering in public security universities. 

To improve the operational efficiency of the digital government, this paper firstly expounds the relevant cases of digital government data sharing in Guangdong-Hong Kong-Macao Greater Bay area in digital government. Then it explores the basic pro‐ cess and effect of multi department data sharing in Guangdong-Hong Kong-Macao Greater Bay area, and points out that there are still many problems in multi department data sharing at present. It discusses the coordination strategies of multi department data shar‐ ing in the development of digital government, mainly in five aspects: further improving relevant laws and regulations, building a comprehensive supporting management system, refining the department performance evaluation index system, optimizing the inter‐ est coordination and incentive mechanism, and forming a multi-party supervision and inspection operation force.

Within the context of Emerging Engineering Education, the significance of interdisciplinary learning is becoming increas‐ ingly evident. However, non-computer majors such as automation and intelligent medical engineering often encounter challenges when implementing data structure courses, including limited class hours and students’ weak programming foundations. To tackle these issues, this paper proposes a novel practical teaching model, encompassing elements such as practical pre-study, line-by-line code demonstration, and application scenario integration. Practical effect shows that, the teaching model can enhance students’ learning enthusiasm, reduce the learning threshold, and elevate the quality of teaching. 

With the rapid development of the petrochemical industry, real-time monitoring and scheduling management of railway vehicle transportation have become crucial for enhancing economic benefits and social responsibility. This paper introduces the inte‐ gration application ideas, technical advantages, and practical implementation process of combining 5G and BeiDou high-precision positioning technology in the railway transportation and dispatching system of SINOPEC-SK(WUHAN) PETROCHEMICAL COMPANY LIMITED. Through the integrated application of the train identification system, BeiDou high-precision positioning tech‐ nology, and the enterprise's 5G Internet of Things, online, real-time, high-speed tracking and efficient scheduling of railway vehicles are achieved, which not only improves the overall efficiency of railway transportation, but also reduces operating costs, and signifi‐ cantly enhances emergency response capabilities and safety management levels.

Power system stability has been difficult due to the occurrence of low frequency oscillation in the modern power system. Oscillation is mainly due to sudden changes of frequency, load, voltage, active power and reactive power. In this paper, we provide a damping solution to oscillate the low frequency by using Modified Chameleon Swarm Algorithm. Damping performance and stabil‐ ity analysis of system is done by proposed Modified Chameleon Swarm Algorithm compared with conventional Chameleon Swarm Algorithm and Genetic Algorithm with different operating condition in terms of real power, reactive power and load disturbances. The comparative results confirmed that the proposed controller exhibits higher damping ratio, and better damping of deviations in speed and power angle to improve the stability of the system.

Aiming at the problems of insufficient expression of facial expression features, low recognition accuracy and many pa‐ rameters, an octave convolutional expression recognition method combined with attention feature fusion is proposed. The main inno‐ vation point is to introduce the attention feature fusion mechanism into the model to optimize the fusion of different scale features. Deep separable network is used to replace traditional convolution, which greatly reduces parameters. BN and PReLU are introduced to improve the stability and performance of the model. Experiments show that the accuracy of the model on CK+ and Fer2013 data sets is 98.91% and 74.03%, respectively, showing excellent generalization ability and accuracy.

Recognizing emotions through facial expressions has been widely applied in normal life. Among deep learning algo‐ rithms, Convolutional Neural Networks (CNN) have achieved great success in the field of facial expression recognition. However, it still faces the problem of information redundancy and data deviation, which affects the performance of facial expression recognition algorithm. Therefore, a Multi-scale Image Convolutional Network (GCN) based on landmarks extracted from facial images is pro‐ posed. The proposed method is simulated on CK+, JAFFE, FER2013 and RAF-DB datasets. The results show that the proposed method is superior to AUDN, BDBN, SCNN and other traditional deep learning frameworks, and has higher accuracy on different data sets.

Community detection is a crucial research topic in the realm of complex networks. Understanding and identifying the community structure of a network is essential for uncovering its behavior and function. In this paper, we propose a novel graph em‐ bedding method based on neighbor similarity for community detection. By utilizing the acceptance of nodes and aggregating attri‐ bute information expressions of neighbors, we obtain the vector representation of each node in the network. The final community de‐ tection results are then obtained by directly applying K-means clustering. Our experimental results demonstrate that our proposed al‐ gorithm outperforms other methods, showing significant improvements in both modularity and standard normalization metrics.

With the continuous development of IP networks, all over IP has become an inevitable trend in network development, and synchronous network technology is also changing rapidly. The IEEE1588 protocol has emerged. In order to ensure the security and reliability of 5G networks, the original GPS clock has been upgraded and transformed into a 1588V2 clock carried by a wired net‐ work in the local network 5G base station renovation and construction. It has become a reliable and cost-effective renovation plan for 5G network security. This article analyzes the advantages and disadvantages of the two renovation plans and proposes a solution that combines each other, and adapts to local conditions, providing a reference for future 5G base station clock renovation and con‐ struction.

Electricity quantity prediction is an important basis for power supply units to purchase electricity, and it is the fundamen‐ tal foundation for the stable operation of the electricity economy. This article proposes using the SARIMA-ARCH fusion model to predict electricity consumption. Firstly, the seasonal time series model (SARIMA) is used to model the monthly electricity consump‐ tion time series. The optimal model order is determined through ACF and PACF graph screening, and the basic prediction model of the seasonal time series model (SARIMA) is obtained. Subsequently, the regression residuals of the basic model are subjected to ARCH effect testing, and an autoregressive conditional heteroscedasticity (ARCH) model is established. Finally, this article com‐ pares and analyzes the predicted values of the SARIMA-ARCH model with those of conventional SRIAM and ARIMA. The results show that the prediction accuracy of the hybrid model of SARIMA-ARCH is high.

In the process of the construction of new smart cities, the urban operation center platform, which integrates cutting-edge information technologies such as 5G, big data, cloud computing and artificial intelligence, has been widely used, and has played an important role in improving the public service efficiency and governance capacity of smart cities, as well as realizing the precision and transparency of urban management. This paper proposes a design scheme of smart city operation command and dispatch plat‐ form based on digital twin. The platform consists of 6 modules: user layer, display layer, platform layer, support layer, data layer and front-end access layer, as well as 2 systems: security assurance system and standard specification system, which can realize data shar‐ ing between existing and new business systems. By integrating and utilizing massive data, it provides intelligent decision support for city managers.

The data center of provincial enterprises is one of the important carriers of national network security and informationiza‐ tion construction, and the realization of its autonomy and control should not be limited to the simple replacement of products and components. To realize the autonomy and control of the data center with high quality, from a technical point of view, a 16-indicator evaluation system is established based on four dimensions of business support, architectural agility, network security, and innovative applications to quantitatively analyze the development of autonomy and controllability of data centers of enterprises in Shandong Province, accurately locate the existing problems and put forward practical suggestions, and to provide reference for the develop‐ ment of autonomy and controllability of data centers of enterprises and the high-quality development of network security and infor‐ mationization work.

Experimental equipment is the foundation of practical teaching in universities, and good experimental equipment can pro‐ mote the improvement of teaching quality. However, industrial control safety experimental equipment is relatively scarce, lacks inno‐ vative experiments, and is expensive. In response to the teaching content of industrial control security related course experiments, the development and practice of industrial control network security teaching equipment are explored. The proposed self-made teach‐ ing equipment can be used for student experimental teaching, which is of great significance for improving laboratory construction level, enhancing student practical ability, and cultivating innovative talents.