bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of Convolutional Neural Network
Authors: Du Zhenyuan
Published: October 2024

Over the past decade, the domain of deep learning has emerged as a swiftly expanding area of interest among researchers globally. It offers substantial benefits over traditional shallow networks, particularly in the realms of feature extraction and model fitting. Deep learning excels at uncovering intricate, distributed features from raw data, which exhibit robust generalization capabilities. It has triumphantly addressed challenges that were once deemed intractable within the field of artificial intelligence. With the exponential growth in the volume of training data and a marked increase in computational power, deep learning has achieved remarkable strides and found applications across a spectrum of domains, including but not limited to object detection, computer vision, natural language processing, speech recognition, and semantic parsing, thereby accelerating the evolution of artificial intelligence.
Deep learning encompasses a series of non-linear transformations organized hierarchically, with deep neural networks representing the predominant form of contemporary deep learning methodologies. Inspired by the neural connections found in the animal visual cortex, these networks are characterized by local connectivity, shared weights, and pooling operations. Such attributes not only simplify the network model and curtail the number of trainable parameters but also engender invariance to shifts, distortions, and scaling, thereby endowing the model with enhanced robustness and fault tolerance. This makes the training and optimization of the network structure more manageable.
This paper commences with a retrospective on the evolution of convolutional neural networks (CNNs). Subsequently, it delineates the architectures of neuron models and multilayer perceptron. It proceeds to dissect the CNN architecture, which typically encompasses a sequence of convolutional and pooling layers, culminating in fully connected layers, each serving distinct functions. The paper also elaborates on various enhanced algorithms for CNNs, such as the “Network in Network” and “Spatial Transformer Networks,” while also introducing supervised and unsupervised learning methodologies pertinent to CNNs and highlighting several prevalent open-source tools.
Further, the paper scrutinizes the application of CNNs in various fields, including image classification, facial recognition, audio retrieval, electrocardiogram analysis, and object detection. It posits the amalgamation of CNNs with recurrent neural networks as a potential alternative for training datasets. The research culminates in the design of CNN structures with varying parameters and depths, supported by experimental analysis that elucidates the interplay among these parameters and the ramifications of their configuration. Ultimately, the paper synthesizes the merits and lingering challenges associated with CNNs and their practical applications.
Research of Graphene Electronics and its Applications
Authors: Wang Ziyu
Published: October 2024

Imagine a material as thin as a single atom, yet stronger than steel and more conductive than copper. This is graphene, a two-dimensional wonder material with a unique monolayer hexagonal honeycomb lattice structure. Its exceptional electrical properties, particularly its high carrier mobility, have sparked widespread interest in the scientific community for potential applications in electronics.However, because its conduction and valence bands intersect at the Dirac point, it behaves as a zero-bandgap semimetal, limiting its use in electronic devices. Therefore, opening a bandgap has become a focal research task in the scientific community. This paper explains the inherent conductivity of intrinsic graphene and summarizes how doping can theoretically further enhance its conductivity. Through further research on the development of graphene doping methods, this article primarily introduces how adsorption doping and lattice doping techniques can open the bandgap of graphene. It analyzes the stability of different doping types to achieve their application in nanoelectronic devices.
Enhancing Erasure Resilience in Reed-Solomon Codes: Theory and Applications in Data Storage Systems
Authors: Zhang Yuepeng
Published: October 2024

Reed-Solomon (RS) codes are widely utilized in data storage and communication systems due to their robust error detection and correction capabilities. However, traditional RS codes encounter challenges in addressing the increasing data corruption rates demanded by modern storage systems. This paper aims to overcome these limitations by modifying and extending the structure of traditional RS codes, particularly enhancing their recovery capabilities in the face of significant data loss. We begin by reviewing the theoretical foundations of RS codes and existing extension methods and discussing emerging technologies integrated into RS codes. We then present detailed methodologies for RS code encoding, erasure recovery mechanisms, soft decision decoding, and interleaving coding techniques, followed by a series of experiments designed to test the proposed methods. The experimental results indicate that soft-decision decoding outperforms traditional hard-decision decoding under high signal-to-noise ratio conditions, albeit with increased computational complexity as the list of candidate codewords grows. Interleaved Reed-Solomon (IRS) coding offers improved performance under low signal-to-noise ratio conditions but may introduce additional system complexity due to the interleaving and deinterleaving processes. We conclude with a summary of the research findings, a discussion of the study’s limitations, and suggestions for future research directions.
Further Research in the Chinese Mid-cap Stock Market by Using the Markowitz Model, Capital Asset Pricing Model, and Fama-French Model
Authors: Xu Yukun
Published: October 2024

This article aims to identify the optimal portfolio of 10 mid-cap stocks in the Chinese stock market. Markowitz model is used to analyze the data from January 1, 2024, to July 1, 2024, including 117 trading days in total, and give some advice on portfolio selection. Markowitz’s model uses “Solver” in Excel to generate different portfolios and gets the optimal portfolio when the Sharpe ratio becomes the largest. In the Capital Asset Pricing Model (CAPM) and the Fama-French model, they can judge whether the stock is worth investing by the result given by linear regression in Excel. It turns out that the optimal portfolio of Markowitz has a Sharpe ratio of 2.14. Although the CAPM and Fama-French Model don’t give an exact percentage of each stock, they can take more conditions and factors into consideration to give a better judgment of the amount of the profit of each certain stock. The research fulfills the blank of using these models in the Chinese mid-cap stock market finding some supporting ideas from CAPM and Fama-French Model.
Social Movements and Environmental Activism: Interactive Mechanisms of Leadership, Organization and Resource Mobilization
Authors: Min Zijin
Published: October 2024

The paper focuses on the interactive relationship between leadership, organizational structure, and resource mobilization in environmental protection activities, revealing their pivotal roles. Leadership, as the core driving force, guides the vision of environmental conservation and unleashes the potential of the team. The organizational system serves as the cornerstone for efficient operation, ensuring clear responsibilities, information flow, and resource sharing while fostering team and cultural development. Resource mobilization provides the material foundation for activities, enhancing their momentum and effectiveness through diversified funding and efficient allocation. These three elements are tightly interconnected, forming a synergistic effect that boosts the efficiency and sustainability of environmental protection activities. Strengthening leadership, optimizing organizations, and expanding resource channels are the crucial pathways to constructing an efficient environmental protection mobilization mechanism, holding profound significance for the cause of environmental protection.
Exploring the Communication Impact of New Media Platforms Based on Pet-Based Short Videos
Authors: Zhou Yuchen
Published: October 2024

In recent years, pet short videos have attracted a large number of viewers as a popular topic, and as a result, have raised awareness of pets and stray animals. However, to shoot eye-catching video content, some creators abuse or abandon pets. These problems not only cause harm to the animals but also trigger a social crisis of trust, accompanied by ethical and legal issues. This paper explores the reasons for the popularity of pet-based short videos and the positive and negative impacts on the public and society of this issue. The study will adopt a case study research method to analyze the overall influence of pet bloggers by collecting relevant videos from short video platforms; meanwhile, popular videos and comments involving pet abuse and other incidents will be analyzed for content and public opinion. The final results of the study show that although pet short videos can effectively raise public concern about pets and stray animals, their negative ethical and social effects cannot be ignored, such as pet abuse and blind pet ownership, which hurt society. Future research should further focus on how to improve the ecology of short-form pet content thereby mitigating the negative social and animal impacts of pet-based short-form videos, thus enabling more positive impacts of video communication.
The Impact and Guarantee of Policy Changes to Migrant Children on Educational Equity in Guangzhou
Authors: Feng Yang
Published: October 2024

In 2018, Guangzhou announced that it would adjust the policy on the advancement of the high school entrance examination in other places in 2021 and relax the restrictions on the advancement of children who have migrated with their parents to Guangzhou. It is of great significance to the migrant children who are educated in Guangzhou and take the Guangzhou high school entrance examination. However, there is a lack of adequate investigation into the impact of this change on the migrant children as individuals. This study will conduct interviews with a certain number of samples based on the method of qualitative research, taking Guangzhou as the scale, to explore the subjective feelings of the migrant children about the policy changes, and analyze the impact of the policy relaxation on the academic performance of the migrant children based on their personal family background and school experience. The results show that the relaxation of the policy has a positive and encouraging effect on the academic performance which is not only beneficial to the migrant children, but also ensures a certain degree of educational equity, and also has certain reference significance for the adjustment of education policy.
Early Grammatical Development: Nature or Nurture?
Authors: Wang Yuhan
Published: October 2024

As many people have become concerned about early language acquisition and hold their own different perspectives, the emphasis has been put on this aspect and abundant research studies have been implemented. Both parties held firm to their own views, one believes that can be attributed into input-driven tradition which relies more heavily on our environment as opposed to the other group supports Chomsky’s universal grammar, that considered as early language acquisition aptitude is the nature and innateness of individuals that infants are born with. In this review, we are going to show our views and reveal the weakness of mechanism of universal grammar (UG) and its nature. We show the arguments for and against UG and some ideas about input-driven theory at the same time, to demonstrate our critical analysis to nature and nurture. In my view, I reckon that environment has a universal impact between UG And input-driven, referring to the outcome of previous studies, children might use universal grammar more in their early stage and are affected more by the input-driven in the subsequent learning phase, but both of them cannot be separated from the environment. This research clearly explains how children acquire a language in the early stage, which promotes the development of language pedagogy.
Computation and Applications of Gaussian Integrals in Mathematics and Applied Sciences
Authors: Chen Jiayi
Published: October 2024

Gaussian integrals, particularly those involving the function e−x2 , play a central role in various fields, ranging from physics to finance. This paper explores the computation of Gaussian integrals, beginning with the fundamental integral in the interval −∞ to ∞, which yields π . The author extends this analysis to integrals involving powers of x multiplied by e−x2 , showing their relevance in calculating moments in quantum mechanics and probability theory. Additionally, the author discusses the applications of multivariate Gaussian integrals in machine learning and statistical mechanics, where they are key to solving problems in high-dimensional spaces. Practical examples are provided from quantum mechanics (path integrals), statistical mechanics (partition functions), finance (option pricing models), and machine learning (Gaussian processes). Through these examples, the paper highlights the versatility and universality of Gaussian integrals as essential tools in both theoretical and applied contexts. The integral’s widespread applicability reflects its importance in connecting mathematical theory with real-world phenomena. This work highlights the role played by the Gaussian integral.
Comparison of Machine Learning Models and Feature Importance Investigation of Intelligent Fault Diagnosis Methods for Robots Based on Datasets Across Various Distributions
Authors: Zhang Yiyang
Published: October 2024

Intelligent fault detection is an important component of the industrial and automation fields. However, conventional research on intelligent fault detection mainly focuses on industrial production equipment, while there is little research on intelligent fault detection scenarios for robots. Based on a hexapod robot joints dataset, this paper investigates intelligent fault diagnosis tasks in the field of robotics. Firstly, the dataset is clustered into two new datasets with different distributions through k-means method, which is used to simulate the practicality of imbalanced distribution between healthy and fault data. Afterwards, several multioutput machine learning classification models were established to predict robot joints with faults. In two datasets with different distributions, the larger one is used as the training set and the smaller one is used as the testing set. The article compares the performance of these models with prediction accuracy as the main indicator. And based on the results, the paper selects the model with the highest accuracy for further exploration of feature importance. Finally, the article explains the significance of the results and analyzes possible reasons. The experimental results show that the random forest model better overcomes the problem of data distribution differences and has the highest accuracy. In the random forest model, the importance of position data representing position error is higher than that of slope with respect to the axis data representing angle error. This result may be related to the distribution of feature values and the fact that position data contains more crucial information.
The Impact of Food Processing Flows and the Additive on The Nutritional Content and Values of Food
Authors: Xu Lejia
Published: October 2024

Food processing plays a pivotal role in influencing the nutritional content and value of food. Recent advancements in food processing techniques and the use of additives have led to substantial changes in nutrient levels and bioavailability, especially increasing the safety of food. This paper examines the dual impact of various food processing methods and additives on nutrient levels and bioavailability. Key processing methods such as thermal processing, cold processing, fermentation, drying are classified to primary and secondary processing and being explored for their effects on food nutrients. Furthermore, the role of additives, including natural substances like ascorbic acid, is also discussed in extending shelf life and improving flavor and appearance. Through a comprehensive review of existing literature, this study finds that while food processing can enhance certain aspects like safety and convenience, it can also lead to the loss of essential nutrients and the nutritional value. Besides, the study found that the loss of nutrients may focused on the preparation stage, which also called primary food processing. However, the increase of nutritional value and content may increase in the secondary food processing through the effort and rich experiment of human, like fermentation. The findings highlight the importance to distinguish the function and impact of various food processing methods to the food nutrition. Furthermore, it also emphasizes the need for balanced food processing approaches to optimize nutritional value while mitigating adverse effects on health.
Impact of Pre-trained Weights on MobileNet Architectures for Animal Classification
Authors: Shi Yusen
Published: October 2024

This study aims to address the problem of improving animal image classification accuracy using different versions of MobileNet. Accurate animal classification plays a vital role in biodiversity protection, environmental monitoring, and agriculture. The research is significant because existing studies focus on specific models and datasets, leaving a gap in the comparative performance analysis of MobileNet versions. To address this issue, MobileNet V1, V2, and V3 models were utilized, both with and without ImageNet pre-trained weights. The models were trained on a dataset composed of 30,179 images from two sources, covering 13 animal categories. The experiment involved training the models over 10 epochs using a standard configuration of the TensorFlow framework, with accuracy serving as the primary evaluation metric.The results showed that MobileNet V3Large, with pre-trained weights, achieved the highest accuracy (97.43%), outperforming V1 and V2. Using pre-trained weights consistently enhanced performance, as models without pre-training exhibited lower accuracy and slower convergence. This study contributes by providing a comprehensive comparison of MobileNet versions in animal classification tasks, demonstrating the importance of pre-training and model architecture optimization for achieving high accuracy in image classification.
Discovery: The Impact of Performing Arts on Early Education
Authors: Cai Ziyuan
Published: October 2024

Art is a vital component in our life, performing arts education is crucial for children’s development, positively impacting their cognitive, social, emotional, and academic growth. This paper reviews studies showing that involvement in performing arts like music, dance, and theatre improves critical thinking, creativity, problem-solving skills, and academic performance. It also enhances social skills such as cooperation, communication, and empathy, while providing an emotional outlet for children to express and manage their feelings.
Research of Silicon and Zinc Oxide
Authors: Yang Yiran
Published: October 2024

As electronics develops, zinc becomes more and more useful and electronics producers consume more and more silicon because of it is abundant, it has a mature processing technology and it has a high carrier mobility. Those advantages made silicon perfect for integrated circuits. However, in some cases, silicon is no longer the best choice. Despite silicon’s advantages, there are problems for silicon. For example low breakdown voltage, low thermal conductivity and indirect band gap. Although silicon is still the most commonly used semiconductor, another type of semiconductor, zinc oxide starts to become more and more important in electronics. However, the two materials are used differently. This paper aims to provide the various properties of silicon and zinc oxide, and explain the reasons for their applications in electronics. The two materials are indispensable although they have a lot of disadvantages. Engineers must carefully consider the advantages and limitations of each material for specific applications to make optimal material selections.
An analysis of the reasons why moral intuition cannot be fully trusted in moral judgment
Authors: Gao An
Published: October 2024

Moral intuition is an individual’s moral choice and evaluation based on subconscious moral standards when facing moral situations without relying on empirical evidence and logical reasoning. This article starts with the definition of moral intuition and analyzes whether moral intuition is reliable in the process of moral judgment. The article starts with moral judgment in the moral ambiguity zone and finds that moral judgments based on moral intuition are unreliable in moral dilemmas. Secondly, the article further analyzes the variability of moral standards and explains that moral judgments based on moral intuition may lead to retrospective moral conflicts and regrets after changes in moral standards. Finally, the article further points out that moral intuition is easily influenced by external pressure, which will further affect the effectiveness of moral intuition in moral situational judgment. In summary, the article concludes that pure moral intuition is not worthy of full trust, and a comprehensive judgment of rational analysis and moral intuition can better help us make judgments about moral situations.
Analysis the State-of-art Exoplanets Exploring Schemes: Radial Velocity, Transit and Gravitational Microlensing
Authors: Zhang Chuhan
Published: October 2024

Since the first planet beyond the solar system managed to be examined in 1995, the concept of “exoplanet” has been created. The exoplanet, officially defined as extrasolar planets circling other stars that are not part of the solar system, quickly arose an influential curiosity, as scientists began to consider how many stars and planets truly exist in the periphery of the solar system and what the properties of these objects have. In the following years, a series of detection schemes appeared and applied to this theme. This study has listed five of them that are utilized most in current decades, providing a specific view of the Radial velocity method, Transit method, and Gravitational microlensing method. Each of these three explores exoplanets depends on different theories, like Doppler shifts of the RV method, the light variations of the transit method caused by the shadow of planets, and the subtle peaks of luminance of the microlensing method. By outlining the information of several instruments, the goal is to obtain a comprehensive understanding and concise synopsis of the methods employed in the exoplanetary area, which may provide with fresh inspiration to create more sophisticated appliances by understanding the underlying principles of the ones that already exist.
Research of Gallium Nitride as Next-Generation Semiconductor
Authors: Ye Ziwen
Published: October 2024

In today’s society, semiconductors have become an indispensable material in people’s lives, as they are closely related to daily needs such as internet access, power supply, and computing. The most widely used semiconductor material currently is silicon. Its cost advantage in the early stage made other semiconductor materials lag far behind. As semiconductor manufacturing has progressed to nanoscale and even 3-nanometer technology, problems with silicon semiconductors have become increasingly apparent. As the size of silicon transistors continues to shrink, quantum tunneling effects such as leakage current and gate leakage current have caused power consumption to increase continuously. Moreover, the silicon manufacturing process requires harsh conditions such as high vacuum, high temperature, and multiple steps, which increases the production cost and process complexity. Therefore, people have pinned their hopes on the next-generation semiconductor material, gallium nitride (GaN), as an alternative to silicon. Given its superior properties such as wider bandgap, higher electron mobility, and better thermal conductivity, gallium nitride (GaN) offers promising solutions to overcome the limitations of silicon-based semiconductors.
Research on the Updating and Selection of Greening in Sponge Cities
Authors: Huang Zepeng
Published: October 2024

With the frequent occurrence of urban waterlogging, the concept and construction of sponge city have been carried out in many inland cities. Urban greening is not only an integral and important part of sponge cities, but also has a considerable impact on climate and urban landscape. This paper analyzes the function and existing problems of sponge city greening. In the past planning, the selection of certain trees brought problems such as flying catkins and pollen to people’s lives. Not only are they an allergen for many residents, but they also pose a fire hazard. At the same time, the construction of some green facilities lacks rationality. They are not only difficult to play the role of interception, but also easy to water in the flower bed in rainy weather, resulting in root rot. In addition, this paper also discusses the improvement of tree species and greening facilities. For some problem trees which are planted in a large area and difficult to renew in a short time, some mitigation measures are put forward. Finally, the vertical greening and rainwater gardens which have great potential in the green selection of sponge city are introduced. This paper is devoted to exploring more scientific and reasonable choices in the greening of sponge cities, while alleviating urban waterlogging and minimizing the impact on residents’ lives.
Artificial Intelligence-based Enhanced Brain Tumor Prediction: A Comprehensive Investigation of Machine Learning and Deep Learning
Authors: Yuan Wenqi
Published: October 2024

Brain tumors pose a significant threat to patients’ quality of life and survival rates, with traditional diagnostic methods often falling short due to their time-consuming nature and susceptibility to high misdiagnosis rates. Recent progress in Artificial Intelligence (AI), especially in Machine Learning (ML) and Deep Learning (DL), offer promising alternatives for the prediction and diagnosis of brain tumors. AI models, including Support Vector Machine (SVM), Random Forests, Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models networks, have demonstrated superior performance in processing complex medical data, thereby enhancing predictive accuracy and aiding clinical decision-making. This review systematically evaluates the application of these AI techniques in brain tumor prediction, highlighting their strengths and limitations, as well as the challenges faced in terms of model interpretability, data applicability, and patient privacy. Furthermore, this paper explored future prospects for improving model interpretability, transfer learning and domain adaptation for enhancing model applicability, and federated learning for preserving patient privacy. By addressing these issues, Artificial Intelligence can significantly advance brain tumor diagnosis and treatment, ultimately improving patient outcomes.
Regulated Temporariness Reconsidered: Chinese Migrants in Japan and the Confluence of Policy, Infrastructure, and Transnational Factors
Authors: Ye Qicheng
Published: October 2024

This paper examines how Japan’s immigration policies, the role of migration infrastructure, and pressures and expectations from the home country shape the regulated temporariness of Chinese migrants in Japan. It analyzes the regulated temporariness inherent in Japan’s visa system and considers how the uncertainty faced by temporary migrants intensifies their connections with China. The findings indicate that the migration infrastructure between China and Japan not only enhances migrants’ mobility but also restricts their freedom of choice. Furthermore, globalized social media and migrants’ connections to China transmit “pressure from China,” stemming from the two countries’ war history and the resultant political discourse, to Chinese migrants in Japan. The study reveals that migrant temporariness is not solely driven by migration entry and residence policies, as emphasized in existing migration research theories. Instead, migration infrastructure, historical relations between the two countries, and expectations for future development also shape migrants’ regulated temporariness. Migrants voluntarily enter into regulated temporariness, paradoxically achieving greater flexibility within this regulated state. This phenomenon demonstrates a blurred area between flexible temporariness and regulated temporariness, where both coexist rather than transitioning from one to the other. Consequently, it is necessary to expand our understanding of regulated temporariness, taking into account broader transnational factors and informal influences.
Tumor Immunology Unveiled: Advances, Challenges, and Future Prospects in Cancer Immunotherapy
Authors: Yang Zhuotao
Published: October 2024

Among the most important disciplines in the field of immunology, tumor immunology holds a significant place due to its focus on the interaction between tumors and the immune system. With the advancement of immunotherapy technologies in recent years, tumor immunology has emerged as one of the most exciting areas in cancer research. In this article, we will examine in detail the research progress in tumor immunology, including the mechanisms by which tumor cells evade the immune system, the application of immune checkpoint inhibitors, the development of tumor vaccines and their application in clinical practice, and how the tumor microenvironment impacts immunotherapy.
The Advantage of Board Game with Deep Reinforcement Learning and Causal Inference
Authors: Ming Juncheng
Published: October 2024

With the continuous progress of artificial intelligence technology, the deep reinforcement learning method combining behaviourism and connectionism has shown fantastic performance far beyond the human level in chess games. This paper systematically introduces two mainstream chess games, including the standard algorithms of artificial intelligence technology in chess and Go, including Monte Carlo and deep reinforcement learning, and expounds on their algorithm principles and the core reasons for their strong learning ability. In addition, this paper discusses the limitations of existing methods in interpretability and puts forward the possibility of applying causal reasoning to deep reinforcement learning to solve interpretable problems. This study will provide a valuable reference for practitioners in related fields.
The Research on Factors Influencing Air Pollution
Authors: Qiu Yixuan
Published: October 2024

The rapid development of urbanization contributes to that the people’ life is more and more beautiful and their quality of life is better and better. There are a lot of complex and diversity influencing of air pollutants. This paper finds a lot of articles and reports of government and focus on four air pollutants: carbon monoxide, ozone, nitrogen oxides, and particulate matter, including ultra-fine particles. Air pollution can cause three major environmental problems, including greenhouse effect, acid rain, and ozone hole, which can cause significant damage to the lungs and respiratory tract of the human body, such as chronic lung disease, neurological damage, and heart disease. The purpose of the article is to find some factors that affect air pollution. Using the method of multiple linear regression turns out that there is a significant linear regression relationship between human activities, exhaust emissions from vehicles, ships, airplanes, industrial pollutant, meteorological conditions, landform and PM2.5, while the speed and direction of wind fail the important test.
Studies of Quantum Dots in Light-Emitting Diodes
Authors: Zou Muxia
Published: October 2024

Quantum dots are regarded as a revolutionary material especially in display and lighting applications due to their excellent optical properties. Their high quantum efficiency, high color purity, low-cost solution processability, and tunable emission wavelength, facilitate the realization of their potential for display applications. Therefore, researchers have made a lot of effort in the development and characterization of quantum dot light-emitting diodes (QD-LEDs) to achieve high color purity and brightness in light-emitting devices. Meanwhile, with the aim of commercialization, inefficiency, instability, and cost seem to be inevitable challenges that need to be faced. Progression and challenges of using quantum dots in light-emitting diodes are two aspects that are concentrated on in this review. It also affords methods to tackle these challenges. Lastly, the potential applications of QD-LEDs in displays and lighting are investigated. Their potential to revolutionize various industries are also highlighted. By overcoming current limitations and capitalizing on emerging opportunities, QD-LEDs technology will have further applications. This work will help promote in-depth research on QD-LEDs.
Application of Linear Programming in Automatic Pricing and Replenishment Strategies for Vegetable Commodities
Authors: Bai Zhengyi , Chen Miao , Wang Hanzhang , Wen Zhilin , Zhan Chuwen
Published: October 2024

Vegetable commodities are indispensable parts of the current consumer market. Vegetables possess different characteristics such as seasonality, perishability and so on. Thus, it is critical to formulate reasonable automatic pricing and replenishment strategies for vegetable commodities. However, previous kinds of literature have either ignored some of the attributes of vegetables or restricted their application regions to some specific situations. This review compares the pros and cons of diverse linear programming methods and models in previous research, synthesizes the core method, and finally concludes with two relatively reasonable algorithms for pricing and replenishment strategies. It also gives an evaluation of these two algorithms and proposes suggestions for improvement. The purpose of this review is to provide guidelines for subsequent research to optimize existing methods and models to work out more objective and accurate optimal strategies. Ultimately, vegetable sales and supply can better meet the market demand, which can help rejuvenate economic vitality and bring in higher profits at the same time.
Mathematical Modeling of Demand and Supply: Analyzing Corn Market Equilibrium
Authors: Chen Huxin
Published: October 2024

This article offers a comprehensive analysis of the current state and future development trends of the corn industry, with a specific focus on its significance within China’s agricultural sector. As one of the country’s most important grain crops, corn plays a crucial role in various sectors including animal feed, human food, and as a key raw material in industrial applications. Over recent years, the corn market in China has witnessed substantial fluctuations in both supply and demand, driven by a combination of factors such as extreme weather events, shifts in trade policies, and other external influences. The Chinese government has responded to these challenges by implementing a range of measures aimed at bolstering the industry, including policy directives, financial incentives, and enhanced regulatory oversight. Additionally, the promotion of technological innovation has been identified as a critical component in improving efficiency and productivity within the sector. Looking forward, the corn industry is poised for more stable and sustainable growth, driven by deeper integration within the industry and increased international collaboration. These developments are expected to solidify the industry’s role not only within China but also in the global agricultural economy, ensuring its continued contribution to both national food security and economic development.
Bridging the Disparity: An Optimization Model for Equitable Allocation of Urban and Rural Educational Resources in Western China
Authors: Xu Mulong
Published: October 2024

The persistent disparity in educational resources between urban and rural areas in western China remains a critical barrier to achieving educational equity. This study addresses the critical issue by developing an innovative optimization model. The model aims to allocate financial, human, and physical resources effectively to minimize the educational quality gap between these regions. Using information from the China Education Panel Survey (CEPS) and concentrating on Xinjiang as a representative instance, our design integrates budget constraints, minimum resource requirements, and a unique regional prioritization factor. The goal of the objective function is to reduce the squared disparity in educational quality between city and rural regions. It thinks about variables such as Teacher-Student Ratio (TSR), Per Capita Investment (PCI), and Teaching Equipment Perfection (TEP). Our searchings for show that strategic resource distribution can considerably lessen educational inequalities in between metropolitan and country setups, especially by focusing on underdeveloped rural regions. Sensitivity analysis shows that customizing the local prioritization specification makes it possible for versatile policy strategies, ranging from equitable distribution to plainly sustaining rural regions. This study presents a pragmatic structure for boosting resource allotment in education. It supplies vital understandings for policymakers aiming to promote an extra equitable educational environment in Western China.
Exploring the Cosmos: Impact and Significance of the James Webb Space Telescope
Authors: Niu Junru
Published: October 2024

The James Webb Space Telescope (JWST) marks a pivotal advancement in astronomical research, building upon the legacy of its predecessors by enhancing the observational capabilities and expanding the understanding of the universe. This study explores the transformative impact of JWST, emphasizing its role in studying the formation and evolution of early galaxies, stars, and planetary systems. By utilizing its advanced infrared capabilities, JWST can penetrate cosmic dust and observe celestial phenomena that were previously obscured, thereby providing unprecedented insights into the early universe and the origins of life’s building blocks. The findings from JWST are expected to challenge existing theories and assumptions in cosmology and astrophysics, facilitating a paradigm shift in the comprehension of cosmic structures and the fundamental processes governing them. Furthermore, the telescope’s ability to analyze exoplanetary atmospheres will contribute significantly to the search for life beyond Earth. Ultimately, the JWST not only represents a technological marvel but also serves as a cornerstone for future astronomical discoveries, shaping the conception of the cosmos for generations to come.
Recent Advance of Sustainable Polymers for Packaging Applications
Authors: Li Yutong , Su Wenjing , Yao Tiancheng
Published: October 2024

The extensive application of non-renewable polymers has brought about deep environmental concerns, demonstrating the immediate requirement for new biodegradable and biobased packaging choices. The work offers an in-depth look at a wide array of degradable and biobased polymers like Polylactic Acid (PLA), Polyhydroxyalkanoates (PHA), and Polyvinyl Alcohol (PVOH), as well as starch and cellulose materials, discussing their qualities, uses, and the issues faced in food packaging. These types of green polymers, either in new ways or from renewable sources, offer great advances not only from decomposition into harmless parts but also from the reduction of environmental damage. The essay covers the exploitation side of this material, be it the role of PLA in enzymatic, hydrolytic, or microbial degradations, or the main tactical function of PHA in terms of a water barrier aimed at preserving the quality of stored food. Additionally, the multifunctionality of these substances is represented anew in packaging dairy goods, such as fruits, and vegetables, and specialized applications like edible films or water-soluble bags. The participation of compostable polymers has become increasingly vital in the area of food packaging to meet consumers’ demands and to upgrade the present science technologies aimed at guiding food producers who are looking for more sustainability in their business.
A Research of the Integrated Circuit Design and Optimization
Authors: Zheng Xubo
Published: October 2024

With the scientific and technical revolution going on, integrated circuit has been playing a vital role in our daily life ever since. In IC (Integrated circuit) design and manufacturing, layout of the integrated circuit determines its success or failure. However, the constantly decreasing characteristic size and progressive process bring about some new issues. That is why in post-Moore’s Law period, the design and optimization of layouts deserves attention of the whole industry. A properly designed and optimized layout not only results in better performance while using, but also cut out the cost of manufacture. In this paper, the methodology of integrated circuit layout design and optimization are discussed. For design, the author presented the whole flow of how the layout is generated and principles in integrated circuit design. For optimization, two approaches are analyzed. One is shorting out the critical area. Based on critical area theory and methodology of image processing, layouts can be improved in configuration. The method discussed here makes a modification on the previous way to make the optimization more accurate and efficient. The other method is inserting auxiliary polysilicon, which can counteract the impact of channel length change of MOSFETs and focus error as a unavoidable result of the process improvement.
The Application of Near Field Communication (NFC) in Public Transportation: Current Practices, Challenges, and Future Prospects
Authors: Liu Yigeng
Published: October 2024

In the field of public transportation, the introduction of NFC technology can realize contactless payment and automatic ticket checking functions, significantly improving system efficiency and passenger experience. The article first reviews the development history of the public transportation system and the basic principles of NFC technology, and then analyzes the technical architecture and working principle of the NFC payment system. Through the cases of public transportation systems in many cities around the world, the practical application and effect of NFC technology are demonstrated. This article also verifies the transaction speed, stability and user satisfaction of NFC technology in the public transportation system through a series of experimental evaluations. The results show that NFC technology has excellent performance in terms of processing speed, system stability and user satisfaction, especially during peak hours, its advantages are more obvious. At the same time, the research also pointed out the limitations of current NFC technology applications and the challenges faced in practical applications. In the future, NFC technology will become more complete and is expected to provide passengers with a more personalized and intelligent service experience.
A Study of Thing-Narrative in The Masque of the Red Death
Authors: Ding Ruyu
Published: October 2024

The Masque of the Red Death is one of Edgar Allan Poe‘s most important works, which has been studied by numerous scholars from the perspectives of style, narrative aesthetics, and semiotics. However, the relevant interpretation to date has almost completely ignored the description of things in the novel. At present, there is an obvious “material turn” or “non-human turn” in the field of humanities research, that is, the research perspective turns to the “object” components that have been largely ignored in the past, including animals, plants, minerals, ecosystems, landscapes, places, etc. Therefore, from the perspective of the thing-narrative, combined with the social and historical background of the works, this paper analyzes Poe‘s use of thing-narrative techniques through close reading of the text. Through the in-depth analysis of the interaction between things and characters in the story, the author explains how castles, rooms, and ebony clocks, as cultural symbols, agents with subjectivity, exist as noumenon, influence or predict the fate of characters, and promote the development of the plot. And to further clarify Poe‘s theme in this novel - the inevitability of death.
A Review of Chatbots Application on Supporting International Students’ Mental Health
Authors: Fu Xue , Liu Yichen
Published: October 2024

International students face distinctive challenges, including acculturative stress, homesickness, language barriers, and heightened academic expectations. As a result, their mental health and overall well-being have become subjects of growing concern and significance in academic and support communities worldwide. The emergence of AI technologies, like ChatGPT, opens new avenues for international student mental health interventions. However, no specific Chatbot has been approved as effective for this population. This study aims to comprehensively summarize Chatbot applications in mental health counseling and analyze the challenges of applying them to international students. The study found that prominent Chatbots could demonstrate efficacy in reducing symptoms of depression and anxiety through techniques such as cognitive behavioral therapy (CBT). Chatbots are becoming integral to mental health care, providing accessible and evidence-based interventions and psychoeducation. Compared with traditional in-personal mental health counselling, a Chatbot is more functioning in providing personalized therapy sessions, essay accessibility, real-time responses, and suitable assistance, which could solve current challenges and unmet mental health counselling needs of international students. Despite their potential, ethical considerations and privacy challenges must be addressed to ensure reliable and secure use. Continued refinement is needed for international students’ application to enhance multicultural sensitivity, the accuracy of cross-language interpretation, and personalization. The evolution of Chatbots holds significant promise for enhancing mental health support, particularly for international students navigating and coping with their unique challenges.
The analysis of the causes of depression and anxiety among young people and the corresponding solution
Authors: Liu Yijia
Published: October 2024

While the past few decades have witnessed modernization, they have also seen an epidemic of depression and anxiety among young people. From the sociocultural perspective, the reasons include a change in social values from singular values to pluralism and a shift in social culture from collectivism to individualism, so young people’s self-identity is threatened, and they tend to see others as competitors rather than teammates. Besides, social stigma and disdain for mental illness exacerbate mental problems among young people. This paper suggests that developing quality education, strengthening interpersonal relationships, and de-stigmatizing mental illness may be the solution.