bluuebunny/crossref_metadata_2025 huggingface dataset:
A review of derivative structures of the Miura-Ori
Authors: Sui Chengrui , Chen Junlin , Jiang Wenjie
Published: July 2024

The scholarly paper conducts a thorough examination and evaluation of five primary derivatives of the Miura-Ori fold, a fundamental structure in the art of origami. Each derivative is meticulously studied through the creation of a tangible model, bringing the theoretical concepts to life. The space occupied by each model is precisely measured, providing a practical perspective to the study. These measurements are then compared and contrasted with the outcomes predicted by theoretical deductions, offering a comprehensive understanding of the accuracy of the theoretical models. The paper then transitions to encapsulate the most recent advancements and developments in the intriguing field of Miura-Ori folds. The potential applications of these primary derivatives are not limited to single-layered structures. The paper explores and deliberates on the exciting possibilities of utilizing these derivatives in multi-layered structures, opening up new avenues for practical applications. The paper concludes by charting the course for future research in this field.
Higgs boson and mechanisms
Authors: Liu Yufan , Bai Yutong , Gao Yonghang , Chen Junwei
Published: July 2024

In the contemporary field of physics, the Higgs boson and the Higgs mechanism stand as captivating subjects of research due to their transformative impact on our comprehension of fundamental particle physics. Their discovery not only bridged a significant gap within the Standard Model but also profoundly influenced our understanding of the evolutionary dynamics of the universe. This paper aims to delve deeply into the Higgs boson and its corresponding Higgs mechanism, thereby furnishing further insights into unraveling the enigma of the cosmos. In the subsequent sections, we will meticulously explore the theoretical underpinnings, experimental observations, and relevant research advancements concerning the Higgs boson and the Higgs mechanism. Through this comprehensive investigation, we aspire to enhance our grasp of the fundamentals of particle physics and the mysteries that enshroud the universe.
GPU-based computing analysis for charged-particle production in heavy ion collisions
Authors: Zhang Yuanjia
Published: July 2024

The innovation of this paper lies in the introduction of GPU acceleration, which has greatly speeded up the projects progress. It begins by offering a brief overview of the research history on quark-gluon plasma (QGP) and the corresponding research tool, heavy ion collisions. The paper mainly focuses on data analysis of a significant process within the hadronic collisions, namely, the charged-particle production. The investigation of this process is an integral part of QGP research, as it occurs promptly after the formation of QGP. By applying the tracklet method to the datasets collected by ALICE detector, an estimate of the charged-particle multiplicity density is presented. The finding then suggests a positive correlation between the number of participating nucleons in the collision and the normalized charged-particle multiplicity density. Notably, this dependence on centrality is similar to the research conducted by the ALICE collaboration, but derived from a distinct dataset. It indicates that there should be an underlying mechanism behind the intriguing phenomenological trend waiting for further exploration. These studies have the potential to enhance the understanding of quantum chromodynamics (QCD) and provide insights into the true nature of the universe.
Applications of Fourier transforms in engineering
Authors: Lu Yingguan
Published: July 2024

The Fourier transform was proposed by Fourier in 1807. Fourier transform is a method of analyzing signals, which can analyze the components of the signal or synthesize the signal using these components. Many waveforms can be used as signal components, such as sine waves, square waves, sawtooth waves, etc. The Fourier transform uses sine waves as components of the signal. Due to its excellent properties, the Fourier transform has a wide range of applications in physics, number theory, combinatorial mathematics, signal processing, probability, statistics, cryptography, acoustics, optics, and other fields. This paper focuses on the study of fractional-order Fourier transform in the engineering field, for the equipment of the tiny fault diagnosis method, and according to some existing diagnostic methods, put forward the idea of diagnostic method enhancement.
Evaluation of Chinese herbal medicine's neuroprotective effects: Regulation for neurological disorders
Authors: Niu Qiuyu
Published: July 2024

Chinese herbal medicine is a type of medicine composed of plants unique to China, which has been found to have significant neuroprotective effects in recent years. The research on its neuroprotective mechanism has become a hot topic. At present, neuroprotective mechanisms have been found to be closely related to balancing ion channels, inhibiting apoptosis of nerve cells and promoting nerve growth. In this work, we summarize the recent progress of research on neuroprotective mechanisms of Chinese herbal medicine and the existing clinical trials.The significance of this study is not only to summarize its therapeutic mechanism, but also to improve the status of Chinese herbal medicine at the international level. It is hoped that more international scholars will join in the research of Chinese herbal medicine.
Research progress of PD-1/PD-L1 inhibitors combined with other drugs in the treatment of tumors
Authors: Zhou Shuaichen
Published: July 2024

Cancer as the first killer of the influence of human life and health, researcher use a variety of ways to cure cancer, but has been no effective treatment, along with the innovation of new biotechnology, researcher discovered the cancer cells rely on the Warburg effect in the human body metabolism. And understanding the PD-1L receptor on the surface of tumor cells can help tumor cells to achieve immune escape, which is not easy to be recognized and killed by the PD-1 receptor on the surface of immune cells. After understanding this process, researchers have developed PD-1/PD-L1 receptor inhibitors to help cure cancer by targeting this mechanism, which is also part of immunotherapy. Among the drugs on the market today, it has been confirmed that PD-1/PD-L1 is effective in the treatment of non-solid tumors and a small number of solid tumors. However, because of the characteristics of most solid tumors, PD-1/PD-L1 is limited to play a role, so researchers try to develop PD-1/PD-L1 combined with other drugs to treat more solid tumors. In the literature cited in this article, it can be seen that the combination of PD-1/PD-L1 with other drugs is effective, but the adverse reactions will also increase. This article will discuss the effectiveness of PD-1/PD-L1 combined with other drugs in detail.
Comparing amoxicillin and trimethoprim: Overall performance in combating Escherichia coli-causing urinary tract infection
Authors: Teng Yiou
Published: July 2024

Urinary Tract Infection (UTI) is one of the most prevalent bacterial infections in the world, and 80% of reported UTI cases are derived by gram negative bacteria Escherichia coli. Uropathogenic Escherichia coli is strongly adaptive and resilient against immune response, forming Intracellular Bacterial Communities (IBC) inside uroepithelium cells that constitute the urethra and the bladder. Their rapid absorption of cellular nutrients enables them to advance upward toward the kidney and cause inflammation. Hence, based on the perceived severity of UTI, multiple antibiotics are used to treat UPEC-causing UTI. This article provides a detailed review on the contemporary performance of amoxicillin and trimethoprim, both widely used to treat UPEC-causing UTI for decades. Amoxicillin is a penicillin-derivative that produces bactericidal by inhibiting transpeptidase to disrupt peptidoglycan synthesis, causing rapid-growing bacteria to lyse. It is a well-tolerated drug that can be used on pregnant patients. Resistant UPEC develop beta-lactamase to counter amoxicillin. Trimethoprim, on the other hand, is a dihydropyrimidine antibiotic that impedes the production of tetrahydrofolic acids, producing either bacteriostatic or bactericidal effects. Resistant UPEC transforms the shape of tetrahydrofolic acids-producing enzyme, dihydrofolic reductase, to resist trimethoprim. Although amoxicillin and trimethoprim are competitive inhibitors are effective against a variety of gram-positive and gram-negative species, trimethoprim, in statistical study, is proven to be more effective than amoxicillin in treating gram-negative UTI. Today, the wide-spread resistant strains of UPEC exists for both drugs. Thus, amoxicillin and trimethoprim are implemented mainly through combination therapies, amoxicillin/clavulanate and trimethoprim sulfamethoxazole, respectively. These combinations are more effective than the drugs to be used alone and are able to counter some resistant strains, but UPEC that are resistant to combinations also exists. Regardless, both amoxicillin and trimethoprim are still used as first-line antibiotics, while more powerful antibiotics are reserved for special multi-resistant superbugs.
Meta-analysis of the gut-brain axis and mental disorder
Authors: Yang Hanmo , Zhang Shuran
Published: July 2024

The gut-brain axis refers to the bidirectional information communication network between the brain and the gut. Microbiome and gut-brain axis have been implicated in mental disorders and have become the focus of neuroscience research. A meta-analysis was conducted to summarize the evidence regarding this link. A filtering search was performed using Hollis, PubMed, and Google Scholar to identify considered studies and trials. A keyword search identified 5500 articles. Seven provided analytically valuable and eligible data and were finally included for consideration and analysis. The data offered relevant studies on depression or anxiety, Schizophrenia, and Alzheimer's disease. Of the seven papers, four used correlational analysis (n=952) on depression and anxiety, two (n=384) on Schizophrenia, and one (n=80) on Alzheimers disease. These correlation analyses were designed to investigate the differences in gut microbial abundance between normal individuals and patients with mental disorders. In addition, three studies (n=314) identified a causal link by comparing the changes in mental health status by altering or influencing the gut microbiota. These causal analyses aimed to explore the differences in gut microbial abundance before and after the onset of mental disorders. In this case, mental illnesses were 2.8 to 14 times higher in the experimental groups than in the control groups. The findings support the hypothesis that the health of the gut-brain axis (microbial abundance) is inversely correlated to the incidence of many psychological disorders. However, how the gut-brain axis facilitates the pathogenesis of mental disorders remains to be further studied.
mRNA therapeutics in diseases: Light inspect into a new horizon
Authors: Li Yanhui
Published: July 2024

An in-depth analysis of messenger RNA (mRNA) therapeutics and their potential to treat a wide range of diseases is provided in this thorough review paper. mRNA therapy has a great deal of potential for treating various terminal illnesses. By introducing mRNA as a vaccine or therapeutic agent, recent developments in biotechnology and molecular medicine have made it possible to produce almost any functional protein or peptide in the human body. This offers a unique chance to prevent and treat various challenging illnesses, many of which have genetic roots. Moreover, the utilization of in vitro transcribed mRNA has brought about a revolution in the production process, rendering it more efficient, adaptable, customizable, and cost-effective when compared to traditional methods. For a long time, researchers have aspired to enhance various aspects of mRNA, including its stability, immunogenicity, translation efficiency, and delivery systems, with the aim of achieving an efficient and secure means of delivering mRNA. In this comprehensive review, we will delve into the essence of mRNA and mRNA-based therapies, encompassing their fundamental principles, methods of production, diverse applications, outcomes, and constraints. We will underscore the importance of optimizing mRNA and its delivery systems to ensure the success of mRNA therapy, all while addressing the pivotal challenges and prospects in the battle against cancer and COVID-19.
Medication affecting the correlation of aggression development and Attention-Deficit/Hyperactivity Disorder (ADHD)
Authors: Wang Tianqi
Published: July 2024

Attention-Deficit/Hyperactivity Disorder (ADHD) affects 3-7% of children in the U.S. Magnetic studies have shown a decrease in the corpus callosum in children diagnosed with ADHD. ADHD may have a greater impact on the white substance in certain areas of the brain. The cortical thickness as well as the levels of serotonin in the brain is also attributed as a contributing factor establishing the correlation between the two disorders. There are some clinical trials that indicate a medication that can treat both ADHD and aggression. The most common medication for disrupting the link is the use of stimulants and non-stimulants which are used in treating ADHD, the reduced aggression levels are attributed to effective treatment results in ADHI. Antipsychotic medications are used in treating aggression in cases of maladaptive aggression which does not respond to stimulants. Hence, the goal is to treat one disorder (ADHD) and use other combinations like behavioral treatment to reduce the levels of aggression.
Exploring disease therapeutic strategies by modulating the TNF-/NF-KB signalling pathway
Authors: Ouyang Conghao
Published: July 2024

In recent years, the number of patients with rheumatic diseases such as ankylosing spondylitis has been increasing, with over 3 million young people suffering from the disease. One main causal factor for these diseases is tumour necrosis factor-a (TNF-a), a pro-inflammatory cytokine that triggers mutations and apoptosis of various cell types by activating multiple signalling pathways. NF-KB is an essential intracellular nuclear transcription factor that participates in the inflammatory and immune responses of the organism and regulates apoptosis and stress responses. Excessive activation of NF-KB has been linked to inflammatory changes in various human diseases, such as rheumatoid arthritis, cardiovascular diseases, etc. Therefore, inhibition of the NF-KB signalling pathway by drugs may become a therapeutic approach. Among multiple signalling pathways that induce inflammation, the TNF-/NF-KB signalling pathway is considered the most important. This review will discuss the relevant diseases for which modulation of the TNF-/NF-KB signalling pathway is the main therapeutic strategy. These strategies include targeted inhibition by blocking the TNF-a/NF-KB signalling pathway, modulation of inflammatory factor transmission at the nanoscale by ribonucleotides, and Oncolytic virus therapy. Some of these strategies are effective and have been applied to treat multiple diseases; others are still in the exploratory phase, but the potential of these exploratory therapies is enormous and promises to overcome the limitations of traditional therapeutic strategies.
The application of different biomaterials in food package
Authors: Tan Yiyang
Published: July 2024

Nowadays, food packaging is the most essential part in the food industry, as it is the key part to stop food from contamination. In the 20th century, most of the food package used plastic products. However, it turns out that it contains lots of problems, especially for the natural environment, as it can hardly finish degradation. As a solution of this, many scientific researchers state the proposal of biomaterials due to lots of advantages, such as recyclability, degradability and sustainability. Actually, there are various types of biopolymers for food packaging, like polysaccharides and proteins. To be more specific, biopolymers like tannic acids, chitosan and nano-chitin get the most potential.
The comparison of sodium sulfacetamide and erythromycin in treating trachoma
Authors: Bu Junmeng
Published: July 2024

Trachoma is a common eye infection caused by Chlamydia trachomatis and there are many medicines for it already. Erythromycin is a type of antibiotic and sodium sulfacetamide is an antibacterial drug, they are both commonly used for treating trachoma. This passage contrasts the use of sodium sulfacetamide and erythromycin in the concepts of synthesis process, targets, and the resistance built up. Sodium sulfacetamide is a synthesized substance, generally contains 2 steps: prepare sulfacetamide and then let it hydrolysis to form the salt. It works by impede the synthesis of folic acids. If the Chlamydia tachomatis start to get folic acids from host cell, then sodium sulfacetamide cant work any more. Erythromycin is produced by fermentation, it works by bind to ribosomes of the trachoma to impede synthesis of protein. The Chlamydiae can built up resistance by transporting erythromycin out, changing structure of their ribosomes, or destructing the erythromycin molecules. They dont share many things in common, but the importance of them is significant, and they represents the main treatments of trachoma.
The mechanism of MRSA drug tolerance and the comparison between vancomycin and daptomycin in the treatment of MRSA infections
Authors: Xu Chun , Li Yuming , Pan Yan , Li Yanzi , Zhang Yanning
Published: July 2024

Methicillin-resistant Staphylococcus aureus (MRSA) is a superbug that is resistant to multiple antibiotics commonly used in medication and has caused severe infections in humans for hundreds of years. This review examines the MRSA drug resistance mechanism and compares the efficacies between two antibiotics, daptomycin and vancomycin, in treating MRSA infections. The resistance mechanism of MRSA alters the structure of inactivating the antibiotics. Therefore, the MRSA resistance to daptomycin and vancomycin is analyzed to show how MRSA reacts to different antibiotics preferred in therapy. Moreover, the efficacies of two antimicrobial drugs were analyzed by the research and data from journals and magazines. Combined with rifampin, two antibiotics show more potent efficacies with fewer doses, and could be an alternative therapy to conventional individual antibiotics only.
How paternal MDD history and depression symptoms affect offspring's OGM
Authors: Fang Baizi , Xia Jiayi , Zhu Ruowen
Published: July 2024

Major depressive disorder (MDD) is a series of depressive symptoms mainly caused by abnormalities in the patients genetic system or dramatic changes in the acquired environment. Specific autobiographical memories can be difficult to retrieve due to a phenomena known as overgeneral memory (OGM).Parental depression increases the risk of cognitive vulnerability in offspring, a type of cognitive vulnerability associated with the intergenerational transmission of depression .Only the potential effects of motherly depression on the development of OGM in descendants are examined in the current study; however, we had little acquaintance about the prospective impressions of paternal depression on the development of OGM in offspring. We can conduct an assessment for children aged 8 to 14 who lived with their parents every 6 months in 2 years. A complete assessment of whether the fathers with MDD history should be conducted in the beginning, the study was grouped by this followed by comparing the results with the current social data to draw a conclusion. This paper only provides theoretical experiment design and possible results about how paternal MDD history and depressive symptoms affect offspring OGM, which needs further research in the specific social samples. We try to find how paternal MDD history and depressive symptoms affect offspring OGM, and find ways to prevent adolescent depression.
Transmission of X-linked recessive inheritance in the population
Authors: Feng Jiarui
Published: July 2024

X-linked recessive inheritance refers to diseases caused by X-chromosome recessive disease-causing genes. Specific examples include genetic diseases such as human red-green color blindness and hemophilia. The current rate of red-green color blindness: 8% males 0.5% females. This study is based on the SIR model for the conversion of concomitant x recessive genetic disorders in the total population. We theoretically derive the disease-free equilibrium of the proposed dynamic system, as well as the basic reproduction number. Based on the derived basic reproduction number, further, discuss the impact of the transmission rate of the companion x genetic disease and the transmission rate of the disease gene in the population. The results have implications for controlling the emergence of genetic diseases in the population.
Analyzing the Impact of Barbie Movie on the Barbie Brand
Authors: Gao Zhuofei
Published: July 2024

On July 21, 2023, Barbie, the intellectual property (IP) movie of the same name as the
Barbie brand, triggered a worldwide pink storm upon its release and drove the Barbie brand's
popularity along with its box office success, making Barbie, which is now 65 years old, once
again gain a very high level of attention and gain a great deal of revenue. Based on this
phenomenon, the purpose of the exploratory was to explore the reasons why the movie would have
such a great impact on the brand early. This article mainly took the Barbie movie as a case
study, combined the existing data to analyze the business of the Barbie brand, and concluded
that the reasons for the success of the Barbie brand's popularity include the launch of the
Barbie IP movie that conforms to the brand's tone, the implementation of the strategic policy of
co-branding with a number of brands, and the recent explosion of the topic of "feminism." At the
same time, this article put forward suggestions for these three reasons why the Barbie brand has
been able to continue to be hot: the Barbie brand can continue to launch IP movie sequels; to
strengthen the screening of brands that are consistent with the values of the Barbie brand for
co-branding; and to expand the scope of brand association.
Risk Assessment of Banks and Financial Institutions When Interest Rate Hikes
Authors: Kang Ningxin
Published: July 2024

This article delves into the multifaceted risks precipitated by a bank's decision to
increase interest rates. It commences with an overarching overview contextualized within The
Times, elucidating the significance of the subject matter. Subsequently, it delineates various
extant risks, each accompanied by a succinct exposition and an outline of corresponding
mitigation strategies. Following this introductory phase, the article conducts a meticulous
analysis of these identified risks, methodically dissecting liquidity risk, reputation risk,
market risk, and systemic risk. Each risk is meticulously defined, substantiated with historical
precedents, and elucidated with comprehensive management methodologies. This paper underscores
the myriad risks attendant to a bank's interest rate hike. This synthesis serves to underscore
the overarching theme that such policy decisions not only reverberate through the financial
ecosystem but also necessitate vigilant risk management protocols to safeguard against potential
ramifications. Finally, the article concludes by summarizing the numerous risks posed by a
bank's interest rate hike. It emphasizes the need for careful consideration and proactive risk
management to mitigate potential consequences.
BYD's Strategic Planning Study for the Future New Energy Vehicle Market
Authors: Zhang Tianze
Published: July 2024

With the rapid development of science and technology and the increasing awareness of global
environmental protection, new energy vehicles have become an important direction for the
transformation of the global automobile industry. As one of the leaders in the global new energy
vehicle market, BYD is actively responding to the challenges and opportunities of this market
transformation with its profound accumulation in battery technology, electric vehicles and
hybrid vehicles. However, the new energy vehicle market still faces many uncertainties and
challenges, such as technological innovation, market competition, policies and regulations, and
infrastructure development. In order to meet these challenges, BYD must develop and implement an
effective strategic plan to ensure that it maintains its leading position in the highly
competitive market and to promote the sustainable development and progress of the new energy
vehicle industry. Through an in-depth study of BYD's strategic planning, this paper expects to
provide useful references and insights for BYD and other new energy vehicle companies. Next,
this paper will comprehensively analyze BYD's strategic planning and implementation paths in
terms of its technological innovation, key financial indicators and cooperation and competition.
Through the in-depth analysis of this study, it can better understand how BYD has carved out a
path of sustainable development in the new energy vehicle market.
Analyze the Impact, Reasons, and Optimization Strategies of the Image of AI Anchors on
Consumer Purchase Intention in Live Streaming E-commerce
Authors: Li Jinpeng
Published: July 2024

The importance of live streaming e-commerce in online shopping is increasing with the
continuous development of live streaming technology. Artificial intelligence (AI), as a
disruptive force, has introduced virtual anchors in the live streaming field, replacing live
anchors for product introduction and interaction. The virtual anchor image, with its
anthropomorphic appearance, interacts with the audience in real-time and is gradually being
applied in various fields such as e-commerce, advertising, and social platforms. This study
reviews how the image of AI anchors in live streaming e-commerce affects consumer trust and
personalized experience, and further explores its impact on consumer purchase intention.
Research has found that virtual anchor images can be divided into human and animal images based
on their appearance, and different images have different impacts on consumer purchasing
intentions. Humanized virtual anchors establish trust relationships and stimulate purchase
intentions through rich expressions and actions; Personalized and customized design enhances the
attractiveness and sense of identity of virtual anchors. Finally, regarding the application of
enterprises in the field of virtual anchors, this article proposes anthropomorphic design,
personalized image, and cultural adaptability strategies to improve the commercial efficiency
and brand image of virtual anchors.
The Research about the Connection Between Housing Prices and Unemployment
Authors: Zhang Songhai
Published: July 2024

The aim of this research is to investigate the potential correlation between housing prices and unemployment rates in London. Specifically, the study focuses on analyzing the relationship between the average housing price and the unemployment rate in the city. To achieve this objective, this paper employs statistical methods such as the t-test and regression analysis to determine whether there is a significant connection between the two variables. Additionally, a line chart is utilized to visually represent the changes in housing prices over time, providing a clearer understanding of the trends and patterns. By conducting this comprehensive analysis, we hope to gain insights into the potential impact of unemployment on housing prices in London.
Analysis of Investment Value of Beverage Industry Enterprises: Evidence from Coca-Cola Company
Authors: Zhuang Jiarong
Published: July 2024

Financial indicators and operational efficiency are crucial for the long-term development of enterprises. This article takes Coca-Cola Company as an example to conduct a detailed analysis of the investment value of the enterprise from various aspects such as current financial performance, operational efficiency, and competitive landscape. Explore the future development trends of Coca-Cola Company by reviewing and reviewing its financial statements, operational indicators, competitive positioning, and economic factors that influence the company. Research data shows that although Coca-Cola has maintained profitability, its operating profit margin has slightly declined in the past few years. By doing so, it is more profitable in certain aspects than competitors such as Pepsi and Monster Beverages. The organization faces obstacles to constantly changing consumer choices through healthier choices and supply chain/geopolitical chaos. This may require Coca Cola to diversify its product portfolio and supply chain to maintain expected profits. This study has important practical significance for investors in the beverage industry and researchers interested in this topic.
A Random Forest-based Prediction for Liver Cirrhosis
Authors: Wei Haitao
Published: July 2024

Prediction of cirrhosis through timely screening and diagnosis with effective medical and lifestyle interventions can be taken to delay the disease progression and improve treatment effectiveness. Therefore, early cirrhosis prediction helps reduce associated complications and improve quality of life. This study established a random forest-based algorithm and compared its performance with other machine learning models based on an Indian Liver Patient Records. The dataset was divided 30% of the test set and 70% of the training set. The results showed that the Accuracy, Area under the curve (AUC) and Recall values of Random Forest (RF) model were 0.73, 0.76 and 0.88, respectively, which demonstrated the proposed model can predict the results better than other models. The proposed model for predicting liver cirrhosis in the future will continue to be improved and perfected to improve the accuracy of liver cirrhosis prediction and treatment effect, thereby reducing the health risks and burdens brought by the disease.
Development and validation of a GBDT-based for detection of diabetes
Authors: Wu Xunze
Published: July 2024

Improved early detection and intervention can lessen the substantial health and financial burden of type 2 diabetes (T2D). In order to achieve early prediction of diabetes, an approach for diabetes prediction based on Gradient Boosting Decision Tree (GBDT) was proposed in the present study. The feature selection approach based on GBDT was designed to identify the most relevant features, where GBDT builds a powerful model by combining multiple weak classifiers, which can reduce the risk of overfitting and improve the model generalization ability. Condition prediction was performed based on GBDT and the filtered features, and the truncation value of the feature was calculated. The experimental results in the paper showed that the Area Under the Curve (AUC) value of GBDT was 0.9788, which was a big improvement compared with other studies; the AUC value based on glycated hemoglobin level was 0.7307, and the cutoff value of glycated hemoglobin level was about 6.8, which was very accurate. The prediction of diabetes based on GBDT can help patients to understand whether they have diabetes initially based on their own glycated hemoglobin values, and it can also help clinicians to make more objective judgments in clinical diagnosis in order to judge the patient's situation and subsequent monitoring of the condition, making an excellent contribution to the control of the patient's condition.
SGANnet: Super-resolution guided asymmetric stereo matching network
Authors: Bao Jingyao , Yu Hongfei , Zou Yongjia , Cao Yang
Published: July 2024

With asymmetric resolution stereo images as input, existing stereo matching algorithms significantly decline in prediction performance. To address this, we introduce SGANet (Super-resolution Guided Asymmetric Stereo Matching Network), a model that employs unsupervised training methods to overcome the difficulty of acquiring ground truth disparity. For the lower resolution side, this paper designs a stereo guided super-resolution module (SGSR), where the network generates a super-resolved image enriched with details guided by the higher resolution side. Additionally, for this module, we propose a feature consistency loss based on the image's feature space to measure the similarity between the real and super-resolved images. Experimental results on the autonomous driving dataset KITTI demonstrate the effectiveness of the SGSR module and the feature consistency loss in improving the disparity prediction performance of asymmetric resolution stereo images.
Utilizing principal component analysis to enhance machine learning in bankruptcy prediction: A comparative investigation
Authors: Ma Xinming
Published: July 2024

In the global economic environment, bankruptcy prediction is essential for managing financial risks. With the advancement of machine learning techniques, accurate prediction using these methods has become a research focus. This study explores the efficiency and accuracy of improved machine learning models for bankruptcy prediction through principal component analysis (PCA). By utilizing a dataset from the banking industry in Taiwan, this paper compares the performance of PCA with and without processed data using logistic regression modeling. The research methodology includes data preprocessing, PCA downscaling, and subsequent model training and testing. The key research question is whether PCA preprocessing can significantly improve the operational efficiency and predictive accuracy of the model. It is found that the model with PCA outperforms the model without PCA in terms of processing time and accuracy. This suggests that PCA can effectively improve the performance of bankruptcy prediction models and provide a more effective tool for financial risk management. These findings provide useful insights into other areas of financial analysis using machine learning and support the value of applying PCA in predictive modeling.
Innovative research on AI-assisted teaching models for college English listening and speaking courses
Authors: Luo Yun
Published: July 2024

This paper explores the innovative application of artificial intelligence (AI) in the construction of teaching models for college English listening and speaking courses. By leveraging advanced AI technologies, educators can enhance the effectiveness of language instruction and provide personalized learning experiences. This study examines the theoretical foundations, practical implementations, and the impact of AI-assisted teaching on student engagement and performance. Through comprehensive analysis and discussion, we highlight the potential of AI to transform traditional language education, address challenges, and improve learning outcomes. The findings suggest that integrating AI into college English courses offers significant advantages in terms of adaptability, interactivity, and efficiency, paving the way for future educational innovations.
Housing data visualization and analysis
Authors: Tong Yuxuan
Published: July 2024

Data visualization is a powerful tool that can assist individuals and organisations in comprehending vast amounts of data and extracting valuable insights from it. The most significant function of data visualization is to make recommendations by figuring out the essence of the occurrence of the data. This paper will take housing data as an example, raise relevant questions, and reveal the logic behind the data and the relationship between variables through data visualization, linear regression, and other statistical methods.
Analysis and identification of ancient glass artifacts based on component data
Authors: Zhang Xinyu , Zhang Yuyan
Published: July 2024

This study explores the classification of glass artifacts based on their chemical composition. Using the stratified chi-square test method, the correlation between the chemical characteristics of glass artifacts and their susceptibility to weathering was analyzed. A decision tree model was constructed to predict and analyze the types of glass artifacts. To further study the types of glass artifacts, factor analysis was employed to reduce the dimensionality of the data, simplifying the data structure. Based on the reduced data, the K-means clustering algorithm was used to further classify the glass artifacts into three subcategories.
Optimizing urban ecosystems through green and blue infrastructure: Strategies for sustainability, resilience, and justice
Authors: Yang Yanxi , Li Xiaohan , Wang Duruo , Liu Zuhang
Published: July 2024

This article examines the roles of Green and Blue Infrastructures (GBIs) in enhancing urban sustainability, resilience, and environmental justice. It highlights how GBIs promote biodiversity, reduce pollution, and increase climate resilience through integrating technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and digital twinning. These innovations improve the management and operational efficiency of urban planning. The study emphasizes the socio-economic and health benefits provided by GBIs, which incorporate natural elements into urban areas to foster environmental equity and resource accessibility. Advanced technological interventions are crucial for optimizing GBI functionality, ensuring urban environments are resilient and sustainable. The paper advocates for a holistic approach involving community engagement and policy reforms to distribute GBI benefits equitably, aiming to create urban ecosystems that are environmentally sound, socially just, and economically viable.
A review of applications of deployable structure
Authors: Wen Mingzhao , Ye Linghao , Pu Hongrui , Ye Yuanqi
Published: July 2024

Simple and reliable deployable structures already play a role in every aspect of our daily lives, from shopping bags and baby carriages to large buildings such as balconies and stadium roofs. In recent decades, people have been more interested in the various applications of deployable structures, for example, aerospace engineering. This paper reviews some examples in the field of deployable structures, including from the basic elements to more complex ensembles such as planar double-chain linkages and deployable antennas. Due to its properties including lower cost, smaller volume, and multiple usages, deployable structures have been extensively applied to the design and manufacture of satellites and other devices for space activities. Sizeable space antennas, as a key technology currently developed for space probes, are reviewed in this paper by using formulas to explain their functions and highlight abilities. The system of paraboloid cable net is also reviewed due to some benefits. We have discussed and summarized some challenges in applying deployable structures, along with strategies for potential solutions.
Design and control of the Six-DOF robotic manipulator
Authors: He Ruohan , Wang Yinrou , Li Tingyi
Published: July 2024

This paper presents the building process and simulation of a robotic manipulator. Robotic manipulators are used in various scenarios, such as industry and agriculture. It would be of particular interest to learn how to systematically design and control a manipulator to follow a planned trajectory. Hence, a CAD model is developed in this paper. There is a rough finite element analysis for two cases in this study, which could easily find out how the product reacts to real-world forces and vibration. In order to solve real-world problems safely and effectively, this effort intends to build the mechanical design of the manipulator by SOLIDWORKS and simulate trajectory tracking capability using MATLAB.
Improving the crashworthiness of B-Pillars in side-collisions
Authors: Guo Jiayu , Liu Shuyang , Fu Yu , Liu Ricky , Shi Jincheng
Published: July 2024

The B-pillar plays a crucial element in protecting passengers during side collisions. However, the current technology used in B-pillars is insufficient in providing adequate protection. Therefore, this research aims to investigate ways to improve the crashworthiness of the B-pillars. The study utilizes a 2020 Toyota Yaris due to its prevalence and poor side-crashworthiness and a right body measuring 80cm 60cm 40cm to simulate side collision. The results show that the velocity and acceleration of the upper B-pillar are greater than that of the lower B-pillar, indicating that the welding spot should be placed at the lower end of B-pillars, and the pillars thickness should be increased to enhance side crashworthiness. Furthermore, the study discovers that PVC, when combined with nano-materials, ABS resin, when combined through physical mixing, and SMC materials experience no significant deformation during collision while possessing an incomparably low density. Therefore, the study proposes the use of these stronger materials as an alternative to steel. The cost and feasibility of using these materials in mass production is also analyzed.
Wireless communication channel modeling based on ML
Authors: Chen Xuanyu , Zhu Weiqi , Shi Yiheng , Zhong Yuxuan
Published: July 2024

Nowadays, wireless communication plays an important role in various fields of our lives, and in order to ensure good communication performance, reliable channel models are important. In this paper, we present a comprehensive survey of Machine Learning (ML) based channel modeling methods to help with the estimation and prediction accuracy on wireless channel modeling. To begin with, we start with a review of the traditional methods of wireless communication channel modeling, basically empirical method and deterministic method. Then, we introduce several ML methods to address limitations of traditional ways will be used for channel modeling, such as Support Vector Machine (SVM), Random Forests, autoencoder, Deep Learning, etc. Lastly, we demonstrate the application of ML methods including deep learning, SVM, and random forests on wireless channel modeling. Through learning the underlying channel distribution, Deep Learning methods have already demonstrated remarkable performance in channel modeling for different scenarios in some previous learning. Also, by training with suitable kernels and conducting sufficient training iterations, optimal hyperplanes can be identified, Support Vector Machines (SVM) can then be utilized to predict channel characteristics based on input features. Random Forests can identify the most relevant features influencing the channel and optimize system design by handling complex and high-dimensional data through iterative feature selection and splitting criteria. These methods achieve competitive accuracy with respect to traditional methods, however, there are still challenges and issues to be addressed in the application of machine learning methods for communication channel modeling.
Dynamics and mitigation strategies of knock in hydrogen-fueled internal combustion engines
Authors: Zhong Anye
Published: July 2024

In the context of utilizing hydrogen as a fuel for internal combustion engines, it is crucial to address its tendency to induce knock more readily under certain conditions compared to gasoline. Knock not only reduces fuel efficiency but also has the potential to inflict detrimental effects on engine components. It is generally believed that knock is primarily caused by the spontaneous combustion of the end-gas mixture. Current research focuses on the frequency, intensity, and statistical characteristics of knock signals.This study aims to introduce two prevalent and effective methods for mitigating knock: increasing the Exhaust Gas Recirculation (EGR) ratio and water injection into the engine. Experimental observations suggest that at low rotational speeds (n=900 rpm), the amplitude spectra of knock induced by hydrogen and gasoline show similarities. However, at higher speeds (n>3000 rpm), the amplitude of hydrogen-induced knock not only exhibits a greater number of peak values but also intensifies in strength. Notably, the efficacy of increasing the EGR ratio diminishes at high speeds, potentially due to the propensity of methane to also cause knock under high-temperature conditions. In contrast, water injection, while generally effective, may adversely impact the lifespan of engine components.In summary, effective suppression of knock in hydrogen-fueled internal combustion engines is vital for accelerating the adoption of hydrogen in the power sector and making significant contributions to environmental conservation. This paper aims to delve into the analysis of these methods effectiveness and proposes directions for future research.
Research on urbanization of middle and south Hebei from the perspective of high mobility
Authors: Yang Anqi , Tan Miaomeng
Published: July 2024

With collaborative development of Beijing-Tianjin-Hebei region, we work on studying the history, culture, industrial system of the middle and south areas of Hebei, and sorted out the characteristics of their urbanization development. It has showed that there are so many wonderful rural enterprises with the prosperous rural economy, but being lower level of urbanization developing in above areas. Most of farmers have to work in both towns and farms at different time. As result, it caused serious environmental problems for lack of the proper mode of urbanization. The farmers from these poor areas will face more challenges for surviving and improving their lives. It is supposed that the future development of urbanization will promote reasonable population and rural industries gathering from large cities to towns. At the same time, the clean energy and renewable resources should be further developed and used to avoid the continuous global environmental pollution and climatic deterioration. What the government and industrial planning departments need to do will be to build the platform of information industry for villages and towns, to promote resources to be integrated effectively and the industry upgrading fully. Therefore, the problems about development of urbanization will be solved gradually.
Advanced AI and ML techniques in cybersecurity: Supervised and unsupervised learning, reinforcement learning, and neural networks in threat detection and response
Authors: Meng Xianghui
Published: July 2024

In the rapidly advancing field of AI and ML, this paper explores their pivotal role in transforming cybersecurity. Highlighting the integration of sophisticated techniques like deep learning for intrusion detection and reinforcement learning for adaptive threat modeling, it emphasizes the shift towards AI-driven cybersecurity solutions. The study meticulously analyzes supervised and unsupervised learning's impact on threat detection accuracy and the dynamic capabilities of neural networks in real-time threat identification. It reveals how these methodologies enhance digital defenses against complex cyber threats, underscoring the theoretical underpinnings and practical applications of AI and ML in cybersecurity. The paper also discusses the challenges and future directions, contributing significant insights into the evolving landscape of cybersecurity technologies. This comprehensive research background sets the stage for understanding the unique contributions and potential of AI and ML in strengthening cybersecurity measures.
情感人工智能时代的用户体验塑造：人机共情的新路径
Authors: 郭 景
Published: July 2024

随着情感人工智能技术的迅猛发展，其在社会各个领域的应用不断加深，尤其在塑造用户体验方面展现出前所未有的潜力。本研究旨在呼吁整合多学科的丰富知识，从全面而深层次的角度探讨人机共情在服务接触中对用户体验的影响机制，以及如何通过情感层面的营销管理来提升用户体验。为此，本文回顾了人机共情的发展背景和理论基础，强调其在人机交互中的关键作用。其次详细论述了人工智能作为交互式代理在建立共情能力方面的相关技术准备。最后通过理论阐述指出人机共情是一种双向互动过程，包含人工共情能力与共情反馈行为两个主要维度。本文不仅为人机共情的理论和实践提供了新的研究视角，也为营销管理领域带来了新的用户体验塑造策略。