bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting students’ E-retention in online learning during COVID pandemic: Evidence from the Students of Higher Education
Authors: Masood Amna , Noman Ahsan
Published: March 2024

Purpose

On account of prevalent crisis of COVID-19, the challenges of pandemic compelled students to pitch in the advanced and extensive environment of e-learning. Thence, the study of epidemic change in the satisfaction and online learning outcomes require absolute investigation to inquire e-retention among students of higher education institutions.

Design/methodology/approach

This study employed the PLS-SEM technique to analyze the research results through Smart PLS software. Additionally, data were collected from students of higher education through a questionnaire.

Findings

The findings of this research reflect that all factors i.e., Direct Instructions/Lecture, Instructor-Learner Interactions, Learner- Learner Interactions, Internet Self-Efficacy, Perceived Social Presence have a positive and significant impact on Online Learning Outcomes and Online Learning Satisfaction. In response, these factors positively and significantly affect E-retention.  

Originality/value

Higher education institutions and policymakers are the prime beneficiaries of this research paper. The paper makes a vital contribution to the literature by considering the role of online learning outcomes and online learning satisfaction for e-retention prediction in higher education, especially at the time of Covid-19.
Utilizing Adeno-Associated Virus Vectors for Advancing Cancer Gene Therapy
Authors: Fan Yiming
Published: March 2024

Adeno-associated virus (AAV) has proven highly effective as a delivery vector in clinical trials for gene therapy. The safety and efficacy of AAV have led to its application as a novel approach for cancer treatment. Many successful clinical trials of cancer have demonstrated the feasibility of this approach. This review mainly introduces the structure, characteristics and application of AAV in tumor models. The promise of gene therapy in addressing diverse diseases has become evident, with AAV vectors playing a pivotal role in its achievements. AAV's exceptional safety profile and efficacy in gene therapy have spurred their application as a novel approach in the realm of cancer treatment. Clinical trials in this area have yielded impressive results, underlining the potential of AAV in combating cancer. This review provides a comprehensive insight into AAV's structural attributes and unique characteristics, emphasizing their significance in the context of tumor models. AAV's ability to efficiently deliver therapeutic genes to cancer cells, coupled with minimal risk, marks a significant breakthrough in oncology.
Research Progress in Inhibiting Tumor Cells Through Sugar Metabolism in the TME
Authors: Chen Yiwen
Published: March 2024

In the immune microenvironment, the sugar metabolism of tumor cells determines their own growth, and the disturbance of metabolic process is considered as an important feature of tumor cell growth. Compared with normal cells, the greatest change in the metabolic process of tumor cells is the occurrence of aerobic glycolysis. Even under sufficient oxygen conditions, tumor cells will still absorb a lot of glucose through glycolysis to provide energy for cells, and many important rate-limiting enzymes in the glycolysis process are expressed in different forms in tumor cells and have an important impact on tumor growth. However, the specific mechanism of this conversion and the targeted enzyme's process are not clear. This article analyzes the difference in sugar metabolism between tumor cells and normal cells, specifically summarizes and analyzes the inhibitory effect mechanism of pyruvate kinase and lactate dehydrogenase on tumors, and obtains the conclusion that targeted inhibition of PKM and LDH can inhibit the growth and reproduction of tumor cells. Exploring the inhibition of tumor cell dependence on glucose, studying the glycolysis pathway of tumor cells and targeted drugs inhibiting key enzyme activity can help us further understand the metabolic characteristics and growth mechanism of tumor cells, provide new targets and strategies for tumor treatment, and provide a reference for anti-tumor combination therapy and individualized treatment for patients. However, at present, the research on targeted drug toxicity and side effects has not been solved, and the potential side effects and toxicity are not studied enough. Future research should focus on individualized treatment for patients, pay attention to the differences in the activation degree of glycolysis pathway in tumor cells, and bring better treatment results to tumor patients.
The Mechanism of Action of Cancer Therapeutic Vaccines and Their Application Prospects
Authors: Li Mingrui
Published: March 2024

The complexity of cancer as a global health problem has also led to the development of various novel therapeutics, including prophylactic vaccines. Scientific breakthroughs and technological developments have changed cancer research; since cancer is a complicated disease with many factors, more and better vaccines are required. The research stresses the worldwide influence of cancer and modern therapeutic approaches, such as vaccination. With advances in molecular and cellular biology, cancer therapeutic vaccination studies have been advanced; the discovery of tumor markers and advances in immunology and cancer therapies have changed. In contrast to chemotherapy and radiation, immunotherapy revolutionized treatment. The cancer therapeutic vaccines elicit antigen-specific T cell responses, helper T cell stimulation, and innate immune cells; the research indicates that these vaccines use various means to arouse the body's defensive mechanism against cancer. Complex tools are explained by figures 1 and 2, which depict CD4+ T cells and innate immunity cells. Vaccines can be peptide/protein-based, whole-cell- or nucleic acid-based by application or clinical trial. Each category is looked at for its significant successes, triumphs, and failures: dendritic cell vaccines, the efficiency of antigen presentation, and clinical trial success. Cancer-therapeutic vaccines and immune checkpoint inhibitors are synergistic. Expanding the range of cancer vaccine antigens depends on finding added tumor-associated antigens and immunological checkpoints. However, there are flaws in cancer therapeutic vaccines, despite progress; tumor heterogeneity, immune evasion, and Safety are among concerns today. Recognizing these problems sets the stage for later chapters of optimization and new approaches.
Reward-Based Decision-Making in Depression or At-Risk Population
Authors: Lin Yiyan
Published: March 2024

This paper discussed the recent studies on the behavioral and neural patterns of individuals with depression and those with familial risk in reward-based decision-making. Current theories for depression and reward-based decision-making were presented so that they can pave the way for investigation of the interaction between the two under scrutiny. The paper argues that depression influences individuals’ aspects of reward-based decision-making, including thinking strategies, flexibility, reward perceptions, and motivation. At-risk populations inherited the same patterns. According to the empirical studies from recent years, results mainly support the argument. Both clinical and at-risk groups have conservative thinking strategies, inflexibility in demanding situations, and lower perceived reward salience. Furthermore, at-risk populations can obtain the same reward amount even though their neural signals indicate a diminishing reward salience. Future studies, such as longitudinal studies to scrutinize the effect of depression on clinical and at-risk populations, should seek insight into the long-term impact. Moreover, in the future, more studies should investigate the critical period for at-risk populations adopting inflexible thinking patterns so that clinicians can prevent depression in time.
The Effectiveness of Art Therapy for Depression and Relevant Factors
Authors: Wu Yuheng
Published: March 2024

Art therapy (AT) is a method of treating psychological illnesses and improving mental health that uses artistic means. Depression is a medical condition that could cause changes in one's feelings, thoughts, and behaviors. This paper focuses on testing the effectiveness of art therapy on depressive symptoms with its relevant factors by analyzing the most recent research progress in this field. With the evaluation of the paper, it can be concluded that depression symptoms can be reduced by art therapy. However, there are some limitations in the research reviewed in this article. Most research used subjective measures, which might not reflect the objective outcomes of participants. Additionally, most studies did not follow participants for a long period after the therapy. Future research could be done with more objective methods and procedures to test the long-term effect of art therapy on depression. This review can provide some suggestions to the development of effective treatments for individuals with depression.
Optimization Research and E-commerce Design of Gastrointestinal Electron Microscopic Endoscopy Images Based on Convolutional Neural Networks
Authors: Zhang Jie , Huang Yuting , Yuan Yezhi , Fan Pei , Li LIangyu , Qin Yi
Published: March 2024

The research team backtracked the gastrointestinal electron microscopy manifestations of patients based on the pathological classification of clinical institutions. Through data backtracking, we obtained the diagnostic significance images of the patients. The diagnostic significance images were classified and implemented using convolutional neural networks, with Class A being benign lesions, Class C being malignant lesions, and Class B being non lesions. Based on existing data, we obtained a confusion matrix, and a report was made on the parameters of convolutional neural networks. This convolutional neural network has decent accuracy and can preliminarily meet the needs of clinical institutions, providing a powerful tool for clinical work.
Prediction of Infecting Cancer based on Logistic Regression Model
Authors: Luo Fusheng
Published: March 2024

Cancer is one of the most fatal contributors towards the increasing mortality rate of mankind. This represents an important topic to study for the sake of the welfare of humanity. However, the traditional manual diagnosis and prognosis procedures of this disease are quite time-consuming, even for a professional medical practitioner. Thus, a model with robust power of predictions regarding the state of the tumour (i.e., probable cancer) would benefit most patients from the toxic side effects and additional medical services fees incurred by inessential treatment. To this end, the Logistic Regression Method is applied to derive a powerful model combining an algorithm from machine learning criteria – Learning Vector Quantization. There are two phases in building this model, phase 1 is the pretreatment of our data from Kaggle, including the process of normalization, classification and feature selection. From feature selection, 14 variables are extracted based on their level of importance. Thereby, models are built on these 14 variables and one output Y, consisting of 0 or 1, derived from the classification process. These 14 variables have a huge impact towards the prediction process since they significantly reduce the work needed for the procedure. Phase 2 is applying the relevant methodology to produce our model and examine its efficiency. To test the ability of the trained logistic models to recognize cancer, we analyzed residual samples that were not previously used for the training procedure and correctly classified them in all cases. The evaluation of the model combines methods of the AUC-ROC curve as well as the confusion matrix, which are powerful statistical approaches. The AUC value after calculation is , which strengthens the validity and efficiency of the model. Besides, the confusion matrix reveals an accuracy of 0.9787 (out of 1). The repercussions of this model can be utilized in the field of forecasting the probability of cancer from concrete measurements of the tumor. This may refrain from the exorbitant expenditure on the usage of certain delicate medical machines, like X-ray. Moreover, this provides foundation statistics for the application of modern AI technology in the cancer prediction region.
AI-Aided Diagnosis For Neurodegenerative Diseases: Prospects And Challenges
Authors: Chen Chuanzhe
Published: March 2024

Neurodegenerative disease (ND) represents a chronic disease characterized by loss of neuron function and death, such as Alzheimer's disease, Parkinson's disease, etc. It’s difficult and complex to diagnose ND, due to the requirement for the synthesis of multiple biomarker data, such as genes, proteins, images, etc. The advent of artificial intelligence (AI) technology, particularly through machine learning (ML) and deep learning (DL) algorithms, introduces novel methodologies and tools for ND diagnosis. These methodologies extract pertinent information and patterns from massive, multidimensional, and non-linear data, assisting doctors to render more precise, prompt, and objective assessments. This article provides an overview of the updated implementation status of AI in ND diagnosis, assesses the advantages and contributions of AI to ND diagnosis, as well as the existing limitations and challenges, and offers insights to the future development direction of AI in ND diagnosis.
From Conception to Revelation: Non-Invasive Prenatal Testing Explored
Authors: Chen Zhilei , Wang Baichuan , Yuan Yishan , Zhang Luhan
Published: March 2024

Prenatal testing plays a crucial role in addressing the inherent unpredictability of genetic disorders that can affect unborn children. Non-Invasive Prenatal Testing (NIPT) has emerged as a groundbreaking tool in the realm of prenatal care, enabling the early detection of chromosomal abnormalities. By examining fetal DNA present in the mother's bloodstream, NIPT has achieved remarkable accuracy rates, surpassing invasive techniques that carry inherent risks. However, with these advancements come ethical considerations. The results of NIPT can significantly influence a mother's decision regarding the continuation or termination of pregnancy. This decision can be further complicated by religious beliefs, which often play a pivotal role in guiding choices related to pregnancy. Moreover, receiving a positive result from NIPT can subject expectant mothers to profound emotional stress, highlighting the need for comprehensive counseling and support services. In addition to these ethical concerns, practical limitations persist. The cost of NIPT remains a substantial barrier for many families, potentially limiting its accessibility. Furthermore, NIPT's accuracy can be influenced by factors such as gestational age, with tests often yielding inconclusive results when fetal fractions are low. Despite these challenges, technological innovations, including microarrays and Next Generation Sequencing (NGS), have bolstered the capabilities of NIPT. These advancements enable a more comprehensive assessment of fetal DNA, enhancing the overall quality of prenatal care. As NIPT continues to evolve and integrate with broader genomic technologies, society must engage in thoughtful reflection on its implications. The potential normalization of pregnancy terminations based on genetic information underscores the importance of ethical and societal discussions surrounding the use of NIPT, with a focus on preserving informed patient choices and ensuring comprehensive support for expectant parents facing difficult decisions.
Advancements in the Application of Proteogenomic in Assisting the Discovery of Novel Cancer Antigens
Authors: Zhao Di
Published: March 2024

Over 18.1 million patients worldwide affected by more than 100 different types of cancer. Cancer is one of the critical focuses of medical research today. Researchers have discovered numerous types of cancer, with many patients and a lack of effective unified treatment methods. Therefore, it is urgent to study the pathogenesis and treatment methods. Proteogenomic is an interdisciplinary field focusing on genomics, transcriptomics, and proteomics, which has shown significant effectiveness in tumor analysis and treatment development. Its comprehensive analysis capabilities can help scientists and physicians accurately discover, predict, and treat cancer. This article summarizes the relevant content of using proteogenomic in analyzing and treating various cancers in recent years. It explores the role of proteogenomic in assisting the discovery of new antigens for cancer immunotherapy. This review highlights the latest advancements in proteogenomic and its significant findings in cancer analysis and treatment, suggesting a promising future for its integration into clinical practice.
PD-1 Blockade in Treatment of NSCLC: A Comprehensive Review of Clinical Efficacy, Safety and Cost-effective
Authors: Li Zeyue
Published: March 2024

Non-small cell lung cancer, also called NSCLC, makes up 85% incidence in lung cancer, presents a considerable challenge for its high mortality rate and poor prognosis. In recent years, the invention of programmed cell death 1 (PD-1) inhibitor nivolumab, which is a type of immune checkpoint inhibitors, emerging as a groundbreaking drug, has offered a potential solution to tackle the inescapable drawbacks of traditional cancer treatments. Several clinical trials assessing nivolumab's efficacy and safety in the form of monotherapy or combination therapy, demonstrated that nivolumab has remarkably increased the progression-free survival and overall survival for NSCC, in both first- and second-line. Notably, compared to chemotherapy drugs, nivolumab showed a favorable safety profile with fewer immune-related adverse events. Given the high cost and escalating use of nivolumab, finding more cost-effective treatment regimen is imperative. The purpose of this review is to delve into the clinical efficacy and safety of PD-1 inhibitor nivolumab as immunotherapy remedy against NSCLC, providing some therapeutic recommendations for cost-effective. While continued research and development are vital to further enhance nivolumab's potential as a more potent tool to combat cancer.
CRISPR/Cas9 Mechanism and Applications Progression
Authors: Liu Shangyang
Published: March 2024

CRISPR (short for Clustered Regularly Interspaced Short Palindromic Repeats) and the accompanying endonuclease Cas9 are potent tools for gene editing in many applied disciplines. The CRISPR/Cas9 system primarily functions through two components, in which the single guide RNA (sgRNA) directs the Cas9 protein with endonuclease activity to carry out precise DNA cleavage. The cell's inherent repair mechanisms allow it to complete the subsequent reconnection of the broken genes. Gene knock-in or knock-out can therefore be achieved via this approach. This system was typically applied to prokaryotes in early studies. Its role as a component of their defense mechanism has been thoroughly studied. With rapid development over the past ten years, this system can now be effectively used in a multitude of eukaryotic cells, including yeast cells and mammalian cells. Because of its accuracy, effectiveness, and low cost, CRISPR/Cas9 has been widely used for human disease treatment and has shown great potential in gene therapies for hereditary diseases. However, since more late-stage clinical trials are required to validate its safety, its applications still face many challenges. This review investigates the mechanism of CRISPR/Cas9-mediated gene editing technology and highlights its recent applications in Alzheimer's disease, Sickle cell disease, and Duchenne muscular dystrophy, which are difficult to treat with current approaches. Some challenges the CRISPR/Cas9 system has encountered at this stage are described, and its prospects are also examined.
Therapeutic Effect Analysis of Cervical Cancer Vaccine
Authors: Xie Fuman
Published: March 2024

Cervical cancer is a very widespread carcinogenic condition that poses a significant threat to women's health. The primary cause of this condition is mostly linked to persistent infection with high-risk strains of human papillomavirus (HPV), which are recognized for their ability to promote the development of cancer. Due to HPV infection, both therapeutic and preventative HPV vaccinations have been developed. Therapeutic vaccines can eradicate viruses and virus-infected cells by stimulating cellular immunity. Notwithstanding the availability of preventive HPV vaccinations, HPV screening, surgery, radiotherapy, and chemotherapy, the therapeutic effect on cervical cancer patients is limited. Therefore, therapeutic vaccines have emerged as a new treatment option. The cancer-causing proteins E6 and E7, which are consistently detected in cervical cancer and precancerous lesions, are crucial in the progression and sustenance of cervical cancer. Consequently, these cancer-causing proteins present an encouraging opportunity for the research and creation of therapeutic cervical cancer vaccines. The multiple therapeutic vaccines against cervical cancer will be briefly discussed in this article, including their mechanisms, development methods, and clinical effects.
Application of Glucagon and Glucagon-Like Peptide Agonists in Disease Treatment
Authors: Guo Jinqi , Hu Haibin
Published: March 2024

With the continuous advancement of medical technology, glucagon has been confirmed to be closely related to many diseases, such as diabetes, glucagonoma cell hyperplasia and neoplasm (GCHN), hepatitis and neurological diseases, which take numerous life each year. The identification of several diseases, like Mahvash syndrome as an autosomal recessive inherited pancreatic neuroendocrine tumor syndrome associated with GCGR inactivation mutations deepens our understanding of GCG signal pathway. As disease mechanisms are better understood, more novel drugs are being developed to treat these diseases. Glucagon-like peptide agonists, such as cotadutide, are shown to possess neuroprotective properties in the context of neurodegenerative diseases, also exhibits potential in ameliorating blood glucose control, weight management, and liver fat reduction, offering a promising avenue for developing novel treatments. The linkage between GCHN and GCGR gene inactivation highlights the potential role of GCGR in tumorigenesis, emphasizing its relevance in oncological research. In summary, these findings provide comprehensive insights into neuroprotection, metabolic regulation, genetic disorders, and oncology, offering a holistic view of the potential implications of these entities in various medical contexts.
Enhancing Cancer Immunotherapy: A Novel Approach through the Integration of Oncolytic Virotherapy with CAR-T and NK Cell Therapies
Authors: Zhang Yunan
Published: March 2024

A two-pronged approach to fighting cancer, oncolytic virotherapy (OV) combines direct tumor cytotoxicity with potentiation of anti-tumor immunity. Combining OVs and cell therapies, such as CAR-T and NK cells, has been proposed as an enhanced therapeutic approach. Additionally, it has been demonstrated that adding oncolytic virotherapy to combination medicines increases their therapeutic potential. This article explains the mechanisms underlying oncolytic viruses, the beneficial interactions between them and immune cell therapies, as well as the drawbacks and difficulties of this strategy. We highlight the novel approach of combining CAR T/NK cell treatments and oncolytic virotherapy, highlighting current developments and speculating on the future trajectories of this frontier in cancer immunotherapy.
A Machine Learning Pipeline for Cervical Cancer Prediction
Authors: Huang Yubo
Published: March 2024

Cervical cancer ranks as the fourth leading cause of cancer-related deaths among women and often remains asymptomatic during its initial stages. Therefore, it’s very important to predict it. The study's objective is to explore cervical cancer risk variables and develop an ensemble prediction model to forecast cervical cancer more precisely. The cervical cancer data were represented by 11 risk factors and a dependent variable consisted of Hinselmann, Schiller, Cytology, and Biopsy. SVM, decision tree, and random forest methods are compared for prediction, with models trained on 75% of the data and evaluated using confusion matrices. Precision and recall metrics assess model performance, with recall being vital in critical situations like identifying cervical cancer patients. The comparison was made among these three methods and compared the ranking result of risk factors with the ground truth, random forest method offers high recall (0.8821) and accuracy (0.8744). Recognizing risk factors can enhance cure rates and female patient survival. And the cervical cancer prediction system enhances medical care and reduces the cost. Data mining techniques in the medical sector facilitate diagnostic and prognostic apps for early treatment of life-threatening diseases.
Gene Editing's Sharp Edge: Understanding Zinc Finger Nucleases (ZFN), Transcription Activator-Like Effector Nucleases (TALEN) and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)
Authors: Liu Qinya , Sun Qianyuan , Yu Jiajun
Published: March 2024

At present, the main gene editing tools encompass TREN, Zinc Finger Nucleases (ZFN), clustered regularly interspaced short palindromic repeats (CRISPR), and Transcription Activator-Like Effector Nucleases (TALEN). In this study, we introduce an overview of the three gene editing methodologies and discuss their current clinical applications. In addition, we suggest some trends and future applications within the field of gene editing. ZFNs represent one of the pioneering technologies, demonstrating significant efficacy in mitigating a multitude of genetic diseases and finding applications in agriculture. Yet, this technology contains intricate processes and produces substantial costs when implemented. TALENs have already been employed across various domains. In the medical field, they have been successfully applied in the treatment of leukemia in infants. However, TALENs are being replaced by CRISPR due to the superior efficiency of CRISPR. CRISPR, consisting of six components, exhibits considerable promise in the medical realm, particularly in the context of treating diseases such as Alzheimer's disease (AD). In the realm of genetic engineering, it can collaborate with B cells to rectify specific genes within the human genome, which have been tested in experiments. In the future, it can be used in many fields, including agriculture and nucleic acid testing.
Five traditional Chinese medicine formulations for addressing COVID-19
Authors: Zhang Junyan
Published: March 2024

Covid-19 has spread worldwide, with its initial outbreak occurring in China. The Chinese government employed not only Western medicine but also traditional Chinese medicine (TCM) in their response. TCM, due to its potential to enhance human immunity, was considered a valuable tool in prevention. Furthermore, the analysis of TCM recipes has shed light on their efficacy. Some of the five recipes, including LHQW, XFBD and LCDD, have been found to have the capacity to enhance the patients’ immunity responses. Furthermore, specific formulations like LHQW, XFBD, and HSBD have the capability to disrupt the attachment of COVID-19 to host cells by impeding its interaction with the ACE2 receptor. It is worth noting that Western medicine (WM) has limitations, thus, leading to the exploration of a combined approach involving both TCM and WM. Clinical observations have demonstrated that the treatment combining TCM and WM is more effective compared to treatments with either approach isolation. However, further research is needed to identify more favorable treatment strategies.
Side Effects and Solutions of CAR-T Treatment
Authors: Yao Zhijie
Published: March 2024

Cancer poses a significant threat to human health. It is a kind of complex and severe diseases characterized by the expanding of abnormal cells in the whole body due to uncontrolled growth. If left untreated, cancer with metastasis can invade nearby tissues, spread to other parts of the body, and impair the normal functioning of organs and systems. There are some treatment methods for it, such as surgery, chemical therapy and radiation. The field of immunology has made tremendous strides in understanding how the immune system works and how it can be harnessed to fight disease. Immunotherapy has become a rising solution in recent years. CAR-T therapy belongs to immunotherapy using genetically modified T cells with receptors of tumor antigens to specifically eliminate cancer cells, which is a promising approach for cancer patients. However, there are still some issues that remain to be resolved to make it wider using in treatment of cancer patients. For example, CAR-T therapy occurs severe side effects sometimes, even induced patients die, although it has been approved application in clinic since 2017, however, limited studies investigated its adverse reactions. Therefore, in the present, the author provides a comprehensive overview of the side effects of CAR-T therapy from reduction of therapeutic effects of antigen escape, to induction patients die of severe cytokine storms, then discusses corresponding treatment measures, which purpose is to help guide treatment for patients, make clinical staff focus on its side effects and provide some ideas for research and development in the future.
A Reflective Study of Computerized Database Application Technology and Maintenance
Authors: Yang Shiqi
Published: March 2024

In the era of digitalization, computer databases have become an indispensable technological support across various industries. They not only house a vast amount of data but also enable efficient management and swift retrieval of this information. Concurrently, advancements in database application technology and maintenance have become crucial in ensuring the stable operation of information systems. However, contemporary computer database technology still faces numerous challenges such as data compatibility, automation levels, and flexibility in data transmission. Therefore, this study focuses on the key application techniques and maintenance mechanisms of computer databases, exploring measures to enhance data compatibility, improve production automation, strengthen the flexibility of data transmission, and bolster technical support. These efforts aim to drive the development of computer databases towards efficiency, stability, and security, thus laying a solid data foundation for the sustainable development of the entire information society.
Development of Design Principles of Group Awareness Tool for Facilitating Computer-supported Collaborative Argumentation
Authors: Zhao Xin
Published: March 2024

As one of the forms of collaborative learning, collaborative argumentation can improve students’ argumentation skills, promote effective content learning and cultivate critical thinking. Especially in the field of Computer-supported Collaborative Learning (CSCL), such as conflicts between group members, free-riding behavior, and unequal participation occur frequently. At the same time, in the CSCL environment, it is difficult to coordinate one’s actions with other group members’ actions. These problems are mainly caused by learners who cannot obtain group awareness information about accompanying learners and the current group. Therefore, this study according to the previous research, the initial design principles and initial specific guidelines for the group awareness tool were identified. After that, three educational technology experts reviewed the initial design principles and guidelines. The modified specific guidelines were applied to the two-week classroom teaching of 45 first-year students in a class. In addition, a learners’ response survey was conducted on the students who actually participated in the computer-supported collaborative argumentation activities. Finally, the results of questionnaire surveys (n=45) and interviews (n=5) were analyzed, and the final specific guidelines were developed. According to the research results, collaborative argumentation based on group awareness tool solves the problem that general collaborative argumentation activities can’t obtain timely information on the cognitive, behavioral, and social awareness of accompanying learners and the current group study situation. It can continuously maintain their study motivation and promote interaction among learners to improve the quality of computer-supported collaborative learning.
Water Body Information Extraction from Remote Sensing Images based on PSPNet
Authors: Zhang Jing
Published: March 2024

Remote sensing image has the characteristics of real-time, periodicity and wide monitoring range. It can quickly and accurately obtain water area, distribution and other information, which is of great significance to the utilization and development of water resources, agricultural irrigation, flood disaster assessment and so on. Since traditional water information extraction methods only use part of image band information, the accuracy of water information extraction is low and has certain limitations. In recent years, convolutional neural network technology has developed rapidly and achieved good results in water information extraction from remote sensing images. Therefore, in this paper, Pyramid Scene Parsing Neural Network (PSPNet) was used to extract water information from Ziyuan-3 multispectral remote sensing images, to make sample sets of water, and train the convolutional neural network model. Compared with the traditional normalized difference water index (NDWI) and support vector machine (SVM), the results show that PSPNet has the highest accuracy and the lowest misclassification rate.
Optimizing Matrix Capsule Networks for Contraband detection Research
Authors: Yan Zhiming , Li Xinwei , Yang Yi
Published: March 2024

An optimized matrix capsule network is proposed for the problem of contraband in parcels with different poses, different sizes, random occlusion and sample imbalance. The network improves the recognition accuracy with the help of the matrix capsule network's recognition ability for object poses, and is mainly composed of a multi-branch feature extraction network and a side branch matrix capsule network, which is used to extract large and small targets; the side branch matrix capsule network uses a larger capsule convolution kernel, which is capable of detecting larger targets, and uses the operation of randomly discarding the capsules in the side branch, which makes the parameter amount to be reduced while enhancing the learning ability of the network. The region of interest of the network is obtained by using heat map approach with the help of weight back propagation mapping back to the original map to localize the contraband. Through a large number of experiments on the SIXray dataset, it is proved that the network in this paper improves the detection accuracy by 9.43% and the processing speed of the model by about 1/3 compared with the original capsule network.
Research on Multi-Sensor Data Collection and Mapping Methods
Authors: Du Kang , Song Dalei , Zeng Yimei
Published: March 2024

In today's information society, the importance of collecting data through multiple channels continues to increase. This article focuses on the use of drones for large-area forest information collection and tree analysis. In complex environments, there are a series of problems in obtaining tree mapping information with a single sensor, such as single use scenarios, low accuracy, limited viewing angles, and high requirements for computing power. To address these challenges, this paper proposes a Bayesian-based multi-sensor fusion strategy to overcome the limitations of a single sensor by integrating depth camera and lidar data. Experimental results show that the proposed method significantly improves the accuracy of mapping surrounding trees and the ability to collect information. The multi-sensor fusion strategy effectively reduces the problem of repeated data processing and improves the comprehensiveness of collected data. It meets the requirements of real-time performance and accurate collection of surrounding scenes.
Research on Image Stitching Technology for Road Surface Cracks
Authors: He Chunlin , Wu Jiaye , Yang Yujie
Published: March 2024

Image stitching refers to the combination of multiple images with a certain over lappingarea rate acquired by the image acquisition device into one image, which has a full picture of each image. Image stitching technology is an important part in the field of digital image processing. With the increasing requirements for image quality and the limited resolution of ordinary cameras, it is impossible to obtain large angle of view and clear image at the same time. In this paper, the key technologies involved in pavement crack image stitching are intensively studied, including image feature point detection and matching, internal and external camera parameter estimation and optimization, and image fusion. The work done in this paper and the main innovations include: (1)Two main feature detection and description algorithms, SIFT algorithm and ORBalgorithm, are studied. In view of the fact that the SIFT algorithm has redundancy when the Gaussian pyramid is established, this paper appropriately reduces the number of groups and maintains the number and stability of feature point detection. Combined with the advantages and disadvantages of the improved SIFT algorithm and ORB algorithm, and the performance of the two algorithms in the detection of pavement cracks, the improvedSIF'T algorithm is used as the feature point detection algorithm, and rBRlEF algorithm for describing the feature points in the ORB algorithm is used as the feature point description algorithm. The description algorithm is called the BSIF'T algorithm. Experiments show that the robustness of BSIFT algorithm is similar to that of SIFT algorithm, which is better than ORB algorithm and faster than SIF'T algorithm. (2)Image fiusion algorithms are studied, including best seam-line search and image fusion. Aiming at the ghost phenomenon that may occur in overlapping regions, this paper proposes a best seam-line search algorithm based on distance transform.
Application of Exercise Recommendation Model Based on Deep Reinforcement Learning
Authors: Dao Juwei , Hong Li
Published: March 2024

In order to consolidate the learning achievements of students at a certain stage, Teachers often provide students with corresponding exercises before or after the beginning of this stage. Therefore, making the exercises appropriately challenging is one of the main goals of adaptive online learning systems. However, because each student's learning status is different, a student's learning situation in the same period of time will also be different. Therefore, it is also very challenging for students to choose appropriate exercises. In this paper, we propose a new method for problem recommendation. Firstly, we use GCKT model to model the user's answer sequence. Obtain the students' mastery of each concept. The learned user and problem representation are integrated into an extended framework to predict the likelihood of user mastery of the problem. Then use this as the basis for recommending exercises. On this basis, the deep reinforcement learning technology is used and the knowledge tracking model is used as a student simulator. The difference in the performance of the student simulator on all the exercises before and after solving the exercises provided by the exercise recommendation model is used as a reward, Make the model learn what kind of exercises can improve students' ability to the greatest extent, and recommend such exercises to students. Finally, the experiment is carried out in the actual use environment. The results show that the model has better performance than the current common recommendation models.
Targeting TP53 to Treatment of Cancers: Chance and Challenge
Authors: Hu Yanxin
Published: March 2024

P53 is capable of controlling cell proliferation, senescence and apoptosis, as well as DNA repair through regulation associated genes expression. Nearly 50% of human malignancies harbor mutations in TP53 gene, which were identified as a common feature of many cancers, often leading to the accumulation of abnormal cells and uncontrolled cell growth. Research into therapeutic strategies targeting mutp53 began nearly 20 years ago. However, few drugs have entered clinical trials, and with the exception of China's Gendicine, none have yet been approved for direct patient treatment. With the development of p53-specific targeted therapies and drug delivery systems, targeting P53 to develop effective cancer treatment strategies has been focused on by researchers in recent years. This review mainly summarizes the relationship between P53 and cancer, lists some examples to introduce cancer therapy targeting P53, and analyzes the current chance and challenge of P53 therapy in cancer. The purpose of this paper is to provide new ideas and directions for the research of targeting P53 in cancer.
Application of UV-vis spectroscopy in the detection and analysis of substances
Authors: Chen Zhenkun
Published: March 2024

Ultraviolet-visible (UV-vis) spectroscopy is a spectral measurement technique that relies on Lambert-Beer's law. Due to its user-friendly nature, rapid analytical capabilities, broad applicability, and non-destructive properties, it has gained significant prominence across various scientific disciplines. In the realm of environmental science, UV-vis spectroscopy can be employed for effectively detecting water quality by determining the concentration of heavy metals and related chemicals in water sources. Within the field of clinical medicine, UV-vis spectroscopy plays an indispensable role in disease diagnosis and patient health monitoring. Agriculture and plantation industries greatly benefit from UV-vis spectroscopy due to its capability in facilitating precise analysis of soil and plant nutrients. The field of materials science heavily relies on ultraviolet-visible spectroscopy for the examination of optical properties pertaining to various materials. Moreover, UV-vis spectroscopy finds extensive application in organic chemistry for compound identification and purity evaluation. This research aims to provide a comprehensive overview of multiple applications of UV-vis spectroscopy while exploring its potential synergies with specific technologies across different fields.
Study of CITE-seq protein expression prediction method based on LSTM
Authors: Zhao Wenrui
Published: March 2024

CITE-seq consists of mRNA sequencing and labeled antibody sequencing. This paper predicts labeled antibody sequences based on LSTM neural network. Research shows that LSTM algorithm has a reliable application in CITE-seq protein expression prediction technology, and the prediction accuracy rate reaches 95%. The LSTM algorithm proposed in this paper is relatively accurate and can simulate the gene sequence of proteins well. The CITE-seq data training model learns the potential relationship between RNA and protein, realizes the prediction of protein expression by using scRNA-seq data, greatly reduces the cost of CITE-seq test experiment, and improves the experimental efficiency.
Method to Recover from Meniscus Tearing
Authors: Li Yichen
Published: March 2024

A torn meniscus, a prevalent knee injury, holds significant importance due to its impact on post-surgery outcomes. Failure to undergo timely and appropriate rehabilitation can result in numerous complications, including muscle atrophy and limited joint mobility. Current research has made strides in assessing the efficacy of various rehabilitation protocols for patients with meniscus tears, aiming to restore strength and functionality to the affected knee. Despite progress, challenges persist, such as determining the optimal timing and intensity of rehabilitation exercises and addressing individual variability in recovery rates. Thus, the crux lies in selecting tailored treatments that address the specific nature and severity of the meniscus injury, aiming to mitigate post-operative complications like muscle atrophy and ensure comprehensive recovery.
Analytical study on the current state of development of the companion industry
Authors: Yuan Yu , Chen Xingchi , Zhang Qirui , Xu Ruihan , Jiang Jing
Published: March 2024

Chaperone Service are designed to save time and effort by assisting patients with hospital visits, including registration, queuing, and billing. This article summarises the theoretical foundations of the professional escort field, developments, the definition of the concept of the escort service industry, the concept of the escort, the distinction between the escort and the traditional care worker and the "ticket scalper" robbing the number on behalf of the patient, as well as the demand for talents and competencies of the escort and the traditional care worker and the "ticket scalper" robbing the number comparison. The article also analyses in detail the healthcare resource models in the escort service industry, including escort service platforms, hospitals' own escort channels, and cooperation between hospitals and escort agencies. These analyses help to understand the current situation of the escort service industry, the definition of the concept, the comparison of the talent demand, and the different service models, so as to better develop this industry.
Analysis of Different Treatment Methods for Breast Cancer
Authors: Zhang Taisen
Published: March 2024

This research discusses the mechanisms of current medical treatments for breast cancer and a brief analysis of the treatments. The aim of this research is to present the different cancer therapies and to select the most promising and promising therapies for breast cancer based on the performance of each therapy, where chemotherapy, radiotherapy, and immunotherapy for breast cancer are discussed. By analyzing the various therapies, it is concluded that all three therapies selected for discussion have shown a significant increase in efficacy over their traditional counterparts, but at the same time, the risks and potential shortcomings of the newer therapies have been demonstrated in experimental trials. Through the analysis of this research, it is hoped that there will be a combination of techniques and breakthroughs in therapies for breast cancer. Each therapy has its own shortcomings, and the combination of chemotherapy, radiation, and immunotherapy with current cancer therapies will be the future of cancer breakthroughs.
The impact of insomnia on depression: a prospective cohort study based on the UK Biobank
Authors: Wen Zhiyu
Published: March 2024

Major Depressive Disorder (MDD) is a serious global mental health condition that has gotten worse recently due to its influence on well-being and quality of life. Data from the World Health Organization (WHO) reveal a prevalence of 3.8% for depression globally. Depression's link with various complications necessitates exploring its etiology for effective prevention and management. Insomnia, prevalent in 10-20% of adults, is closely associated with depression. The causal relationship between insomnia and depression is of particular interest. Using the UK Biobank database, a prospective cohort design investigated the independent association between insomnia and depression risk, adjusting for potential confounders. Data were analysed with descriptive statistics, ANOVA, χ2 tests, and multivariable Cox models. Several models accounted for confounders and evaluated insomnia's relationship with depression. R Studio 2022.07.2 facilitated the analyses. The 173,809-participant cohort displayed diverse characteristics. This study, centered around Model 1, highlighted a significant association between depression and insomnia(P<0.05). Model 2 and Model 3 further validated this connection, maintaining a statistically significant difference in insomnia(P<0.05) after adjusting for additional factors. This study, utilizing the UK Biobank database, highlights the independent association between insomnia and depression risk. Findings underscore insomnia's pivotal role across demographics, reinforcing the multifaceted nature of this link.