bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of blockchain’s consensus algorithms Comparative Analysis and Future Directions of Blockchain Consensus Mechanisms
Authors: Yang Boyuan
Published: December 2024

This paper provides a comprehensive review of blockchain consensus algorithms, classifying them into public chains, consortium chains, private chains, and special chains. It explores the evolution of these algorithms, from the early Nakamoto Consensus in Bitcoin to advanced mechanisms. The review highlights the varying degrees of decentralization, security, and scalability among these algorithms, addressing their advantages, limitations, and the ongoing research aimed at overcoming existing challenges. By comparing traditional and novel consensus mechanisms, this paper aims to provide a valuable reference for both new and seasoned researchers, fostering further innovation in the development of efficient and robust blockchain systems.
Cognitive Issues and Countermeasures of Lacquerware Product Users
Authors: Zhang Wenxin
Published: December 2024

This article aims to explore the current situation, existing problems, and corresponding optimization strategies of user cognition of lacquerware products. By analyzing the characteristics of lacquerware products, the main problems of insufficient public awareness of lacquerware products, deviation in price and cost-effectiveness perception, and disconnection between functionality and modern life are revealed. Propose optimization measures to address these issues, aiming to enhance public awareness of lacquerware products and promote the healthy development of the lacquerware market. Research has shown that by comprehensively applying these strategies, users' interest and acceptance of lacquerware products can be effectively enhanced, providing strong support for the inheritance and innovative development of the lacquerware industry.
International Media Analysis: Critical Analysis Comparing News Reports in Listed West/South Digital Media
Authors: Zhang Jianan
Published: December 2024

This paper delves into the narrative perspectives of Western and Southern media in news reporting on four themes: war, culture, politics, and Olympic sports. The study reveals that Western media (such as BBC and CNN) tend to adopt narratives that align with the Global News Flow theory and the framework of cultural imperialism, emphasizing individual suffering, the consequences of political and military actions, and the global influence of Western values and innovations. In contrast, Southern media (such as CGTN, Al Jazeera, and WION) actively challenge the dominance of Western mainstream narratives by focusing on local perspectives and realities, employing de-Westernized news narratives. They seek to alter the narrative structure of international news and highlight the development achievements and contributions of Southern countries in culture, sports, and other fields. These counter-narratives align with post-colonial journalism theory, offering potential pathways for Southern countries to gain more voice and challenge Western hegemony in the international news domain. This study contributes to uncovering power dynamics in global news reporting and provides insights for addressing cultural hegemony.
A Research on The Economic Consequences of The Implementation of Dual-Class Share (DCS) Structure: A Case Study Based on Kuaishou Technology
Authors: Feng Xueyao
Published: December 2024

Following the abolition of the dual-class share structure in 1989, the Hong Kong Exchanges and Clearing Limited re-launched the "Weighted Voting Right" mechanism. In February 2021, Kuaishou Technology listed in Hong Kong with a dual-class share structure, which has been regarded as the initial public offering of “the first stock of short video” drew the attention of many investors. Particularly, numerous international scholars have examined the performance of the dual-class share structure. This paper examines the economic consequences of the three dimensions based on the IPO of Kuaishou Technology. Taken together, by adopting a dual-class share structure, managers are more likely to face lower unsystematic risk and firms are less likely to face acquisition risks, as well as higher innovation performance and thus greater growth potential with a particular emphasis on operation capacity and profitability, even if the performance is not obvious in the short term. The contribution of this paper is to provide practical experiences for other Internet companies with the aim of promoting dual-class share structure and corporate sustainability.
A Study on the Self-Presentation of Male Beauty Influencers on Douyin
Authors: Pan Yifei
Published: December 2024

With the continuous advancement of the internet and the evolution of mobile technology, the media landscape has experienced significant changes. Short video platforms have gained popularity due to their engaging content format and wide reach, leading to new directions in mobile social media. Beauty content on short video platforms aligns well with the current trend of "appearance value." Contemporary males exhibit characteristics different from those of previous eras, as male beauty influencers on platforms like Douyin use short videos to present diverse images and find new avenues for growth. The strong gender fluidity associated with the combination of men and beauty has entered the public eye frequently, and the self-presentation behaviors of male beauty influencers have garnered much public attention. This study aims to discuss concepts related to male beauty influencers and self-presentation, analyze the themes of male beauty influencer videos, and provide suggestions for their self-presentation, contributing to research in this area.
Convertible Bonds: A Supplementary Exit Path for Private Equity Funds
Authors: Huang Wenqian , Xiong Yangqi
Published: December 2024

With the rapid development of China's economy, private equity funds have become an important force to help unlisted small and medium-sized startups quickly become bigger and stronger. As an important financing channel for enterprises, private equity funds can provide a large amount of financial support for target enterprises, and in all aspects of their investment operations, the choice of exit method is particularly important as the core link that directly affects returns. Therefore, this paper takes the case study as the research method, and selects SoftBank Group's gradual reduction of Alibaba shares through the issuance of convertible trust bonds as a case study, and studies the use of convertible bonds as a supplement to the IPO exit method of private equity funds. The study finds that convertible bonds, as a supplement to the IPO exit method, have more efficient, lower risk and low cost financing capabilities, effectively solve the problem of single exit path, improve the speed of private equity fund exit liquidation, and reduce the loss range while retaining the profit growth space.
Exploration on the Construction and Practical Teaching of Cultural Industry Management Major Based on AIGC Technology
Authors: Dong Shu
Published: December 2024

With the rapid development of AIGC (Artificial Intelligence Generated Content) technology, its impact on various fields is becoming increasingly significant. This paper focuses on the field of cultural industry management and explores how AIGC technology can be integrated into the curriculum construction and practical teaching of this major. By analyzing the characteristics and advantages of AIGC technology, this paper outlines strategies for its application in updating the curriculum content of cultural industry management, innovating teaching methods, and strengthening practical teaching segments, and proposes corresponding implementation paths and support measures to enhance the quality of teaching in the field of cultural industry management and cultivate high-quality professional talents who are capable of meeting the needs of cultural industry development in the new era.
Application of Single Chip Microcomputer in Smart Home Products
Authors: Lu Xiukang , Zhu Junnan , Long Taitang , Xiong Jingbo
Published: December 2024

This article explores the application of Single Chip Microcomputers (SCMs) in smart home products. SCMs, compact computing devices integrating a processor, memory, and I/O peripherals, are ideal for IoT-enabled smart homes due to their real-time processing, energy efficiency, and affordability. Their core characteristics include low power consumption, real-time processing, compact size, affordability, versatility in communication, making them indispensable in smart home ecosystems. SCMs are widely used in smart lighting systems, thermostats, home security systems, smart appliances, and voice-controlled systems, enhancing user convenience, energy efficiency, and security. Technological enablers like IoT connectivity and sensor integration facilitate the integration of SCM-based smart devices. However, security concerns remain critical. Future trends include the integration of AI and Machine Learning and the adoption of 5G technology, enhancing the functionality and sophistication of smart home systems.
Influence of Financial Technology on City Carbon Emission — Based on Urbanization Mediation
Authors: Zhu Peiying
Published: December 2024

In the context of rapidly advancing digital technology, it is crucial to examine how the digital economy influences urban performance, particularly considering its intermediary role in the urbanization process. This paper specifically explores the impact of financial technology on urban CO₂ emissions using both a double fixed-mode approach and an intermediate effect model. The findings highlight two key points: first, the development of financial technology significantly affects a city's CO₂ emissions, and second, the degree of influence varies depending on the extent of financial technology adoption. This heterogeneity suggests that different cities experience different impacts based on their level of digital financial integration. Consequently, it is imperative to recognize and harness the role of digital finance in fostering a green and low-carbon economy. By leveraging digital finance, cities can implement more effective strategies for reducing carbon emissions and advancing towards becoming low-carbon cities. This underscores the importance of integrating digital financial tools and technologies in urban planning and environmental policies to promote sustainable urban development.
Case Analysis of Tencent’s Cross-Border Merger and Acquisition of Sumo Group
Authors: Huang Linghe
Published: December 2024

As the process of global economic integration continues to accelerate, more and more companies are beginning to implement global development strategies, and cross-border M&A are an important way to achieve it. This paper takes Tencent's cross-border M&A of Sumo Group as an example. It first introduces the two parties and the process of the M&A, then analyzes the motivations of the M&A from multiple aspects, and uses the event study method and financial indicator method to analyze the performance of the M&A. The research results show that the final value of the cumulative excess rate of return in the ten trading days before and after the M&A date on July 19, 2021 is positive, and multiple financial indicators have performed well in the long term, indicating that Tencent has achieved positive market performance and financial performance after the M&A. Based on the analysis and conclusion, this paper enriches the literature on cross-border M&A in the game industry, and also has certain implications for cross-border M&A of other game companies.
The Chinese Painting-style Animation Innovation in the AI Era
Authors: Hou Qunshan
Published: December 2024

In the era of artificial intelligence, Chinese painting-style animation is undergoing an unprecedented transformation. The application of AI technology not only improves the efficiency of animation production but also brings new possibilities for innovation to creators. The impact of the AI era on the animation industry is multi-dimensional. AI technology can automate many repetitive tasks, such as the generation of in-between frames, the drawing of characters and scenes, and its application in various other aspects, thereby shortening the production cycle, reducing actual time costs, and improving timeliness. Its detailed functions, such as AI-assisted scriptwriting and directing to generate creative storylines, plots, and even scripts, provide new ideas for animation creation. One of the most admired and sought-after aspects is the operability and personalization of AI. By analyzing audience preferences and behaviors through various cases and traffic analysis, it can provide personalized content recommendations using animation production techniques, meeting the psychological needs of different audience groups. In terms of image and video processing, it can be used to enhance the visual effects of animation, such as automatic coloring, background rendering, image clarity and blurring processing, etc. For the design of characters and scenes, designers can quickly generate concept drawings of characters and scenes through subjective expression, including composition positioning, character body dynamics, and simulating different styles and schools of painting techniques. In terms of sound, it is also excellent, capable of simulating specific sound effects, generating and improving dubbing sound effects and sound quality, enhancing the auditory experience of animation, and through analysis techniques such as voice recognition and natural language processing, allowing animated characters to use lifelike body movements and facial expressions to interact with the audience, providing a better interactive experience.
The Impact of Digital Construction on High-Quality Economic Development from The Perspective of New Quality Productivity
Authors: Wang Yonglong
Published: December 2024

This article explores the impact of digital construction on high-quality economic development from the perspective of new quality productivity. New quality productivity is an advanced form of productivity formed by the improvement of the quality of the constituent elements of productivity, emphasizing the role of new production factors such as knowledge, technology, and data.  Digital construction, as an important manifestation of new quality productivity, reshapes the driving force and structure of economic growth through the deep integration of information technology and various fields of the economy and society.The article analyzes the theoretical basis and empirical research of digital construction on high-quality economic development through literature review, and points out that digital construction can promote high-quality economic development through new quality productivity.Based on theoretical analysis and empirical research, the article constructs a model that includes the level of digital construction, new quality productivity, and high-quality economic development.Using panel data from 30 provinces in China from 2011 to 2022, conduct empirical testing through a fixed effects model.The results indicate that digital construction significantly promotes high-quality economic development and has a positive impact by improving the level of new quality productivity.New quality productivity plays a partial intermediary role between digital construction and high-quality economic development.Based on this, the article proposes policy recommendations such as strengthening technological innovation support, improving digital infrastructure construction, and cultivating digital talents, in order to promote the empowerment of new quality productivity by digital construction and achieve comprehensive and coordinated economic development. This study provides theoretical basis and practical guidance for understanding the deep impact of digital construction on high-quality economic development.
China's Carbon Emission Trading Mechanism: Realistic Challenges and Practical Pathways
Authors: Liu Shuhan , Tang Wenjie , Zhang Junhao
Published: December 2024

Carbon emissions trading serves as a pivotal mechanism for carbon reduction and significantly contributes to the realization of China's dual-carbon objectives. China will implement a nationwide carbon trading market from 2021, but it is still in the early stage of development, and there are still a lot of imperfections. Through an analysis of the current status of the development of China's carbon emissions trading mechanism, this study identifies several challenges within the domestic carbon emissions trading market mechanism, including the "overly singular allocation of quotas, lack of market activity, and difficulties in supervision and verification." In order to overcome these obstacles, this paper proposes a series of recommendations, including gradually increasing the proportion of paid auction quotas, accelerating the expansion of carbon market participants, and applying digital technologies for carbon emissions monitoring and data collection. This paper then put forward suggestions such as gradually expanding the proportion of allowances to be used in the auction, accelerating the expansion of carbon market participants, and applying digital technology to the monitoring of carbon emissions and data collection, with a view to providing certain theoretical support for the future optimisation and research of China's carbon emissions trading market.
Applications and Challenges of AI Chips in Autonomous Driving Technology
Authors: Huang Tianhao
Published: December 2024

The rapid development of autonomous driving technology puts great demands on high-performance computing, and AI chips play a crucial role in it due to their efficient parallel computing capability. The aim of this review is to explore the application of AI chips in different layers of the autonomous driving system, including the perception layer, the decision-making layer, and the execution layer, and to analyse their key technical issues in terms of power consumption management, computational efficiency, scalability, reliability and security. In addition, this paper discusses the impact of the manufacturing process on the performance of AI chips, as well as its future development trends and challenges.
Promoting Sustainable Investment Through Designing and Pricing Green Financial Derivatives
Authors: Li Yuexin
Published: December 2024

As climate change and environmental degradation escalate, the financial sector faces growing pressure to adopt sustainable practices. This paper examines designing and pricing of green financial derivatives and their role in promoting sustainable investment. Through references review and case studies, the study assesses the influence of regulatory frameworks like the EU's EIA Directive on the environmental integrity of green derivatives. The findings emphasize the crucial role of EIAs in ensuring that green financial products meet sustainability goals and highlight the increasing importance of ESG derivatives. However, the effectiveness of these derivatives is hindered by challenges such as their synthetic nature, lack of direct ownership of underlying assets, and complexities in ESG reporting. The study also identifies the need for traditional pricing models to evolve by incorporating sustainability factors, with advanced data analytics and technologies. In conclusion, robust regulatory frameworks, effective market incentives, and improved investor education are essential for promoting sustainable investment. Additionally, it is significant for public-private collaboration to scale sustainable finance initiatives and the integration of ESG criteria into financial decision-making to achieve both financial and sustainability objectives.
The Current Situation and Trend of Sino-US Trade Development in the Context of Economic Globalization
Authors: Chen Shu
Published: December 2024

The evolution of trade and commercial relations between China and the US is particularly noteworthy, given the significant impact of economic globalization and its role as a fundamental pillar of global economic stability and progress. The trade volume between China and the US is steadily increasing, and this is happening at the same time as the trade structure is changing significantly. The process of commerce between China and the US is not without its difficulties, though, as seen by the US's gradual growth in trade protectionism, the frequency of trade disputes, and the imbalance in trade relations. This paper provides an in-depth analysis of the dynamic evolution trend of China-US trade in the context of economic globalization and discusses the current status and challenges of China-US trade relations, as well as the future trend based on global economic, political, and technological development trends. In addition, the paper forecasts how China-US trade relations will develop in the future in light of new technological advancements, policy adjustments, and shifts in the global economic landscape. It also offers policy recommendations and strategic adjustment guidelines to encourage collaboration between the two sides and produce win-win outcomes.
Predictive Modeling of Personal Medical Insurance Costs: Analyzing Key Factors and Interactions
Authors: Wang Xuerui , Tuo Siwei
Published: December 2024

This study aims to use Kaggle's dataset to predict personal medical insurance costs, including age, sex, BMI, number of children, smoking status and regional variables. Through comprehensive statistical analysis and model improvement, we have determined important forecasting factors and interactions that affect charges. It is mainly found to include non-linear relationships between age and medical expenses, as well as the significant impact of interaction between BMI and smoking. The enhanced regression model combines these interactions and non-linear effects, showing great improvements in terms of interpretation capabilities, the R^2 value is 0.9642. The results provide actionable insights for insurance policy formulation, health management programs, and risk assessment, demonstrating the importance of considering complex variable interactions in predicting medical expenses. Future research should continue exploring these relationships to further refine predictive models.
Analysis of JD Logistics Acquisition of Deppon Express
Authors: Zhang Xinjuan
Published: December 2024

With the rapid development of the Internet economy, logistics has fully penetrated all aspects of human beings’ lives, and many logistics companies occupy the logistics market. To cope with market changes and for other reasons, many companies choose the way of acquisition. Since 2014, China's logistics enterprises have mushroomed, forcing enterprises to restructure and acquire. The policy environment created by the government also encourages enterprises to make acquisitions to achieve the optimal allocation of market resources. This study will take the acquisition of Deppon Express by JD Logistics as an example, describe the entire acquisition event, and analyze whether the acquisition is successful by combining the existing triple risks and corporate performance. Based on the research content, it is found that the acquisition is beneficial and brings broad development prospects to the logistics market, but the acquisition also brings risks, and JD Logistics faces the pressure of accounts receivable in the performance of the company. Therefore, enterprises should evaluate the risks in advance and make reasonable decisions.
Research on Paths to Enhance the Quality of Cultural, Sports, and Tourism Consumption Scenarios in Liaoning
Authors: Wang Nan
Published: December 2024

With the continuous improvement of the socio-economic development level, people's living standards have been ensured, leading to a greater focus on spiritual satisfaction. This shift provides new opportunities for the tourism industry. During the process of driving economic growth and social development, the integration of culture, sports, and tourism industries has emerged as an important force. The development of these integrated industries, as a new model of industry integration in China, has been found to have a high degree of industry fit through systematic practical application. This integration not only enables effective resource consolidation and optimal allocation but also creates more attractive and competitive products and services to meet diverse consumer demands. Analyzing from the consumer perspective, the integration of culture, sports, and tourism can further stimulate consumers' potential, and how to leverage this integration to enhance consumption levels has become an urgent issue for relevant departments and enterprises. This paper comprehensively analyzes the importance of improving the quality of cultural, sports, and tourism consumption scenarios, proposes solutions to problems encountered during development, and thus promotes high-quality development of the industry.
Digital Transformation and Business Model Innovation: Navigating Opportunities and Challenges
Authors: Cai Zhuolin
Published: December 2024

Organizations' operations and market competition are being transformed by digital transformation, which is increasingly acknowledged as a critical factor in business model innovation. This paper investigates the influence of digital transformation on business model innovation, emphasizing the opportunities and obstacles it presents. The research employs a comprehensive literature review to identify key themes and insights, synthesizing findings from ten academic publications. The research indicates that digital transformation promotes innovation and creativity, enhances consumer experiences, improves operational efficiency, and opens up new revenue streams. Nevertheless, it also presents obstacles, including cultural resistance, data security concerns, technological integration issues, and skills deficits. The paper offers strategic recommendations for organizations to effectively navigate these challenges by analyzing successful case studies. Furthermore, it identifies research deficits and recommends future research directions to enhance comprehension and utilization of digital transformation. The findings emphasize the significance of seamless technological integration, continuous employee training, comprehensive data security measures, strong leadership, and a clear digital strategy in the pursuit of successful digital transformation.
Tesla's Acquisition of SolarCity: Analysis of Impact on Electric Vehicle Technology in New Energy Enterprises
Authors: Zhang Xin
Published: December 2024

With the risen of the electric vehicle industry, the requirement of the market for electric vehicle technology grows as well. The development of new energy plays an important role in electric vehicle technology development at present. As an influential company in electric vehicle and energy development, Tesla’s action of acquiring SolarCity, which is the largest rooftop solar panel installation company in the US, attracted a large amount of attention in the field of new energy and is typical for analyzing the development of new energy and electric vehicle industry. This study takes the acquisition case as an example, based on the literature review method and empirical research method, to verify Tesla's overall ability improvement after acquiring SolarCity. Analyzing the acquisition case can show the positive impact of the acquisition behavior by new energy enterprises on the development of electric vehicle technology and provide effective guidance for the future acquisition behavior of new energy enterprises.
Gender Equality: Analysis of the Current Status and Suggestions for Future Development
Authors: Zhang Xiaohan
Published: December 2024

This study centers on the current situation and future direction of gender equality. In the backdrop of global modernization, although women have extensively participated in social production and awakened to their rights consciousness, structural gender inequality remains deeply entrenched. Men still dominate social resources and decision-making processes. Traditional family concepts restrain women's freedom, and gender discrimination in the workplace remains prevalent. Despite the vigorous emergence feminist movements, improper thoughts and online public opinion have aggravated the confrontation between men and women, impeding the progress of gender equality. This study conducts a comparative analysis of the stances on gender equality at home and abroad, traces the historical roots of gender inequality from prehistoric society to capitalist society, and reveals the underlying social structure and economic foundation. A critical examination of the current gender equality ideology reveals issues such as an excessive emphasis on ideology while neglecting the realistic foundation, the pursuit of equality yet tacitly accepting male “privileges”, women's empowerment confined by the male-dominated framework, blurred boundaries between equality and consumerism, differentiated interests within feminism, and the confrontation between men and women. Based on this, this study proposes future strategies for promoting gender equality, emphasizing the need to transform the realistic foundation, enhance women's social value, increase women's access to social resources and voice, and strengthen social consensus and cooperation to drive the substantive development of the cause of gender equality.
Forecasting China's Consumer Price Index (CPI) Based on Combined ARIMA-LSTM Models
Authors: Liu Yu
Published: December 2024

This study aims to construct an efficient consumer price index (CPI) forecasting model to provide policymakers, investors, and businesses with more accurate forecasts of future price levels and inflation trends. in this study, a combined model that integrates autoregressive integrated moving average (ARIMA) with long short-term memory (LSTM) networks is introduced. The model first captures the linear trend of CPI data using the ARIMA model, and then inputs the residuals into the LSTM network to predict the nonlinear part. The model is trained and tested using monthly Chinese CPI data. The findings indicate that the ARIMA-LSTM hybrid model outshines the single ARIMA model regarding prediction accuracy, its predicted values are closely aligned with the actual values, and the model residual series passes the Q-test, which suggests that the model exhibits a strong fitting capability. The article also introduces the evaluation indexes of the model and compares the prediction performance of the single ARIMA model and the ARIMA-LSTM hybrid model. Finally, the article concludes that the ARIMA-LSTM hybrid model has high accuracy and reliability in CPI forecasting, which provides a powerful tool for forecasting future price trends.
A Cutting Edge Review of Cross-Cultural Marketing and Consumer Behaviour Analysis - A Data-Driven Strategy
Authors: Li Xinya
Published: December 2024

Cross-cultural marketing faces unprecedented challenges and opportunities in the context of globalisation and digitalisation. This paper provides an overview of key topics in cross-cultural marketing, discusses the importance of consumer behaviour analysis, and focuses on the application of data-driven marketing strategies in cross-cultural marketing. Through in-depth research and case studies, this paper reveals the key role of data in understanding consumer needs, optimising marketing strategies and improving market competitiveness, providing effective strategic advice and practical guidance for international companies.
The Enlightenment and Influence of Chinese Painting on Animation Art
Authors: Hou Qunshan
Published: December 2024

Chinese painting emphasizes "artistic conception" and "spiritual charm", and its expression is often implicit and poetic. This aesthetic concept, which focuses on emotional expression and spiritual connotation, has a profound influence on animation art. The application of Chinese painting has enriched the visual expressiveness of animation, enhanced emotional rendering and artistic conception, allowing animation works to display a unique Eastern aesthetic. The brush and ink techniques and line application in Chinese painting have been borrowed and innovated in animation, such as ink animation, which is directly inspired by Chinese painting techniques. By simulating the effects of ink painting, a unique animation style is created. It contains rich elements of traditional Chinese culture, such as myths and legends, historical stories, etc. The use of these elements in animation not only spreads Chinese traditional culture but also gives the animation works a profound cultural connotation. In modern animation creation, the elements and concepts of Chinese painting are continuously innovated and developed, constantly integrating modern technical means to present traditional Chinese painting elements in a new form, promoting the development of animation art. The enlightenment and influence of Chinese painting on animation art are all-round, enriching the artistic expression of animation and promoting cultural dissemination and artistic innovation.
Neutrosophic Sentiment Analysis Method Using Orange Data Analysis
Authors:   Regional Autonomous Community University of Los Andes Riobamba. Ecuador, Janneth Janneth ,   Regional Autonomous Communities University of Los Andes Ambato. Ecuador,   Regional Autonomous Communities University of the Andes Ibarra. Ecuador,   Universidad de la Habana, La Habana, Cuba,   Tashkent State University of Economics, Uzbekistan, Ortiz Monica Alexandra Salame , Posso Alipio Absalon Cadena , Rodriguez Joffre R. Paladines , Baxtiyerovna Bekbayeva Feruza
Published: December 2024

The present work tackles an urgent problem in the area of data analytics that is the shifting of sentiment against language in regards to human cognition. Although the science of data mining and machine learning has done much to address the problem of these tools, their scope is still limited regarding the management of human language which has inherent uncertainty and ambiguity. This research seeks to address this gap by illustrating how to apply a machine learning tool in a way that embraces the so-called uncertainty neutrality using the orange data analysis tool for analysis of visualized data. It is also important to note in the research that the combination of neutral and intelligent analysis with using applications such as orange increases the efficiency of sentiment classification and expands the theoretical scope of sentiment data analysis. Their findings underscore that this perspective seeks to illuminate details which other methods tend to ignore and hence offer a much more nuanced understanding of human cognition. Practically, this research presents an efficient paradigm as the new framework can be employed in market intelligence, evaluation of public policy and intelligent interface design, among others. As a result, this research does not only contribute to the body of knowledge within the profession of data science but also explores new dimensions in understanding human cognition.
Stem Cells and Regenerative Medicine: A Review of Artificial Intelligence Techniques for Stem Cell Differentiation
Authors:   Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA, Nima Nima ,   Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03, Hradec Králové, Czech Republic, Abdollahzadeh Benyamin
Published: December 2024

Following this background, this review discusses the advanced technologies such as AI, micro-fluids, and automated platforms that this differentiation protocol could help achieve in regenerative medicine. Stem cell research, essential in tissue engineering, disease modeling, and drug development, is challenging through heterogeneity, scalability, and reproducibility, as observed in differentiation procedures. Machine learning and deep learning patterns have become more effective in predicting cellular behavior, tracking cellular stations, and optimizing differentiation methods for current stem cell technology. These methods also reduce observer bias, increase the throughput of high-throughput screening, and advance modeling to precise therapeutic applications. At the same time, microfluidic and automated systems provide nearly perfect control over differentiation stimuli, recreating the in vivo conditions with the ability to control spatial and temporal gradients. This integration between AI and microengineering has promoted 3D culture systems, lab-on-a-chip technologies, and biosensors, enabling reproducible and efficient differentiation results. There is still much to accomplish, such as the problem of obtaining uniform stem cell populations or decoding the tissue context. This field incorporates several interdisciplinary advancements such as stimuli-responsive systems and computational modeling; it envisages new horizons in regenerative medicine, transforming stem cell-based therapeutic technologies to their optimum level for personalized medicine and other advanced tissue engineering applications.
Innovations in Machine Learning Models for Hepatitis Diagnosis and Disease Progression Prediction
Authors:   Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt, Marwa Marwa ,   Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia, Lim Wei Hong
Published: December 2024

Chronic liver disease (CLD) is a group of conditions for which up to half of the global population remains at risk and causes serious complications: liver cirrhosis and liver cancer. Therefore, early diagnosis and proper treatment of these diseases enhance the prognosis of patients suffering from CLD. This review paper explores how machine learning (ML) techniques are used in practice to diagnose, prognosis, and treat chronic liver diseases. Continuing with more specific examples of collected data from the results of several studies, their more comprehensive implementation is expected to improve the respective management processes and the detection of liver disease in patients more accurately. The review further discusses the various ML methods, including supervised and unsupervised learning, neural network, and ensemble learning, also applied to the estimation of risk felt by the patients, suggesting a course of treatment or how far the disease has progressed. While the inclusion of ML technology in the field of Hepatology is progressing well, some issues like model diversity, applicability of models, and concerns about ethics still pose challenges. This paper points out the importance of working in teams from various fields to develop appropriate mechanisms for dealing with these issues and adequately use ML for clinics. In conclusion, the results indicate that there is a possibility that ML will change the management of chronic liver diseases, which in turn will lead to the development of innovative treatment methods and better patient management.
Machine Learning Approaches for Malaria Risk Prediction and Detection: Trends and Insights
Authors:   Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA, Ahmed Ahmed ,   Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA,   Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA,   Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA, Gaber Khaled Sh. , Rizk Faris H. , Mohamed Mahmoud Elshabrawy
Published: December 2024

The current review summarizes the latest trends in malaria literature, emphasizing transmission ecology, new diagnostics and treatment. It stresses the additional focus on the transmission, according to the spatiotemporal models and predictive analytics, which help identify periods and the locations with the most significant risk, noting that these processes should consider the environmental factors. The change in the diagnostic approach, especially the introduction of artificial intelligence techniques such as deep learning, has improved the rate and precision at which malaria parasites are diagnosed in resource-limited countries where time is of the essence. Furthermore, there have been significant advances in drug discovery due to machine learning applications that have made it quicker to find new antimalarial drugs in the face of drug resistance. Despite these developments, there are still problems such as drug resistance, socio-economic disparities, and the environment that are being altered and still require an integrated and transdisciplinary approach. Combining these determinants is indispensable for eliminating these challenges and further promoting global efforts to control malaria.
Machine Learning in Public Health Forecasting and Monitoring the Zika Virus
Authors:   School of ICT, Faculty of Engineering, Design and Information Communications Technology (EDICT), Bahrain Polytechnic, Isa Town, Bahrain, El-Sayed El ,   Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt,   Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan, Eid Marwa M. , Abualigah Laith
Published: December 2024

The Zika virus is a severe public health threat all across the world, owing to its spreading mechanism through Aedes mosquitoes and its ability to result in extreme neurological diseases, which include the congenital Zika syndrome and the Guillain-Barré syndrome, amongst others. Conventional monitoring techniques often fail because many asymptomatic cases render early diagnosis challenging. Machine learning (ML) techniques can be seen as a constructive development in addressing this challenge, which entails predicting and tracking the spread of diseases such as Zika through extensive and complex datasets. Data analytic ML systems also enhance early warning systems and situational uplift by using data from social media, climate history, and genetics. This helps reasonably to predict the mosquito population biologically and the environmental factors that favor the spread of the virus for a more practical approach from the public health sector. Over and above, some issues are still pending, especially regarding the quality of data, understanding the models and how to apply such models within the current health systems. These factors must be solved to implement ML successfully in surveillance practice. This review provides an overview of the issue, stating the potential of machine learning applications in the development of public health, whose actions focus on Zika and other diseases transmitted by vectors.
Optimizing AI Models for COVID-19 Detection and Forecasting: A Comprehensive Study
Authors:   School of ICT, Faculty of Engineering, Design and Information  Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain, Abdelhameed Abdelhameed
Published: December 2024

This systematic review explores the use of artificial intelligence (AI) and machine learning (ML) during the COVID-19 disease outbreak. AIML models may interpret medical images, auditory input, and patient records to diagnose early enough, thus enhancing the likelihood of positive patient outcomes. Coupled with optimization algorithms, deep learning methods have predicted COVID-19 from chest X-rays and CT scans with unprecedented high accuracy. This review, therefore, synthesizes the existing literature and looks at the significant emphases, gaps, and potential trends of applying AI in diagnosing COVID-19 and forecasting outbreaks. Further, the advancement of AI and ML in this domain needs to be known to enhance global preventive diagnostic techniques for future pandemics.
Political and Religious Impacts on COVID-19 Responses in Tanzanian Healthcare Facilities: A Qualitative Cross-Sectional Survey
Authors: Edward Augustine Magwe
Published: December 2024

Coronavirus disease (COVID-19), a virus-related illness that affects the respiratory system, first appeared in Wuhan province, China, in December 2019. By mid-January 2020, cases of COVID-19 were reported in numerous Asian countries, signaling the beginning of a global health crisis. Within a mere four months, the virus had spread to all continents. A qualitative cross-sectional survey was conducted from 24 August to 3 October 2022 among healthcare workers in the Dar es Salaam, Arusha, Dodoma, and Mwanza regions. A total of 96 participants were involved from 24 healthcare facilities in all four regions. Individual interviews and key informant interviews were recorded using the Kobo Toolbox and analyzed thematically. The study revealed that many participants felt that politics misled society rather than educating. The way healthcare workers in Tanzania expressed their views on the impacts of politics in fighting the outbreak of COVID-19 was found to threaten the safety of healthcare workers and general public health. A significant number of healthcare workers also expressed that religion hurt the fight against COVID-19 in their facilities and the general society. However, A few health workers believed that religion had a positive influence, while others felt that it had advantages and disadvantages depending on its application. In Tanzania, politics and religion misled the community to a large extent rather than educating it. Clean politics and good religious practices should be used when fighting pandemics such as COVID-19.
Prediction of the Kinematic Viscosity of Libyan Crude Oil as a Function of its Specific Gravity as One Input Data
Authors: Salah Idris
Published: December 2024

The prediction of the kinematic viscosity of some Libyan crude oils based on its specific gravity as one input parameter has been investigated. The correlation was developed in order to improve on the accuracy of existing correlations and to assist oil kinematic viscosity where the knowledge of viscosity of uncharacterized crude oils is required. The data used to build the correlation consist of measurements from 4 crude oils from various locations around Libya, the range from 0.81 to 0.84 g/cm3, resulting in kinematic viscosity values ranging from 5 – 11 mm2/s. The correlation can be used as far as the specific gravities’ values are available. When a single specific gravity measurement is available at 15oC, it can predict the viscosity at 40 oC with an average absolute deviation (AAD) of 6.58%, which is an improvement in accuracy compared to previously published correlations (Puttagunta) with the kinematic viscosity at 37.8 as one input for kinematic viscosity prediction was 7.17%. Finally, when a t-test which is an inferential statistic used to determine if there is a significant difference between the means of experimental and theoretical data of kinematic viscosity, the difference was considered to be not statistically significant. It is worth noting that the required input for this equation is only the specific gravity.
Bagaimanakah Dampak Analisis Kebutuhan Dasar Siswa Terhadap Hasil Belajar Siswa di SMA Islam NU Pujon pada Mata Pelajaran Kimia?
Authors: Munfarikha Ninik , Zuhria Viki Arina , Islam Rukmana Fachrul , Weri Firda , Sahar Adnan , Obina Wilfrida Mayasti
Published: December 2024

A safe and comfortable school is a school that is able to foster students' intrinsic motivation through a positive culture, namely by meeting the basic needs of students as the basis for determining an appropriate learning model to get learning outcomes that meet the KKM. This study aims to determine the impact of the analysis of students' basic needs on the achievement of learning outcomes, especially in chemistry subjects at SMA Islam NU Pujon. The sample of the basic needs analysis research is 398 students of NU Pujon Islamic High School for the 2024/2025 school year and the research sample for student learning outcomes for chemistry subjects is classes XI-4 and XII-2 with a mixed method explanatory design research method. The results of the study were obtained that the results of the analysis of the basic needs of NU Pujon Islamic High School students for the 2024/2025 school year with the need for pleasure 25.7%, the need for affection and acceptance 25.4%, the need for freedom 23.5%, and the need for power 25.4%. The average student learning outcomes for chemistry subjects were obtained in class Xl-4 of 77.9 and XII-2 of 79.22. The findings showed that the learning outcomes of students for Chemistry in the two classes were above the school KKM