bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Machine Learning for Subsurface Geothermal Energy Development
Authors: Zhu Yanying
Published: December 2024

Geothermal energy, which derives heat from the Earth's core, presents a promising renewable resource for meeting sustainable global energy needs. Nevertheless, challenges including high initial costs, technical risks, and complex underground conditions have limited its widespread adoption. Recent advancements in Machine Learning (ML), a subset of Artificial Intelligence (AI), offer innovative solutions to these challenges. This paper presents a comprehensive review of the application of ML techniques in geothermal energy development, focusing on exploration, drilling, reservoir characterization and engineering, as well as production/injection engineering. Various ML algorithms including neural networks, clustering methods, and decision trees, have been employed to analyze complex geological and operational data. These applications have led to improved identification of geothermal resources, optimized drilling operations, enhanced reservoir management, and increased production efficiency. While ML integration offers significant advantages, limitations like data quality issues and computational demands persist. This paper highlights the need for interdisciplinary collaboration, data sharing, and increased investment in research and development to overcome these challenges. The ongoing advancement of AI technologies is anticipated to drive innovation in geothermal exploration and development, enhancing the efficiency, reliability, and economic viability of geothermal energy as a cornerstone of sustainable energy systems.
Progress in Application of Nano-technology in Silicon-Based LIBs
Authors: Jin Mile
Published: December 2024

With the growing global demand for clean energy and efficient energy storage technologies, improving the performance of lithium-ion batteries, as one of the most mainstream energy storage devices, has become the focus of scientific research and industrial attention. Lithium-ion batteries (LIBs) have been widely used in portable electronic devices, electric vehicles, smart grids, and large-scale energy storage with their advantages of high energy density, long cycle life, no memory effect, and environmental friendliness. However, with the increasing battery performance requirements in these fields, traditional lithium-ion battery materials have gradually exposed their limitations in energy density, cycle stability, and safety. In this context, nanosilicon, a new anode material, has become a research hotspot in lithium-ion batteries due to its unique physicochemical properties and excellent electrochemical performance. Nano silicon, i.e., silicon materials with dimensions on the order of nanometers, has a larger specific surface area, shorter lithium-ion diffusion paths, and higher mechanical stress tolerance than bulk silicon. These properties make nanosilicon exhibit higher lithium storage capacity, faster charge/discharge rate, and better cycle stability in lithium-ion batteries. Therefore, this paper focuses on the progress of the application of nanosilicon in LIBs and the future development direction.
Cultural Creativity and Market Response in Cross-border Co-branding: An Application of Cultural Marketing Theory
Authors: Wang Xuan , Zhao Xinqi , Zhao Yiran
Published: December 2024

This paper discusses the specific application of cultural marketing theory in the practice of cross-border joint marketing and its market effect. Cross-border co-branding, as an emerging marketing method, creates unique cultural creativity by integrating cultural elements of different brands, aiming to enhance brand interaction and market appeal. Based on the theory of cultural marketing, this study analyzes the generation mechanism and expression forms of cultural creativity in cross-border co-branding and its psychological impact on the target market, and reveals how cultural creativity becomes a bridge connecting brands and consumers' emotions. This paper evaluated the market response of cross-border co-branded marketing, including the enhancement of brand awareness, the enhancement of consumers' purchase intention and the expansion of market share, and further verified the applicability and effectiveness of cultural marketing theory in cross-border co-branded marketing. This paper not only enrichis the application field of cultural marketing theory, but also provides theoretical guidance and strategic suggestions for enterprises in the practice of cross-border co-branded marketing.
Research on the Application of Epoxy Resin Adhesive in the Restoration of Cultural Relics
Authors: Zhang Xijing
Published: December 2024

Epoxy resin adhesives, as a versatile and highly efficient bonding material, have been widely employed in the field of cultural heritage restoration in recent years. Their unique chemical stability and superior mechanical properties have allowed them to excel in various types of artifact restoration. This article examines the fundamental characteristics of epoxy resin adhesives and, in conjunction with their application status in the restoration of paper, wood, ceramic, and metal artifacts, explores the optimal application measures and future development directions. Research indicates that epoxy resin adhesives not only effectively restore the original appearance of artifacts but also extend their lifespan; however, there are certain limitations in their application that require further research and technological advancements to overcome. Moving forward, with the continuous emergence of new materials, the role of epoxy resin adhesives in cultural heritage restoration is expected to become even more significant.
CRISPR-Cas9 and Immunotherapy for Cancer
Authors: Zhong Yifei
Published: December 2024

Genome editing is part of the key research topics today. Researchers found, that the system of CRISPR-Cas9 can use Cas9 protein to manipulate the actual gene parts, playing a genuine editing role. Nowadays, the system of CRISPR-Cas9 is using widely in many area, like the cultivation of genetically modified crops, industrial production and the treatment for illness and so on. Except for those, the CRISPR-Cas9 system can be also with other method like immunotherapy to achieve the treatment in different types of cancers. In the development of specific cancer, they achieve continuous proliferation by capturing the host immune checkpoint. With the aberrant expression of PD1 in cancer cells, they achieve immune evasion through interaction between PD1 and PD-L1, which are currently the target of cancer treatment. This paper utilize the treatment of non-small-cell as an example to expound the knockout of PD-1 by CRISPR-Cas9 system, artificially constructed T-cell can keep activating T-cell proliferation.
Research on the Impact of Green Credit on Enterprise Development
Authors: Bao Shuqi
Published: December 2024

This study delves into the significant impact of green credit on corporate development, with a particular focus on its role in reducing financing costs, improving operational performance, and enhancing environmental outcomes. As green credit emerges as a crucial financial instrument, it offers companies preferential financing options that encourage the adoption of environmentally sustainable practices. The research findings indicate that green credit not only reduces the cost of capital but also drives substantial improvements in both financial and environmental performance. This dual benefit is especially pronounced in high-polluting industries, where the adoption of green credit facilitates compliance with environmental regulations and strengthens market competitiveness. Furthermore, the study examines how businesses are adapting their strategies in response to green credit policies, including optimizing their financing structures and investing in sustainable technologies. Overall, the research underscores the vital role of green credit in fostering a sustainable economy by aligning financial incentives with long-term environmental and economic goals.
AI Technology Promotes the Effective Development of Chemical Enterprises
Authors: Jiang Sheng
Published: December 2024

At present, subject to the overall sluggish economic environment, various industries have generally suffered a severe test. The chemical sector, in particular, has been hit hard by the multiple pressures of tightening demand, slowing economic growth and falling profitability. However, the rise of artificial intelligence has brought positive effects to the development of the chemical industry. With the continuous progress of artificial intelligence technology in recent years, it is not only used to reduce labor costs and shorten working time, but also shows broad application potential in deeper applications such as intelligent operation, design assistance, and data modeling. This paper first summarizes the current predicament of China's chemical industry, and then discusses some problems existing in the development of chemical enterprises. Finally, this paper will discuss how to optimize the planning process of chemical enterprises, simplify production links, strengthen production management and improve the intelligence of operation processes, to study the specific impact of artificial intelligence platform on enhancing the competitiveness of chemical enterprises, in order to promote the transformation of China's chemical industry to high-quality development.
Visual Deep Learning Model Improvement, Comparison, and Integration: Prediction of Pneumonia Based on Chest X-Ray Images
Authors: Niu Ziang
Published: December 2024

Using machine learning to process lung medical images can greatly improve hospital efficiency and save costs. With the increase in the number of patients, the demand for pneumonia pathologic recognition systems is increasing. Therefore, the organic combination of the two is of great significance to reduce the pressure on the medical and health systems. This paper presents a deep-learning method to identify and predict pneumonia. Using Convolutional Neural Networks, ResNet, and DenseNet as well as improved and integrated models, the known pneumonia images and normal lung images were used as training sets to identify lung images and determine whether pneumonia is present. The results showed that the original CNN and the improved ResNet network had the best effect, and the F1-score for pneumonia recognition reached 0.88. Therefore, this paper integrated these two neural networks. Finally, the F1-score of the integrated model reached 0.89, which was able to predict more accurately. This paper provides a new idea for selecting, integrating, and applying the model in the medical field.
Enhancing the performance of lithium-ion batteries by different types of nanomaterials
Authors: Li Jiayang
Published: December 2024

This research analyzes the application performance of nanomaterials in lithium-ion batteries (LIBs). Composite materials made of silicon and carbon have shown promise as a way to greatly improve the performance of LIBs. Silicon offers substantial improvements over traditional graphite anodes. The significant volume expansion of silicon during cycles of charge and discharge, however, brings several difficulties, including electrode deterioration and mechanical stress. Advanced structural designs, including nanostructuring, hollow particles, and Si@C core-shell architectures, have been developed. These developments preserve high capacity and cycle stability while mitigating volume variations. In order to further increase the cycle life and specific capacity of silicon anodes, carbon-based materials like graphene and carbon nanotubes (CNTs) are essential for boosting the conductivity, flexibility, and structural integrity of silicon anodes. Despite these advancements, engineering challenges remain, particularly in terms of complex fabrication processes and cost-effective scalability. Future trends will focus on optimizing material design, improving manufacturing processes, and exploring new composite and coating technologies. These advancements could enable the nanomaterial to become a key component in LIBs, with improved capacity retention, safety, and manufacturability.
Comparison of Prediction Models for Gestational Diabetes Mellitus: Logistic Model and Random Forest Model
Authors: Chen Yanru
Published: December 2024

This study aimed to develop and evaluate gestational diabetes mellitus (GDM) prediction models based on the logistic regression approach and Random Forest algorithm. GDM, defined as intolerance for glucose when first diagnosed during pregnancy, influences 5-12% of pregnancies worldwide and is a serious threat to the mothers' and infants’ health. Therefore, doctors and mothers must predict and intervene in GDM as early as possible. Data from 1012 women who were pregnant from laboratories in the Kurdistan Region was collected for this study, including age, number of deliveries, weight, height, body mass index (BMI), genetic information, and GDM diagnosis. Among them, 21.4% of pregnant women were diagnosed with GDM. By data preprocessing, division of training and test sets (80% training set, 20% test set), and using the SMOTE-Tomek Links sampling method to deal with data imbalance, it constructed logistic regression (LR) and random forest (RF) models, respectively. A grid search algorithm optimized the random forest model with parameters (n_estimators=300, max_depth=20, min_samples_split=10) and performed five-fold cross-validation. These two models were evaluated on the test set and the outcomes showed that the RF model had a good performance in terms of accuracy (0.875, 95% confidence interval (CI) [0.8261, 0.9239]), precision (0.874, 95% CI [0.7199, 1.0000]), specificity (0.979, 95% CI [0.9536, 1.0000]), and area under curve (AUC) value (0.740, 95% CI [ 0.6611, 0.8153]), which were superior to the LR model. According to the study, the random forest algorithm shows better performance in GDM prediction.
Investigation On the Path to Enhancing the Motivation and Participation of Higher Education Students in Practical Education
Authors: Ouyang Zhiwei
Published: December 2024

With the continuous improvement and development of higher education, the position of practical education in higher education has become increasingly important, and the participation of practical education also affects the quality of educational development. The importance of practical education has been widely recognized. The low motivation and involvement of students in practical education activities have received widespread attention, and there are still shortcomings in the content, process, and means of practical activities. This article analyzes the paths to enhance the motivation and participation of higher education students in practical education. The reasons for the low motivation and involvement of students in practical activities are that students' participation and goals are not clear, practical activities pursue quantity rather than quality, and the school society lacks effective guidance for college students' social practice. Based on this, this article proposes the following suggestions: stimulate interest to meet personal development needs, pursue the unity of quantity and quality of practical activities, and improve the encouragement mechanism of schools and groups.
Overview of the research status of the application of digital technology in commercial banks
Authors: Ge Yuehan
Published: December 2024

This paper mainly discusses the advantages and disadvantages brought by digital technology to the current commercial bank market. The purpose of the study is to analyze the specific application of digital technology in commercial banks and the prospect of future improvement. Through a specific analysis of the relevant studies on the promotion of production efficiency of commercial banks by digital technology, the relevant studies on the risk control role of digital technology, and the relevant studies on the marketing of digital technology, the conclusion is drawn that the application of digital technology in commercial banks has brought a lot of convenience, but it is also accompanied by technical failures, network security, data privacy, customer acceptance and other challenges. Banks need to take a multi-layered approach to these issues to ensure that digital technology can truly bring value to business development and customer service.
Exploring Organic Small-Molecule Solar Cells: From Fundamental Principles to Advanced Device Architectures
Authors: Wang Tianhao
Published: December 2024

Organic small-molecule solar cells (OSCs) have gained attention as strong contenders for next-generation photovoltaics, thanks to their distinct advantages like adjustable energy levels, flexible molecular design, and the potential for low-cost production. This paper reviews the crucial elements of OSC technology, with an emphasis on its operational mechanisms, material innovations, device architectures, and the latest advancements in research. The working principles, including exciton generation, charge separation, and collection mechanisms, are discussed in detail. Furthermore, the review explores the donor-acceptor materials, with an emphasis on how molecular engineering has optimized the efficiency and stability of OSCs. The comparison between single-layer and bilayer device structures is analyzed, highlighting their respective advantages and limitations in terms of efficiency, stability, and recombination losses. Finally, the latest breakthroughs in OSCs are presented, underscoring the significant progress in power conversion efficiencies and long-term operational stability. This paper seeks to offer an in-depth analysis of the current advancements and future potential of small-molecule organic solar cells.
Research on the modernization of grassroots emergency management capacity ——Based on the empirical study of the explosion of Jiayi Chemical Plant on 3.21 day
Authors: Wu Wenya , Bian Ruose , Li Nan
Published: December 2024

In order to reduce the harm of emergencies and safeguarding the safety of people's lives and property, this paper focus on the modernization of grassroots emergency management capabilities, so as to improve the ability of grassroots units to deal with emergencies. In the research process, the literature research method and case analysis method were used to analyze and summarize the development process and current situation of grassroots emergency management.
Predicting COVID-19 Mortality Rates and Identifying High-Risk Areas Using Machine Learning Models
Authors: Ding Guanlin , Zheng Jingyang
Published: December 2024

The COVID-19 pandemic has wreaked havoc on global society, exerting unprecedented strain on national healthcare systems. This public health catastrophe has compelled governments and communities to reassess their preparedness for health crises. As of April 2024, the virus has reached 181 countries, with more than 120,000 confirmed cases reported. Research employing various machine learning models—including random forest, LASSO, and multiple regression—has been conducted to predict the mortality rate of the COVID-19 pandemic. These models consider multiple factors, such as the rate of confirmed cases, the number of hospital beds per thousand people, the duration of infection, the total vaccinated population, and the median age in each country. Initial findings indicate that the rate of confirmed cases, hospital bed availability, and the duration of infection are the most significant predictors of mortality rates. Based on these models, countries have been categorized into low- and high-risk groups. Consequently, the capacity of clinical facilities in terms of beds per thousand people can also be forecasted, enhancing readiness for potential future outbreaks. This proactive approach could offer critical insights for governmental and health organizations, potentially mitigating the impact of subsequent COVID-19 waves.
ESN Neural Network-Based Electrical Submersible Pump Model
Authors: Wang Chuan , Long Zhen
Published: December 2024

This paper introduces an Electrical Submersible Pump (ESP) model based on the Echo State Network (ESN) neural network. ESPs are commonly used in oil extraction, and their performance is influenced by a variety of factors, making accurate modeling crucial for optimizing operations and maintenance. ESN is a type of recurrent neural network with unique dynamic memory capabilities, making it suitable for handling time-series data and modeling nonlinear dynamic systems. The article details the ESN network structure and its application in the ESP model, including how to use ESN to predict performance parameters and diagnose faults in ESPs. Compared with traditional models, the ESN model shows significant advantages in prediction accuracy and computational efficiency. The results indicate that the ESN-based ESP model can effectively simulate the dynamic behavior of ESPs, providing a powerful tool for real-time monitoring and fault prevention of ESPs.
Research on the Business Model Challenges for the Media Industry in the New Era
Authors: Liu Junjie
Published: December 2024

This paper examines the business model challenges faced by the media industry under the influence of technological advancements, aiming to explore the issues and opportunities encountered during the transition from traditional media to digital and intelligent media. By analyzing the development of the media industry through various conventional media, digital transformation, "Internet Plus," and intelligent media, the paper reveals the profound impact of technological progress on the media industry and investigates the evolution of media formats and shifts in business models. The main research content focuses on how advanced technologies such as cloud computing, big data, and artificial intelligence have driven the transformation and upgrading of traditional industries, especially in sectors like manufacturing, agriculture, and services. The paper also highlights the rise of personalized content recommendation and online-to-offline (O2O) business models, exploring new business models emerging in intelligent media related to content production, distribution, and user engagement. The conclusion indicates that technology is the core driving force behind the evolution of the media industry. With the continuous development of technologies such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI), user experiences and content distribution methods in the media industry will be further revolutionized. However, as opportunities arise from technological advancements, challenges related to data privacy and security are also emerging. In the future, the media industry will face the challenge of balancing technological innovation with regulatory oversight to ensure sustainable development.
Insurance Fraud Prediction Model Based on eXtreme Gradient Boosting
Authors: Lu Mengda
Published: December 2024

As the volume of insurance transactions continues to increase, combating insurance fraud has become increasingly important. One of the key challenges is how to predict fraud based on high-dimensional data. In the current research landscape, deep learning methods that perform well often require large amounts of data, but their training and deployment also pose significant computational challenges. This paper, based on the Alibaba Tianchi dataset, explores the feasibility of constructing a low-cost prediction system through feature engineering and parameter tuning. Additionally, this paper verifies the robustness of the eXtreme Gradient Boosting (XGBoost) model in handling high-dimensional sparse features and imbalanced data.
Application Of Ai-Assisted Medical Imaging
Authors: Song Jialin
Published: December 2024

Artificial Intelligence (AI) has rapidly gained widespread attention and has made unprecedented strides in recent years, significantly influencing various sectors, especially the medical field. Its applications have revolutionized healthcare by assisting doctors in making faster, more accurate, and data-driven decisions. This paper aims to provide an in-depth analysis of AI's role in medical imaging, focusing on its applications in interpreting X-rays, CT scans, and MRIs. These imaging techniques utilize cutting-edge image recognition algorithms, with Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) being the most prominent. AI not only improves diagnostic precision but also reduces human error, leading to better patient outcomes. Furthermore, the paper discusses current trends in AI-driven medical imaging and explores potential future directions, emphasizing how AI could continue to advance medical diagnostics and treatment planning, paving the way for more personalized and effective healthcare solutions.
Research on Supply Chain Management of Fresh Produce Under the Blue Ocean Strategy
Authors: Wang Rui
Published: December 2024

As competition among enterprises intensifies, it is imperative for businesses to utilize the Blue Ocean Strategy tools to prepare alternative strategic actions for exploring new market spaces, thereby maintaining long-term competitive advantages. Nueva Pescanova enhances consumer trust and confidence in its products using the tamper-proofing, blockchain-based solutions. It also addresses the Global Dialogue on Seafood Traceability (GDST) standards using the IBM Food Trust platform. This paper employs the Blue Ocean Strategy to analyze Nueva Pescanova’s approaches, suggesting that Nueva Pescanova focuses on customer needs and adopts rule redefinition and value innovation to uncover new market opportunities.
Research on the Correction Model for Measuring the Rheology of Oil-Based Drilling Fluids in Straight Pipes Under Pulsating Flow
Authors: Yang Hai , Wei Zeqiang
Published: December 2024

The currently common method for the automated real-time accurate measurement of drilling fluid rheology is the straight pipe measurement. However, the pulsating flow generated by the pumping equipment often leads to significant data errors in pressure difference and flow measurement, resulting in low accuracy of the calculated rheological parameters. To address the issue of data errors caused by pulsating flow, this paper proposes a correction model for straight pipe drilling fluid rheology measurement under pulsating conditions. Through research and theoretical analysis of the pulsating flow produced by diaphragm pumps, we derived the variation laws of outlet fluid flow rate, pressure, and diaphragm pump operating frequency, resulting in an expression for outlet flow velocity, which provides initial parameters for subsequent simulations. We then conducted 3D modeling and simulations of the measurement pipe in the actual experimental setup, obtaining a correction model for fluid rheology measurement under the influence of pulsating flow based on numerous simulation results. Finally, comparative experiments were conducted on a constructed experimental platform, comparing the results at different flow rates, viscosities, and temperatures against a rotating viscometer as the standard. The average deviations for the corrected rheological parameters were found to be 2.69% for apparent viscosity (AV), 2.362% for plastic viscosity (PV), and 3.056% for yield point (YP), effectively reducing the data errors in pressure difference and flow measurement caused by pulsating flow.
Off-grid Automobile Wireless Charging System Based on Photovoltaic Energy Storage
Authors: Zi Yifan
Published: December 2024

With the development and popularization of new energy vehicles, more and more electric vehicles have entered the public's vision, and gradually replaced the traditional fuel vehicles to become a good partner for people to travel. However, electric vehicles have problems such as slow charging process, short driving range, insufficient number of charging piles, and the energy structure of municipal power dominated by thermal power also restricts the emission reduction ability of electric vehicles. The establishment of off-grid optical storage wireless charging system will greatly alleviate this situation. The system uses photovoltaic as a source of all energy, which can be distributed in the open space on both sides of the highway and municipal roads, and the charging base is arranged in the safety zone on both sides of the road or at the traffic light intersection. Its energy storage facilities can ensure the stability of the photovoltaic system power supply and meet the needs of car charging. By optimizing the energy storage battery capacity, photovoltaic panel layout and number, inverter and wireless charging system circuit structure will greatly improve the efficiency and reliability of the charging system, making it possible to use on a large scale.
Research on the Business Management and Operation Models of Mainstream Multinational Companies: The Case Analysis Based on Apple and Huawei
Authors: Xu Junjie
Published: December 2024

This paper examines the business management and operational models of two global technology giants, Apple and Huawei. By analyzing Apple’s Directly Responsible Individual (DRI) management system and Huawei’s Integrated Product Development (IPD) model, the study highlights how these companies’ foster innovation, improve operational efficiency, and adapt to market changes. Apple’s DRI system centralizes decision-making authority and emphasizes individual accountability, promoting rapid innovation through expert leadership. In contrast, Huawei’s IPD model focuses on cross-functional collaboration, enabling a fast response to market demands and fostering structured product development. The comparison provides insights into how different management models contribute to corporate success in the technology industry. The study aims to offer valuable lessons for multinational companies seeking to enhance competitiveness through innovative management strategies, particularly in fast-evolving market environments.
Research on the Upgrading of Industrial Structure in Developing Countries
Authors: Shao Yudong
Published: December 2024

This article primarily explores how developing countries can achieve the dual economic development goals of fairness and efficiency through the upgrading of industrial structure in the context of globalization. The upgrading of industrial structure is key to enhancing competitiveness and promoting sustainable development. The article reviews the concept, theories, and current research status of industrial structure upgrading both domestically and internationally. By comparing and analyzing successful and unsuccessful cases, it reveals the general patterns and characteristics. The article mainly discusses the balance strategies and efficiency challenges in the process of industrial structure upgrading, proposing strategies such as strengthening technological innovation, optimizing resource allocation, promoting industrial chain collaboration, and enhancing international cooperation. These suggestions aim to help developing countries plan their upgrading paths scientifically, overcome obstacles, and seize opportunities. The upgrading of industrial structure is crucial for the sustainable economic development of developing countries, and it points out future research directions to deepen research in this field and provide more theoretical and practical guidance for developing countries.
The Impact of Covid-19 Pandemic on the Incidence of Tuberculosis
Authors: Lin Yanlin
Published: December 2024

During the outbreak of Covid-19, data showed that the mortality and infection rates of tuberculosis (TB) rose for the first time in nearly a decade. Through the registration data of tuberculosis before and after the COVID-19 outbreak in the United States, this article will predict and assess whether the novel coronavirus will have some impact on the prevalence of tuberculosis, and whether the impact of tuberculosis will increase as the pandemic progresses. This will aid government to take certain measures to approach tuberculosis during the outbreak. Through ARIMA modeling and prediction of TB case numbers with STATA, this study concluded that COVID-19 increases the prevalence of TB, and with the pandemic progressing, the more severe the situation of COVID-19 is, the more cases of TB there are. The innovation of this article is to use STATA software to analyze the impact of novel coronavirus on tuberculosis and offer suggestions for reducing the prevalence of tuberculosis amidst the epidemic. Residents should wear masks more often to prevent airborne transmission of Mycobacterium tuberculosis. Meanwhile, government should improve the identification and diagnosis of mycobacterium tuberculosis medical technology to reduce the incidence rate from the source.
Research and Analysis of the Impact of Significant Genes on Lung Cancer in the Context of Global Exacerbation
Authors: Sun Shuo
Published: December 2024

Lung cancer ranks among the most prevalent and severe cancers globally. This study investigates the correlation between specific genes and lung cancer and aims to develop a gene expression-based model for predicting lung cancer. Machine learning analysis of gene expression data from 551 individuals revealed that genes such as CLDN18, GKN2, LYVE-1, GPIHBP1, and CLIC5 play significant roles in the onset and progression of lung cancer. The mechanisms of action of these genes were analyzed to understand their potential in the diagnosis and treatment of lung cancer. Additionally, a comprehensive prediction model was constructed, achieving an accuracy of 99.2%. Genes such as CLDN18, LYVE-1, and GPIHBP1 were found to be upregulated in lung cancer tissues, suggesting their potential as therapeutic targets. A thorough review of literature concerning these key genes and their impacts on humans was also conducted to further validate the study's accuracy and practicality. This research contributes to new approaches in the early detection and treatment of lung cancer, offering valuable insights and serving as a reference for future studies.
Lack of Parvalbumin leading to autism behaviors in mice
Authors: Sun Yiru
Published: December 2024

This literature review examines the role of parvalbumin (PV)-expressing interneurons in the development of autism spectrum disorder (ASD), emphasizing the critical impact of PV deficits on the balance between excitatory and inhibitory (E/I) neural transmissions. PV neurons play a key role in maintaining this balance, and their deficit leads to increased inhibitory synaptic transmission and disrupts the E/I equilibrium. This disruption is linked to core ASD-like behaviors in mouse models, including social interaction deficits, communication impairments, and altered sensory processing. These findings suggest that targeting PV expression may offer a potential therapeutic approach for mitigating ASD symptoms.
Online Prediction of Tool Life Based on Multi-source Data Fusion
Authors: He Ling , Xu Yun , Li Qi
Published: December 2024

To address the challenges of difficult data acquisition and low prediction accuracy in tool life prediction, a method for data collection from FANUC numerical control machine tools and prediction of tool life based on the SSA-BP neural network is proposed. Data such as tool running time, spindle load rate, spindle load, X-axis load rate, Y-axis load rate, Z-axis load rate, X-axis current, Y-axis current, and Z-axis current are collected. By optimizing the core parameters of the BP neural network with the SSA algorithm, a tool life prediction model is constructed. Compared with traditional BP neural networks and GWO-BP neural networks, experimental results show that this model has the closest tool life prediction values to the actual values and better network stability, making it more suitable for tool life prediction.
The Rise of Genderless Clothing and Its Impact on Gender Relations
Authors: Lin Zixuan , Wu Zixuan
Published: December 2024

The concept of genderlessism, which rejects the traditional gender consciousness associated with clothing, has led to a shift in the way clothing is perceived. The design process for clothing is no longer constrained by traditional gender norms, resulting in the emergence of genderless styles that challenge traditional gender roles. This paper takes the gender consciousness of fashion design as its point of departure and traces the genesis of genderless fashion design, analyses the evolution of the masculinisation of women’s clothing, the feminisation of men’s clothing and the blurring of gender lines in the process of genderlessness. It presents a case study approach to examine the reasons for the popularity of genderless fashion and its characteristics in terms of the influences of factors such as lifestyles, feminism and local cultures. It also analyses the characteristics of genderless fashion design in the East and the West. By examining the historical characteristics of genderless clothing, the influence of genderless style on public dress and social aesthetics, and the development of genderless clothing, it can be concluded that societal tolerance of all gender groups is increasing. Furthermore, the advent of gender-free clothing enables designers to shift their focus from the conventional dichotomy of gender to a more holistic approach that encompasses the human experience. This shift in perspective allows for a diversification of social group aesthetics, challenging traditional perceptions of gender and paving the way for a more inclusive and equitable society.
An analysis on the Relationship between Diversified Product Lines and Brand Loyalty in the Ice Cream Industry —— Taking Dairy Queen Ice Cream China Company as an Example
Authors: Chen Yanyan , Qian Haotian , Zhu Yu
Published: December 2024

This article takes Dairy Queen (DQ) Ice Cream China Company as a highly representative sample, cleverly using a combination of case analysis and empirical research to explore the relationship between diversified product lines and brand loyalty in the ice cream industry in depth and comprehensively. Firstly, the paper elaborated on the development background of the ice cream industry in detail from the long river of history, the development of ice cream has a long history, evolving from the ancient Western legend period and constantly undergoing changes over the long years. However DQ ice cream currently faces many severe challenges. At the market level, the phenomenon of store closures is often seen, which reflects the difficulties they face in the fierce market competition. Competitors are emerging like mushrooms after rain, putting DQ under enormous external pressure. After in-depth analysis of industry trends and competitive landscape, it is clearly found that DQ has problems in multiple key areas and is under tremendous competitive pressure. In terms of product quality, DO can no longer fully meet the increasingly high quality requirements of consumers. In the service process, the efficiency and quality of its services need to be improved.
The Application and Research of Flight Scheduling Strategies
Authors: Ni Ruowei
Published: December 2024

The rapid development of the aviation industry has increased the complexity and importance of flight scheduling. This paper studies scheduling policies from both temporal and spatial perspectives. Three standard scheduling policies, First Come First Serve policy (FCFS), Small Job First policy (SJF), and Shortest Remaining Processing Time policy (SRPT), are introduced in a modularized form. In this paper, we explore the roles of the three policies in dealing with different scenarios in the airport scheduling process: FCFS prioritizes task arrival time for normal scheduling, SJF prioritizes task execution time for runway scheduling under the pressure of high throughput and SRPT prioritizes the remaining task execution time to cope with delays promptly. The dynamic system of flight scheduling should be able to cope with a variety of possible situations, combine various modules efficiently and improve them to solve the problems. The research in this paper can provide reference ideas for the design of subsequent dispatch models.
Skin Repair based on Traditional Chinese Medicine Combined with Dressing Therapy: Application and Mechanism
Authors: Xiao Keqin
Published: December 2024

This paper aims to examine the evolution of traditional Chinese and Western medicine dressings in wound treatment, highlighting the promising effects and extensive potential of newly developed dressings that integrate the strengths of both approaches. Our research indicates that conventional Western medicine dressings often exhibit inadequate efficacy and necessitate cumbersome replacements, leading to significant discomfort for patients and imposing unnecessary burdens on healthcare professionals. Conversely, innovative high-tech Western dressings demonstrate superior performance and ease of maintenance, gradually making their way into clinical practice. Traditional Chinese medicine boasts a wealth of historical prescriptions for wound treatment, with specific formulations demonstrating remarkable efficacy in promoting wound healing. Investigations into the underlying mechanisms suggest that these effects may be linked to the modulation of the Wnt/β-catenin signaling pathway in wound cells, contributing positively to therapeutic outcomes. Thus, the synergistic integration of these methodologies holds great promise for advancing wound recovery strategies. Current studies have substantiated that the combined approach of traditional Chinese and Western medicine yields more effective treatment results. With the ongoing advancements in artificial intelligence, materials science, and modern practices in traditional Chinese medicine, the future of wound management through integrated dressings is poised for significant enhancement.
An Improved Two-Point Magnetic Target Localization Method
Authors: Wu Kun
Published: December 2024

This study addresses the existing limitations of methods for determining the location of magnetic targets. Traditional methods either require geomagnetic background field information or fail to obtain stable solutions in specific cases due to matrix inversion when geomagnetic background field is not required. In order to solve this problem, this paper first adopts a new sensor array, then rewrite the equation of its method, and then uses the least squares method (LMS) to solve the magnetic target position, solve the possible instability problem in the process of single point solution and finally achieve the magnetic target positioning. Simulation results show that the method can accurately and uniquely determine the position of a magnetic dipole in the presence of a geomagnetic field, while experimental results verify the superiority and practicality of the method.
Analysis of Development Strategy of Building Decoration Industry under the Background of Downward Development of Real Estate Economy
Authors: Pan Lu
Published: December 2024

This article explores in depth the development strategy of the building decoration industry under the background of the downturn in the real estate economy. Faced with challenges such as reduced demand, intensified competition, and rising costs brought about by the adjustment of the real estate market, the construction and decoration industry urgently need to formulate and implement effective development strategies to cope with current difficulties and seek long-term development. This article first analyzes the current situation of the industry and points out the specific impact of the downturn in the real estate economy on the industry. Then, this article elaborates in detail on the development strategies that the construction and decoration industry should adopt during the downturn in the real estate economy. Finally, conclude and provide recommendations as well as prospects. With the advancement of national policies and the upgrading of consumer demand, the building decoration industry will usher in new development opportunities and continue to move towards green environmental protection, intelligence, and personalization.
The Application of Artificial Intelligence in Classroom: Advantages, Challenges, and Suggestions
Authors: Pan Yushi
Published: December 2024

The application of artificial intelligence (AI) in education has received widespread attention, especially in personalized learning and teaching management, where it has shown significant potential. However, current research still has limitations, especially in addressing the ethical and privacy challenges of AI technology and the decline in students' self-discipline. This paper analyzes the application of AI in the classroom and explores its positive impacts on enhancing teaching efficiency, personalized learning, enhancing teaching tools, and supporting special education. The study found that although AI has significant advantages, its widespread use has also led to problems such as technological dependence, academic dishonesty, privacy breaches, and moral bias. Based on this, the paper suggests that AI literacy and critical thinking should be cultivated when introducing AI technology in the classroom to ensure the rational use of technology. Additionally, clear ethical norms and privacy protection strategies should be established to balance the relationship between technological progress and educational ethics, thereby driving the comprehensive improvement of educational quality.
On A Novel Neutrosophic Numerical Method for Solving Some Neutrosophic Boundary Value Problems
Authors:   Department of Mathematics, Faculty of Science, Jadara University, P.O. Box 733, Irbid 21110, Jordan, Ahmed Ahmed
Published: December 2024

In this paper, we study a novel numerical method for finding the neutrosophic numerical solutions to some neutrosophic boundary values problems in differential equations of high orders. The proposed method based on neutrosophic numerical collocations of higher degree polynomials as an approximation to solve the problems. In addition, we provide many mathematical proofs about the existence of the solutions with many different examples and numerical tables that clarify the validity of the proposed method.
Neutrosophic Approaches to Soliton Solutions for Nonlinear Time-Fractional Coupled Jaulent–Miodek System Using a Modified Laplace Adomian Dec omposition Method
Authors:   Department of Mathematics, Jordan University of Science and Technology, Irbid 22110, Jordan, Kamel Kamel ,   Department of Mathematics, Al-Gunfudah University College, Umm Al-Qura University, Mecca 21955, Saudi Arabia,   Department of Material Engineering - College of Engineering, Shatrah University Thi-Qar 64001 - Iraq,   Department of Mathematics-Faculty of Science, Philadelphia University, Amman 19392- Jordan,   Department of Mathematics-Faculty of Science, Philadelphia University, Amman 19392- Jordan, Almalki Adel , Ajeel Mahmood Shareef , Abu-Shams Azza I. Abu , El-bashabsheh Sajeda El
Published: December 2024

This paper presents a modified Laplace Adomian decomposition method (MLADM) to solve the nonlinear time-fractional coupled Jaulent–Miodek system. The proposed approach provides convergent series solutions with easily computable components, demonstrating both accuracy and simplicity in its application. By employing the Caputo fractional derivative, this study establishes a robust framework for analyzing nonlinear behavior in fractional differential equations. The effectiveness of the method is validated through comparisons with previous studies, with results illustrated using graphical representations. The solutions proposed herein are significant for modeling complex and dynamic real-world phenomena across various scientific disciplines. All computations and graphical results were carried out using Mathematica, emphasizing the method’s reliability, precision, and ease of application to nonlinear fractional systems. The study of fractional nonlinear systems is crucial for modeling complex, dynamic, and uncertain processes, which are core aspects of neutrosophic science. By addressing the intricate behavior of the nonlinear time-fractional coupled Jaulent–Miodek system, this work advances mathematical models that encapsulate uncertainty, indeterminacy, and complex interactions. Such an alignment with the principles of neutrosophic science underscores the relevance of our approach to the objectives of the International Journal of Neutrosophic Science, highlighting its potential to enhance the understanding and practical applications of complex systems.