Journal of Globe Scientific Reports

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.

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.

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.

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.

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.

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 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.

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.