Mental health is a complex issue influenced by a variety of circumstances, including economic issues like income, work, and social support. The purpose of this article is to look into the impact of economic factors on mental health outcomes, namely income, employment, and social support. The paper begins with an introduction to the topic, followed by a review of the available literature on the association between economic circumstances and mental health outcomes. The section on research gaps emphasizes the need for additional study on the long-term consequences of economic recession on people with mental health problems, as well as the influence of changes in individual and household income on mental health and well-being outcomes. The approach used to study the impact of economic factors on mental health, including the use of multiple regression models, is then described in the paper. Finally, the report provides the study's findings and examines their implications for mental health policy and practice.

The purpose of this study is to analyze the impact of different positions, levels of expertise and company size on salary levels in the field of data science and to make salary projections for data science professionals in 2024. This research project can help data science professionals to understand what are the important factors that affect salary levels and to understand the salary environment and trends in the data science field in 2024. With the explosive growth of big data, the oversupply of data science jobs and the precise hiring needs have led to significant changes in the salaries of data science related careers from year to year. Data is thoroughly cleaned and preprocess sed to maintain data quality and consistency, including handling missing values and removing outliers. Descriptive analysis techniques were then used to understand the current state of data science salaries, calculating data such as mean, median and standard deviation. Time series modeling was used to determine how key factors affect pay levels over time. To further investigate salary trends, ARIMA was applied to visualize the evolution of data science salaries from 2020 to 2023, and then to forecast average salary levels for different positions in the data science field in 2024. In summary, the important factors affecting data science salaries and the trend of salaries for different careers in data science in 2024 are analyzed, and a detailed analysis is provided with salary as a key factor to provide valuable recommendations for data science stakeholders.

In the past few decades, China has skilled in fast urbanization. Millions of people migrate from rural to city areas so that they can find better opportunities. After all, large-scale population migration will inevitably lead to the growth of the labor force and the concentration of the population, but it may also have the opposite result due to political system factors. This form of transition has had a massive effect on China’s economic system and society. China's economic development and urbanization have also been important issues for people to pay attention to, especially after the announcement of the 13th Five-Year Plan. Among them, Shenzhen and Northeast China after the reform and opening up are very typical examples, and reliable conclusions are bound to be drawn through the study of them. This essay will focus on reading the complex dating among China's economic system and urbanization primarily based on this history.

This thesis examines two different types of market challenges, the financial crisis, and the pandemic crisis, and seeks similarities and draws conclusions by summarising and analyzing the coping strategies of industries under the 2008 financial crisis and comparing them with those of related industries under the current pandemic crisis. It is found that cost control and enhancement of corporate image are two commonly used strategies during the crisis, with the former helping to maintain competitiveness during the crisis and the latter facilitating the enhancement of brand value. In addition, new market development and the development of new business models are also effective strategies to cope with crisis. The study cases include Tesla, China Southern Airlines, and McDonald's, and by analyzing the coping strategies of these companies during the crisis, some strategies that can cope with both the financial crisis and the pandemic crisis have been derived. However, given the limitations of the number of cases, the coverage of the findings of this study could be improved. Future research could further explore coping strategies in public relations crises and political crises.

This study investigates the showrooming phenomenon, wherein consumers physically inspect products at brick-and-mortar stores before making online purchases. It categorizes the driving forces – price comparison, convenience and accessibility, brand awareness and emotional attachment, and personal characteristics– and underscores its pivotal role in influencing consumer choices and reshaping traditional retail dynamics. The research systematically dissects these forces, commencing with comparative pricing and digitally enabled convenience, progressing to brand influence and emotional attachment, and culminating in exploring individual characteristics. While showrooming equips consumers with informed decision-making capabilities, it presents challenges such as diminished profitability for conventional retailers. This analysis accentuates the pressing need for empirical validation and further investigation to fill existing gaps. The paper imparts a comprehensive comprehension of showrooming's far-reaching implications, empowering consumers and prompting retailers to swiftly adapt to an evolving retail landscape.

This article provides an overview of the use of data analytics for customer segmentation and personalization in marketing strategies. The article reviews the various approaches, advantages and challenges of using data analytics to gain insights into customer behavior and preferences. The paper also discusses the role of emerging technologies in improving data analytics capabilities for effective segmentation and personalization by examining a large body of literature. In this work, I have compiled this review by understanding and delving into the changing evolution of the traditional retail industry in the digital marketing era, the application of data analytics in modern marketing, and the impact of novel technologies such as artificial intelligence in informing strategic marketing decisions such as market segmentation and customer segmentation, and improving the efficiency of operations management. The results of the review highlight the importance of using data-driven approaches to shape modern marketing practices and provide practical insights for companies aiming to optimize customer engagement and maximize profits.

This paper uses the data of some African countries and G7 developed countries from 2010 to 2019 to study the impact of foreign direct investment (FDI), economic freedom and corruption on the real gross domestic product (GDP) growth rate of African countries. From the results of simple regression, we can draw a conclusion that the increase of FDI helps promote economic development, and it is significant. The results of the multiple linear regression show that FDI and economic freedom have a significant impact on the real GDP growth rate, and the results of the impact of FDI and economic freedom on the real GDP growth rate are the same as those of the simple regression, which are positive and negative, respectively. However, corruption is no longer significant for real GDP growth in the multiple linear regression. The above results suggest that African countries can increase the extent to which FDI controls economic freedom to achieve economic growth while minimizing government corruption. African policymakers can aggressively attract FDI, control the degree of economic freedom, and fight corruption.

This article focuses on the impact of post-Brexit immigration policy on the UK labour market. The article points out the potential for large fluctuations in employment numbers due to the fact that the Brexit period coincided with the COVID-19 pandemic, and focuses on sectors such as tourism and trade. High inflation has also led to large differences in wage levels between different markets. The article is roughly divided into three different labour markets, high, medium and low, to be investigated. To minimize the impact of inflation, the authors chose to use average real wage data rather than nominal wages throughout the data collection process. The authors also rely on sources of labour mobility affected by immigration policy to make their conclusions as verifiable as possible. In conclusion, immigration policy has had a significant impact on the UK labour market, particularly in sectors such as tourism and trade.

Because of the improvement of the performance of modern computer hardware and the continuous development of algorithms, the application of artificial intelligence is more widely used in all walks of life. In this work, the application of artificial intelligence technology in the fields of finance, medical care, industry, information, education and social life, especially in the manufacturing industry, has formed an unstoppable trend. For future careers, the arrival of artificial intelligence is also thought-provoking. In addition to bringing a lot of new jobs, but also let some low-cost, labor-intensive jobs disappear, causing great pressure on the job market, the employment threshold has significantly increased, familiar with artificial intelligence managers and experts pay much higher than manual workers, so that the income pattern of workers gradually prolonged. The arrival of AI technology has also triggered changes in the labor market, with labor market and automation technologies have largely replacing repetitive and more basic skilled workers.

This article focuses on the key factors influencing customer loyalty within Starbucks' coffee brand business. To comprehend which factors have the greatest impact on coffee purchasing behavior, we employed various statistical methods and modeling techniques, including logistic regression and hypothesis testing, utilizing the R programming language. Our results reveal that Starbucks customers' price evaluations and spending patterns significantly influence their behavior. We also found that customers perceive the prices as somewhat expensive, and spending less than 20RM per purchase is the most crucial factor in fostering loyalty. We subsequently formulated these factors into a binary logistic regression equation to establish their relationship with loyalty, which is statistically reliable. Moreover, we conducted a series of analyses to identify potential causes. It is estimated that customers who spend between RM 20 and RM 40 may find the product reasonably priced and may develop brand loyalty or even a coffee addiction. Finally, we propose a series of solutions to address these findings.

This article explores the challenges and implications of integrating artificial intelligence (AI) into digital security within the context of the rapidly evolving digital economy. It highlights the significance of data security in the digital age, particularly in cross-border data flows, and the need for effective policies to ensure safe and orderly data exchange. The study employs various methods, including technical evaluation of blockchain technology's potential in addressing data security challenges, and analyzes the impact of data protection standards on the European digital economy. The results emphasize the critical role of blockchain in decentralized data control, shedding light on the complexities of regulating data processors in a highly open and decentralized environment. The article also delves into the complexities of international cooperation in cross-border data regulation, suggesting the need for global international legal norms to harmonize data governance legislation. In conclusion, the article advocates for the integration of blockchain technology as a crucial component of a global cross-border data security system, offering technical innovation in tandem with legal frameworks. It underscores the importance of actively participating in the development of global data regulation standards to ensure data security in an interconnected digital world.

This literature review provides a comprehensive overview of key developments in quantitative trading theory, machine learning-based financial time series forecasting, deep learning-based financial time series forecasting, and modern quantitative investment strategies. It highlights seminal contributions from renowned scholars and researchers in the field. This review first explores the Efficient Market Hypothesis (EMH) proposed by Eugene Fama in 1970 and its empirical validation, which lays the foundation for understanding stock market dynamics. It then focuses on the application of machine learning to financial time series forecasting, including Hull’s Delta strategy, Hujll J’s multifactor model regression series, and Junhua Chen’s seminal work in 2009, which emphasizes the role of machine learning in solving the challenges of financial time series data. Finally, an overview of the development of deep learning in financial time series forecasting is presented, including the comprehensive model of Bowie et al., the success of Junhua Chen’s Deep Belief Network (DBN), and the sequence data processing capabilities of the Multi-stage Attention Network (MAN) model. In terms of modern quantitative investment strategies, it covers research areas such as EBIT/EV-based stock ranking studies, higher-order moment analysis, frequent trading strategies, and investor sentiment indicators. In summary, this literature review showcases the evolution of quantitative trading theory, the emergence of machine learning and deep learning in financial time series forecasting, and the development of modern quantitative investment strategies, offering valuable insights and tools for investors, researchers, and practitioners in financial markets.

This study focuses on the effectiveness of the Dechow F-SCORE model in monitoring financial fraud in the Chinese financial market. At the same time, the research also aims to thoroughly evaluate the advantages and deficiencies of the F-SCORE model. To implement research, six pairs of listed companies have been selected from different industries, and each company includes a company that has been involved in financial fraud cases and a company that has not had financial fraud. The annual report data of these companies conducted a comprehensive analysis in the vertical and horizontal directions. However, the results of the study show that when the F-SCORE model is applied to compare the financial fraud of these companies, the results are not noticeable. The results of the data analysis show that the F-SCORE model does not seem to detect financial fraud in Chinese-listed companies effectively. The conclusion of this study pointed out that the application of the F-SCORE model in Chinese listed companies is limited, and it may require more improvement and customization to adapt to the exceptional circumstances of the Chinese financial market. This also emphasizes that financial fraud monitoring involves various methods and tools to meet the needs of different regions and markets.

This paper explores the multifaceted relationship between product placement in American blockbuster movies and its influence on consumer behavior. With the backdrop of an evolving advertising landscape, the study delves into practices of brand integration in films, investigated through “seen,” “mentioned,” and “used” product placement. The paper delves into factors affecting consumer attitudes and purchase intentions, underscoring the importance of authenticity in brand integration and emphasizing the need for marketers and filmmakers to align their strategies with consumer preferences, build familiarity, and alleviate skepticism. This research sheds light on the dynamic interplay between brands, movies, and consumer behavior. As brand integration continues to evolve, these findings offer a roadmap for creating meaningful and impactful brand experiences within the cinematic realm, benefiting both industries and moviegoers.

The passage summarizes the key points from the provided passage about green finance, its channels, impact on small and micro-enterprises, influencing factors, and challenges. It highlights the various channels of green financing, such as green bonds, sustainable bonds, and green loans. Additionally, it discusses secondary market investment in green stocks and green bonds. The passage emphasizes the importance of government policies, banking institutions, non-banking sectors, and investors in promoting green finance. Furthermore, it points out the challenges faced by green finance, including information asymmetry, high costs, and the lack of clear green standards for small and micro-enterprises. Despite these challenges, the passage underscores the potential for green finance to drive sustainability and economic growth, particularly for smaller businesses. In summary, the passage provides an overview of the concepts, mechanisms, and impacts of green finance, highlighting its role in environmental conservation and sustainable development, especially for small and micro-enterprises.

Blockchain technology is an emerging technology, which has received much attention since its inception due to its data immutability and decentralization. Due to the special characteristics of blockchain, it can solve the financial services puzzle in terms of transactions and trust and can even help the financial services industry upgrade. In this review paper, we are gathering previous research papers focused on exploring the functionality of blockchain technology within the financial services sector and dividing them into two categories. One of the papers is about how blockchain works in financial services, and the other is about the impacts of blockchain in finance services. Then, we divided the article into three introductory directions according to the causal logic: the role, challenges, and development suggestions of blockchain technology in financial services. We also put forward suggestions for developing blockchain technology combined with the financial services industry.

With the rapid development of the Internet and media, the traditional coffee marketing model has been unable to meet the current consumer demand, and China's coffee industry has also undergone changes. Luckin Coffee has changed the traditional marketing methods by means of fission marketing, new retail and other strategies, and has quickly conquered the Chinese market. Therefore, this paper mainly takes Luckin Coffee as an example, and uses SWOT model to analyze the internal and external environment of Luckin Coffee from four aspects: strengths, weaknesses, opportunities and threats. At the same time, based on the analysis of consumer behavior and the existing status of Luckin Coffee, Luckin Coffee also has a SWOT model. It points out the positive factors of Luckin Coffee's popularity among consumers and the adverse effects after the financial scandal, compares and analyzes with Starbucks coffee, which occupies a larger share of China's coffee market, and finally explains the existing problems and puts forward corresponding countermeasures and suggestions.

In this study, we introduce a four factor equal-weight scoring model, utilizing SSE A-shares from 2019 to 2023, aimed at identifying stocks of high investment potential. We innovatively incorporate the E/I factor to assess the intrinsic investment value of stocks, integrating it with PEG, RSI, and beta to ensure a comprehensive information capture. While assuming the feasibility of short-selling in the A-share market, our proposed strategy offers insights for future statistical arbitrage strategies in this market, underscoring the significance of risk hedging. Our preliminary analysis suggests that the influence of the four factors on a stock's intrinsic value is not strictly linear. Consequently, we employ a factor rating approach to enhance their explanatory power regarding a stock's intrinsic investment value. Initial results indicate that while our original strategy effectively manages risk, it compromises on returns. However, post-adjustment, the refined scoring model demonstrates robust profitability, yielding an annual return of 23.967078% and a Sharpe ratio of 1.146165. These findings validate the efficacy of our proposed strategies, offering traders a novel investment direction.

As the academic debate about sweatshops has become more complex, public attention to sweatshops has become more and more widespread, but the interests of sweatshop workers have not been completely improved. This paper will use the method of literature review to discuss how to effectively promote the improvement of workers' working conditions and raise their wages from the perspective of the development history of sweatshops and the government. The government should implement labor protection laws to protect the legal rights of workers in domestic and foreign factories. And in the continuous development of the national economy, for these workers to improve the working environment in the factory or find alternative jobs for them, and raise the minimum wage. With the deepening of research on sweatshops and the strengthening of the government's role in protecting workers' rights and interests, the interests of sweatshop workers in developing countries can continue to be improved.

League of Legends is one of the most successful games in the world, hosting the largest and most attended events in the world and the largest number of grassroots players. Based on this, we studied why League of Legends events are so much bigger than other games, such as Honor of Kings, Counter-Strike: Global Offensive, and why its game elements and culture are so widely spread. Through the study of nearly 6 years of game videos and the intuitive feeling of the league of Legends elements in the city. We found that League of Legends events can be better and better not only due to the strong support of sponsors, but also due to the diversity of events, and the existence of the game community has greatly promoted the spread of League of Legends, but also buried the hidden danger of the deterioration of the game environment for League of Legends.