With the popularization of the internet and social media, the impact of network public opinion on the social level cannot be underestimated, and sentiment analysis in network public opinion is of utmost importance. This paper collects, organizes, and summarizes relevant literature on sentiment analysis in network public opinion research in China, adopting bibliometric methods to analyze the quantity of literature, co-occurrence of high-frequency keywords, and evolution of theme keywords. Following the pro‐ cess of sentiment analysis in network public opinion research, this study analyzes the main research content, ideas, and methods in data collection, data processing, sentiment recognition(classification/tendency),sentiment evolution. Finally, this study points out fu‐ ture research directions, and gives the working directions of next steps.

To address the problem of poor localization performance caused by the small number of fingerprint points and the lack of representativeness of fingerprint features in the offline phase of fingerprint-based indoor localization, a CSI-based indoor localiza‐ tion method is proposed using Inverse Distance Weighted (IDW) interpolation and amplitude-phase fusion network. The IDW inter‐ polation algorithm is used to generate a large-capacity fingerprint library. Then, a parallel convolutional neural network (CNN) is used to process the amplitude and phase to obtain the positional fingerprint features. Finally, a novel integrated architecture fusing Random Forest (RF) and Multilayer Perceptron (MLP) is used for classification to obtain the estimated positions of the target loca‐ tion samples.

This study designs an intelligent sleep massage pillow system, aiming to provide users with effective sleep protection. The system adopts TGAM brain wave detection, temperature and humidity control, massage control, Bluetooth, WIFI and other module technologies to control smart sleep products in real time. It is a highly intelligent and fully automated process. In addition, the Sleep Care APP also has a sleep staging algorithm, automatically generates sleep depth analysis reports, and provides users with personal‐ ized sleep plans. It also uses an AI smart assistant to analyze users’ sleep conditions in real time, helping users improve their sleep experience more scientifically.

Based on the core technology of the next generation Internet IPv6, which has the characteristics of large enough address space, higher security, better service quality and supporting mobile terminal equipment, the information development of universities is gradually promoting the IPv6 network deployment and IPv6 network application of information system. However, in the process of promoting IPv6 network deployment and access of application systems in universities, there are still some problems that some un‐ derlying network devices do not support IPv6 protocol, and security devices cannot achieve comprehensive control. In order not to change the current school network architecture, without affecting the current IPv4 network access, this paper puts forward the imple‐ mentation of IPv6 network deployment and access for college Web application system based on the intelligent DNS and reverse proxy. This technology has the characteristics of simple principle, strong stability, high security and easy maintenance. Through link‐ age deployment, IPv6 network access and IPv4 access security reinforcement of the school application system are realized, which provides reference practice for the deployment of IPv6 network application.

With the increasing influence of mobile Internet on young readers in university libraries, the proportion of new generation readers using QQ instant messaging software is increasing, text mining of library QQ group text information can be used to under‐ stand the public sentiment of the library, which can be used for library public sentiment warning and provide strong public sentiment response capabilities for library decision-makers. Web crawler technology is used to crawl chat records in QQ groups from Septem‐ ber 2022 to December 2022 as library public sentiment data, then performing data preprocessing operations such as deduplication and cleaning on the original public sentiment data. Then, Tsinghua University's Thulac segmentation technology is used to extract keywords and calculate their weights from the public sentiment data, and them the WordCloud library is used for visualization. Next, the spaCy library is used to to calculate specific emotional tendencies and scores for public opinion data, and finally the emotional tendencies of library public opinion are analyzed through experiments.

With the rapid development of computer science and technology, computer technology has enormous potential in the field of education, especially in the application of modern medical practice. This paper aims to explore the reform of the practical teach‐ ing system of integrating basic medical theories and applications into computer science in the context of New Engineering, achiev‐ ing the integration of information technology practical teaching and medical knowledge, improving students' learning effectiveness, promoting practical ability improvement, and cultivating students' innovative thinking. The methods and achievements of the reform around the determination of talent cultivation goals for the crossover of computer major and modern medicine, the construction of practical teaching system, and the exploration of the education model for the integration of industry and education are introduced in the paper.

To solve the problem that the recognition efficiency of different types of attention mechanisms is too low in the person reidentification task, and the focus is too much on extracting single-granularity information, which makes it difficult to extend to realworld application scenarios, a fine-grained module based on local attention is proposed. The obtained coarse-grained feature repre‐ sentation is divided into multiple parts, guiding the network to extract finer-grained local clues. In addition, considering the short‐ comings of a single branch in extracting multi-granularity clues, a multi-branch fine-grained network is designed to further extract richer clues. Experimental results on mainstream datasets verify the effectiveness of the network proposed in this article

In traditional teaching, due to differences in teachers, the knowledge connections between different courses are often over‐ looked, resulting in excessive repetition or failure to integrate new and old knowledge points. To make the knowledge points more smoothly connected in teaching, we help students to establish a knowledge connection system. Firstly, we organize a teaching salon where teachers fill out a teaching knowledge connection table to find fusion points between courses. Secondly, we use holographic projection technology to build a holographic projection pyramid. Finally, we use holographic projection during the teaching to help students recall previously learned knowledge and better integrate it with new knowledge, and understand new information. To sum up, holographic projection technology visualizes models in courses, deepens students' impressions, increases visual stimulation dur‐ ing learning, and enhances students' interest in learning. This innovative form of teaching tool and teaching method helps to deepen students' understanding and absorption of knowledge.

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.

Aiming at the accidents caused by household gas leakage, such as human poisoning, ?re and explosion, this paper proposes the design and implementation of home kitchen environment remote monitoring system based on cloud platform technology. The system uses STM32 chip as the core processor, analyzes the real-time data collected by the sensor, compares the preset threshold for strategic control, and realizes the intelligent control of the gas electronic valve, alarm and intelligent window in the kitchen. And the data is regularly uploaded to the cloud platform for storage through the ESP8266 communication module, and the storage data of the cloud platform is invoked by the mobile terminal in real time to carry out remote dual control with human intervention on the kitchen state to realize reliable and safe management. 

In response to the current situation of multiple computer major courses with strong correlation being mutually indepen‐ dent and lacking comprehensive application in teaching, this article proposes an iterative teaching model of multi-course fusion proj‐ ect. The teaching of multiple courses revolves around the same large project, breaks down the large project into a series of iterative teaching tasks from easy to difficult, adopts an online and offline hybrid teaching method, and introduces the popular TAPD research and development platform in the industry as teaching assistance. Take the integration teaching of JAVA Program Design and Data‐ base Principles and Applications for example, this article introduces the teaching design and specific implementation process of this model in detail. 

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.

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.

This paper explores the construction of virtual simulation experiments based on course groups, optimizes and integrates the experimental teaching content of core courses within the course groups from a systematic and global perspective, focuses on the connection and collaboration of experimental content between courses, and establishes a progressive virtual simulation experimental teaching platform through the organization and implementation of experimental teaching for course groups. It provides a good solu‐ tion for the experimental teaching reform of cloud computing and automated operation and maintenance course group under the background of New Engineering.

SDN adopts a centralized control method, achieving visualization and dynamism of network management, and is currently a new type of network architecture. However, at the beginning of SDN design, security issues were not considered. In the current situation where DDoS attacks are so prevalent, it is necessary to conduct research on attack detection under SDN architecture. After analyzing the SDN architecture mechanism, this article studies the location deployment of attack detection modules and designs a multi-level collaborative detection scheme for DDoS attacks. It designs an independent modeling scheme based on ensemble learning algorithms to address the problem of attack detection modeling. It extracts two sets of samples from the KDDCUP99 dataset without replacement, and uses SVM and adaBoost algorithms for independent modeling and sample combination modeling. The use of the adaBoost algorithm has a certain improvement in the classi?cation performance of samples, and modeling alone has no impact on the classi?cation performance of samples. It also shows higher classi?cation performance than the total samples on teardrop. The experimental results show that independent modeling for collaborative detection of forwarding plane and control plane has certain feasibility. This scheme starts from reducing the burden on the SDN centralized control center, providing a guarantee for e?ective network management, and has certain guiding signi?cance for attack detection of SDN architecture. 

In the era of big data, it has become a consensus that various business departments can stimulate data value based on the accumulation of existing business data. However, due to the lack of uni?ed data standards across di?erent business systems, disorganized metadata, data silos, and low-quality data problems constantly emerge, hindering the e?ective utilization of data and necessitating necessary governance. Among them, data lineage analysis is one of the key tasks of metadata management, which is of great signi?cance for data traceability and data governance. However, traditional methods for constructing data lineage often face high computational complexity, poor accuracy, and high execution costs. To overcome these issues, a data lineage construction method based on the similarity calculation of data tables is proposed: by text feature representation of the three elements of data table naming, table structure, and data ?elds, using TFIDF to calculate the similarity of data tables, and further constructing the data table lineage relationship through the improved Jaro-Winkler Distances algorithm to verify the ?eld overlap and table name similarity. The results show that the algorithm has a signi?cant e?ect on the construction of data table lineage, facilitating the smooth progress of data governance work.

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.

 The problem with previous studies of social One Class Collaborative Ranking (OCCR) algorithms is that they simply integrated the user's social network information into their model, without taking into account the transmission of social trust networks between users. To solve this problem, a new social one class collaborative ranking recommendation algorithm (TrustOCCR) based on CLiMF model and the newest TrustMF model is proposed, which aims to improve the performance of social one class collaborative ranking recommendation by integrating twofold sparse data, i.e., implicit feedback data and the transitive social trust network data. Experimental results on practical dataset show that our proposed TrustOCCR algorithm outperformed existing state-of-the-art OCCF approach over di?erent evaluation metrics, and that the TrustOCCR algorithm possesses good expansibility and is suitable for processing big data in the ?eld of internet information recommendation. 

In order to conduct scienti?c research and educational teaching on quantum computing, simulation tools for quantum computing have emerged. Vizard is a virtual reality development platform software that is suitable for building 3D models and can be quickly developed using Python language. A quantum bit visualization simulation model for candy boxes is proposed based on the Vizard platform. This model uses quantum state lollipops to describe the probability amplitude of quantum bits. The candy radius represents the modulus and the angle between the candy stick and the horizontal plane represents the auxiliary angle. Opening the box lid causes the quantum state lollipop to collapse. Using quantum state lollipops, quantum gate components are simulated and quantum circuits are constructed using quantum gate components. The candy box model is easier to describe the superposition state of quantum bits than traditional Bloch balls, and it is interesting and easy to understand, suitable for quantum technology beginners and teenagers to popularize quantum technology education.

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.

Due to the in?uence of earthquakes, groundwater levels can undergo abnormal changes. The prediction study of water level change has important theoretical and practical signi?cance for the analysis of possible earthquake precursors and the mitigation of secondary disasters. In this study, the earthquake selected occurred on April 13, 2015, at 10:28 a.m., in Jianshui County, Honghe Hani and Yi Autonomous Prefecture, Yunnan Province. By obtaining groundwater level data from observation wells in the vicinity of the earthquake, the dataset is divided based on the theoretical concepts of earthquake active and non-active periods. Subsequently, an LSTM model is employed for training. The di?erences between the predicted values and the actual values are observed. Experimental results indicate that the model can identify abnormal changes in groundwater levels caused by earthquakes, providing valuable insights into studying groundwater level anomalies as earthquake precursors. 

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.

This device uses STM32F407 as the core to achieve waveform recognition and parameter measurement of periodic signals. The device consists of a core calculation module, buttons, display, signal conditioning circuit, and zero crossing detection circuit. It can achieve multi-channel gain sampling of periodic signals, measure parameters such as peak to peak value and frequency of waveforms, and identify waveform types such as sine waves, square waves, and triangular waves. This system achieves precise measurement of a wide range of voltage through a multi gain signal conditioning circuit, precise measurement of frequency through a hardware zero crossing circuit, real-time waveform acquisition through frequency calculation of sampling frequency, and recognition of waveform types through model matching. It has the advantages of wide voltage and frequency measurement range, high measurement accuracy, accurate waveform recognition, and fast response speed.

With the popularization of cloud computing technology and the development of containerization technology, Docker and SpringBoot have become indispensable tools for modern software development and deployment. However, this widespread use also comes with security risks. This article provides an in-depth analysis of the unauthorized access risks of Docker API and SpringBoot Actor. When these critical components are exposed to unauthorized access, attackers may exploit these vulnerabilities to perform malicious operations, such as deploying malicious containers, tampering with application con?gurations, or stealing sensitive information. These behaviors may not only lead to service interruption and data leakage, but also cause serious reputation and ?nancial losses to the enterprise. 

With the development of communication technology, chaotic communication technology has attracted the interest of many researchers for its resistance to multipath fading and strong confidentiality, among which Differential Chaos Shift Keying (DCSK) is the most widely studied non-coherent digital modulation scheme. Generalized Carrier Index Differential Chaos Shift Key‐ ing (GCI-DCSK) is the integration of the index modulation technique on the basis of DCSK, which further improves the system per‐ formance. In this paper, the GCI-DCSK modulation scheme is combined with the relay collaboration technique to extend the cover‐ age of the communication network and improve the communication quality.

Power demand response is an important means to maintain the balance of power supply and demand in the peak period, and peak clipping is the main way of smart grid to achieve power demand response. In order to enable enterprises using hybrid ?ow-shop production to better participate in peak clipping demand response(PCDR) and optimize production scheduling, this paper introduces PCDR in the hybrid ?ow-shop scheduling problem, establishes a new problem model, and proposes an improved Sparrow Search Algorithm(ISSA) for model solving. Aiming at the problem that the standard SSA is prone to local optimization, the ISSA improves the local search ability of SSA by adding KMeans clustering replacement strategy. The experimental results show that the proposed model and algorithm can provide a better peak clipping production scheduling scheme, meeting the needs of enterprises to implement PCDR scheduling.

The service perception experience of high-speed rail network users is signi?cantly impacted by network issues arising from frequency cutting, Doppler e?ect, and carriage penetration loss in the context of a high-speed railway private network. This article ?rstly summarizes the network problems existing in the current high-speed rail private network. Then, starting from the solution strategies for coverage problems, digital twin technology is applied to the planning and construction of high-speed rail private network sites. At the same time, an innovative edge beam collaborative rolling scheme is proposed to enhance the coverage quality of the high-speed rail private network in response to the problem of "dark under the light" at the site. In addition, the paper presents an optimization strategy for achieving super cell overlapping coverage based on CPID, and provides a comprehensive summary of antenna selection schemes tailored to di?erent scenarios along the railway. Finally, this paper presents relevant optimization solutions to address the frequency o?set caused by the Doppler e?ect, mutual interference resulting from di?erences in public-private network coordination, and the perception of low network speed among high-speed rail users. Leveraging the VMAX big data monitoring platform, combined with user tracking and high-speed rail line trajectory maps, precise identi?cation and optimization of high-frequency private network users leaving private network communities have been carried out, e?ectively improving the network perception of high-speed rail users.