The COVID-19 pandemic has had a significant impact on people’s mental health, especially increasing concerns about issues like depression and anxiety. This study analyzed weekly internet search data to observe how public interest in these mental health problems in Canada changed before and during the pandemic. Time series analysis methods were used, such as Seasonal-Trend Decomposition (STL), ARIMA modeling, and change point detection. The results showed that during the early stages of the pandemic, there was a large increase in searches for "depression" and "anxiety." The ARIMA model created a scenario where the pandemic did not happen, showing what the search patterns might have looked like without it. Change point detection also identified key moments, like the March 2020 lockdown, when search behavior changed significantly. Overall, the pandemic worsened public concerns about mental health, with noticeable differences compared to pre-pandemic trends. This shows that there is an urgent need for stronger mental health support during national crises. Future research could use clinical data to further validate these trends.

With the advent of globalization, tourism, as a pivotal service industry, is assuming an increasingly pivotal role in market competition. Digital transformation has emerged as the predominant trend in tourism development. This study conducts an extensive analysis of the impact of digital transformation on location-based services (LBS) within the tourism industry. Drawing upon an extensive review of the nature and characteristics of tourism digitization, this paper delves into the trajectory of development, core functions, and current applications of LBS technology in the tourism sector. Specifically, the technical architecture of LBS is examined, with a detailed description of the sequential implementation of GPS, Wi-Fi/Bluetooth indoor positioning technology, and map API integration while underscoring the significance of data security and privacy preservation. By means of variable identification and hypothesis formulation, a theoretical framework for LBS, shaped by the forces of digital transformation, has been established and proposed. The framework employs both quantitative research methodologies, such as structural equation modeling and multiple regression analysis, as well as qualitative approaches, including case study analysis, to garner a diverse array of data sources. These sources encompass online survey responses and content extracted from social media platforms. The research findings indicate that digital transformation has brought about a substantial impact on LBS within the tourism industry, yielding benefits in areas such as personalized services, improved user engagement experiences, and optimized service delivery processes.

Online shopping has become an integral part of modern daily life and has made important contributions to the development of the retail industry. In China, it has made a great contribution to economic growth and progress. However, with the influx of more and more manufacturers into the market, the industry is becoming more and more competitive. As a result, some consumers have suffered the impact of inferior products, and this vicious competitive situation is not what the market wants to see. Not only that, the supply chain also faces many problems such as information asymmetry and inventory overhang. To change this situation, the management and optimization of the supply chain is crucial. This paper aims to deeply study the supply chain of online shopping platforms, analyze it from multiple dimensions, propose corresponding optimization strategies, and provide references for research and practice in related fields based on the publication standards of academic papers.

Stock price forecasting is a key subject in the financial field. Accurate stock price simulations are crucial for investors to make decisions about when to buy or sell stocks for profit. Under the current environment of global economic fluctuations, it is particularly urgent to develop and implement an effective stock price forecasting model. In this study, the VN30 composite index of Vietnam from 2016 to 2023 was selected as the research object, and the ARIMA model and ETS model were used to predict the index. The results show that the ARIMA model outperforms the ETS model in forecasting accuracy, which is reflected in its lower root-mean-square error (RMSE) values. This indicates that the ARIMA model can provide a more accurate prediction of the VN30 index in Vietnam. This study compares ARIMA and ETS models for predicting Vietnam's VN30 index closing prices. It advances financial market research, offering valuable insights for investors, policymakers, and analysts. By enhancing prediction accuracy, it supports informed decision-making, market transparency, and policy formulation, while also providing guidance for model selection and optimization in future studies.

With the advent of the digital age, digital economy has become a major propulsion for the global economy. For manufacturing, digitalization has brought unprecedented opportunities and challenges, transformed production processes and impacted aspects such as marketing and supply chain management. Especially in China, digital transformation has brought notably changes to manufacturing. This paper examines the relationship between the digital economy and manufacturing development by analyzing data from 30 provinces in China from 2011 to 2022. This paper adopts the entropy method to construct a comprehensive evaluation system of digital economy and manufacturing development and uses the panel regression model to conduct an empirical analysis. The results show that the expansion of the digital economy significantly supports manufacturing development, especially in terms of increasing productivity, cutting costs, and allocating resources as efficiently as possible. Additionally, this paper finds that digital lifestyles and digital knowledge environments significantly foster manufacturing development. On the other hand, technological innovation and skilled labor are the key factors for manufacturing development. This paper provides theoretical and practical insights for the integration of digital technologies and the real economy, offering guidance for high-quality and sustainable manufacturing development.

ByteDance, one of the world’s leading technology companies, has experienced rapid financial growth, driven primarily by its flagship products TikTok and Douyin. This study examines ByteDance’s financial performance from 2020 to 2023, focusing on revenue growth, profitability, cash flow, and key financial ratios. ByteDance's revenue surged from $34 billion in 2020 to an estimated $85-$90 billion in 2023, demonstrating a compounded annual growth rate (CAGR) of approximately 30%. Despite facing challenges in profitability due to heavy investments, the company has made strides towards achieving a break-even point by 2023. Strong liquidity and manageable leverage ratios further reinforce ByteDance's financial stability. In addition to its financial analysis, this study also evaluates ByteDance’s global market competitiveness and the company's diversification into new sectors like e-commerce, gaming, and enterprise software. ByteDance’s strategic expansion across North America, Europe, and Asia-Pacific has positioned it as a strong competitor in global markets. This analysis underscores ByteDance's remarkable ability to sustain high growth rates, adapt to regulatory challenges, and diversify its revenue streams, positioning it as a leading global technology company.

Accurate prediction of infectious disease cases has always been a research focus in the field of medicine and health. This paper studies the application of time series models in the forecasting of influenza cases in China using the SARIMA and ETS models. The dataset used was collected from the China Public Health Science Data Center from January 2012 to December 2018. The COVID-19 period was excluded to avoid anomalies. Both models were trained on the historical data and tested to see their performance in doing predictions for the year 2018. Based on the evaluation metrics, RMSE and MAPE, the ETS model, one of the best models selected for comparison, was better at modeling the seasonal patterns of influenza, in comparison with SARIMA (0, 1, 0) (1, 0, 0) (12). These findings indicate that the ETS model can make more precise forecasts with wider confidence limits, which may be useful for public health planning and management in China regarding influenza.

This paper uses a comparative analysis method to explore the differences in specific measures, relevant policies, and implementation effects in the field of stray animal management between China and the United States and analyzes the impact of these measures and policies on social stability and economic development in the two countries. Through comparative analysis, this paper believes that the overpopulation of stray animals is a common problem faced by China and the US. With the continuous increase in the number of stray animals, China and the US are facing increasingly severe public health challenges, biodiversity damage, lack of welfare of stray animals, and the pressure of sterilization, sheltering, and adoption of animals is also worsening. This paper aims to explore feasible improvement paths in the field of stray animal management for China and the US through a relatively comprehensive comparison. Meanwhile, this paper encourages future research to focus more on extracting more effective management measures from a global perspective to more effectively deal with the derivative effects of stray animal problems, thereby continuously improving the stray animal management systems of various countries and having a positive impact on the sustainable development of the social economy.

In today's highly competitive market environment, the rapid development of information technology and the rising expectations of customers are profoundly affecting all walks of life, especially the hotel management industry. Competition between hotels has largely transformed into competition between supply chains. However, there are many challenges and problems in hotel supply chain management. Improving the resource integration capability and allocation efficiency of the hotel supply chain becomes a key issue that hotel companies need to sort out urgently. The main purpose of this paper is to probe how to innovate the hotel supply chain management model by citing big data analysis technology. Specifically, this paper aims to build a supply chain database information system through data analysis and real-time monitoring to help hotels achieve efficient supply chain management. On this basis,this paper will analyze in depth about the future development trend of hotel supply chain management, to provide useful reference and inspiration for the sustainable development of the hotel industry.

In this study, the ARIMA (1, 1, 0) model forecasts the Nikkei 225 index from September 2022 to September 2024. Initially, the difference operation and ADF test are used to guarantee that the data is smooth, and then an optimal ARIMA model is chosen. Meanwhile, ARIMA (1, 1, 0) is considered the most appropriate model based on AIC and BIC criteria. The model shows an excellent prediction ability, particularly over the next 30 days, indicating that the market trend is consistent. However, the confidence intervals increasingly broaden with time, increasing in forecasting uncertainty, particularly in the face of market volatility, which limits the model's accuracy. Furthermore, while ARIMA models perform well in short-run forecasting, advanced versions such as GARCH may be more effective in dealing with complex market volatility. Additionally, combining external macroeconomic variables, such as the GDP growth rate and inflation rate, might improve the model's long-run forecasting accuracy. Therefore, future research can further improve the forecasting effect by introducing more exogenous variables or using more complex models.