bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Enterprise Development and Brand Marketing of European Denim Brands - Taking Diesel as an Example
Authors: Peng Chenyue
Published: March 2024

Jeans originated in the western United States in the 19th century and were overalls for farmers and workers in the western United States at that time. Jeans became popular and popular among young people in the early 20th century and became a fashion trend. The Italian denim fashion brand Diesel is best known and most iconic for its jeans. Diesel was once a denim leader, known for its bold and creative denim designs, gaining a global following. Diesel's brand positioning has been at a high level from the beginning, and it is often associated with well-known brands to enhance brand value and awareness. Now that the Y2K aesthetic is making a comeback, Diesel is quickly re-entering the industry conversation by capitalizing on current fashion trends and establishing a strong street style among world-famous stars and influencers with a huge social media presence. However, Diesel lacks brand recognition, and the brand needs to be upgraded. In contrast to Balenciaga, which has a similar style but has already joined the ranks of luxury goods, the main reason for Diesel's failure to enter the ranks of luxury goods is that the brand does not have enough historical depth and does not bring enough influence to the fashion world.
Enhancing Credit Card Fraud Detection on Imbalanced Datasets
Authors: Liu Chao
Published: March 2024

Abstract. The use of credit cards is becoming more and more popular in today's society, especially now with the prevalence of electronic payments. Electronic payments are available through bank apps or payment processors like PayPal, Alipay, etc. Without using cash, people are taking advantage of the convenience of them both online and offline. In addition to bringing people the benefits of efficiency and convenience, credit card fraud has also emerged and caused a great deal of economic losses for cardholders, as well as causing great trouble for banks. The primary goal of this work is to identify fraudulent transactions in an unbalanced dataset. The dataset comprises credit card transactions from just two days in 2013 in Europe. In this study, the original data, the original data with Stratified-KFolds, and the undersampled data will be compared. It is found that undersampling, although it reduces the accuracy by a small amount, can greatly improve the detection of fraudulent transactions. Meanwhile, this study uses different models, one is Logistic Regression, and the others are all Tree-Based method. The study analyzes their confusion matrices, ROC curves, and Precession-Recall curves. The results show that in the undersampling rate dataset, the recall, precision, F1 score and accuracy of Xgboost are optimized to 93.2%, 97%, 95%, and 95% respectively, and the AUC of both the ROC curve and the Presicion-Recall curve are optimized to 99%, so this study concludes that XGboost is the best performer. With excellent algorithms, we can better avoid the leakage of information and loss of money in real life.
Robustness Analysis on Stock Market Prediction Method
Authors: Ji Haonian
Published: March 2024

Stock prediction is a vital approach used to forecast stock’s future prices by analyzing historical data. This technique plays a crucial role in assisting investors in making informed decisions in the stock market. There are various methods employed for stock prediction, with the most common being traditional statistical methods and machine learning methods. Traditional statistical methods involve models such as ARIMA and GARCH, while machine learning methods involve LSTM and GRU models.This research paper aims to compare the performance of SARIMA , LSTM and GRU models in stock prediction. To enhance their performance, the models will be optimized by incorporating DWT-ARIMA-XGBoost[1], LSTM-XGBoost[2] and GRU-XGBoost techniques. These methods will then be deployed to predict stock prices, with a subsequent comparison of the results obtained from the three models. After comparing those models, this paper selects four models that have high level of prediction to study their generalization. This analysis will provide valuable insights into the effectiveness and suitability of these models for stock prediction tasks. The paper evaluates these models based on their prediction accuracy, generalization ability.
Stock Investment Strategies with Time-Varying Co-Movements Between Energy Market and Global Financial Market
Authors: Liu Xiang , Wang Xue
Published: March 2024

One of central challenges in guiding investment mangement is how to intelligently identify investment strategies. This paper sets up a deeping learning model for realizing stock investment strategies with time-varying  co-movements between energy market and global financial market: It uses LSTM neural network approach to predict energy stock price, and employs the improved time domain connectedness measures of Diebold and Yilmaz to test the spillover mechanism of market volatility shocks. By  the propsed model,  this paper explores the time pattern of volatility spillover between energy market and stock price in major global financial markets (mainly including stock market) from January 3, 2000 to December 31, 2020.
The Economic Development Process of Russia after the Dissolution of the Soviet Union
Authors: Song Zihan
Published: March 2024

Since the dissolution of the Soviet Union in 1991, the overall economy of Russia has maintained a positive development trend. Among them, oil, natural gas, and mineral resources are some of the most important industrial pillars in Russia, accounting for over 50% of the total revenue of the Russian government. Russia's economy is inseparable from its energy. The single economic structure of excessive dependence on energy resources has also brought many problems to Russia. The economic crisis in 2008, the 14-year Crimean War, the 19-year COVID-19, and the 22-year Russian-Ukrainian War are all great tests of Russia's economic stability. This article mainly discusses the economic development process of Russia from 1991 to the present and elaborates and analyzes Russia's economic situation, structure, and characteristics by sorting out the difficulties encountered by Russia over the past 30 years and corresponding policies. By analyzing the economic development process of Russia, this study can discover many factors that affect the economy and the importance of publishing correct policies, which has great learning significance.
How Different Degrees of Land Reform Affect Economic Development: Comparative Analysis Based on China and India after 1950s
Authors: Zhou Shangjun
Published: March 2024

Whether and to what extent land reform is undertaken is of great importance to a wide range of developing countries as it relates to national economic development. This paper makes a comparative analysis of land reforms in China and India after the 1950s. China's land reform was radical. The landlord class lost control of the land, and China's landless peasants disappeared. India's land reform was more conservative. The landlord class still held a lot of land for a long time, and there are still a large number of landless peasants in India after the 21st century. The comparative analysis shows the differences in land reform policies. This difference led to different results in the development of both agricultural area and industry area between China and India, which indicate that different degrees of land reform have significant impacts on national economic development. The conclusion shows that the land reform which changes land ownership from landlord class to peasant class has a positive effect on economic development. But the land reform of land collectivization does not necessarily play a positive role in the national economic development.
Research on Tesla's Localization Strategy Development in China
Authors: Wu Shinghei
Published: March 2024

Tesla Inc. has been continuously pushing the global automobile industry towards a more environmentally-friendly, smarter direction, the majority of the public believe that Tesla wanted new forces to help it fight against the traditional gasoline car manufacturers. And Tesla has shown its great localization strategy in China, there are multiple factors that contribute to its success, such as setting up the gigafactory in Shanghai, fully considering consumer demand, implementing successful market strategy and applying industry-leading technologies and safety. This research focused on Tesla Inc.'s history in China and the current market condition, intended to delve deep into Tesla's localization in the China market and offered a more comprehensive understanding about Tesla's market and localization strategy. Specifically, this paper explored how Tesla leverages its global influence to affect the China electric vehicle market and compared it with the current study in order to provide a more multi-dimensional perspective to understand the characteristics of Tesla in China, thus providing reference for the development electric vehicle market, and providing a strong base for future research.
Exploring the Company's Risk Management in Equity Investment --Taking Shandong Hanlin’s Betting Agreement as an Example
Authors: Peng Jinyue , Sun Qianya , Wan Yulin , Xie Huize
Published: March 2024

With the full restoration of normalized operation of the social economy, China's equity investment market is hot to pick up. In equity investment, while the investing company obtains greater benefits, there also exists the possibility of loss brought by market risk and non-market risk, so it is necessary to improve the risk management measures of the equity investment company to improve its risk prediction and response ability.It is necessary for investing company to ensure that the investment firm has the ability to deal with all risks that may arise and can proactively address and anticipate them. This paper explores and analyzes the reasons for the failure of Shandong Hanlin's betting agreement, and further summarizes the risk management measures that should be taken by the company in equity investment to promote the healthy and sustainable development of China's equity investment market.At the same time, it also expounds the types of risks that may exist in the investee company, the methods of risk control and the way of selection.
Analysis of Turkey's Investment Potential as an Emerging Market
Authors: Yuan Jiawei
Published: March 2024

The global economy is in a context of rapid change and Turkey is standing out with strong and stable economic growth. As a candidate for EU membership, Turkey is one of the largest countries in Europe, and its favourable geographical location makes it an important energy and logistics corridor linking Europe, the Middle East, the Caucasus and Central Asia. In this paper, by analyzing some important indicators between Turkey and the US, this research tries to provide investors with some guidance on whether it is worthwhile to invest in Turkey
Discussion About the Key Performance Indicator of the Segmentation Strategy and Macro Environment of the Privately Offered Fund
Authors: Xie Yutong
Published: March 2024

This paper focuses on the meaning of privately offered fund and its classification, studies its segments, and analyzes the core focus metrics of segmentation strategies. Secondly, it gives a better presentation of the characteristics and trends of privately offered fund through the above analysis. Finally, it discusses the macro environment suitable for privately offered fund segmentation strategy.
The influence of e-commerce algorithms on Consumers’ behaviour
Authors: Wei Zihan
Published: March 2024

With the development of the Internet and data analysis technology, the e-commerce plat-form continues to optimise the e-commerce algorithm system, improve consumers' online shopping experience and willingness, reduce the time cost of consumers' retrieval of commodity information, achieve accurate marketing, and increase sales. Therefore, it is of great significance to understand the functions and characteristics of the algorithm and how does it affects consumers' shopping decisions. Based on collating relevant literature on consumer decision-making, online shopping behaviour, and algorithm recommendation system, a process model of an algorithm system affecting shopping decisions is constructed. Conduct a questionnaire survey to analyse the impact of the algorithm system on shopping decisions through a series of data. The following conclusions are drawn: The influence of the algorithm system is inevitable and strong, but it still needs to be improved, such as improving the accuracy of product recommendations. In addition, the development of an algorithm recommendation system is optimistic.
Risk management practice for foreign banks in the Chinese financial sector: a case study of HSBC China
Authors: Chen Jiaoyang
Published: March 2024

This dissertation uses a single case study approach with a mix of qualitative and quantitative analysis of secondary data for research process. It aims to answer the research questions of how does risk management framework of foreign banks work in China’s regulatory and economic environment. Through an in-depth analysis of the risk management practice of HSBC Bank (China) in 5 years period, the author finds risk management policies and assessment procedures are always align with organization’s risk appetite and strategy. In addition, based about HSBC, the portfolio with higher capital requirement has lower return due to the extra cost. And there is no clear evidence that stricter supervisions will impact the efficiency of risk management.
Abnormal Returns: Takeover Prediction Modelling - Shanghai Stock Exchange
Authors: Zhang Ningdan
Published: March 2024

Around the Merge and Acquisition (M&A) announcement date, the targets will experience dramatic increases in their share price. The movement of share pricesbrings investment opportunities. To successfully predict the future targets and make an investment strategy, investors can obtain the abnormal return from the increment of the share price. However, many articles of research show that even successfully predicted large numbers of targets, within the investment portfolio, are still unable to show the abnormal return. To make a successful investment strategy, researchers summarized the potential negative factors of the portfolios' return. For example, the investment timing, the distressed firms which share the common characteristics with predicted targets. This paper motivated by the potential improvement space for target portfolio's return, attempts to rationalised a successful investment portfolio. Through selecting the determinate characteristics of targets, building a prediction model, studying its abnormal return and analysing the influence of a distressed firm on the abnormal return, this paper determines that the targets can be distinguished from non-targets with the characteristics of inefficient management, misevaluation, high sales growth, low liquidity and larger free cash flow; consistent with determinant characters, the prediction model has a good predictability; however, good predictability could not produce the abnormal return; furthermore, even the empirical result shows that the target portfolio actually involves a certain percentage of the distressed firms which have significant differences from no-distressed firms, the portfolio can still not obtain abnormal return by screening the distressed firms using its characteristics.
Empirical Study on the Impact of Capital Structure on Profitability in Blockchain Industry-Based on the Data Analysis of 127 Listed Companies
Authors: Zhao Min
Published: March 2024

Under the background of the rapid development of the digital economy, a new round of technological revolution represented by blockchain is emerging, and blockchain and other high-tech industries have become the technological support for economic development in the new era, which has attracted the great attention of countries around the world. How does the current capital structure of China's blockchain-listed companies generally affect their profitability? This paper adopts the data indicators of 127 blockchain-listed companies in 2022 for factor analysis and multivariate model regression to further analyze the impact of capital structure on profitability status. The empirical results show that: 1. The gearing ratio and long-term debt ratio of listed companies in China's blockchain industry are negatively correlated with profitability, and the effect of the gearing ratio on profitability is greater compared with the long-term debt ratio; 2. The equity ratio is positively correlated with profitability.
Under the Framework of a Green Economy: A Study on the Prospects of China's International Trade Development
Authors: Wang Yifei
Published: March 2024

China has made significant progress in green economic development, but it also faces challenges such as green trade. This study provides an overall analysis of the development pattern of green trade in eastern, central, and western China. It then focuses on the eastern region, using specific foreign trade data from the Jiangsu, Zhejiang, and Shanghai areas to analyze the impact of foreign trade on the efficiency of the green economy. The study aims to explore the prospects of China's international trade development in the context of a green economy. The research reveals that China has made positive strides in both import and export trade and environmental protection. However, there is still a need to enhance trade balance, promote trade development in central and western regions, and strengthen environmental management and regulation by businesses in terms of ecological benefits.
SARS-CoV-2/Covid-19 cambió el estilo de vida
Authors: Sánchez Blanco José Miguel
Published: March 2024

El  autor  realiza un   breve  análisis acerca  de   la  enfermedad que padeció en el año 2019, relatando desde el punto de vista de dos años atrás los posibles efectos que  tendría la pandemia en el presente.
La pandemia como oportunidad de construcción
Authors: Ramírez Alzate Jazmín
Published: March 2024

La pandemia por el covid 19 ha traído consigo un sin número de dificultades de orden social y económico que, a diario vemos reflejadas en cientos de personas desesperadas por conseguir un empleo para cubrir los gastos básicos que les permitan tener una vida digna o angustiadas por tener a sus familiares en un hospital a causa de un virus silencioso y mortal. Los estudiantes no son ajenos a esta realidad, pues sin duda han llevado gran parte de esta carga, al tener que “aprender a aprender” de una manera completamente diferente a la que estaban acostumbrados.
Células infectadas y no infectadas por SARS-CoV-2
Authors: Muñoz Ana Luisa
Published: March 2024

La Investigadora Ana Luisa Muñoz, nos muestra dos imágenes de células infectadas por SARS-Cov-2, obtenidas en el laboratorio en el año 2021. Un año después de la aparición del virus.
El enigma de la Stevia: aún queda mucho por descubrir
Authors: Enriquez Ramos Juan Diego
Published: March 2024

La Stevia es una planta de América del Sur que se cultiva en varias regiones. Contiene un componente dulce llamado glicósido de esteviol que la convierte en un edulcorante bajo en calorías. El uso de la Stevia ha aumentado, pero aún hay incertidumbre sobre sus efectos a largo plazo en la salud humana. LaOrganización Mundial de la Salud (OMS, 2022) recomienda precaución con los edulcorantesno calóricos, porque podría aumentar el riesgo de enfermedades cardiovasculares y diabetes.
Alegría
Authors: Gil Reyes Norman Esteban
Published: March 2024

El mural, creado en el año 2023, invita a estudiantes, profesores y personal administrativo a contemplar nuestra universidad desde una perspectiva de amor y colaboración. "Alegría" es un fragmento de las hermosas palabras concebidas por el equipo que ha embellecido el corredor principal de la institución.
An investigation into the short-circuit characteristics of Sic MOSFET power devices
Authors: Zhang Yexu
Published: March 2024

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) devices exhibit substantial prospects for application under extreme operational conditions, including elevated temperatures, high voltages, and high frequencies. Nevertheless, owing to their distinctive material and structural attributes, SiC MOSFET devices are not devoid of challenges, with the short-circuit phenomenon constituting a pivotal avenue of inquiry. The short-circuit effect pertains to the abrupt escalation of leakage current that these devices might undergo under elevated voltage conditions, thereby exerting a perturbing influence on their stability and reliability. Investigations into the short-circuit effect predominantly revolve around two dimensions: one involves comprehending its underlying physical mechanisms, while the other centers on identifying commensurate remedial approaches.With respect to the underlying physical mechanisms, researchers have discerned that the elevated breakdown field strength and augmented carrier mobility intrinsic to SiC materials engender an augmentation in leakage current, consequently giving rise to the short-circuit effect. Furthermore, factors such as oxide layer anomalies and surface states are also conceivable catalysts for the surge in leakage current. To rectify this predicament, scholars have proffered a panoply of stratagems, encompassing the optimization of material synthesis processes, enhancement of oxide layer quality, refinement of device structural designs, and incorporation of protective circuitry, among others. In summation, the investigation of the short-circuit effect in silicon carbide MOSFET devices is fundamentally aimed at attaining an in-depth comprehension of its causative mechanisms. Moreover, it endeavors to proffer efficacious resolutions conducive to augmenting the reliability and steadfastness of these devices within high-temperature and high-voltage environments, thereby facilitating their widespread integration within the ambit of high-performance power electronics.
Learning based multi-robot coverage algorithm
Authors: Song Mingzhe
Published: March 2024

Multi-robot coverage algorithm is essential in exploration, search and rescue, tracking and other tasks. Nowadays, global planning-based approaches are difficult to solve the actual deployments of very large robot team coverage problems. In this article we use the heuristic algorithm based on graph neural networks to solve the multi robot coverage algorithm. Firstly, we discretize the coverage task and encode it into a graph. The location of graph and the robots are nodes. Then we design a graph neural network controller and use imitation methods to train the controller. The controller will generate the solution that is not inferior to the expert through imitating an open-loop expert solution based on VPR. Finally, we designed a graph neural network architecture to perform zero shot generalization on large maps and teams, enabling the system to be extended to larger map teams. It is difficult for the expert. And we successfully use this model to simulate 10 quadcopter and a number of buildings in a city. We also prove the GNN controller is better than the method based on the planning in the exploration task.
Sensor and sensor fusion technology in autonomous vehicles
Authors: Duan Bo
Published: March 2024

The perception and navigation of autonomous vehicles heavily rely on the utilization of sensor technology and the integration of sensor fusion techniques, which play an essential role in ensuring a secure and proficient understanding of the vehicle's environment.This paper highlights the significance of sensors in autonomous vehicles and how sensor fusion techniques enhance their capabilities. Firstly, the paper introduces the different types of sensors commonly used in autonomous vehicles and explains their principles of operation, strengths, and limitations in capturing essential information about the vehicles environment. Next, the paper discusses various sensor fusion algorithms, such as Kalman filters and particle filters. Furthermore, the paper explores the challenges associated with sensor fusion and addresses the issue of handling sensor failures or uncertainties. The benefits of sensor fusion technology in autonomous vehicles are also presented. These include improved perception of the environment, enhanced object recognition and tracking, better trajectory planning, and enhanced safety through redundancy and fault tolerance. Lastly, the paper discusses the advancements and highlights the integration of artificial intelligence and machine learning techniques to optimize sensor fusion algorithms and improve the overall autonomy of the vehicle. Following thorough analysis, the deduction can be made that sensor and sensor fusion technology assume a critical function in facilitating efficient and secure autonomous vehicle navigation within intricate surroundings.
A comprehensive overview of the application of artificial intelligence in language learning
Authors: Zhou Ziru
Published: March 2024

With the rapid advancement of artificial intelligence, AI technology has made significant progress in the field of language. Machine translation has become the dominant method, replacing manual translation due to its convenience and speed. This article will discuss three different aspects: translation, information retrieval, and language artificial intelligence. In the translation section, three distinct translation models will be analyzed, using Google Translate as a foundation. These models have transformed the translation industry and improved accuracy and efficiency. In the information retrieval section, the differences between semantic search involving AI and traditional keyword-based search techniques will be explored. Semantic search, driven by AI, provides more accurate and relevant search results by understanding the context and intent behind user queries. The impact of these advancements on search engine optimization (SEO) practices will also be discussed. Furthermore, the article will delve into the types of speech recognition and classify speech recognition technologies. Finally, the article will summarize the entire content and provide an outlook on future developments.
Application of artificial intelligence technology in the automotive field and existing problems
Authors: Mao Bowen
Published: March 2024

With the development of technology and science, loads of companies try to apply AI to the automotive industry. The automotive industry is at the cusp of a technological revolution, with Artificial Intelligence poised to redefine the way vehicles are designed, operated, and experienced. Nowadays, people still dont have a deep understanding of the application of artificial intelligence in cars. In academia, scholars have briefly analyzed some applications without integrating the known applications. Thus, this paper will integrate multi-dimensional applications, including autonomous driving, car maintenance, and car manufacturing. It will specifically analyze the application and methods of artificial intelligence in automatic driving, the application of automobile maintenance and the practical use and method of AI in the process of automobile manufacturing. Also, it will show the existing problems and challenges that hinder the seamless integration of AI into the automotive industry. The article concludes by providing a summary and outlook for the entire piece.
Stock price forecasting using machine-learning methods
Authors: Chen Qinqing
Published: March 2024

The stock market is essential in the economic growth of the nations in which it operates, and stock price prediction is of great significance to investors and government departments, as stocks provide both high reward and high risk. Nowadays, stock price prediction makes extensive use of machine learning algorithms. A large number of machine-learning models are available for predicting stock prices in the existing literature. In this article, the K-Nearest Neighbor (KNN), Random Forest (RF), Long Short-Term Memory (LSTM), and Gate Recurrent Unit (GRU) methods are applied to construct models to make stock forecasting based on Airbnb's historical stock data. The stock data are collected from 10th December 2020 to 19th August 2022. In addition, the accuracy of these four different models is analyzed and compared through Mean Square Error (MSE), Mean Absolute Error (MAE), and Resolvable coefficient (R^2) score metrics. The result shows that the LSTM and GRU models perform better than KNN and RF, with GRU showing the best results.
Severitys prediction of car accidents in PA and model comparison
Authors: Chu Wenchuan , Qu Xuanrun
Published: March 2024

This study aims to provide an analytical and predictive framework for understanding and forecasting the probabilities of varying severities of traffic accidents in Pennsylvania, based on the US Accident dataset dated up to March 23. Initially, the dataset and variable descriptions are provided for comprehensive understanding. During the data preprocessing phase, variables undergo a thorough examination for missing values, and suitable imputation methods are selected for data completeness. Further, feature selection and data cleansing are executed to prepare the dataset for model training. The study predominantly utilizes three machine learning algorithms, Logistic Regression, Random Forest, and Support Vector Machine, to construct predictive models. The performance of these models is meticulously evaluated for accuracy and compared through specific data sampling techniques. Overfitting checks, feature importance elucidation, and in-depth discussions on model performance variations are also included. By navigating through this multifaceted analysis, the study aims to shed light on the strengths and weaknesses of different modeling approaches for this particular problem context, thereby providing valuable insights for future research endeavors.
Simulation and analysis of C-V2X data transmission in different traffic scenarios based on MAC layer scheduling algorithms
Authors: Zhuang Shangjie
Published: March 2024

With the rapid development of communication technology, vehicles are becoming more and more intelligent, and Vehicle to Vehicle (V2V) communication has become an important cornerstone for the future intelligence of vehicles. Because of the high-reliability and low-latency characteristics of 5G networks, many applications can be realized by using Vehicle to Network (V2N) communications. Different applications have different requirements for Quality of Service (QoS), which should be met by the network, and the base station can use different scheduling methods to satisfy these different requirements. This paper focuses on the scheduler at the MAC layer in 5G base stations. The scheduler must handle both video signals and other communication signals transmitted by the vehicle, and must also consider Hybrid Automatic Repeat reQuest (HARQ) retransmissions. We performed a C-V2X signaling network transmission simulation by using OMNeT++. We considered urban and motorway, two public transportation scenarios. Different types of data are set to be transmitted in two scenarios. We used different scheduling schemes for data scheduling, then investigated the performance of these scheduling schemes for transmitting data. The methods and simulation experiments mentioned in this paper are important for practical generalization.
Review of High Dimensional Neural Network Potentials
Authors: ZOU YICHEN
Published: March 2024

Over the past few years, machine learning potentials (MLPs) have been fully developed and can now be applied to a variety of large-scale atomic simulations. MLPs encompass a number of different machine learning algorithms, of which neural network potentials are the most dominant and representative, and have achieved great success. This review focuses on the second generation of neural network potentials, high-dimensional neural network potentials (HDNNPs). While HDNNP has accomplished noteworthy results in the implementation of ion diffusion, nuclear magnetic resonance parameters, defect formation, and thermal conductivity, it still encounters hurdles in the compilation of training datasets, transferability, accuracy constraints, and the management of multiple chemical species. In spite of this, HDNNP remains a highly promising methodology. Looking forward, we anticipate the development of more specialized software for HDNNP to augment research efficiency and lower the barrier to usage.
Comparative analysis of the KL-UCB and UCB algorithms: Delving into complexity and performance
Authors: Wu Chenyue
Published: March 2024

This paper embarks on a meticulous comparative exploration of two venerable algorithms often invoked in multi-armed bandit problems: the Kullback-Leibler Upper Confidence Bound (KL-UCB) and the generic Upper Confidence Bound (UCB) algorithms. Initially, a comprehensive discourse is presented, elucidating the definition, evolution, and real-world applications of both algorithms. The crux of the study then shifts to a side-by-side comparison, weighing the regret performance and time complexities when applied to a quintessential movie rating dataset. In the trenches of practical implementations, addressing multi-armed bandit problems invariably demands extensive training. Consequently, even seemingly minor variations in algorithmic complexity can usher in pronounced differences in computational durations and resource utilization. This inherent intricacy prompts introspection: Is the potency of a given algorithm in addressing diverse practical quandaries commensurate with its inherent complexity. By juxtaposing the KL-UCB and UCB algorithms, this study not only highlights their relative merits and demerits but also furnishes insights that could serve as catalysts for further refinement and optimization. The overarching aim is to cultivate an informed perspective, guiding practitioners in choosing or fine-tuning algorithms tailored to specific applications without incurring undue computational overheads.
Current status and applications of time-to-digital converters
Authors: Huang Hanzhi
Published: March 2024

Time-to-Digital Converter (TDC) is widely used to realize time interval measurement. The high-precision time measurement technique has important applications in the fields of laser ranging, particle identification, and radioactive nuclear medicine engineering. Based on the existing literature research and data, this paper studies the application areas of TDC in the present development and analyzes the future prospects of TDC applications. The research results showed that: TDC, based on signal screening, realizes time interval measurement as the ultimate purpose of building the system and, at the same time, completes the function of multi-pulse time interval measurement, which can meet the needs of more diversified measurements in the experiments. In the circuit structure, it can identify the feedback output in the all-digital phase-locked loop (ADPLL) and the reference clock phase and frequency information between the feedback output and the reference clock in an ADPLL. It is also promising for use in other areas of high-precision time measurement and processing of circuit signals.
Analysis of UAV data communication stability method in extreme environment
Authors: Liu Weile
Published: March 2024

In order to get in touch with the affected area smoothly after the disaster, this paper uses drones as air base stations. Their high mobility and sensitivity make them apt for such critical roles. However, during extreme weather conditions, these unmanned aerial vehicles (UAVs) encounter various challenges that can impair their stability and, consequently, their ability to serve as reliable air base stations. As a result, signal transmission can be interrupted or severely limited. Among the common factors affecting UAV stability are strong winds, temperature fluctuations, heavy rainfall, and electromagnetic interference. These factors can cause the UAVs to deviate from their intended flight paths, drop in altitude, or even lose connection with the ground control. As a consequence, the reliability of communication, so critical in emergency situations, gets compromised. To address the problem of degraded UAV stability, several solutions have been proposed and implemented. One approach involves enhancing the drone's onboard stabilization systems, incorporating advanced algorithms that allow it to autonomously adjust to environmental changes. Another strategy is the deployment of a network of drones, ensuring redundancy. If one drone faces difficulties, another can take over, maintaining the communication link. Furthermore, advances in materials science have led to the development of UAVs with more robust structures, capable of withstanding the rigors of adverse weather conditions. Lastly, the use of ground-based boosters or repeaters can amplify signals, ensuring that even if a UAV's transmission is weak, the communication remains uninterrupted.
A study on fruit classification using convolutional neural network for image recognition technology
Authors: Shi Haipeng
Published: March 2024

Artificial intelligence is being incorporated into more aspects of our daily lives. Agricultural production in China is at a critical juncture due to the rising fruit production and dwindling labor force, necessitating the adoption of mechanization and intelligent systems. Image processing is particularly suitable for this field. Image classification technologies in fruit categorization are examined in this research. The results of this study can help improve the development of intelligent, lightweight machinery for agricultural output. This, in turn, will enhance the efficiency of fruit cultivation, harvesting, and trading and alleviate labor constraints. Two popular convolutional neural network models, namely ResNet50 and MobileNetV2, were employed in this study. The study utilized two optimizers: SGD and Adam. The evaluation results revealed that the ResNet50 model, employing SGD optimization, achieved the highest accuracy of 95.57%. Despite its lower accuracy of 92.19%, the MobileNetV2 model demonstrates higher efficiency than ResNet50 due to its lower hardware requirements, rendering it suitable for operation on compact devices.
Apple, Microsoft, and Amazon stock price prediction based on ARIMA and LSTM
Authors: Xia Xinyao
Published: March 2024

Along with the advancement in technology and data analysis, efforts have been made to use various factors to predict the stock market to improve portfolio performance and ensure an efficient market. This study explores two stock forecasting models, Long Short-Term Memory(LSTM) and Auto Regressive Integrated Moving Average (ARIMA), and evaluates their performance on the same datasets of historical stock prices of Apple, Microsoft, and Amazon. While ARIMA exhibited superior performance in terms of Root Mean Squared Error(RMSE) and R2 metrics, the research acknowledges inherent limitations, such as the exclusion of potential external influencing factors and the simplistic approach toward LSTM optimization. Despite ARIMA's commendable forecasting ability in this context, the dynamic and non-linear nature of stock prices suggests the potential of hybrid models and broader datasets for future research. The findings underscore the significance of selecting appropriate forecasting tools in the volatile domain of stock investments and pave the way for a more holistic and informed approach to stock price prediction.
Adversarial attack against deep learning algorithms for gun category detection
Authors: Lu Fan
Published: March 2024

Contemporarily, many deep learning methods have been generated for weapon detection. The weapon detection technology could be used in investigating violent cases. However, the existing gun detection models lack adversarial attack verification for special types of firearms and special picture samples. This study investigates the efficiency of Fast Gradient Sign Method(FGSM) adversarial attack in the field of weapon detection and the influence of weapon category on the attacks result. The dataset is scraped from IMDBF.com and the model being attacked is MobileNetV2, created by HeeebsInc in 2020. As a result, using FGSM methods, adversarial samples generated in film and television graphics containing pistols and rifles can effectively decrease the accuracy of the weapon detection model above. Besides, it is observed the difference of eps needed in attacking different types of gun graphics like film pictures and collection photos. These results verify that some weapon detection models have weak anti-interference, which may provide some ideas for future attacks like BIM or PGD attack.
Research on LOAM algorithm in terrain reconnaissance of robot
Authors: Yi Tao
Published: March 2024

This study investigates how the Lidar Odometer and Mapping (LOAM) algorithm, a kind of optimized SLAM algorithm, help robots build the terrain accurately. Some experiment results from different researches and papers were used to prove that the LOAM algorithm can successfully construct the map in 3D and make the researchers clear about complex terrain and make the robots move autonomously and safely without encountering the obstacles. In this paper, we refer other researchers papers to support our viewpoints and make it clear to convey the LOAM algorithm how to run and the principle of Odometry and Mapping process. We do not go into too much details about the content of the code, our main goal is to introduce and explain the advantages and disadvantages of the algorithm, and its efficiency and accuracy in creating maps. Our analysis showed that the LOAM algorithm can build topographic maps to achieve the goal of automating robot operation. We conclude that LOAM algorithm is an effective tool to help robots run autonomously and scan terrain.
Een scoping review van effectieve interventies ter ondersteuning van kinderen in armoede
Authors: Keinemans Sabrina , Samuels Lieke , Weling Joost
Published: March 2024

In 2022 leefde 9,2% van de Nederlandse kinderen en jongeren in armoede. Vaak hebben deze kinderen en jongeren te maken met materiële en immateriële achterstanden, die hen op alle levensdomeinen belemmeren in hun ontwikkeling. Professionals kunnen en willen kinderen in armoede graag ondersteunen, maar geven aan dit een moeilijke taak te vinden. Effectieve interventies kunnen professionals helpen bij de uitvoering van deze taak. 
Dit artikel rapporteert over een scoping review die is uitgevoerd om inzichtelijk te maken welke kennis in academische literatuur beschikbaar is over effectieve interventies ter ondersteuning van kinderen in armoede. Aan de hand van een gefaseerd selectieproces met inclusiecriteria zijn 26 artikelen uit diverse wetenschappelijke databases geselecteerd voor de review. In deze artikelen worden 29 interventies beschreven. Uit de analyse van deze artikelen blijkt dat de meeste interventies gericht zijn op het verbeteren van academische, cognitieve en/of sociaal-emotionele vaardigheden. 
Weinig interventies zijn toegespitst op financiën of op structurele oorzaken van armoede. In de discussie wordt gepleit voor de komst van meer interventies met deze focus en voor meer effectiviteitsonderzoek naar bestaande interventies.
Beledigende Broccoli 2.0_Armoede krijg je gratis_Misschien moet je iets lager mikken
Authors: Boekhoven Jeroen
Published: March 2024

Om armoede te bestrijden is kennis van armoede nodig. Niet alleen wetenschappelijke kennis en vakkennis, maar ook ervaringskennis. Het principe dat je niets doet voor mensen zonder die mensen erbij te betrekken wordt steeds meer omarmd door beleidsmakers en sociale professionals. En terecht, want goed armoedebeleid en goede interventies kunnen niet zonder de kennis van mensen die getroffen worden door armoede(beleid). 
Deze drie boeken  zijn geschreven door auteurs met ervaringskennis op het gebied van armoede. Er zijn overeenkomsten tussen de ervaringen van Tim ‘S Jongers, Stella de Swart en Milio van de Kamp. Toch verschillen hun verhalen erover aanzienlijk van elkaar. Dat komt niet alleen door het verschil tussen hun ervaringen met armoede, het komt ook doordat hun perspectieven op armoede zo verschillen.
Sinergi Kepemimpinan dan Literasi: Upaya Kepala Sekolah dalam Mengembangkan Perpustakaan Sekolah
Authors: Masrufa Binti , Ramandani Ananda Desi
Published: March 2024

This research analyzes the principal's strategy for developing the library at SMPN 5 Jombang. The research method is descriptive qualitative, with data collection techniques conducted through interviews, observation, and documentation. The research subjects consisted of the school principal, the head of the library, and class VIII students. The results of the research show that the principal of SMPN 5 Jombang implemented four strategies in developing the library, namely the strategy of advising by forming a library organization, the strategy of reprimanding indirectly through curriculum waka, the strategy of appreciating by giving praise and appreciation, and the strategy of receiving suggestions through coordination meetings. Apart from that, the school principal also makes efforts to develop library management, including completing collections, improving the spatial layout, improving services, increasing interest in reading through library programs, and supervising through annual evaluations. The results of this research reinforce the critical role of school principals in developing libraries as a vital means of supporting learning. Library development requires commitment and active participation from all school members, as well as continuous efforts to optimize library functions to support improving the quality of learning and achieving educational goals in schools.
Major evidence of the relationship of cardiovascular disease and tooth extraction: a systematic review
Authors: Tognolo Maria Angélica , Pianesso Taise , Gomes Heloísa Soler , Scriboni Andreia Borges
Published: March 2024

Introduction: Cardiovascular diseases can present a congenital origin, that is present from birth, such as interatrial and interventricular communication, defects of the artery, coronary arteries, or acquired coronary artery anomalies that involve several risk factors such as smoking, alcohol, old age, and sedentary lifestyle. Thus, the acquired ones are the most frequent in the routine of a dental practice, so the dentist should be able to attend to those patients who need special care, regarding the correct use of medications due to the risk of drug interactions, the type of local anesthetic to be used and the management of patients who use anticoagulant therapy. Objective: It was to develop a concise systematic review to highlight the main clinical outcomes of the relationship between cardiovascular diseases and tooth extraction. Methods: The research and development of the work were carried out from December 2023 to February 2024 in the databases Scopus, PubMed, Science Direct, and Scielo, following the PRISMA rules. The quality of the studies was based on the GRADE instrument and the risk of bias was based on the Cochrane instrument (Funnel Plot). Results and Conclusion: A total of 112 studies were found that were subjected to eligibility analysis. The final sample had 36 eligible studies and 21 studies were described in the systematic review. Most studies showed homogeneity in their results, with X2 =63.9% >50%, with p<0.05. It was concluded that the virtual reality technique was effective in controlling blood pressure and heart rate within an acceptable range and can help in the management of blood pressure and heart rate during tooth extraction in patients with hypertension. It is necessary for the dental treatment of these patients with heart disease a good knowledge of the subject and requires medical and dental coordination. Dental surgeons need to be prepared to identify possible medical and dental emergencies and take steps to prevent or treat them effectively and quickly.