bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Analysis of ESG Ratings and Their Impact on Equity Valuation
Authors: Luo Tianjiao
Published: December 2024

In accordance with studies on 2,000 large-cap corporations headquartered in nearly 50 countries, significant differences exist in ESG disclosure quality and transparency. This article aims to conduct a comparative analysis of ESG ratings and their impact on equity valuation. By analyzing factors influencing enterprise valuation and theoretical frameworks explaining the potential links between ESG and corporate valuation, addressing a critical blank space in existing papers. It contributes to redefine how ESG factors influence corporate performance and equity valuation, providing valuable perspectives for policymakers, investors and managers navigating the evolving landscape of sustainability. Furthermore, this article proposes some targeted policy recommendations. For instance, incentivizing sustainable practices can promote the importance and accuracy of ESG indicators in equity valuation. Governments can develop fiscal incentives for corporations adopting robust ESG practices. Tax breaks or subsidies can be typical examples. This would encourage more enterprises of various ownership types to prioritize sustainability in their operations and reporting.
Evaluation of the Effectiveness of Tuberculosis Intervention in the US
Authors: Zhang Yangjia
Published: December 2024

This study evaluates the effectiveness of tuberculosis (TB) interventions implemented in the United States by comparing actual TB incidence and mortality data with predicted data that assumes no interventions. The analysis focuses on two key intervention periods: the introduction of strict infection control measures and enhanced environmental controls in 2005, and the update to TB screening guidelines for healthcare personnel in 2019. This study trained the Auto-regressive Integrated Moving Average (ARIMA) model on pre-2005 data in order to forecast the number of tuberculosis cases and the proportion of cases overall that did not get these interventions. The predicted data was then compared with actual post-intervention data to assess the impact of these public health measures. Results show that without these interventions, TB cases and mortality rates would have remained significantly higher. The actual data, in contrast, demonstrated a marked decline in both metrics following the implementation of these measures. The findings underscore the critical role of comprehensive public health interventions in reducing the burden of TB. The long-term impact of these measures is evident in the sustained reduction in TB incidence and mortality, highlighting the importance of continuous monitoring and adaptation of TB control strategies to maintain effective disease management.
Study on the Impact of Digital Economy on Green Development
Authors: He Dantong , Xu Weichen
Published: December 2024

Green development is a crucial tactic for attaining sustainable development since, in the setting of the world economy's rapid expansion, environmental challenges are playing a bigger role in human development. Since the advent of the digital era, green economic development and digital technology have been progressively integrated. It is not only important to save the environment and maintain ecological balance, but it's also a crucial step in advancing economic modernization and transformation and attaining sustainable development. It is a significant factor propelling China's economic expansion. China, with the second largest economy in the world, needs to promote a green economy to support long-term development goals. The importance of the digital economy on green development is examined in this research. Through literature review methods, this article investigates how the digital economy can improve resource efficiency and reduce energy consumption and emissions through technological innovation and management optimization. The study found that the digital economy significantly contributes to the green development of various industries, but it also faces challenges such as increasing energy consumption and data security. In the future, to create a situation where the economy and the environment benefit from each other, it will be required to further integrate the digital economy with green development, foster technological innovation, and enhance the legislative framework.
Analysis of Low-Threshold 1D Perovskite Photonic Crystal Laser
Authors: Xie Yufeng
Published: December 2024

By using the bandgap effect and light localization effect, photonic crystal lasers can decrease the optical mode volume and lower the laser threshold, so satisfying the requirements for device shrinking and photonic integration. Given their wide range of potential applications in fields including light-emitting diodes and sensors, they are attracting considerable interest. The laser threshold is the minimum energy density required to cause laser oscillation and is a crucial specification for the practical application of laser devices. Perovskite materials, equipped with their exceptional optical gain characteristics, are well-suited for the development of low-threshold lasers. By combining them with photonic crystal lasers, it is possible to realize low-threshold or even threshold-free pumped lasers. This paper categorizes photonic crystal lasers according to their photonic crystal structures and examines the advancements in research on perovskite photonic crystal lasers in attaining low minimum thresholds. This work investigates the prospective capabilities of perovskite photonic crystal lasers, with the objective of achieving laser output that is low-threshold or near-zero-threshold, high-power, and of high quality.
Study on the Current Situation and Promotion Strategies of Tourism Catering in Zhaoqing
Authors: Chen Haiming , Kong Xuejuan
Published: December 2024

This paper, through questionnaire surveys and interviews, finds that the development of tourism and catering industry in Zhaoqing City faces several issues. These include a lack of prominent local catering characteristics, insufficient promotion of local tourism catering, serious homogenization, a lack of innovation, unstable service quality, strong seasonality in tourism, and inadequate future planning. Based on the analysis of these problems, the development strategies summarized are: firstly, to enhance the quality of tourism catering products and services and create distinctive brands; secondly, to strengthen industry management and policy support to promote healthy industrial development; thirdly, to promote industry integration and innovation to expand development space.
Electric Aviation: Pioneering the Future through Advances in Electric Vehicle Technologies
Authors: Zhi Yicong
Published: December 2024

This paper explores the technological advancements in electric vehicles (EVs) and their potential applications in electric aviation. Core EV technologies such as battery systems, electric motors, and electronic control systems are analyzed to understand their relevance to aviation. The evolution of various battery types, including lithium-ion, solid-state, and next-generation alternatives, is emphasized, focusing on their critical role in addressing energy density challenges that electric aircraft will face. Additionally, this paper discusses the technical, regulatory, and manufacturing hurdles that must be overcome for electric aviation to become commercially viable. From battery weight and endurance limitations to energy-efficient propulsion systems, the complexities of translating EV innovations to aviation are assessed. The paper concludes by examining future possibilities for electric aviation, highlighting how breakthroughs in EV technology could lead to sustainable air travel. Through this lens, the technological trajectory of EVs provides key insights into the opportunities and challenges of developing electric aircraft.
Challenges with the Ultimate Energy Density with Batteries
Authors: Liu Yuxin
Published: December 2024

Lithium batteries have become integral to modern technology, powering everything from portable electronics to electric vehicles and large-scale energy storage systems. Their widespread adoption is driven by their high energy density, long cycle life, and lightweight nature, making them the preferred choice for numerous applications. However, the performance of lithium batteries is heavily influenced by the properties of their electrolytes, which play a crucial role in ion transport and overall battery stability. This paper explores key advancements in the development of electrolytes that enhance the performance of lithium batteries. It discusses the role of conductive materials like carbon nanotubes and graphene in improving ionic conductivity, as well as the benefits of solid-state electrolytes such as LISICON and garnet-type materials. Additionally, the paper examines strategies to improve voltage and chemical stability through the use of fluorinated solvents, advanced electrolyte formulations, and protective coatings. These innovations are critical for increasing the energy density, safety, and longevity of lithium batteries, paving the way for the next generation of high-performance energy storage solutions.
Effects of Materials on Environment and Economy of Public Transport
Authors: Zhu Ying
Published: December 2024

The global shift towards sustainable development has underscored the importance of minimizing the environmental footprint and optimizing economic efficiency across all sectors, including public transportation. The materials used in public transport vehicles has substantial implications for both environmental and economic performance. The choice of materials utilized in the construction, maintenance, and operation of public transport systems plays an important role in achieving these goals. The document aims to examine the aim of selecting environmentally friendly and economically viable materials for public transport vehicles, along with the subsequent effects these choices have on both the environment and the economy. Public transportation is a cornerstone of modern city planning, facilitating mobility, reducing congestion, and promoting social equity. As the world faces with the challenges of climate change and economic pressures, the materials used in public transport infrastructure and vehicles are under increasing scrutiny for their impact on both the environment and the economy aspects.
The Impact of Technological Advancements in the Luxury Car Market: A Case Study of Mercedes-Benz Sales in the U.S.A
Authors: Fang Yuan
Published: December 2024

This study investigates the influence of technological advancements on Mercedes-Benz's market performance in the U.S. luxury car sector, focusing on the period from 2013 to 2023. Employing multiple regression analysis, the research examines the relationship between key variables: technological adoption rates, sales volume, average selling price, and revenue. The study reveals that while the average selling price significantly enhances revenue, technological adoption contributes positively, though with a less pronounced effect. However, taking such measures as using electric cars and car driving automation technologies has helped Mercedes-Benz to sustain its competitive advantage. Even if this is initially expensive and consumer acceptance or penetration varies, it restricts their immediate revenues. Multiple regression and ANOVA analyses also support the importance of these factors, stressing that an effective pricing policy and constant innovations are crucial for increasing revenue. The result of the study shows that Mercedes-Benz can maintain the leading position in the luxury car segment only if it invests in technological developments and follows the changes in consumer trends. That can only underline the need for further development of technologies in the automotive industry, a situation luxury brands must face in the long run, as they must satisfy consumer demand for high-end features to generate constant revenues.
Research on the Influence Path of Humorous Advertisement Types on Customer Engagement
Authors: Zhong Xueyang
Published: December 2024

Customer Engagement has become an increasingly popular topic in recent years. Higher customer engagement implies stronger brand power and is a key factor for businesses to capture market share. In the current trend of short video marketing, more and more brands are incorporating humor into their advertising campaigns. Therefore, it is important to explore the mechanisms of customer engagement formation under humorous scenarios to validate marketing practices. This study discusses the formation mechanism of customer engagement in humorous advertising contexts through three experiments. The research found that humorous advertisements promote the formation of customer engagement by reducing the psychological distance between the brand and the customer. Additionally, brand strength and perceived value play moderating roles in the paths from humorous advertising to psychological distance and from psychological distance to customer engagement, respectively. This study enriches the understanding of the formation mechanism of customer engagement in the context of content and these findings will help advertising practitioners better understand and utilize humor advertisements to improve customer engagement.
A Review of Factors Influencing Brand Attachment
Authors: Wang Ronglin
Published: December 2024

In the whole process of consumer purchase decision-making, not only rational factors play a role, but also irrational factors or emotional factors play a very important role, that is, consumers will be driven by their own emotions towards the brand and make a purchase, consumer brand emotion is an important part of the consumer-brand relationship, which has a very important role in brand management, and brand attachment belongs to the special emotion of consumers to the brand, which mainly describes a kind of emotion like mother-child relationship. This paper summarises the influencing factors of brand attachment in recent years, mainly including consumer perspective, brand perspective, and consumer-brand relationship perspective for classification, and puts forward the idea of future research on brand attachment based on this.
Research Status and Optimization of Small Molecule Organic Solar Cells
Authors: Huang Shengsheng
Published: December 2024

Organic solar cells, particularly those made from small molecule materials, have garnered significant attention in recent years due to their numerous advantages. One of the most notable benefits is their clear material molecular structures, which allow for precise regulation of energy levels. This clarity also extends to purification methods, which are simple and straightforward. Additionally, small molecule materials are highly adaptable and can be used in a variety of applications, ranging from building-integrated photovoltaics to wearable devices. This article delves deeper into the development status and application prospects of organic solar cell technology, with a particular emphasis on the benefits of small molecule materials and their manufacturing processes. By examining the potential applications of organic solar cells in different fields, this article provides valuable insights and guidance for those interested in studying and applying organic solar cell technology. Overall, this article highlights the exciting opportunities that exist for the advancement of clean energy technology through the use of organic solar cells.
Application of different nanomaterials for lithium-ion batteries
Authors: Lyu Loubin
Published: December 2024

With the growing demand for environmentally friendly transportation and the development of electric vehicles, using nanomaterials to enhance lithium-ion battery performance, especially in capacity, charge rate and cycle life has become important. Nanomaterials such as graphene, carbon nanotubes (CNTs), and silicon have received significant attention for their unique physical and chemical properties, including superior electrical conductivity and large storage capacity. This research discusses the role of these nanomaterials in improving lithium-ion battery performance. Graphene’s two-dimensional structure and high electrical conductivity allow for faster charge/discharge rates and increased battery capacity. With their one-dimensional structure, CNTs are very strong and electrically conductible. Silicon with high theoretical capacity can increase battery energy density, although its volume expansion during cycling has challenge for long-term stability. As well as giving examples of these nanomaterials in lithium-ion battery, for example, graphene and CNTs can use as conducting additives in electrode and a new nanostructure of silicon (silicon nanotubes) used in anode to enhance lithium-ion battery performance.
The Influence of Parental Involvement on Childrens Academic Performance
Authors: Lai Simai , Wu Wansi
Published: December 2024

Due to the fact that many people today, especially students, are paying more attention to the standard of education. Therefore, the impact on parental involvement on childrens academic performance is becoming more and more essential. Reviewing the research that has been done in the past years, authors find that the definition of parental involvement varies from macro to micro, and the relationship between the parental involvement and childrens academic performance is similar in different regions or countries. Moreover, whether parents are attending parent-teacher conferences and whether they assist or give a supported learning environment to their child can lead to children having a higher performance in education. Other factors like parents regularly communicating with teachers and understanding the challenges and strengths can create a positive environment for their child. However, over pressuring their child towards their education may lead to many negative impacts. As a result, by preventing negative impacts from happening, and analyzing these data, recommendations can be made like suggesting parents to be more involved in their childrens activities that are related to education and paying more attention to the feelings of their children whilst being with them.
Research on the Impact of International Student Policies on Students' Choice and Mobility of Studying Abroad
Authors: Dai Hanying
Published: December 2024

In recent years, the impact of international education policies on student mobility has attracted significant scholarly and policy attention, underscoring the need for a deeper understanding of the underlying mechanisms that influence these patterns. Despite extensive studies, there remains a gap in comprehensively exploring how various components of these policies interact to shape student choices. This paper aims to bridge that gap by conducting a comparative analysis of international education policies across multiple countries, focusing on three critical dimensions: academic support, living conditions, and career development opportunities. The findings highlight that the availability of scholarships and access to educational resources play a pivotal role in attracting high-caliber international students. Furthermore, robust living support systems, including housing and healthcare, combined with flexible post-graduation work visa policies, are shown to significantly boost student retention rates. Based on these insights, the paper recommends optimizing scholarship programs, expanding living support initiatives, and enhancing career development pathways to improve a country's attractiveness to international students. Such measures not only facilitate the influx of global talent but also bolster a country's competitive edge in the international education sector.
Implications of the Critical Period Hypothesis for Oral English Instruction in Primary Schools in China
Authors: Zhao Zekai
Published: December 2024

Oral language instruction has long been overlooked in primary school English education in China. However, with the rapid pace of globalization and the growing demand for English proficiency, oral skills have become a crucial part of students overall language competence. Based on the Critical Period Hypothesis (CPH), investigating the sensitive period for language learning at different ages and its implications for educational practices has become a key approach to improving educational quality. Therefore, this paper seeks to explore how to enhance the quality of oral language teaching in primary school English classrooms in China through the application of the CPH. It specifically focuses on adopting effective teaching strategies to help students overcome challenges in oral language learning, thereby fostering the holistic development of their language skills. In addition, this study reviews and analyzes relevant literature, with a focus on recent research findings. And the results indicate that teaching strategies based on the CPH, such as group role-playing and dubbing activities, can effectively improve students oral language skills. These methods provide students with more opportunities for language practice, thereby addressing the shortcomings of oral language teaching. However, further experimental research is needed to validate the effectiveness of these teaching strategies.
The Connection Between Dunhuang Murals and Dunhuang
Authors: Dong Hao , Yang Yongjiang
Published: December 2024

Dunhuang painters are a subgroup within the category of artisans. Although they represent only one type of artisan, their significance in Dunhuang is undeniable. The work of these painters goes beyond mere artistry; the historical materials they have left behind are of great importance. Through their images, they tell the story of the development of ancient civilization. Every mural and every sculpture not only represents the manifestation of Dunhuang civilization but also serves as a vital addition to the larger narrative of Chinese history. Dunhuang painters are both ancient and contemporary figures. The ancient Dunhuang painters left us with inexhaustible spiritual and material wealth. Contemporary Dunhuang painters continue to provide us with a spirit of innovation and an unbroken legacy that evolves with the times.
A comprehensive evaluation of monoclonal antibody therapy for triple-negative breast cancer (TNBC)
Authors: Liang Chen
Published: December 2024

TNBC is one of the subtypes of BC, Its cancer cells do not contain estrogen receptors or progesterone receptors, it does not contain HER2 receptors, or have a low content, which makes its therapeutic means limited and has a poor prognosis and is prone to tumor recurrence. Recently, Monoclonal antibody therapy, as one of the main forms of targeted therapy, has gradually gained attention and application in the treatment of TNBC. However, despite being a primary form of targeted therapy, monoclonal antibody treatment has only recently garnered attention and application in TNBC treatment. One significant challenge is the high incidence of side effects and associated symptoms, for which there is currently no effective solution. This article provides a comprehensive summary of the key landmark substances that are present in the pathological changes of TNBC, with particular emphasis on those substances that can impact prognosis. The review covers the mechanism, clinical effects, side effects, and combination therapy involving TNBC mAbs. At the conclusion of the article, new findings regarding TNBC research are presented, along with new opportunities for precision medicine for TNBC patients. This study concludes that monoclonal antibody therapy holds promise in the treatment of TNBC; however, further research is necessary to optimize treatment regimens and improve patient survival rates. This article's goal is to be a reference for future research on the side effects of TNBC, with a focus on improving patient prognosis and therapeutic outcomes.
Deciphering Biological Function Difference Between MO::L1 and MO::L2/3 in Mouse Brain from Spatial Transcriptomics Data
Authors: Piao Minrui
Published: December 2024

Parsing brain structure is crucial in neuroscience research, and the advent of spatial transcriptomics offers unprecedented molecular opportunities. In this study, we utilized 10X Visium spatial transcriptomics to analyze the mouse hindbrain, focusing on the MO::L1 and MO::L2/3 regions. By integrating artificial priors with the SpaceFlow method, we identified spatial organization and conducted differential gene expression analysis. Key findings include the identification of TGF-beta and Hippo signaling pathways, crucial for neurodevelopment and injury repair. Our results highlight the roles of differentially expressed genes in developmental processes, cellular regulation, and signaling pathways, providing insights into brain region functions. Additionally, we discuss current research gaps, suggesting that future studies should explore dynamic gene expression changes and leverage advanced computational models to enhance our understanding of the nervous system, thereby advancing precision medicine.
Advances in the targeted therapy for breast cancer
Authors: Zhang Yaoyun
Published: December 2024

In 2022, 670000 people died because of breast cancer (BC). Notwithstanding their age and gender, women make up about half of the total number of instances of BC without any specific risk factors. In 157 out of 185 nations in 2022, BC will be the most frequent cancer among women. With the continuous progress of research, BC has achieved great breakthroughs, such as the introduction of BC immunotherapy, which is a revolutionary treatment for some patients; In addition is the introduction of antibody-coupled drugs (ADCs) that can target the delivery of potent chemotherapy drugs into breast cancer cells. This paper analyzes the treatment of some targets in BC, and obtains the application effect of some new drugs in breast cancer, which buys time for clinical treatment such as surgery. However, there are still some drugs with unclear effect on breast cancer treatment, and some drugs are only used alone. Future studies can pay more attention to the aspects of drug combination therapy.
Simulation Assisted Improvement of Plastic Degradation Enzyme PETase based Machine Learning Tools
Authors: Tian Ziming
Published: December 2024

Polyethylene terephthalate (PET) plastic is one of the most widely used plastic primarily due to its flexibility, endurance, and low cost. However, the plastics one-time use nature and long degradation time have led to massive waste accumulation, damaging our ecosystem, health, and biodiversity. While previous degradation methods are ineffective due to their high cost and low efficiency, the discovery of two enzymes PETase and MHETase in the bacteria Ideonella sakaiensis to degrade PET and mono(2-hydroxyethyl), a reaction intermediate in PET degradation, respectively, sparked the idea of a sustainable approach to degradation. Ever since, many approaches, including directed evolution, rational protein engineering, and computational redesign strategies, have optimized PETase in terms of its thermostability, catalytic activity, and more. This study proposes the incorporation of newly developed machine learning-based computational tools, including MutCompute, AlphaFold, and DiffDock, into a holistic protein engineering process to predict optimal PETase mutations. Here, in-silico experiments using machine learning tools as well as molecular dynamics simulation and interactions analysis screened for large amounts of PETase mutants in a time and cost-saving manner. Degradation assay coupled with mass analysis and high-performance liquid chromatography techniques then experimentally characterized PETase and its chosen mutants; thus, further screening found the most viable PETase mutant. Using various strategies, the project directly tackles one of the major global issues sustainability by bio-recycling PET. The research also aims to pave the way for introducing a new, imitable process for the more effective and resource-efficient engineering of all proteins.
The review of WRKY transcription factor family functions and its abiotic stress responses in plants
Authors: Zhao Tianyu
Published: December 2024

As sessile organisms, plants may encounter various abiotic stresses during their growth, such as low temperatures, high temperatures, drought, and salinity, which can lead to impaired growth, injury, or even death. Transcription factors, as key proteins influencing gene transcription, play a crucial role in plant responses to abiotic stress. Among them, the WRKY transcription factor family is one of the largest in plants and plays a significant regulatory role in plant growth and development. This paper reviews the functions of the WRKY transcription factor family and the current research status on their roles in plant responses to abiotic stress. It discusses how WRKY transcription factors regulate plant hormone signaling pathways and stress signal transduction pathways to enhance plants' ability to cope with abiotic stress.
Reducing the immunosuppressive microenvironment in pancreatic adenocarcinomas through cell-type specific targeting of myeloid derived suppressor cells
Authors: Ren Christopher , Li Tina , Liu Christina , Yang Yana , Zheng Asher
Published: December 2024

Pancreatic adenocarcinoma is one of the leading causes of cancer related deaths worldwide and currently lacks effective treatments. The immune-suppressive microenvironment and high malignancy has made conventional targeted therapies and immunotherapies ineffective for most patients. Thus, there is an urgent need for alternative treatments to enhance therapeutic efficacy. Through a comprehensive literature review, we propose a novel CD300ld antigen-specific liposome cargo delivery system for targeting MDSC mediated immune-suppression in pancreatic adenocarcinoma. Hypothetically, this approach could exhibit enhanced immune engagement and significant anti-tumor effects through the artificial expression of CD47 inhibitor and suppression of STAT3-dependent immunosuppressive cytokines. Further validation in primate models, combined with conventional immune check-point inhibitor (ICI) treatments, could be conducted for a more comprehensive understanding of this model.
A Scoring System that Leverages Single Cell RNA-Sequencing Data to Predict Cell Therapy Outcome in Solid Tumor Treatment
Authors: Li Yuwen
Published: December 2024

Cancer, a prominent global health concern, involves the uncontrolled growth and dissemination of abnormal cells, forming tumors that can infiltrate nearby tissues and metastasize to distant sites. Early and precise tumor detection and treatments are crucial to impede their spread and mitigate disease progression. This study presents the development of a scoring system that harnesses single-cell RNA sequencing (scRNA-seq) data to predict the suitability of solid tumors, specifically glioblastoma (GBM) and lymphoma, for advanced cell therapies such as CAR-T, NK cell, and TIL therapies. By integrating key cellular factorsincluding tumor-infiltrating cells, immunosuppressive cells, and immune escape markersthis system provides a quantitative assessment of a tumors potential responsiveness to these therapies. Validation with patient-derived samples has demonstrated the system's potential to improve precision in therapy selection, offering a promising tool for personalized oncology. This research not only aims to enhance the effectiveness of cell therapies for cancer patients but also lays the groundwork for applying similar methodologies to other cancer types, advancing the integration of precision medicine in cancer treatment.
CAR T-cell Therapy for GBM
Authors: Pang Zilong
Published: December 2024

Glioblastoma multiforme (GBM) is the most lethal and common primary malignant brain tumour. It has a 100% mortality rate and poor prognosis, and is currently incurable. Conventional treatments, such as surgery, can only extend patients survival to a very limited extent and are often accompanied by severe side effects. In recent decades, chimeric antigen receptor (CAR) T-cells therapy has emerged as a novel immunotherapy with great potential in treating various tumours, raising high expectations for its application in GBM. This paper reviews the structure and function of CAR design and discusses three antigens on GBM that have completed clinical trials: EGFRvIII, HER2 and IL-13Ra2. Although each antigen-specific CAR T-cell therapy face its own challenges, the results from clinical trials have been overall inspiring. However, CAR T-cell therapy for GBM is still in very early stages and requires further research and exploration.
Application of 3D Printing in Cardiomyopathy
Authors: Ji Yulin
Published: December 2024

Cardiomyopathy encompasses a diverse group of heart muscle diseases that can lead to severe complications, including heart failure and arrhythmias. Traditional research methods, such as animal models and two-dimensional cell cultures, struggle to replicate the complex three-dimensional architecture of heart tissue, hindering the development of effective therapies. Recent advancements in 3D printing technology, particularly in bioprinting, present novel opportunities to overcome these limitations. This paper explores the applications of 3D printing in cardiomyopathy, highlighting its role in heart modeling, tissue engineering, drug testing, and regenerative medicine. Patient-specific heart models created from imaging data allow for enhanced surgical planning and improved communication between healthcare providers and patients. Tissue engineering efforts, utilizing 3D-printed scaffolds and bioprinting techniques, aim to restore heart function by regenerating damaged tissues. Furthermore, 3D-printed heart tissue models facilitate more accurate drug testing and disease modeling, enabling researchers to better understand disease mechanisms and assess therapeutic efficacy. Despite its promise, challenges such as the complexity of heart tissue, scalability, and regulatory considerations remain. Future research should focus on refining 3D printing technologies, developing advanced bio-inks, and ensuring the integration of engineered tissues with native heart structures. Overall, the integration of 3D printing into cardiomyopathy research and treatment holds significant potential to transform patient care and improve outcomes, paving the way for personalized medicine approaches in managing this complex group of diseases.
Current HPV therapeutic vaccine development and impact on cervical cancer prevalence and mortality
Authors: Wang Qinheng
Published: December 2024

Cervical cancer is considered to be the fourth most common cancer in the women's population globally, as reported by WHO in 2024. We have achieved great success in the research and development of HPV prophylactic vaccines, and HPV prophylactic vaccines have effectively reduced the prevalence of HPV. While 90% of HPV infections can be stopped by having injections of HPV prophylactic vaccine, there is currently no effective method that can prevent the spread of HPV induced malignancies and cervical cancer after infection takes place in unvaccinated individuals. This forces people who are diagnosed with cervical cancer to choose more painful treatment options; this high mortality rate and painful treatment increase awareness of the development of HPV therapeutic vaccines. Current HPV therapeutic vaccines are designed to target E6 and E7 oncoproteins, activate CD8+ T cells, clear the infection and prevent further development of infected cells. 9 HPV therapeutic vaccines are under development, with 8 in phase 2 clinical trials and one in phase 1. The development of the HPV therapeutic vaccine has the potential to reduce the mortality of cervical cancer, thereby improving the quality and lifespan of affected individuals. This article will summarise the development, advantages and disadvantages of HPV therapeutic vaccines to determine whether they can substantially help reduce the incidence of cervical cancer.
Interaction Between Gut Microbiome and Neurodegenerative Diseases: Current Status, Challenges, and Future Prospects
Authors: Fan Xinyue Edwina
Published: December 2024

The intricate relationship between the gut microbiome and neurodegenerative diseases has become an emergent topic in biomedical research. This study delves into the interplay between intestinal microbial communities and the pathogenesis of neurodegenerative conditions such as Alzheimer's and Parkinson's disease. Drawing on theoretical frameworks that suggest a bi-directional 'gut-brain axis', the hypothesis driving this investigation postulates that the gut microbiota may influence central nervous system dysfunction directly through microbial metabolites or indirectly by modulating immune responses. Employing a combination of metagenomic analysis and neurophysiological assessments, the research elucidates the compositional and functional alterations in the gut microbiota of individuals with neurodegenerative diseases. Findings highlight a significant correlation between specific microbial profiles and disease progression, laying the groundwork for potential therapeutic interventions targeting gut microflora. The novelty of this study lies in its comprehensive analysis of the microbiome's role in neurodegeneration, providing valuable insights for the advancement of microbiota-centered therapies in clinical practice.
Comparison of State-of-art Machine Learning Models Performances: Evidence from BTC, BNB, ETH, USDC and USDT
Authors: Yi Zimeng
Published: December 2024

As a matter of fact, cryptocurrency price prediction has become a major trend for financial research especially based on the state-of-art machine learning techniques. This study explores the effectiveness of six machine learning (ML) models in predicting the daily prices of five major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Tether (USDT), BNB (BNB), and USD Coin (USDC). The ML models applied include the Ordinary Least Squares (OLS) model, Random Forest, Long Short-Term Memory (LSTM), XGBoost, LightGBM, and Transformer. The results demonstrate that traditional regression and tree-based ensemble models, particularly OLS and LightGBM, are more effective in predicting cryptocurrency prices, especially for BTC, BNB, and ETH. LightGBM consistently outperforms OLS in minimizing prediction errors, while Random Forest and XGBoost also yield strong, though slightly less accurate, results. Conversely, LSTM and Transformer models underperform, indicating their limitations with the specific characteristics of cryptocurrency data. These findings suggest that traditional and tree-based models are better suited for cryptocurrency price prediction. Future research should investigate the incorporation of additional market indicators and advanced ML architectures to enhance predictive accuracy.
Trust Application Advantages in Corporate Bankruptcy Reorganization: A Case Study of HNA's Bankruptcy Reorganization
Authors: He Zilong
Published: December 2024

Bankruptcy reorganization is a systematic approach that helps enterprises escape financial distress, mitigate bankruptcy crises, and restore independent management capabilities. Traditionally, the debt-to-equity swap model is employed to dilute shares in order to achieve debt repayment. However, this method does not fully protect the interests of creditors. By analyzing the trust scheme used in the HNA bankruptcy reorganization case, this article summarizes the process and advantages of using trust in corporate bankruptcy reorganization. Based on theoretical and practical conditions, and in conjunction with the current state of the economy and the bankruptcy reorganization market, the article discusses the various models of trust in corporate bankruptcy reorganization, as well as the conditions under which different models apply. The analysis indicates that trust has advantages when applied to corporate bankruptcy reorganization and shows promising prospects for the future.
A Comparative Study of Traditional and Machine Learning Approaches for E-Commerce Sales Forecasting
Authors: Sun Lijia
Published: December 2024

An increasing number of users are relying on online shopping in recent years. E-commerce is a rising field that shows great potential in business. A precise prediction on e-commerce sales could greatly affect the business plan of a company, influencing the marketing strategy, supply chain management, production, and many other aspects. Thus, e-ommerce sales prediction has become an attention-grabbing topic in the current business world. This study compares the performance of traditional statistical approaches and recent machine learning algorithms on e-commerce sales forecasting based on two datasets with and without time series data obtained from Kaggle. By comparing the performance metrics, including MAE and RMSE, machine learning methods outperformed in datasets without time series data, and statistical models achieved a better result in datasets with time series data. Thus, a trend that traditional models like ARIMA are better at time series data and machine learning methods have an obvious advantage in non-time series data has been proposed. Both of the models have their own strengths in corresponding fields.
The Role of Climate Change in Catalyzing the Decentralized Green Economy: How U.S. Cities are Pioneering Sustainable Industry Clusters
Authors: Guo Qinyuan
Published: December 2024

Climate change is accelerating the shift from traditional, carbon-intensive industries to decentralized green economies in U.S. cities. Urban hubs like Silicon Valley, Boston, and Austin are emerging as global leaders in clean technology, biotechnology, and renewable energy by capitalizing on their unique regional strengths. These industry clusters not only enhance economic resilience but also drive innovation and sustainability. The article explain different actions that U.S. cities do to deal with climate change and it will also use comparison and examples to illustrate the contents. The study attributes their success to robust innovation ecosystems, supportive policies, and strong academic and research institutions. However, challenges such as inconsistent funding, policy variability, and workforce retraining persist. Despite these obstacles, ongoing technological advancements, evolving policies, and global trends are expected to sustain the momentum of green industries. The U.S. model provides critical insights for other regions aiming to transition to sustainable economies. This study underscores the essential role of urban innovation, collaboration, and strategic policymaking in addressing climate change and ensuring long-term economic and environmental resilience globally.
ARIMA Model Application in Predicting NVIDIAs Stock Price
Authors: Yeung Kitung
Published: December 2024

The volatility of stock prices in the technology sector has drawn significant attention from both investors and researchers, particularly in the rapidly evolving GPU market. This study aims to address the challenges of accurately forecasting stock prices by applying the ARIMA model to NVIDIA's stock, a prominent player in the GPU industry. Using monthly data from July 2014 to July 2024 obtained from Yahoo Finance, the data is preprocessed through first and second-order differencing to ensure stationarity. Several ARIMA models are constructed, with ARIMA(2,2,1), the time series value is determined by the combination of the first two autoregressive terms (after two differencing) and the previous error term, and ARIMA(0,2,1), the model depends only on the previous error term, demonstrating the best fit based on AIC and BIC criteria. The predictive accuracy of these models is compared by calculating key statistical indicators, such as RMSE, MAE, MAPE, and Symmetric MAPE. The findings suggest that while both models offer valuable insights, ARIMA(021) provides more precise forecasts. However, the limitations of ARIMA models in capturing non-linear and sudden market movements are acknowledged, emphasizing the need for incorporating additional real-world factors in future analyses. This study provides practical recommendations for investors and researchers to understanding and predicting stock market trends.
Environmental Protection and Sustainable Development by Clothing Brands
Authors: Chen Min
Published: December 2024

The central aim of this study was to investigate the contribution of major clothing production brands towards international sustainable development goals (SDGs). Using a case study method, the research evaluated the environmental impacts of conventional materials used in clothing manufacturing and examined the reasons for recycling these materials after consumption. Additionally, the study assessed the environmental implications of recycling efforts by Patagonia and Levi Strauss. Key findings revealed that clothing discarded in landfills, if not recycled, emits greenhouse gases contributing to global warming. Although companies are motivated to recycle old garments to reduce their ecological footprint, the process is complex and costly, posing challenges for practical implementation. Despite these difficulties, the case studies of Patagonia and Levi Strauss demonstrate how these two apparel manufacturers contribute to international sustainability goals by diverting waste from landfills, reducing carbon emissions, conserving natural resources, and alleviating poverty through job creation. The research concludes that, as exemplified by Patagonia and Levi Strauss, sustainable fashion helps protect the environment and promotes the circular economy model, serving as a prototype for other industries.
Real Estate Market Data Analysis: Correlation Between Housing Prices, Living Space, Building Age and Location
Authors: Huang Renyun
Published: December 2024

The real estate market plays a significant role in economic activity, with housing price fluctuations affected by a variety of factors. Understanding these factors is crucial for investors, developers, and policymakers. This study analyzes the relationship between housing prices and variables such as living space, age of construction, age of renovation, and geographic location using data visualization techniques. The results indicate a positive correlation between living area and housing prices, and show that the distribution of building and renovation ages highlights peak periods of construction and renovation activity. In addition, location has a significant impact on housing prices, which may be related to factors such as land prices, transportation convenience, etc. This study collects data and performs statistical analysis to present and interpret the data through data visualization techniques. The findings suggest that house prices are positively correlated with living area, the distribution of building and renovation ages follows a specific periodic pattern, and location has a significant impact on house prices. This study offers a new perspective on the dynamics of the real estate market and provides valuable insights for guiding investment decision and shaping policy.
How Fintech Affects Systemic Financial Risk - Evidence from Chinese Commercial Banks
Authors: Wu Tong
Published: December 2024

Against the backdrop of the rapid development of fintech, digital transformation has become an important tool for the banking industry to deal with systemic financial risks. Taking Chinese commercial banks as the research object, this paper explores how FinTech affects systemic financial risk, especially its role on non-performing loan (NPL) ratio. Through heterogeneity tests and main regression analyses, the paper examines the specific impact of the total digitisation index and its subdimensions (strategic digitisation, operational digitisation, and managerial digitisation) on the NPL ratio. The findings show that banks' digital transformation significantly reduces the NPL rate, and in particular, strategic digitisation has the most prominent effect in reducing credit risk. In addition, digital transformation further reduces credit risk by improving the stability of loan growth rates and deposit growth rates. The research in this paper provides important practical guidance for the banking industry on how to effectively use fintech to control systemic financial risks in the process of promoting digital transformation, and provides a theoretical basis for the formulation of relevant policies.