bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Impact of RMB Exchange Rate on China's Industrial Structure Adjustment
Authors: Wang Jiawei
Published: January 2024

This paper firstly summarizes the current situation of China's current industrial structure as well as the change of exchange rate system, systematically describes the research object of this paper, and outlines its main features. After that, this paper analyzes the impact of the real effective exchange rate of RMB on China's secondary and tertiary industries for the purpose of the study, takes into full consideration the main factors affecting the industrial structure, selects a large number of control variables, and completes the empirical analysis of China's overall as well as the eastern, middle and western regions by using the panel data of the country's 30 provinces in the 16-year period from 2005 to 2020. In addition, a variety of robustness tests were conducted to fully ensure the stability and interpretability of the model. It is finally concluded that the appreciation of RMB exchange rate will significantly benefit the development of China's tertiary industry, promote the industrial structure from the secondary industry to the tertiary industry, and complete the upgrade of industrial structure. Specifically, for every 1% increase in the real effective exchange rate of RMB, the proportion of tertiary industry in the GDP of the whole country, the east, the center and the west will increase by 0.7850%, 0.7171%, 1.0186% and 0.4873% respectively. Besides, this paper also verifies the indirect impact of the exchange rate on the industrial structure through the three paths of price effect, foreign trade openness and FDI, and verifies the existence of the intermediary effect. According to the empirical results, the price effect, FDI, and the increase of foreign trade openness will promote the advanced industrial structure of China. But both FDI and foreign trade openness are negatively correlated with the exchange rate, which is the core variable of this paper.
Design of a Prairie Garbage Collection Robot Based on Unmanned Aerial Vehicles
Authors: Gu Xuze , Liu Jiaxun , Li Yixuan
Published: January 2024

In response to the increasing concern over environmental protection, particularly with regards to garbage pollution and degradation in prairie scenic areas, a solution is proposed involving a multi-rotor unmanned aerial vehicle (UAV) platform equipped with a mechanical arm for efficient prairie garbage collection. The potential applications of UAV technology incorporating a mechanical arm are discussed. The design of the mechanical apparatus is elaborated, encompassing components such as the UAV's mechanical arm, body frame, and telescopic pole. Consideration factors for the UAV frame are addressed to ensure design balance and stability. By integrating image transmission devices, visual recognition, and control systems, an automated process is achieved for identifying garbage locations, flying to the garbage position, grasping the waste, and successfully depositing it into the collection unit. Research findings demonstrate that the UAV equipped with a mechanical arm offers a proficient solution for garbage retrieval in prairie terrain, mitigating risks and costs associated with cleaning efforts.
Examining the Impact of Green Innovation Management on the Brand Image of Contemporary Fashion Industries
Authors: Bai Yinghua , Ge Rui , Mei Yudi , Yang Kefan
Published: January 2024

This study investigates the impact of green innovation on contemporary fashion brands' brand image. It contends that integrating sustainability into innovation can yield positive outcomes beyond financial gains. The research employs quantitative methods, utilizing surveys to gather data from Zara customers. The findings highlight a positive correlation between consumer purchasing frequency and their willingness to pay for green products. By carrying out cross analysis, an ascending pattern was found between these two items. This pattern indicates a growing demand for sustainable options. The study emphasizes that addressing consumers' environmentally friendly needs can enhance brand image, especially once fundamental and competitive attributes are met. The study also finds the positive relationship between education and income level, and willingness to pay for green products. This research underscores the importance of integrating sustainable practices into innovation strategies, as a means to ensure brand resilience, consumer perception, and long-term success in an environmentally conscious era.
Sensory Inputs Guiding Cognitive Behaviors and Decision Making
Authors: Lin Jiarui
Published: January 2024

Understanding human behaviors and higher cognitive functioning has long been an ultimate goal for psychologists and neuroscientists. Sensory inputs are pivotal in shaping the intricate landscape of complex behaviors and decision-making processes in both humans and animals. The distinct pathways through which the sensory information is perceived from the environment are referred as sensory modalities. Each sensory modality corresponds to a specific type of sensory input or sense, such as vision, hearing, or tactile, enabling us to gather information about the world around us. Sensory inputs integrate information of various sensory modalities at different levels of nervous system and networks, are filtered by attention, forming the perception of the external environment. Through the interaction with various neural circuits and networks, the sensory inputs activate memories and emotions, leading to further information processing. The conversion of sensory information into behavior and decision making is a complex neuronal process involving context evaluation, emotional arousal, memory activation and reward system prediction, with sensory inputs impacting immediate reactions and shaping long-term memory and future responses. This paper is a systematic review of recent literature studies that explain cognition and behaviors, providing an overview of the multifaceted interplay between sensory perception and the execution of complex behaviors, while highlighting the significance of sensory processing in cognitive neuroscience and behavioral science.
Co-movement of commodity futures and stock markets
Authors: Yang Yixi
Published: January 2024

Navigating through the complex interface of commodities and stock markets, this paper elucidates the pivotal role of commodities in sustaining China's expansive economic and infrastructural panorama. Amidst significant urbanization, industrial advancement, and burgeoning population, China's vast commodities sector plays a vital role in satisfying domestic consumption, facilitating export operations, and underpinning sustained growth. Exploring the intricate linkage between commodities and stock markets, the study reveals a positive correlation between commodity futures and stock market dynamics, enriching the discourse on their co-movements within an emerging economy framework like China. The research, while offering invaluable insights into the complexities of these relationships, also lays the groundwork for future studies, inviting exploration into the causal dynamics and global perspectives. The findings, though illuminative, underscore the necessity for holistic, judicious investment and policy decision-making, reminding stakeholders to meticulously consider broader financial landscapes and market peculiarities alongside analytical insights, thus balancing data-driven decisions with strategic foresight in the vast, interconnected world of financial markets.
The Influence of Geographical Location and Economic Location on the Industries of Representative Resource Cities in the Pearl River Delta Region
Authors: Ni Zicheng
Published: January 2024

The Pearl River Delta, a prominent economic powerhouse in China, is home to resource-rich cities like Guangzhou, Shenzhen, and Zhuhai. This study explores how geographic and economic location profoundly influence the industrial development of these cities, shaping their economic landscape. Geographic positioning, defined by factors like proximity to coastlines, natural resource distribution, and transportation networks, plays a pivotal role. Coastal cities excel in manufacturing and trade, while those with fertile land thrive in agriculture and tourism. Economic factors such as market demand, labor availability, cost competitiveness, and access to capital also significantly impact industrial composition. These factors interact dynamically, fostering industrial clusters, influencing transportation infrastructure, and guiding resource utilization. Based on these findings, recommendations for optimized industrial planning are proposed, including industry diversification, infrastructure enhancement, environmental considerations, and urban integration. Policy suggestions aim to balance economic growth with environmental sustainability, workforce development, innovation promotion, inclusive growth, and regional collaboration. This research provides valuable insights for policymakers and urban planners to formulate sustainable development policies in the Pearl River Delta and similar regions, ensuring continued economic prosperity.
Comprehensive Analysis on Topological Structure of Modular Multilevel Converters
Authors: Hu Rui
Published: January 2024

This paper discusses the importance and application of modular multilevel converter (MMC) in the field of power conversion. Firstly, the article starts with the basic concept of MMC and introduces its key role in high-power electrical energy conversion. Subsequently, to address the complexity of MMC, the article describes in detail the common topologies of MMC, including diode-clamped multilevel topology, flyover capacitor topology, H-bridge cascade topology, and switched-capacitor multilevel topology, and also emphasizes the different scenarios in which they are applied to power systems. The last section describes the capacitor voltage balancing strategy in MMC. Several common capacitor voltage balancing strategies, including hardware clamping method, capacitor voltage closed-loop method, and capacitor voltage sequencing method, are comprehensively discussed in this paper, and the principles, advantages, disadvantages, and applicability of each strategy are thoroughly analyzed. Ultimately, this article offers academics and engineers a thorough review of MMC and offers valuable references for further study and real-world applications in the areas of power system conversion and energy management.
Multi-factor Stock Forecasting Based on Regression and Machine Learning Models
Authors: Liu Jiawen
Published: January 2024

In some stock predictions, shallow learning is better than deep learning. With this in mind, this study will adopt a machine learning model and a regression model to predict Chinese stocks to help the stock timing strategy. Different from the previous usage of plenty of indicators as stock features for prediction, this study selected specified multi-factor data and compared the prediction effects of foreign multi-factor data models and China's multi-factor data models to verify. It is found that the prediction effect of the Chinese new salience multi-factor data model which has a better ability to explain market anomalies is better. However, factor models with higher correlations have poor results. Compared with randomly selecting factors or selected factors weight by model, choosing a specific factor model in advance can not only improve calculation efficiency, but also improve prediction accuracy. In addition, compared with the regression model, the prediction results of machine learning also have better performance results.
Deep Learning-Based Lightweight Target Detection
Authors: Jin Zuyi
Published: January 2024

With the rapid development of computer vision technology, target detection techniques have also been widely used in various fields. However, traditional target detection methods usually require a large amount of computational resources and complex model structures, which pose challenges to the realization and deployment of target detection. Therefore, lightweight target detection methods arise. Lightweight target detection methods refer to reducing the computational and parametric quantities of the model as much as possible while keeping the detection accuracy unchanged. The emergence of this method can not only improve the speed and efficiency of target detection, but also make the target detection technology more adaptable to embedded devices and other resource-limited scenarios. In this paper, through the summary of the deep learning method, the impact on the accuracy and the reduction of computation is compared to give a reference to the subsequent embedded systems or small computational amount of system lightweighting.
Brian Electrical Activity Acquisition and Decoding Algorithms For Using BMIs
Authors: Dai Yuanbo
Published: January 2024

Physical disabilities in limbs often lead to severe impacts on personal life and work. However, BMI technology offers hope for amputees or paralyzed individuals to regain control and motor abilities. By reading signals from the brain's motor cortex, BMI technology enables the control of robotic arms, cursors, and more, facilitating functional recovery. The key lies in identifying and extracting information related to specific tasks or intentions from brain signals, where the decoder determines the accuracy, reliability, and sensitivity of the final movements. This paper aims to provide a brief overview of the decoding steps in BMIs. The paper will discuss how the raw signals are acquired from the brain, including invasive BMI and noninvasive BMI, along with the respective advantages and disadvantages of these two approaches. Then, it will explore how these signals are processed into specific commands through both linear and nonlinear algorithms. Additionally, the paper will discuss the strengths, limitations, and feasibility of different decoding methods in human-computer interaction applications. This review offers researchers a preliminary understanding of the acquisition and decoding algorithms for brain electrical activity in BMIs. By evaluating the strengths and limitations of different algorithms, we can provide guidance for designing and improving BMI systems, thereby supporting the achievement of more accurate and efficient human-computer interaction.
Research On Vegetable Replenishment and Pricing Based on LSTM Neural Network Prediction Modeling
Authors: Pan Junmin
Published: January 2024

Vegetables in fresh food supermarkets face challenges with pricing and replenishment since the majority of vegetable varieties have a short shelf life. Fresh food supermarkets must replenish goods based on sales history and demand. In this paper, the pricing and replenishment decisions of fresh supermarkets are predicted using the random forest and LSTM neural network prediction models. The findings indicate that the prediction model in this paper can have a good predictive effect and that the decisions made by fresh supermarkets regarding pricing and replenishment are related to the historical time series data. This paper's study can assist in resolving price and replenishment issues in fresh food stores, which will have some positive economic effects.
A study on the treatment of Parkinson's disease and cognitive impairment with multiple system atrophy by intranasal injection of insulin
Authors: Guo Yuhang
Published: January 2024

Parkinson's disease (PD) and multiple system atrophy (MSA) are both common neurodegenerative diseases. To investigate the effects of intranasal insulin injection on motor function and cognitive impairment in patients with Parkinson's disease (PD) and multiple system atrophy (MSA).Thirteen PD patients and one MSA patient participated in the experiment . Using a random number table method, the participants were assigned to an experimental group (n=8) and a control group (n=6). The experimental group received insulin injection, while the control group received physiological saline injection. Both groups received nasal injection for 4 consecutive weeks. Compare the general information, language fluency (FAS), Montreal Cognitive Assessment Scale (MoCA), Beck Depression Inventory (BDI), and Parkinson's Unified Rating Scale (UPDRS) results between the two groups. Compared to the control group, the experimental group had a significantly higher FAS score, but there was no significant difference in the FAS score before and after treatment in the experimental group, and the prognosis score improved compared to before. In the experimental group, a MSA patient showed improvement in FAS scores during the four week treatment period, with no significant progression of the disease. There was no significant improvement in UPDRS score, BDI score, and MoCA score before and after treatment, the difference was not significant statistically. The use of insulin therapy for cognitive and motor disorders related to PD is safe and feasible. Intranasal insulin may improve functional motor function and cognitive performance in Parkinson's disease.
Evaluation of the Effect of Gel Materials on the Concrete Life
Authors: Zhang Taotao
Published: January 2024

The life of concrete is the main factor affecting the safe service life of a building. The reason is that concrete, as the main structure of a building, plays an important role in structural support and resisting the erosion of natural environment. However, there is growing interest in the lifespan of concrete. At present, the life of some concrete may not be able to meet the requirements of some specific environments such as high temperature, cold areas and deep sea. Based on the existing experimental data and research, the action mechanism of gel material and its influence on concrete are analyzed in this paper. In addition, this paper also summarizes the shortcomings of different types of gel materials, so that designers and construction departments can have a better understanding of gel materials. These findings may contribute to a deeper understanding of gel materials and their potential to improve the life of concrete.
The Development Status, Challenges and Problems of Green Finance in Different Countries
Authors: Shi Zhongruyi , Shao Tongyu
Published: January 2024

This article summarizes, analyzes, and compares existing research on green finance, covering various topics related to green finance such as methods of green finance financing, how to make green finance profitable, how technology and policies can drive green finance, and the challenges faced by green finance. The study primarily focuses on three major countries: China, the United Kingdom, and the United States. It examines the challenges faced by the development of green finance in these countries and proposes solutions to current development issues. The analysis suggests that green finance has significant potential in addressing climate change and improving social environments. However, there are challenges such as lack of awareness about green finance, policy inconsistencies among countries, and the absence of incentive mechanisms. This study has important practical significance for the healthy development of green finance in the future.
Various Flow in a Slender Tube: Mach Number and Domain Shape Influenced by Boundary Obstacles Via Small Disturbance Equations
Authors: Wu Juankai
Published: January 2024

With the exponential advancements in computer hardware and software in recent years, computational fluid dynamics (CFD) has emerged as a highly efficient alternative to traditional fluid mechanics studies. Its prowess in accurately predicting and analyzing flow mechanics has led to widespread adoption across diverse engineering disciplines. This research delves into a numerical analysis of flow conditions utilizing the small disturbance equation (SDE). The study bridges the gap between mathematical formulations and computational language, exemplified through Python code, by harnessing the successive over-relaxation (SOR) method, Neumann boundary conditions (BC), and the ghost point technique. The research underscores its predictions' precision in subsonic and supersonic scenarios by meticulously evaluating the resultant errors. This accuracy is further corroborated by examining overarching flow patterns and Mach number distributions. However, the program's outputs indicate a discernible lack of precision in the transonic case, with errors surpassing acceptable thresholds. This discrepancy is hypothesized to stem from potential mischaracterizations of points at the inflow and outflow boundaries. This study, thus, offers invaluable insights while also highlighting areas necessitating further refinement.
Stock Value Analysis Based on the DDM Model: An Example of Company COSCO
Authors: Tian Fengyuan
Published: January 2024

The stock market is unpredictable and there are many ways and means to analyze stocks, DDM is still worth to be analyzed and discussed as one of the less common but very basic and important analytical means. This paper, will focus on GGM in DDM and apply it to COSCO Shipping Holding to find out whether DDM is applicable to this stock and whether this stock is worth investing in. In this paper, it obtains the relevant data from the company's annual report as well as the financial data website, calculate and derive the final estimate by using the DDM formula. The estimated value will be compared with the actual value to determine whether the stock is worth investing in or not. In the end, the estimated value calculated by DDM is much larger than the actual stock price, which means that the stock is seriously undervalued and may bring great returns according to the principle of DDM. Combining the actual situation and objective factors, this paper concludes that the principle and formula of DDM are very important, but it is not applicable to all situations. The necessary conditions are more demanding, and they need to be combined with many other theories to analyze many aspects.
Bicycle Sharing Demand Analysis and Operation Community Division based on Riding Spatial-Temporal Data -Taking Beijing as an Example
Authors: Wu Muran
Published: January 2024

Under the background of vigorously building sustainable cities, shared bicycles have developed rapidly, and some operation and management problems have gradually become prominent. In recent years, the research based on spatial-temporal big data of shared bicycles mostly focuses on the analysis of hotspots in time and space, and the current management of shared bicycle is mostly divided into operating communities by administrative divisions. The construction of bicycle operation management is not carried out in combination with the characteristics of spatial-temporal data and actual needs. Based on the shared bicycle order data in Beijing, this paper analyzes the basic spatial and temporal characteristics of shared bicycles, and uses geographically weighted regression model to analyze the impact of various types of built environments on the demand distribution for shared bicycles. Finally using Fast Unfolding algorithm to identify communities with close demand for shared bicycles. The study finds that the overall utilization rate of bicycle resources is currently low, and various types of POIs have different ranges and degrees of impact on parking demand, as well as the division of bicycle communities does not fully coincide with administrative divisions. This study helps to understand the overall demand and impact characteristics of shared bicycles, provides a basis for the development of differentiated strategies for the delivery, management, and scheduling of shared bicycles, and helps urban managers to optimize the design of the shared bicycle system.
Impact of Digital Financial Inclusion on Small and Medium-Sized Enterprises
Authors: Yu Xiaoke
Published: January 2024

With the rapid development of digital technology, Digital Inclusive Finance (DIF) has emerged and gradually become an important part of financial services. DIF expands service coverage and financing channels through digital technology, bringing a new kind of development opportunity for Small and Medium-Sized Enterprises (SMEs). This paper analyses the enterprises broken down into different regional and different property rights systems in order to study the degree of impact of DIF on SMEs from the perspectives of financing constraints and transformation and upgrading. It then draws the conclusion that DIF has a facilitating effect on SMEs. It can alleviate the financing constraints and reduce the financing cost of SMEs through DIF, which can help to reduce the financing cost of SMEs and improve their development. financing constraints and reduce financing costs, thus promoting innovation and upgrading of SMEs. The effects of digital financial inclusion on SMEs vary depending on location and ownership structures. From a regional perspective, digital financial inclusion has a greater impact on SMEs in the central and western regions than it does on SMEs in the eastern regions. From the perspective of property rights, non-state-owned SMEs have a greater impact on digital financial inclusion than state-owned businesses.
Analysis of Biosensors and Their Frontier Application in the Medical Field
Authors: Gu Weizhong
Published: January 2024

With the progress of science and technology, the application of biosensors is more and more widespread. At present, traditional medical detection has the disadvantages of long cycles, low accuracy, and a cumbersome process. Because biosensors perfectly meet the requirements of timeliness and accuracy in medical detection, they are increasingly used in the field of cutting-edge medicine, which greatly improves the efficiency of diagnosis and treatment, reduces the probability of misdiagnosis, and reduces the pressure on the medical system. It is crucial for the early detection of the illness, particularly for the early detection of malignancy. Biosensors have bright prospects and high value in the field of modern medicine. In this paper, the composition and operation principle of biosensors is introduced. The frontier applications of biosensors in some medical fields are analyzed, and their advantages and disadvantages are obtained. The future development of biosensors is prospected, aiming to provide a certain direction for its future development.
The Technical and Industrial Challenges Faced by Fuel Cells—A Case Study of PEMFC And SOFC
Authors: Hao Shuai
Published: January 2024

Fuel cells are a proficient and environmental technology that directly transmutes chemical energy into electrical energy. Within this domain, Proton Exchange Membrane Fuel Cells (PEMFCs) and Solid Oxide Fuel Cells (SOFCs) are at the forefront of small-scale applications and industrial power generation, respectively. The efficacy and economic feasibility of PEMFCs are contingent upon key components, such as the proton exchange membrane and mass transport layers. Consequently, contemporary research is geared towards the optimization of these components to augment performance while concurrently mitigating costs. Conversely, the operational capacity of SOFCs at elevated temperatures has spurred research efforts aimed at material enhancement and the diversification of applications. These advancements encompass the integration of additives, the exploration of novel membrane fabrication methodologies, and the development of innovative structural designs for fuel cells. Although there have been progressive strides in the realm of hydrogen-powered vehicles and fuel cell power plants, their assimilation into the market has been somewhat languid, with commercialization still facing a myriad of challenges. Notwithstanding these hurdles, it is anticipated that with the ongoing evolution of technology and a concomitant reduction in costs, fuel cells will persist as a pivotal component in the quest for energy cleanliness and efficiency.
A study on vegetable stocking-pricing decision-making in supermarkets based on regression analysis and planning models
Authors: Ma Mingyu
Published: January 2024

Vegetable products in the superstore require high freshness and must meet market demand while maximizing profit. This study proposes a pricing strategy based on the proportion of sales for each product category. Using data from the previous four weeks, a relationship model is developed. Additionally, a BP neural network regression is used to analyze the relationship between vegetable sales and cost-plus pricing. A planning model is then established to determine the replenishment quantity and daily maximum profit, with the objective of maximizing profitability. Adjustments to the replenishment program and pricing strategy are made to accommodate changes in sales conditions. By extracting sellable goods data and using prediction results, the planning model is further refined by setting constraints such as ordering limits, historical pricing records, and quantity limits. This model enables the determination of replenishment quantities and pricing strategies for each individual product, ultimately maximizing profit for the superstore.
The Influence of Block-Chain Technology in Chinese Smart Logistics
Authors: Zhong Jiaqi
Published: January 2024

Block-chain technology is an advanced database mechanism that allows information to be shared transparently within an enterprise network. The scale of Chinese logistics market is developing rapidly, whether it is e-commerce express delivery, cold chain transportation, international trade logistics and other fields, it is developing rapidly. Through an overview of block-chain and smart logistics, this paper discusses the feasibility and advantages of using block-chain in smart logistics. Including block-chain technology and the decentralization of smart logistics transaction subjects, efficient transaction mechanism, and transaction process security to discuss the important role of block-chain in smart logistics. At the same time, it analyzes the practical application of block-chain in SF Express, and analyzes the integration and development of block-chain and smart logistics. And at the end, it puts forward suggestions and prospects for the development of block-chain in smart logistics in the future.
Impact of the Business Environment on the High-Quality Development of International Trade Based on Data from 30 Provinces in China
Authors: Zhou Murong
Published: January 2024

Superior growth in international trade is an important part of economic development. A good business environment is a necessary condition for economic development as well. This paper picks data from 2013 to 2021 and uses 30 Chinese provinces as research goals, excluding Tibet. Based on the new development concepts of innovation, coordination, green, openness, and sharing, this paper establishes the measurement system of international trade. The business environment is evaluated from four aspects: the public supply level, human resource level, market environment level, and government services level. The connection between the business atmosphere and the superior growth of international trade is empirically studied through the fixed effect model. The entropy weight method is used to assign weights to the indicators. The robust test of the regression model was carried out by the TOPSIS method. This paper also does the fractal regression and heterogeneity analysis. The research finds that: (1) Improving the business environment plays a big part in encouraging the high-quality development of international trade. However, the size of its impact varies from region to region and from level to level of economic development; (2) The business environment mainly affects international trade through human resources, market capability, and government services. The impact of public supply is significant but relatively weak; (3) The business environment has a gravely detrimental effect on the openness relative index of international trade, which includes the index of international commerce dependence. According to the research conclusion, three suggestions are put forward: improve the level of government affairs, pay attention to the introduction of talents, and optimize the market structure.
Analysis of the Configuration for State-of-art AI Chips and Applications
Authors: Lin Yangrui
Published: January 2024

As a matter of fact, the artificial intelligence chips provide core hardware computing support for the rapid development and application of artificial intelligence technology in recent years. In reality, they are the key driving force for the efficient, rapid application and energy-saving operation of artificial intelligence technology and are also the strategic commanding heights of the era of artificial intelligence. The purpose of this study is to analyze and summarize the architecture and practical application of the most advanced artificial intelligence chips. On this basis, this study will first analyze the development process of AI chips. At the same time, the classification, main architecture and working principle of AI chips are introduced. In addition, the study explains the application of AI chips in the existing mainstream market. Finally, this study will analyze the limitations of its research as well as look forward to the future. Overall, these results shed light on guiding further exploration of AI chips.
Architectural Model Design Research Based on Social Morphology Mapping
Authors: Zhang Qinrui
Published: January 2024

This study aims to explore the issue of declining social mobility in China, against the backdrop of China's economic development since the initiation of reform and opening-up in 1978. This study introduces an innovative urban spatial concept that vertically distributes three major social strata within a building, aligning them with different functional spaces. By closely integrating production resources and factors of production, this study regulates workers' labor time on the one hand and encourages them to maintain substantial purchasing power on the other hand. This novel perspective constructs a distinctive urban landscape. Since the initiation of economic reforms and opening up in China, the economy has experienced vigorous growth. However, social mobility has gradually decreased. Against this backdrop, this study takes the three major social strata as its foundation and reimagines the spatial arrangement within cities. By integrating social strata with functional spaces, the spatial manifestation of various strata's status and roles is the goal. Meanwhile, the introduction of production factors guides the behavior of workers. On one hand, labor time is regulated to seek more production resources. On the other hand, workers are encouraged to maintain higher purchasing power. However, it's worth noting that the current notion of "humans should be doing the same things" is gradually evolving into a utopian aspiration. Through this study, the aim is to encourage individuals to reconsider the connection between social mobility and class, along with exploring ways to establish a more inclusive and fair future within urban environments.
Analysis of the Principle and Configuration of AI Chip and the State-of-art Applications
Authors: Lu Zhexi
Published: January 2024

AI chips are specialized hardware components designed to accelerate AI operations, making them crucial for various industries. This study provides a comprehensive overview of artificial intelligence (AI) chips, their structure, principles, fabrication process, applications, limitations, and future outlooks. The article discusses the components commonly found in AI chip structures, including processing units, memory hierarchy, interconnects, and specialized hardware units. It delves into the principles guiding AI chip design, such as specialization, parallelism, low precision arithmetic, and hardware-software co-design. The fabrication process of AI chips involves intricate steps, including design, mask generation, deposition, lithography, and testing. Applications of AI chips span data centers, edge computing, autonomous vehicles, healthcare, NLP, and more. Despite their advancements, AI chips have limitations, such as energy efficiency and specificity, but the future holds promise with improvements in energy efficiency, specialized acceleration, hybrid architectures, and quantum AI chips. Overall, AI chips have transformed the AI landscape, setting the stage for continued innovation and impact in various sectors.
Predicting US Airbnb Listing Prices by Machine Learning Models
Authors: Yang Yuchen
Published: January 2024

This paper addresses the prediction of Airbnb property prices using 2023 open data through the application of machine learning methodologies. In the context of the flourishing sharing economy, accurate price prediction within the short-term rental market holds great significance for hosts and users alike. Drawing on the 2023 Airbnb open dataset, the study employs three distinct models – Linear Regression, Random Forest, and XGBoost. Rigorous training, testing, and evaluation of these models reveal insights into their predictive capabilities. The focus centers on assessing model fit using essential evaluation metrics including R-squared, Mean Squared Error, and Root Mean Squared Error. Results demonstrate that the XGBoost model outperforms both Linear Regression and Random Forest. After parameter tuning, the best parameter for XGBoost regressor exhibits the lowest prediction error and highest R-squared value, showcasing its ability to capture intricate patterns within the data. This outcome underscores the potency of advanced ensemble learning techniques for precise property price predictions. The study's implications are substantial, offering hosts and potential guests improved decision-making insights.
Research on the Insomnia Drugs and Comparison
Authors: Song Yiqing
Published: January 2024

Insomnia has become very common now due to the increasing work pressure. Nowadays, people often work overtime until midnight or engage in jobs where day and night are often reversed. The emergence of this new working model is also one of the reasons why insomnia has become common. Insomnia has many disadvantages. Short-term insomnia can cause patients to have poor concentration the next day and feel weak. Long-term insomnia will lead to an increase in cardiovascular and cerebrovascular diseases, and it will also weaken the resistance and increase the patient's risk of disease. This article will mainly compare several common drugs for treating insomnia to analyze which drug is more competitive. During the comparison process, the author will compare advantages, prices, side effects, applicable groups, etc. The result of the comparison is to compare the two drugs in sequence and choose the one with more advantages. The author draws conclusions by reviewing a large amount of information.
CAR T Cell On-Target, Off-Tumor Toxicity in Solid Tumor Treatment: Generation and Management
Authors: Wang Tianyi
Published: January 2024

CAR-T cells involve incorporating T cells with a synthetic receptor to target tumor-specific molecules and this therapy enhances the patients’ own immune system to combat tumors. CAR-T cells are successful to elicit an enduring response to hematological malignancies but remains unsatisfactory during their application in solid tumors. Mostly, OTOT is a main safety concern during trials of CAR-T cells, which has led to destruction of healthy tissues and their normal functions, potentially threatening the patients’ life if damage were done on essential organs. OTOT arises from the CAR-T activation upon normal cells due to presence of tumor-like molecules on their surfaces, accompanied by potential cytokine release syndrome (CRS), which further lyses normal tissues. In this review, the structure and mechanisms of CAR-T cells will be described. More importantly, we will discuss the OTOT generation in CAR-T trials targeting solid tumors and available methods to mitigate this problem for advancing CAR-T application.
Research On Pricing Strategies Based on Time Series Analysis Model and Greedy Algorithm Model
Authors: Li Hongfeng , Jia Yuyan , Zhang Yuzhu
Published: January 2024

The issue of replenishment and pricing arrangements for vegetable categories and individual items in future supermarkets is crucial. This article is based on a time series analysis model to analyze the correlation between different categories of single products, and constructs a dual objective greedy algorithm optimization model, constrained by maximizing profits and minimizing operational loss rates. Predict the total replenishment amount and pricing strategy for 50 types of vegetables, including categories such as cauliflower, leaves, chili peppers, eggplants, edible mushrooms, and underwater acoustic root canals. Next week's predicted sales and prices are: cauliflower 0.381 kg, 11.7331 yuan/kg; Leaves 5.9-6.084 kg, 9.031-8.504 yuan/kg; Chili peppers range from 8.327 to 5.8 kilograms, with a price of 15.632 to 14.793 yuan/kilogram; Eggplant 10.962-11.321 kg, 7.843-7.861 yuan/kg; Edible mushrooms 5.5118-5.374 kg, 12.323-11.476 yuan/kg; The underwater sound root canal costs 3.321-3.378 kilograms, 25.619-15.981 yuan/kilogram.
Current and Future Treatments of Pancreatic Cancer
Authors: Han Ziyu , Luo Qianyuan , Zhuang Ran
Published: January 2024

Pancreatic cancer (PC) is among the most common tumors in the world. Its incidence rate increases with age. More than half of all pancreatic cancer deaths occur in developed regions. The overall prognosis of pancreatic cancer is pretty bad. Only less than 15% of patients can be treated with surgery, and less than 10% of patients are alive after 5 years. The main purpose of the study is to summarize the current and future expectative treatments for pancreatic cancer. To illustrate it more particularly, we also add the basic introduction of pancreatic cancer, including: types of pancreatic cancer, risk factors of pancreatic cancer, symptoms, treatments, new trend, and the problem existing in the modern treatment. Our objective is to let people see and understand the pancreatic cancer clearer and more systematically. The way we study it is by searching information from the internet, picking the most important part, and putting them distinctly. By summarizing the relevant information, the treatments of pancreatic cancer could be improved.
Glioblastoma and the CAR-T cell therapy: A review
Authors: Liao Jingyao
Published: January 2024

Gliomas are the most common and aggressive tumors that develop from the brain. Glioblastomas are also known as grade IV gliomas, indicating them the most aggressive brain tumors. Unlike other cancers that are categorized by stage, gliomas are classified by grade which describes how aggressive the tumor appears under the microscope and its molecular profiles. The grade for gliomas ranges from I to IV, with I is the least aggressive gliomas and IV is the most aggressive one. Glioblastoma originated from glial cells which are a type of brain cells that support and protect brain cells. The common treatments for glioblastomas include surgery, chemotherapy with temozolomide, and radiotherapy. Though patients experience decreases in symptoms and improvement in emotions, the survival rate for the treatments is significantly low. Part of the reason for the situation is the poor efficiency of TMZ due to the medicine penetrating the blood-brain barrier insufficiently. CAR-T cell therapy, an innovative immunotherapy regarded as a self-duplicating drug to treat cancer, is considered to have a better blood-brain barrier penetration, making the therapy a potentially promising treatment for glioblastoma. Besides, the treatment targets and kills tumor cells directly and it is independent of the endogenous immune response, which is greatly repressed in glioblastoma. Unfortunately, due to the restricted number of specific antigens in glioblastoma and their heterogenous expression, results of CART cell therapy for glioblastoma have not indicated clinical benefit. Nevertheless, adjustments can be made in order to improve the CART cell therapy in glioblastoma.
The Prospect of Virtual Technology in Education Field
Authors: Pan Quannuo
Published: January 2024

The virtual technology industry is experiencing exponential growth on a global scale, particularly during and after the pandemic, due to the escalating demand for remote work, online education, digital entertainment, and other related sectors. Currently, virtual technology has made significant progress and breakthroughs across multiple fields and levels; however, it still faces certain technical difficulties and bottlenecks that need to be overcome. This paper focuses on the application of virtual technology in the field of education and its transformative impact on traditional education. From an educational perspective, this paper explores the influence of virtual technology on the current education landscape. Additionally, it analyzes the positive impact and benefits of virtual technology on the overall development of students in education. The paper further discusses and summarizes the transformative changes brought about by virtual technology in the realm of traditional education. For a comprehensive understanding, this paper meticulously categorizes and discusses various applications of virtual technology in the education sector, such as virtual reality experiences, distance learning, cross-cultural communication, art, and creation, as well as vocational and simulation training. The paper concludes with a summary and outlook.
Analysis of the Connection Between Executive Remuneration and Corporate Fiscal Achievement
Authors: Luo Chenjia
Published: January 2024

The interplay between top executive pays and fiscal achievements remains at the forefront of discourse in organizational leadership and governance frameworks. Understanding the connection between executive incentives and performance outcomes is crucial for effective corporate governance. This study dives deep into the tapestry of research that dissects these nuanced interactions. Executive compensation, a critical component of corporate governance, directly influences the behaviors and decision-making of top executives. Simultaneously, financial performance serves as a key indicator of a company's operational health and ability to generate value. According to the analysis, the relationship between both is complete and multifaceted. In some cases, higher levels of executive remuneration are positively correlated with improved financial performance, while negative correlations also exist. The relationship between the two is influenced by characteristics, e.g., corporate governance, industry characteristics, company performance, and individual executive characteristics. Numerous studies are currently available on the relationship between the two. However, it is important to acknowledge the limitations and shortcomings of the existing studies, which may impact the generalizability and applicability of the findings. In conclusion, the nexus between executive compensation and financial output is a complex and multidimensional issue that requires further in-depth research and study.
New Features and Risks of China's Cross-Border Capital Flows and Policy Recommendations
Authors: He Wen
Published: January 2024

With mainland China as its primary focus, this article analyzes the patterns of cross-border capital movements. Examining the characteristics and potential hazards of these flows, the study conducts an in-depth analysis of cross-border capital and provides an exhaustive review of pertinent literature. China's international direct investment outflows increased significantly, while capital inflows decreased, and the scope of foreign direct investments expanded, according to the findings. In addition, the research illuminates forthcoming obstacles, specifically the consequences of the COVID-19 pandemic on the equilibrium of the global economy and the volatility of exchange rates. In order to strengthen China's economic stability, the article concludes with policy recommendations that emphasize the significance of flexible policy mechanisms and the strategic allocation of foreign exchange reserves.