With the popularity of social media, individuals can receive various messages anytime and anywhere, including many rumors.The spread of rumors has a certain impact on individuals and society, which has become a major issue that the country attaches great importance to.This study aims to explore the influencing factors of college students' rumor sharing behavior based on different information sources and the fear psychology generated by college students when facing major public health emergencies.It is found that college students have different sharing behaviors for different types of rumors and information from different sources.Negative emotions such as fear and anxiety can promote their dissemination of rumors.This dissemination behavior is a psychological response, especially influenced by health self-efficacy.Given the findings, the discrimination ability of college students as a student group needs to be strengthened.Society and universities should strengthen educational guidance, provide accurate information, and the country should formulate complete and effective strategies for various online platforms to deal with various rumors.

This study investigates the relationships between social support, interpersonal competence, gender, and academic year among university students. Using the Social Support Questionnaire (SSQ) and the Interpersonal Competence Questionnaire (ICQ-15), data were collected from 400 students. The analysis revealed significant gender differences, with female students reporting higher levels of total social support and interpersonal competence compared to males. Notably, females showed greater friend and family support. Additionally, seniors displayed higher interpersonal competence than freshmen and sophomores, indicating skill development over time. However, social support levels remained consistent across academic years. These findings highlight the necessity for gender-sensitive and developmentally appropriate interventions to enhance student well-being and success. Universities should implement targeted programs to develop interpersonal skills, particularly for male students and early-year undergraduates, fostering a supportive academic environment. Future research should explore additional factors influencing social support and interpersonal competence to further inform effective student support strategies.

This article explores the evolution and impacts of different monitoring paradigms: traditional surveillance, sousveillance, and toutveillance. Traditional surveillance, characterized by hierarchical observation from an authoritative entity, often results in power imbalances, privacy invasion, and social inequality. In response, sousveillance—monitoring by individuals of those in power—emerges as a countermeasure, aiming to democratize oversight but still facing legal and ethical challenges. Toutveillance, which promotes mutual monitoring among all parties, is presented as a potential solution to these issues, leveraging technology such as body cameras to enhance transparency, accountability, and reduce discriminatory practices. The article argues that while each paradigm has its merits and limitations, toutveillance offers a more balanced approach to address the complexities of modern surveillance, privacy concerns, and social justice.

Through the investigation of more than 50 village-level units in Gansu Province, this study discusses the impact of the standardization of the three rights of rural land transfer on the protection of farmers' rights and interests. It is found that the standardized separation of the three rights can effectively improve the protection level of farmers' land rights and interests, and promote the rational utilization and value appreciation of land resources. At the same time, the implementation of standardized measures also provides farmers with more employment opportunities and ways to increase their income, helping to improve the quality of life of farmers. However, there are still some problems and challenges, including the unreasonable price of land transfer, the risk and uncertainty in the transfer, and the weak awareness of farmers' land rights and interests protection. To this end, it is suggested that the government should strengthen the supervision, improve the relevant systems, and increase the training and publicity of farmers, so as to ensure that the legitimate rights and interests of farmers in the rural land circulation are guaranteed, and promote the implementation of the rural revitalization strategy.

For a long time, geographers have revealed various causes of the formation of the Loess Plateau and its evolution process by studying the factors of geological structure, climate environment, hydrogeology and other factors. According to the research results, the formation of the Loess Plateau is not only influenced by the geological structure, but also closely related to various factors such as climate change and hydrogeology. It is worth mentioning that the utilization and management of water resources also play an important role in the development process of the Loess Plateau. Finally, we will put forward corresponding suggestions and countermeasures for the current problems and challenges to promote the sustainable development and ecological environment protection of the Loess Plateau region.

Immunohistochemical detection of colorectal cancer (CRC) plays a key role in guiding targeted therapy and assessing clinicopathological characteristics.This review focuses on the key immunohistochemical markers such as HER2, KRAS, NRAS, BRAF and PD-L1, and discusses the selection of targeted therapy drugs and the clinicopathological relationship.The overexpression of HER2 is associated with the invasion, metastasis and poor prognosis of CRC.Her2-targeted drugs such as trastuzumab provide a new way for the treatment.KRAS, NRAS, and BRAF mutations are similarly associated with a poor prognosis in CRC, but currently targeted agents targeting these mutations have had limited efficacy.As an immune checkpoint molecule, the high expression of PD-L1 is related to the immune response and prognosis of CRC.PD-L1 inhibitors have become an important tool for immunotherapy.Through the detection of these indicators, doctors can more accurately evaluate the tumor characteristics and prognosis of CRC patients, and formulate personalized treatment plans for patients.These immunohistochemical markers not only provide a basis for targeted therapy of CRC, but also deepen our understanding of the clinicopathological characteristics of CRC.

The North Jiangsu Province (NJP) shoal is the largest shoal sea area along the East China Sea coast, and the study of its topographic change and its impact on storm surge wind risk is of great significance for the development of coastal zone resources, environmental protection, and disaster prevention. In this study, we analyzed the multifactorial assessment of topographic changes and wind risk in the shallow shoals of NJP using satellite remote sensing and hydrodynamic modeling, and calculated the Simpson's wind index for the shallow shoals of NJP using the Improved Waterline Method and other methods, taking the shallow shoals of NJP as the study area. The results show that the topography of the shallows in NJP exhibits obvious inter-annual, seasonal and inter-monthly variations, showing a tendency of gradual decrease from the near shore to the far shore in the longitudinal direction, and a tendency of gradual increase from the south to the north in the transverse direction, with the presence of some complex topographic features in localized areas. The Simpson's wind hazard index of the NJP shoal has obvious spatial and temporal differences and variations, in which the Simpson's wind hazard index in the southern and central parts of the NJP shoal is higher than that in the northern part of the NJP shoal, reflecting that the intensity of wind hazards and the degree of risk are higher in the southern and central parts of the NJP shoal than in the northern part of the North Jiangsu Shoal. Research shows that storm surge events are one of the main drivers of topographic change and wind risk, and that factors such as the intensity, frequency, duration, and path of the storm surge affect the magnitude and distribution of wind risk. This study can provide some scientific basis and reference opinions for the topographic changes and tidal flats storm risk management in the shallow flats of NJP.

Augmented reality (AR) is reshaping education by offering immersive and interactive learning experiences. This paper explores the significance of AR in education, emphasizing its role in enhancing engagement, visualization, and knowledge retention. It delves into various aspects, including AR applications, augmented textbooks, and virtual field trips. The discussion includes the benefits of incorporating AR into education, such as personalized learning, real-world simulations, and accessibility improvements. Moreover, the paper addresses the challenges and integration considerations in implementing AR in educational settings, including content development and technology infrastructure. Through a review of AR in education research and projects, the study highlights the transformative potential of AR in shaping the future of learning.

This report takes learning behavior and psychological support as the factors to predict the students' academic performance. A total of 13 factors were selected at the initial stage. Firstly, PCA and MDT algorithms were used for data preprocessing, and we used CNN to extract deep student behavior features. Then, the maximum pooling approach is used to pick the significant features; next, the selected features are used as the input of LSTM. A temporal attention mechanism is introduced at the output of LSTM to allocate attention weights for different weekly student behavior characteristics. This paper divides students' academic performance into several score intervals and makes a multi-classification prediction. This study uses 39 algorithms based on six types to predict the student's academic performance. The result shows that: (1) The average prediction accuracy based on the deep learning model is the highest (85.46%) in the six types of models; (2) The accuracy of the LSTM algorithm (88.36%) outperformed all the benchmark models. (3) The attention mechanism introduced can effectively improve the model's prediction accuracy; the proposed model in this paper outperformed all the benchmark models. Furthermore, the statistical analysis found that (1) Students who have academic confidence (confidence in teachers, courses, and themselves) may obtain higher academic performance; (2) A clear learning motivation is essential to final academic performance; (3) Lack of confidence in others in the study group may ultimately improve students' academic performance by encouraging them to work harder.

This paper focuses on the study of student behavior-based grade prediction. This research investigates grade prediction based on student behavior. After evaluating student behavior data from campus events, an attention-based CNN-LSTM student grade prediction model is developed. Initially, we use Convolutional Neural Network (CNN) to extract deep student behavior features and maximum pooling approach to pick the significant features in student behavior features; the retrieved features are then used as the input of Long and Short-Term Memory networks. A temporal attention mechanism is included at the output of LSTM in order to allocate attention weights for different weekly student behavior characteristics in a reasonable manner, hence improving the model's performance in terms of prediction. Experiments revealed that the student performance prediction model suggested in this paper had excellent performance.

At present, deep learning has been successfully applied in the field of medical diagnosis, and it can effectively improve the diagnostic accuracy by using deep learning to predict brain tumor images. Based on traditional convolution neural networks tend to ignore the problem of key location in brain tumor image information, this paper proposes a door control channel attention conversion unit, door control channel attention switching unit by using the relationship between a small number of parameters to change the channel, under the condition of without any increase in computational cost more significantly improve classification accuracy. Also, the Sequeeze and Excitation block is introduced for the communication between the channels. Finally, a multipath attentional network model (FSnet) for brain tumor image classification is constructed based on ResNeXt network model. Experimental tests on brain MRI data sets show that FSnet has better classification performance than traditional convolutional neural networks.

This paper is mainly based on the prediction of English grades, aiming at the data of students' English grades and personal identity during school, and establishes a fuzzy neural network prediction model based on the spike mechanism and genetic algorithm. First of all, in the aspect of parameter training of neural network optimization design, this paper adopts genetic algorithm as the learning algorithm of fuzzy neural network, so that the network can achieve the global optimum. Then, the spiking mechanism proposed based on the cerebral cortex information transmission mode and the Spiking neuron accumulation trigger (Integrate-and-Fire, IF) model is used to complete the structure growth and pruning of the fuzzy neural network, realize the dynamic adjustment of the network structure during the training process, and improve the performance of fuzzy neural network in English grade prediction application. Finally, after testing, it is found that the fuzzy neural network prediction model based on the spike mechanism and genetic algorithm proposed in this paper has a good performance.

With the rapid development of education for overseas students in China, psychological problems caused by cross-cultural factors have become increasingly prominent. Based on the psychological questionnaire data, in order to make full use of the relationship information between students, this paper proposes a psychological crisis individual identification model based on bipartite graph convolution network model (B-GCN) to make up for the shortcomings of traditional identification methods. Because the GCN model can not realize inductive learning, this paper improves the GCN model based on the structural characteristics of psychological tests, and proposes a bipartite graph neural network model. The model can classify the students who have never appeared in the graph structure, so as to realize the early warning of psychological crisis. Experiments show that the proposed B-GCN model has a good performance.

Artificial Intelligence (AI) is playing a transformative role in healthcare by enhancing diagnostic accuracy, treatment planning, and patient care. This paper explores the significance of AI in healthcare, emphasizing its role in medical imaging, predictive analytics, and personalized medicine. It delves into various aspects, including machine learning algorithms, electronic health records integration, and telemedicine applications. The discussion includes the benefits of AI in healthcare, such as early disease detection, reduced healthcare costs, and improved patient outcomes. Moreover, the paper addresses the challenges and ethical considerations in AI adoption, including data privacy and regulatory compliance. Through a review of AI-driven healthcare solutions and clinical trials, the study highlights the positive outcomes associated with the integration of AI into medical practices.

Quantum computing has emerged as a groundbreaking technology that has the potential to revolutionize computation. This paper explores the significance of quantum computing, emphasizing its role in solving complex problems, cryptography, and simulations. It delves into various aspects, including quantum algorithms, quantum bits (qubits), and quantum supremacy. The discussion includes the benefits of quantum computing, such as exponentially faster calculations, enhanced security, and novel scientific discoveries. Moreover, the paper addresses the challenges and considerations in building practical quantum computers, including quantum error correction and scalability. Through a review of quantum computing experiments and research, the study highlights the positive outcomes associated with the development of quantum computational systems.

Augmented Reality (AR) is reshaping the landscape of education by providing immersive and interactive learning experiences. This paper explores the significance of AR in education, emphasizing its role in K-12 classrooms, higher education, and vocational training. It delves into various aspects, including AR applications, wearable devices, and content development. The discussion includes the benefits of AR in education, such as improved engagement, visualization of complex concepts, and personalized learning. Moreover, the paper addresses the challenges and considerations in implementing AR in educational settings, including access to technology and pedagogical integration. Through a review of AR educational projects and research, the study highlights the positive outcomes associated with the integration of AR into the learning environment.

Blockchain technology has disrupted traditional financial systems and is extending its impact to various industries. This paper explores the significance of blockchain, emphasizing its role in secure transactions, supply chain management, and digital identity verification. It delves into various aspects, including blockchain consensus mechanisms, smart contracts, and decentralized applications. The discussion includes the benefits of blockchain technology, such as transparency, reduced fraud, and enhanced data integrity. Moreover, the paper addresses the challenges and considerations in blockchain adoption, including scalability and regulatory compliance. Through a review of blockchain projects and research, the study highlights the positive outcomes associated with the implementation of distributed ledger technology.

5G technology has ushered in a new era of connectivity, enabling faster data speeds, low latency, and transformative applications. This paper explores the significance of 5G and beyond, emphasizing its role in telecommunications, IoT, and smart cities. It delves into various aspects, including 5G network architecture, mmWave spectrum, and edge computing. The discussion includes the benefits of 5G technology, such as enhanced mobile experiences, autonomous vehicles, and real-time data analytics. Moreover, the paper addresses the challenges and considerations in deploying 5G networks, including infrastructure development and security. Through a review of 5G use cases and research, the study highlights the positive outcomes associated with the evolution of wireless connectivity.

Cybersecurity has become a critical concern in the digital age, with the increasing volume of data and online threats. This paper explores the significance of cybersecurity, emphasizing its role in protecting sensitive information, critical infrastructure, and personal privacy. It delves into various aspects, including cybersecurity frameworks, threat detection technologies, and encryption methods. The discussion includes the benefits of robust cybersecurity, such as data integrity, prevention of cyberattacks, and trust in digital systems. Moreover, the paper addresses the challenges and evolving nature of cyber threats, including malware, phishing, and ransomware. Through a review of cybersecurity practices and research, the study highlights the positive outcomes associated with proactive cybersecurity measures.

Sustainable agriculture has gained prominence as a means to meet global food demands while preserving the environment. This paper explores the significance of sustainable farming practices, emphasizing their role in food security, resource management, and climate change mitigation. It delves into various aspects, including organic farming, precision agriculture, and agroforestry. The discussion includes the benefits of sustainable agriculture, such as reduced environmental impact, improved soil health, and diversified crop yields. Moreover, the paper addresses the challenges and considerations in transitioning to sustainable farming, including economic viability and policy support. Through a review of sustainable agriculture initiatives and research, the study highlights the positive outcomes associated with environmentally conscious farming practices.