bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification and Monitoring of Crop Pests and Diseases Based on Remote Sensing Technology
Authors: Yang Xiucong
Published: December 2024

Identifying and monitoring crop pests and diseases are crucial to agricultural production, and they directly affect crop growth and the quality of agricultural products. This paper reviews crop pest and disease identification and monitoring techniques based on remote sensing imagery, emphasizing their advantages in providing timely and accurate information. The paper mainly summarizes the data sources for crop pest and disease detection, including satellite, UAV, aircraft, ground, and aerial remote sensing data, as well as laboratory monitoring data and seven types of multi-source data fusion. Four remote sensing monitoring techniques were discussed: spectral reflectance analysis, vegetation index analysis, regression model analysis, and spectral differential analysis, and their effects in practical applications were demonstrated through case studies. These methods significantly improve the accuracy and efficiency of pest and disease monitoring. The spectral reflectance analysis method directly reflects the changes in spectral characteristics of crops, and the vegetation index analysis method improves the indicative nature of monitoring by integrating the spectral characteristics of vegetation. The regression model analysis method, on the other hand, provides quantitative estimation of the extent of pests and diseases through mathematical modelling. Spectral differential analysis reveals subtle changes in the spectral profile of the crop and helps in the early identification of pests and diseases.
Analyzing the Decline of China's Real Estate Market: Policy, Market and Future Strategies
Authors: Yang Qingru
Published: December 2024

In recent years, China's real estate market has been unstable and under pressure due to various kinds of factors in the current stage of China's economic development. The central government has actively responded to adjust its economic problems and implemented different policies, but due to obstructions from all aspects, the policies have not been successful. This paper examines the recent decline in China's real estate market, attributed largely to excessive housing speculation and ineffective government policies. Despite initial successes in stimulating housing demand, these policies have failed to address the underlying issues exacerbated by broader economic factors. The paper analyzes the recession from two perspectives: government policies and national economic development and offers recommendations for future actions. It reviews the historical role of real estate in China’s economic growth, explores the causes of the market bubble, and proposes long-term strategies to mitigate the ongoing downturn and safeguard economic stability. According to the conclusions drawn in this article, a more favorable development policy for China's future economy should be to gradually move the real estate market away from finance and towards residential development. However, such a change requires a certain amount of easing time and appropriate judgments should be made based on the development of China's economy.
Comprehensive Financial and Strategic Assessment of Sainsbury’s within a Competitive Retail Landscape
Authors: Duan Shimeng
Published: December 2024

Sainsbury has performed well in brand building and customer loyalty as a retail company with a history of more than 150 years. However, the company has faced many challenges due to the changing economic environment in recent years. Therefore, in response to these problems, this study deeply analyzes Sainsbury’s market performance and financial status, aiming to identify the challenges and opportunities currently encountered by the company and provide feasible suggestions for the company’s future development. This study first uses SWOT analysis to evaluate Sainsbruy’s internal strengths and weaknesses and external opportunities and threats. Subsequently, by comparing the financial indicators of Sainsbury and its competitors, it was found that its liquidity, profitability and capital structure were mostly lower than those of its peers in the past five years. Based on these findings, this study recommends that Sainsbury optimize cost management promptly and take measures to cope with inflationary pressures to enhance market competitiveness and long-term sustainable development capabilities.
Analysis of Current User Stickiness Marketing Strategies and Future Enhancement Means of Online Game Companies: A Case Study Based on miHoYo
Authors: Zhou Jinqiang
Published: December 2024

With the growing influence of miHoYo's game products such as Original God at home and abroad, more and more people know miHoYo through the company's high-quality handheld games. miHoYo holds more than half of the market share in China and has a large number of loyal players. However, as the game content is updated, there is no lack of old players giving up and quitting, how to retain these players for a long time and attract new players to play their own games has become the most important question for all game companies, and miHoYo has given the perfect answer. miHoYo has set up its own social platform software online, set up incentive programmes for creators on its creative platforms and so on, and has also participated in comic shows, advertisements, as well as in the "Manga" and "Battle of the Bulletin Boards".Events such as Comic-Con, advertising campaigns, and linkages with merchandise and tourist attractions create a wonderful and enjoyable gaming community and environment for players, who offline feel the atmosphere of having fun on-site, as well as gaining a sense of empathy between the game and themselves. The combination of the two makes miHoYo's games highly discussed and trafficked both on online platforms and in real life. This makes miHoYo's players more interested in miHoYo's game products, as well as having a strong sense of trust in miHoYo, which finally makes miHoYo's gamers much stickier than other game companies in the market.
Exploring the strategies and benefits of integrating fashion brands with tourism by analyzing the Valentino Pink PP campaign in Anaya
Authors: Yan Jiaqi
Published: December 2024

Using Valentino's “Pink PP” campaign in Anaya as a starting point, this study explores the strategies and benefits of integrating tourism and fashion brands. Through a comprehensive analysis of social media data, tourism statistics, and brand marketing reports, this study finds that brand linkage strategies can effectively increase brand awareness and destination attractiveness, social media communication and word-of-mouth effects play an important role in promoting brand development and tourism prosperity, and immersive experiences can enhance the emotional connection between consumers and brands and promote synergistic development between brands and tourism. Based on the above findings, this study puts forward three recommendations: fashion brands should fully explore their brand influence and enhance their market competitiveness through cooperation with the tourism industry; they should implement innovative marketing strategies by grasping the social media communication and word-of-mouth effects; and they should focus on the emotional resonance with consumers and consumer experience-centered marketing strategies, so as to realize the sustainable development of the brand and the enhancement of its market competitiveness.
AI-Powered Negotiations: Opportunities, Challenges, and the Future of Business Strategy
Authors: Li Hanjie
Published: December 2024

Business negotiations are integral to various aspects of enterprise operations, from recruitment and resource allocation to procurement and international business collaborations. Despite their critical importance, negotiations often remain cumbersome, complex, and time-consuming, with outcomes heavily influenced by the negotiator's skills and external factors, frequently failing to maximize benefits or achieve desired goals. The rapid advancement of artificial intelligence (AI) technologies, particularly large language models (LLMs) like ChatGPT, has seen AI increasingly integrated into daily tasks such as drafting emails and querying legal clauses. This paper investigates the potential of AI and negotiation agents to enhance efficiency and optimize outcomes in business negotiations. Utilizing case studies, the research explores how AI can assist or even replace human negotiators by automating data collection, analyzing historical negotiation patterns, predicting outcomes, and suggesting optimal strategies. The findings indicate that AI negotiation agents can surpass human performance in certain negotiation scenarios, providing consistent, data-driven insights and adapting strategies in real time based on counterpart responses. However, the research also highlights the limitations of AI in understanding human emotions, cultural nuances, and the ethical implications of AI decision-making. The conclusion emphasizes the promising role of AI in revolutionizing business negotiations while underscoring the necessity for human oversight to address emotional and ethical complexities. This study provides a foundation for further exploration into the integration of AI in negotiation processes, aiming to enhance both efficiency and effectiveness in achieving business objectives.
MLP-Based Research in Flood Probability Prediction
Authors: Wang Yanzheng
Published: December 2024

Flood is a kind of natural disaster with great harm. Probability prediction can reduce economic loss effectively. This paper optimizes the prediction of flood probability based on MLP model to reduce the loss. Based on the historical flood data set, a comprehensive correlation analysis using Pearson's correlation coefficient was conducted to find out the influence of each potential factor on the probability of flood occurrence. Based on this, the potential causes of flooding by these factors are examined in detail by taking into account various theoretical and practical aspects, with the aim of revealing the underlying mechanisms and patterns. Two different methods, linear regression and MLP model, are used to establish the corresponding flood probability prediction models. The classical linear regression method is firstly used to predict the probability of flood occurrence, and after rigorous calculation and verification, the obtained prediction accuracy is 99.9250%. In order to further optimize this prediction model to achieve higher accuracy and reliability, MLP is skillfully applied based on five key indicators. Through a series of calculations, the prediction accuracy was successfully increased to 99.9254%. This shows that the improvement is effective and provides a more accurate and efficient method for predicting the probability of flooding. This result not only verifies the rationality and effectiveness of the prediction model, but also provides a solid foundation and strong support for further research and application.
Analyzing The Influence of Oriental Elements on Luxury Brands
Authors: Chen Jiachun
Published: December 2024

With the development of globalization and the deepening of cultural integration, oriental elements occupy an increasingly important position in the world fashion and luxury goods industry. The purpose of this paper is to explore how oriental elements affect luxury brands. In this context, Chanel Coromandel’s high-end jewelry series shows the perfect integration of eastern and western aesthetics with the design of oriental elements, which has become a typical case of exploration. This paper uses the theories of cultural semiotics, brand differentiation, and cross-cultural brand management to investigate and promote the cultural value of luxury goods by incorporating oriental elements. This paper examines how to differentiate luxury goods in the market by incorporating oriental elements and enhancing the image of luxury brands. The paper recommends collaborating with traditional Chinese intangible technology, adopting China's traditional philosophy, and focusing on the exploration and inheritance of jewelry technology. It also reveals the influence of oriental elements on luxury brands, providing guidance for cross-cultural marketing strategies and the strategic deployment of luxury brands in China and other Asian markets.
Orcinus Orca in Captivity: Behavioral and Physiological Effects
Authors: Wu Yaohui
Published: December 2024

The captivity of orcas has been a controversial topic for decades, with significant ethical, behavioral, and physiological concerns. While marine parks promote orca shows as educational experiences, orcas often display abnormal behaviors, including stereotypic behaviors and social disruptions, as well as experience a range of health problems, such as dorsal fin collapse and compromised immune systems. This article examines the history of orca captivity, the profound impact it has on their natural behaviors and health, and the latest research and case studies that illustrate the detrimental effects of such confinement. By exploring alternatives to captivity, such as marine sanctuaries and rehabilitation programs, we can move towards more ethical solutions that prioritize orca welfare. This article also addresses the growing body of scientific evidence calling for the end of orca captivity in favor of humane alternatives that align with conservation and public education efforts. Emerging alternatives, such as marine sanctuaries and rehabilitation programs, provide more humane options that allow orcas to live in the environments that are closer to their natural habitats. By encouraging public education through innovative technologies like virtual and reality, the article emphasizes the importance of shifting away from traditional captivity. Ultimately, this paper advocates for the future welfare of orcas, promoting conservation efforts and ethical practices that respect these remarkable marine mammals.
Digital Archaeology: A Review of the Application of Information Technology in Archaeological Data Management and Analysis
Authors: Xu Kezhen
Published: December 2024

This paper provides a comprehensive review of the current application of information technology in archaeological data management and analysis, discusses the progress and practical application of digital archaeology, and looks ahead to its future development directions and challenges. With the rapid development of information technology, archaeology has gradually introduced digital tools such as database management systems, geographic information systems (GIS), remote sensing technology, virtual reality (VR), and augmented reality (AR). These technologies not only improve the efficiency of data management but also offer entirely new methods for analysis and presentation in archaeological research. This paper evaluates the application of different digital tools in archaeology, analyzes their technical advantages and limitations, and proposes strategies to address issues related to technical complexity, data standardization, and interdisciplinary collaboration. The study aims to provide valuable references for archaeologists, technology developers, and cultural heritage conservation professionals, promoting the further development and application of digital archaeology to achieve the efficient preservation and dissemination of cultural heritage.
The Importance of Club Operation Development to The Development of Sports Events - Take Swimming Events as An Example
Authors: Yan Weize
Published: December 2024

Governments have attached great importance to sports development, with people increasingly paying attention to health management. Through literature review and marketing model analysis, this paper discusses the advantages and disadvantages of club operation of swimming events, and the influence of star effect on market promotion. The results show that club operation can effectively improve the market competitiveness of swimming events, and integrate resources to enhance the efficiency of athlete selection and training. The star effect plays an important role in the promotion, which can increase the popularity and market influence of the swimming club. However, over-reliance on star power and commercial operations can have a negative impact on the career longevity of athletes, the financial stability of clubs, and the brand image. Therefore, clubs should balance commercialization and athlete development, adopt diversified operation strategies, and strengthen cooperation with the government and enterprises to achieve long-term sustainable development.
Chocolate Flavor Factors: From Cacao to Chocolate
Authors: Yin Zixuan
Published: December 2024

Chocolate is a rich food popular all over the world. At present, there is much research on the rheological influence and chemical composition of chocolate, as well as some research on the flavor compounds of chocolate. Their main purpose is to study the effect of chocolate and the change of the composition to make chocolate healthier. However, few studies focused on the summary and comparison of chocolate flavor optimization. In this paper, the production process of chocolate and the horizontal comparison of each process are comprehensively described, and the research on flavor compounds in chocolate is analyzed. The difference in flavor compounds in different kinds of chocolate and the results of four aspects affecting the flavor of chocolate are obtained. This study makes up for the comparison between different kinds of chocolate production processes and summarizes the composition of chocolate flavor compounds and the influencing factors of chocolate flavor, which provides a reference for future related research. However, there are still some problems that cannot be solved, such as the content change of certain flavor compounds under certain processing conditions, to realize the control of chocolate flavor. Future research can also focus on the influence of human senses on chocolate flavor tasting and the improvement methods.
A Marketing Perspective on Rebuilding Football’s Success
Authors: Yang Haotian
Published: December 2024

In recent years, Beijing Guo’an Football Club (F.C.) has seen a significant deterioration in performance and fan involvement, indicative of a wider trend in Chinese football. During the money-driven era of soccer, Chinese football saw a significant influx of cash but ultimately faced financial strain and a decline in competitiveness, particularly during the COVID-19 epidemic. This study examines prospective solutions for Beijing Guo’an F.C. to tackle operational, management, and marketing difficulties. The analysis concentrates on the club's performance statistics from 2014 to 2024, indices of fan involvement, and the factors contributing to the deterioration of management decision-making. The investigation revealed three primary emphasis points: alterations in management, performance outcomes in significant matches, and the disconnection between clubs and viewers. To address the highlighted issues, the proposal outlines a strategy for improving the club while retaining the management through marketing modifications and operational enhancements. Furthermore, communication and community initiatives may reestablish the bond among supporters. The paper helped to revitalize Beijing Guo'an F.C. and may also serve as a reference for other Chinese football teams in the intensifying worldwide competition.
The Impact of The Star Effect and E-Marketing Strategy in Basketball Events -Taking NBA and CBA as Examples
Authors: Zhang Ce , Huang Yixin , Li Liyang
Published: December 2024

Under the trends of digitization and globalization, the marketing strategy for basketball events has undergone rapid changes. The application of network marketing in promoting and broadcasting basketball events has become increasingly prevalent. Through literature analysis, marketing model analysis, and case analysis, this study finds that network marketing significantly impacts tournament organization and the expansion of basketball's influence. Leveraging the star effect can help enhance influence and promote economic value improvement. Established professional leagues like the NBA have experienced the amplification effect of combining digital marketing with traditional media marketing; however, they should also be cautious not to overly rely on the star effect by considering strategic breakthroughs. Developing leagues like CBA should also focus on expanding their network marketing strategies in related fields. Through the combination of different network marketing strategies to expand the promotion of professional basketball competition in the Chinese market, to enhance the sustainable development of Chinese basketball.
Digital Transformation in the Luxury Industry: Louis Vuitton's Strategic Response and Innovation
Authors: Zheng Anqi
Published: December 2024

The study seeks to examine the ways in which luxury businesses, including Louis Vuitton, enhanced their marketing methods in response to the difficulties posed by the pandemic. This paper aims to analyze the influence of these techniques on brand recognition, consumer involvement, and sales effectiveness across several stages of the epidemic. During these time periods, this paper also witnesses the transition from traditional in-person shopping to online shopping, as well as the use of advanced digital technologies like Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI) in marketing strategies. This research has revealed that digital transformation plays a crucial role in preserving market position and fostering client loyalty. This article proposes recommendations for LV's future trajectory in light of its current condition, which entail increasing expenditures in technology and revising its initial digitalization approach. Enhanced integration of nascent technologies can propel the organization to greater heights, ensuring it remains inventive and adaptable, better satisfying the demands of consumers, the market, and the current era.
Comparative Analysis of Tesla and Ford: Investment Recommendations Based on the 7Ps Marketing Portfolio and Porter's Five Forces Model
Authors: Su Tong
Published: December 2024

Investors increasingly focus on identifying favourable investment opportunities in the rapidly developing automotive industry. This study aims to analyse Tesla and Ford comprehensively, focusing on their financial performance, strategic positioning, and product strategies. This study evaluated the investment potential of these two significant companies using financial ratio analysis, Porter's Five Forces Analysis, and the 7Ps framework. The analysis results show that although Tesla has demonstrated significant growth and innovation capabilities, its investments in autonomous driving, battery, and sustainable energy solutions have placed it in a favourable position in the future market. However, with its long history and extensive market coverage, Ford has demonstrated robustness and adaptability in the traditional automotive market. The research conclusion is an investment recommendation based on the comparative advantages and disadvantages of the two companies. Tesla's advantage lies in its forward-looking technology and market leadership, while Ford's lies in its stable financial performance and market adaptability. This analysis provides valuable insights for investors and lays the foundation for future investors' choices and investments.
Forest Fire Monitoring Based on Multi-Source Remote Sensing Data
Authors: Wang Hongwei
Published: December 2024

Forest fires refer to forest fires that are beyond human control, freely spreading and expanding within forests, causing certain harm and losses to forests, forest ecosystems, and humans. Despite the rapid development of science in the world today, humanity has yet to make significant progress in combating forest fires. In response to the current problem of single, real-time, and comprehensive fire monitoring methods in forest fire emergency rescue work. This article explores emergency monitoring methods for forest fires based on multi-source remote sensing data. Based on comprehensive geographic data and previous scholars' specific research on remote sensing data such as drones and satellites in fire emergency monitoring, analyze the applicability, advantages, and disadvantages of various remote sensing data. The conclusion drawn is that unmanned aerial vehicle remote sensing has high spatial and temporal resolution and can implement precise fire extinguishing in small areas, while satellite remote sensing has a large spatial span and can provide information throughout the entire time period. Based on this, this article proposes a framework for emergency fire monitoring, visualizing important areas and facilities with fire development and providing information for cities and counties to extinguish forest fires.
The Impact of Internet Platforms on the Catering Industry
Authors: Li Bowei
Published: December 2024

This paper aims to discuss the effects of Internet platforms on the catering business, particularly on food delivery services and online reviewing and booking platforms like Dianping and Meituan. In the light of the strategy and finance, this paper scrutinises these platforms to understand how they have transformed the restaurant sector, altered customer habits and affected the market. The paper aims at identifying and explaining the different roles that have been introduced in the market by these platforms and the effects that have come along with them. Furthermore, this paper is also an attempt to explore the possibility and threats of those new technologies to let people understand that restaurants must transform and change their strategies because the world is continuously advancing. Finally, the conclusion is made to demonstrate that restaurants have to become more digitised to sustain and thrive in this market. Embracing digital change is essential for future success.
Research on High-Quality Development Models of The Grain Industry Under the New Development Concept
Authors: Wen Wu
Published: December 2024

Ensuring food security is fundamental to national stability, and agricultural stability is key to societal well-being. Under the guidance of the new development concept in this new era, the high-quality development of the grain industry has become a central goal for securing national food security, advancing agricultural modernization, and achieving sustainable development. This study aims to systematically review the existing literature, clarify the current state and primary challenges of high-quality development in the grain industry, and summarize and analyze the foundation of China’s high-quality grain industry development model. Furthermore, it focuses on potential innovative models for high-quality development under the new development concept. This review offers a comprehensive perspective and strong support for the theoretical construction and practical innovation of the grain industry’s high-quality development model. Additionally, the research findings will provide valuable references for future strategies and policy-making in the grain industry, assisting governments and relevant sectors in better understanding how to achieve the dual objectives of food security and sustainable development through innovation and coordinated growth.
Remote Sensing Data Analysis for Urban Planning and Land Use Change
Authors: Xu Jingyang , Zheng Yutong
Published: December 2024

With the acceleration of urbanization, rational use of land resources and effective town planning and layout have become particularly important. Through its advantages of high temporal and spatial resolution, remote sensing technology provides strong data support for dynamic monitoring, precise planning and management of land use. This study thoroughly analyzed urban planning and land use changes over the past decades using remote sensing technology, revealing the advantages of remote sensing application in land resource management. The study shows that remote sensing technology can provide continuous and multi-period land use images, effectively monitor land use changes, and provide scientific basis for land use planning and dynamic adjustment. In addition, remote sensing data's high resolution and automated processing capability significantly improves the accuracy of land use type classification, providing strong support for urban planning and land management decision-making. Integrating remote sensing data with GIS, deep learning and other technologies, this paper hopes further to promote the intelligence and refinement of land use planning, and make significant contributions to achieving the goal of sustainable development.
Challenges and Protection Strategies for Rivers and Lakes
Authors: Zhang Jinsong
Published: December 2024

Rivers and lakes play a crucial role in freshwater supply, maintenance of ecosystems, and the sustainability of humans’ society, economic activities and culture. However, these water bodies are often threatened by pollution, damming, and over-extraction, causing a series of problems for ecosystems and communities. The paper analyzes the challenges faced by rivers and lakes and proposes some measures to deal with these problems. The major challenges include the spread of pollutants, eutrophication, abnormal flow regulation, and lowering water levels, which lead to the loss of biodiversity, threat to human health and economic activities, and the collapse of culture and civilization. This study proposes solutions based on technologies and policies. The technique solutions include Variable Rate Technology and Precision Irrigration System for precision farming and buffer zones for water treatments. For the government, the withdrawal policies for water should be applied to prevent agricultural and industrial overuse and waste of water resources. Besides, the coordinated work between upstream and downstream communities and the participation of the government matters a lot to the conservation of rivers and lakes and the preservation of the cultural and historical legacies related to these water bodies.
The Role of Digital Marketing Strategy in Star Promotion - Taking Taylor as An Example
Authors: Zeng Xinyi
Published: December 2024

With the continuous development of network communication technology, information dissemination, and consumption patterns are closely related to smartphones and network marketing, which leads to a shift in celebrity marketing strategies transmitting from traditional media to multi-channel digital marketing. This paper examines Taylor Swift's marketing strategies across different periods through a literature review, comparative analysis, and model evaluation. By comparing Taylor’s marketing strategies over time, the study reveals that digital marketing can enhance fan loyalty and extend a celebrity's peak period of popularity. At the same time, the increasing influence of stars in the industry brought by digital marketing can also help stars enhance their economic value. However, the findings also indicate that excessive use of digital marketing may diminish fan loyalty and reduce fan base. The study showcases the importance of balancing digital marketing efforts to maintain long-term fan engagement and sustain a celebrity's prosperity in the digital market.
Exploring Reasons of Formula 1 Team’s Underperforming in F1 Racing
Authors: Song Borui
Published: December 2024

The COVID-19 pandemic imposed unprecedented challenges on Formula 1 (F1), intensifying financial and technological disparities among teams. Haas F1, a relatively new entrant to the sport, has struggled with consistent underperformance since its peak in 2018. This paper explores the factors contributing to Haas F1's decline, focusing on financial limitations, reliance on external technology, and ineffective global operations. The 2020 pandemic exacerbated the team’s existing struggles, particularly in securing sponsorship and navigating the new budget cap regulations introduced in 2021. Additionally, Haas’s dependence on Ferrari for essential car components has constrained its technological innovation, leading to further competitive disadvantages. Despite attempts to restructure the team and improve performance through financial and strategic changes, Haas has yet to achieve sustained success. This paper examines Haas F1's case through qualitative analysis and proposes improvements, including diversified funding strategies and increased investment in internal research and development, to enhance competitiveness in the evolving F1 landscape.
Optimizing Water Level Regulation in the Great Lakes: An Integrated Approach Using Genetic Algorithms and Network Flow Models for Sustainable Resource Management
Authors: Tang Yuxuan , Zhang Yuke , Wu Yixi
Published: December 2024

The Great Lakes, as a vital freshwater resource, are significantly influenced by climate change and anthropogenic activities, leading to acute water level fluctuations with profound impacts on various sectors including agriculture, industry, and ecology. This study addresses the need for scientifically sound water level regulation by developing an integrated approach that employs a genetic algorithm and a network flow model. The model is designed to balance the interests of multiple stakeholders and optimize water levels across different periods, thereby ensuring the sustainable use of water resources and ecological balance. Through comprehensive data collection and processing, the study constructs a robust prediction model that accounts for economic activities and ecological protection. The application of genetic algorithms enhances the efficiency and accuracy of the model, providing a practical tool for decision-makers in water resource management. The findings underscore the model's reliability and its potential to support policy-making for sustainable water resource management in the Great Lakes region.
Water level management in the Great Lakes: a study of an integrated optimization algorithm based on Taboo search and genetic control
Authors: Li Zhanghan , Gan Shuming , Lin Zhuoyi
Published: December 2024

This article presents a single-objective Taboo search optimal water level model designed to satisfy the needs of different stakeholders, including shipping and energy interest groups, environmental and ecological protection groups, and recreational and cultural interest groups. In order to optimize the water level of the lake, the article establishes an optimization model for comprehensive satisfaction and describes in detail the calculation method and constraints of satisfaction. Through the Taboo search algorithm, the optimal water levels of five large lakes in each month were obtained after five repeated experiments. In addition, an enhanced elite conservation control model based on a genetic algorithm is presented to satisfy different stakeholder needs by adjusting the water level of the Great Lakes as well as minimizing the time required for the adjustment. Genetic algorithm parameters include probability, population size, and number of termination generations. The superiority of the algorithm is verified through the sensitivity analysis of the control algorithm and the evaluation of real data. The final results show that after adjusting the water level, the stakeholders are more satisfied with the actual water level.
Research on Apple External Quality Grading Method Based on DXNet Model
Authors: Li Chao
Published: December 2024

In the wave of agricultural modernization, intelligent apple selection line technology is a key link in improving fruit quality and production efficiency. There are various methods for grading the external quality of apples, some of which focus on detecting single external features of apples, such as color, size, shape, texture, and visual defects; Other methods are based on deep learning techniques to grade the appearance quality of apples. However, these manual methods for extracting single external features of apples have certain limitations in terms of grading accuracy, and cannot comprehensively and objectively evaluate the external quality of apples. To improve the accuracy and stability of the lossless grading method for apples, this paper proposes a Yan'an apple grading method based on the DXNet model. The convolutional blocks of traditional convolutional neural networks are extracted as feature extractors for apple images, and the learned feature maps are subjected to global max pooling and global average pooling, respectively. The two one-dimensional vectors after pooling are concatenated and input into a classifier mainly composed of fully connected layers for classification.
Effects of Changes in Temperature and CO2 Concentration on Maize Yields in the Loess Plateau
Authors: Ma Chenguang , Lei Na , Zhang Fang , Shi Lei , Zhang Jing , Dai Yilun
Published: December 2024

Addressing the impact of climate change on food security is now one of the most important challenges facing humanity in the twenty-first century. As one of the three major food crops in China, maize is widely planted on the Loess Plateau and is also a major source of local agricultural income. Therefore, accurately assessing the impacts of climate change on local maize production and formulating corresponding adaptive measures are of great theoretical and practical value in reducing the impacts of climate change and guaranteeing the sustainable development of local agriculture. In this study, we calibrated and validated the parameters of the APSIM model using maize field trial data from 2009 to 2012 in Yuzhong County to assess the applicability of the model in the local area. Effect of using measured climate data from 1979 to 2018 and changes in climate data based on measured data and different CO2 concentrations (350 ppm, 450 ppm, 550 ppm, 650 ppm and 750 ppm) on maize yield in the region. The results showed that under the same CO2 concentration gradient, with the increase of temperature, corn yield basically showed a decreasing trend, in which the CO2 concentration at 350 ppm corn yield decreased at the greatest rate, the temperature increased by 5 ℃ compared to the baseline yield decreased by 30%; at the same temperature, with the increase of CO2 concentration, corn yield had a small increase, in the baseline temperature case, CO2 concentration at At 750 ppm, corn yields increased by 9% compared to yields at 350 ppm.
Exploration of the Impact of Different Deep Learning Network Architectures on Click-Through Rate Prediction Models Based on Gated Deep Cross Networks
Authors: Jian Minyu
Published: December 2024

This paper investigates advertisement click-through rate (CTR) prediction models based on Gated Deep Cross Networks and analyzes the impact of different deep learning network architectures on prediction performance. The paper provides an introduction to the background and related research of CTR prediction tasks, as well as a detailed discussion of the model structure and design. In the experimental section, experiments were conducted using publicly available datasets. Various network structure configurations were tested, including changes in activation functions and dropout rates. The results indicate that certain configurations in the experimental group exhibited superior performance compared to the control group, showing significant advantages. Overall, the paper demonstrates the strong performance of Gated Deep Cross Networks in CTR prediction and explores how different network structures affect model performance. Finally, the research work is summarized, and future research directions are proposed, providing valuable insights and references for CTR prediction tasks.
Research on Intelligent Platform Economy Pricing Model Based on Network Traffic and User Profiling Data
Authors: Shi Yiting
Published: December 2024

The platform economy evolved rapidly due to technological breakthroughs, hence new challenges emerged in terms of pricing strategies affected from different user behaviors and market demand dynamics. Traditional pricing methods are not up to this type of challenge, and it requires a model able to be more agile — in real time. The study presents “DeepPrice,” a dynamic pricing model using deep learning namely Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (DRL) to achieve the optimal platform pricing strategies in response to user behavior and market signals. The research has an experimental design for the development and testing of the DeepPrice model. The model is pre-trained by the transaction data of an important e-commerce platform for this task. CNNs learn user profiles and product properties via the encoding layer, while DRL models implement the strategy of adjusting price according to behavior actions in tensor form. Metrics such as platform revenue, user conversion rates, and customer satisfaction are used to validate the model back to the model performance. Our solution helped DeepPrice to generate incremental 20% platform revenue on average and better adjust to the market challenges. It performed better than standard pricing solutions, especially in times of high demand, and effectively personalized price-pointing for top-value customers to drive higher conversions and improve customer satisfaction. This study points to the promise of using deep learning to improve dynamic pricing in platform economies. Flexible & Scalable Solution for any IndustryDeepPrice Nevertheless, challenges regarding the computational cost of implementing personalized pricing strategies and ethical debates surrounding such strategies remain to be studied in more detail. In platform economy, the reinforcement learner has a significant potential to provide a reliable real-time pricing solution with CNN in DeepPrice to improve both profit and customer satidfaction.
A Personalized Stock Market Acquisition Method Based on Scrapy
Authors: Ming Shi , Rasli Roznim Mohamad , Wang Shir Li , Rasli Roznim Mohamad
Published: December 2024

Stock data is of great significance for stock analysis and decision-making. In the field of stock data analysis and mining, the quality and diversity of data have a crucial impact on the depth of research and work efficiency. Traditional data acquisition methods, such as official channels or third-party data source API and SDK interfaces, often provide standardized data. Although these data elements meet basic analysis needs to a certain extent, due to their inherent limitations, it is difficult to support personalized and in-depth research. With the development of the stock market and the advancement of data analysis technology, researchers' demand for data is constantly increasing. They not only require real-time and accurate data, but also hope to obtain more diversified and customized data elements, such as investor posting data, social network data, etc. Behind this demand is the desire to build more refined models, understand more complex market behaviors, and provide more accurate decision support. This paper proposes a stock data collection method based on the Scrapy framework, aiming to address the limitations of traditional data acquisition methods. By analyzing specific stock websites and crawling and processing stock data sources, it shows how to use crawler technology to achieve efficient data collection and analysis. The system improves data acquisition efficiency and system stability by optimizing crawler performance and storage algorithms. This research result provides new ideas and technical support for stock data analysis and mining. space before of 18-point and after of 60-point.
Improving Real-Time Gear Contact Fatigue Inspection with Machine Vision
Authors: Li Hai , Wang Xiaoyang , Wu Shihui , Ye Songze
Published: December 2024

A fast gear image correction algorithm is proposed for the real-time detection of gear pitting failure area in gear contact fatigue test. Based on the fact that gear pitting first appears near the pitch circle of the gear and expands to the root circle [1], the algorithm uses the pitch circle as the basis for the camera scanning line frequency calculation, and uses the pitch circle as the segmentation point to calculate the actual pixel value of the gear meshing surface, which leads to a reduction in the amount of computational parameters. The trigonometric function calculation method instead of the involute parametric equation curve product formula is used, which makes the calculation speed increase. Based on the principle of involute gear meshing and the principle of scanning camera shooting, the pixel value of gear meshing surface imaging is deduced, as well as the formula for calculating the pitting rate of gears based on the pixel value is proposed. The experimental results show that the average absolute error of the gear image fast correction algorithm proposed in this paper is 2.2868%, and the average relative error is 0.11059mm2, the algorithm is relative to the gear mesh surface correction algorithm [2] in the detection of the gear pitting area of the absolute error decreases by 0.1412%, and the relative error decreases by 0.0127mm2, and the computational complexity for the gear mesh surface correction algorithm 1/5 of that of the gear mesh surface correction algorithm, and finally combined with the pixel value-based gear pitting rate calculation formula, thus accurately detecting the time of gear pitting failure.
Is Data Anonymization an Effective Way to Protect Privacy or Not
Authors: Han Yiping , Lu Xinqian
Published: December 2024

This paper examines whether data anonymization is an effective method for protecting personal privacy. With the rapid development of the Internet and artificial intelligence, data has become a key driver of modern societal development, but it also raises ethical and technological challenges regarding privacy protection. Data anonymization protects sensitive data by encrypting it and removing personally identifiable information, aiming to reduce the likelihood of identifying individuals within a dataset. The article analyzes the benefits of data anonymization, including the protection of personal privacy, facilitation of data sharing and transactions, and enhancement of data value utilization, while also highlighting the risks associated with data anonymization, particularly the potential for de-anonymization techniques to re-identify personal data, thereby threatening privacy. The study emphasizes that, despite the risks of data misuse, the rational use of data can bring significant positive value to society. The paper concludes that data anonymization itself is not the problem; the real threat lies in data de-anonymization. To maximize benefits, data anonymization should be used rationally, and risks associated with data de-anonymization should be mitigated through various methods. The article suggests that data collectors should prioritize the protection of sensitive data, and regulatory bodies should strengthen the protection of personal data privacy, adopting technologies such as differential privacy to reduce the risk of data correlation attacks.
Research on Sentiment Analysis Based on Ensemble Learning
Authors: Li Kaiyu , Xie Yi , Li Shicheng , Liu Zhaomin , Liu Di
Published: December 2024

The purpose of this paper is to use the ensemble learning method to analyze the sentiment of tourist attraction reviews, so as to improve the reference value of tourists in travel decision-making. With the popularity of online reviews, how to extract emotional information efficiently and accurately has become an important topic. In this study, random forest, gradient boosted tree (GBDT) and support vector machine (SVM) were used as the base learners, and the ensemble strategy of weighted voting was used to construct the model. Optimize parameters with cross-validation and grid search, and comprehensively evaluate model performance with metrics such as AUC, Log Loss, MCC, and average accuracy. Experimental results show that the classification accuracy and robustness of the ensemble model are better than those of the single model, with an AUC of 0.92 and an MCC of 0.78. The balance between precision and recall was demonstrated through performance surface plot analysis based on different thresholds, and the optimal threshold range was determined. The model can provide reliable sentiment insights for tourism platform users, help scenic spot managers identify user needs, optimize services, and have scalability in other review analysis fields.