Based on a sample of 487 rural elderly people from five counties (cities and districts) in Chengdu City, this paper investigates the main demand drivers affecting rural home-based elderly care service consumption willingness based on structural equation modelling. The results of the study show that (1) the overall satisfaction level of the demand for rural home-based elderly care services in Chengdu is 40%, with insufficient service relevance and low cost-effectiveness; at the same time, the service functions of the basic facilities for the elderly are not sufficiently fulfilled, and the resources are not effectively utilised. (2) The willingness to participate in home care services in rural Chengdu is mainly driven by spiritual and cultural, medical and health care and other demand factors. (3) Life care has a positive effect on rural elderly's participation in home care to a certain extent, but it is not significant. Safety and security indirectly affect rural elderly's willingness to age in place by influencing medical and health care. Therefore, the government, enterprises, communities, and families should work together in an orderly and coordinated manner to form a family-centred, community-based, and professional service-dependent socialised service for the rural elderly living at home, with the main content of solving daily life difficulties, and supplemented by spiritual and cultural services, in order to guard the rural elderly in their later years of happiness.

In the digital era of the economy, digital transformation has become an important path for high-quality development of Corporates. This article is based on the Guotai An database and uses Chinese companies from 2010 to 2021 as research samples to empirically examine the impact of the proportion of female executives on corporate digital transformation. The study found that: (1) The proportion of female executives can enhance a company's level of digitalization. (2) This effect is mainly achieved through increasing the company's research and development intensity, thereby improving its digitalization level. (3) The role of female executives in promoting digitalization is more pronounced in non-state-owned, large companies with dispersed equity ownership.

This paper aims to provide a comprehensive analysis of the near future of the Russian ruble, with a particular focus on its potential profitability as an investment asset. Amidst a backdrop of political tensions, economic sanctions, and ineffective forex interventions by the Russian Central Bank, the ruble has experienced significant volatility. The paper examines key factors influencing the ruble’s value, including political climate, inflation, interest rates, and main exports. The study analyzes the causes and solutions of Russia’s three major devaluations since the dissolution of the Soviet Union in 1992. By analyzing historical exchange rate and other economic indicators, the paper scrutinizes the internal and external indicators of ruble’s devaluation and evaluates the various policy measures taken by the Central Banks of Russia to stabilize the currency, including redenomination and shifts in exchange rate mechanisms. The paper concludes that, given the current political and economic landscape, the profitability of investing in the Russian ruble is questionable and unrecommended. Through this multi-faceted approach, this paper aims to provide a nuanced understanding of Ruble’s past and present, thereby offering valuable insights for policymakers, investors, and scholars interested in the foreign exchange market of Ruble.

This paper constructs a comprehensive index system, measures China’s provincial G-FINANCING INDEX using the VIKOR algorithm, explores the spatial and temporal distribution pattern of green financial development, hotspot regional migration and discrete trends by establishing a Markov spatial transfer matrix, and predicts the future pattern of green financial development in China’s three major economic belts based on the machine-learning algorithm ADABOOST, which finds that: (1) the central region maintains a strong development momentum. The development of green finance in the region shows a scale effect, and there is a positive transmission trend of development momentum between cities. The right side of the nuclear density curve in the western region has a clear trailing trend, and the overall degree of improvement is high. The phenomenon of multi-polarisation exists among city clusters. The city clusters in the eastern region as a whole show extremely high development momentum. (2) Geographic background plays an important role in the process of transferring G-FINANCING INDEX in Chinese cities, and the level of regional G-FINANCING INDEX is the result of the joint action of desired outputs and non-desired outputs, which is dominated by the economic activities in the region, and is specifically expressed in the spatial spillover effect of regional G-FINANCING INDEX is the spatial spillover effect of regional economic activities. (3) The evolution of the G-FINANCING INDEX in the study region has obvious spatial spillover effects, and is synergistic with the type of regional G-FINANCING INDEX, showing the phenomenon of “club convergence”.

The proliferation of short video applications and platforms has paralleled the growing popularity of this media format. In the increasingly competitive landscape of short-form video, companies have adopted numerous strategies to expand their market share, retain active users, and ensure sustainable platform operations. Among these strategies, one of the most pivotal is the utilization of recommendation mechanisms and algorithms to personalize video suggestions for users. For platform users, the additional traffic and exposure facilitated by recommendation mechanisms present a valuable opportunity for their videos to reach a wider audience. This paper delves into the characteristics and commonalities of user-generated content on video platforms influenced by recommendation algorithms, and examines the strategies employed by video creators to harness the increased traffic and exposure provided by these mechanisms, as well as the dynamics between users and the platform. We have randomly selected video data from the "Beeping Beeping" pop-up website for analysis. The study also scrutinizes the actions taken by video producers to exploit the resources and opportunities provided by the recommendation mechanism. Furthermore, it explores the interactions between users and the platform. To carry out this research, we selected a sample of twenty random videos from each of the ten video producers who boast a substantial fan base on the "Bleeping.com" website. While the recommendation mechanism simplifies the user experience, it simultaneously offers varying resources and opportunities to different users. Users who comprehend the platform's recommendation guidelines and underlying algorithms can proactively leverage the mechanism to aid in video creation and dissemination. This approach enables them to actively promote high-quality user-generated content on the video platform during the video creation and upload phases.

As one of the world's largest digital entertainment providers, Netflix plays a crucial role in the entertainment and streaming industry. This article analyzes the main strategies for Netflix's success by using market research data, financial data, and user behavior data. Through analysis, it was found that high-quality original works are the key weapon for Netflix to win. With the help of big data and algorithm analysis, Netflix is deeply loved by consumers. In addition, Netflix's globalization strategy has gained market recognition and allowed it to maintain a competitive advantage in the global streaming media market. This article also reveals some key insights and elaborates on the challenges Netflix is currently facing from competitors and changes in user behavior patterns. Despite facing increasing competition, Netflix is still able to maintain its leading position as a streaming platform, relying on its unique strategy and continuous changes based on users and the market.

As the cornerstone of China's engineering construction, the construction machinery industry plays an important role in the process of foreign trade and export. This fully reflects the important demand for China's construction machinery industry, but also shows the important position of the industry in the international market. At present, China's construction machinery market is open to the outside world, the domestic and foreign construction machinery market competition is fierce, many scholars on China's construction machinery export trade situation and ways to improve the enterprise's global market share to do in-depth research. In this paper, we find that Sany Heavy Industry is ranked in the top of the global construction machinery manufacturers, brand, scale, technology than other domestic construction machinery enterprises have advantages, from the perspective of Sany Heavy Industry through cross-border mergers and acquisitions to improve the level of internationalisation, through a combination of quantitative and qualitative methods, to provide assistance to the international development of China's small and medium-sized construction machinery enterprises.

The internet celebrity effect is a newly emerging effect in the era of big data on the internet, which exerts a unique influence on economic development. Several aspects of the internet celebrity effect align with certain marketing strategies in the modern tourism industry, making it undoubtedly one of the significant drivers of economic development in this sector. Building upon this premise, this paper takes the case of Ding Zhen, a young man from Litang who gained popularity. We conduct an in-depth study through qualitative analysis and comparative analysis, comparing it with Yajiang County to explore the significance and value of the internet celebrity effect. The internet celebrity effect can enhance social attention to tourist destinations, boost regional tourism industry development, and achieve economic growth in the tourism sector.The analysis explores how to leverage the internet celebrity effect to advance the modern tourism industry. It underscores the interconnectedness between the internet celebrity effect and the modern tourism industry, emphasizing the full utilization of the internet celebrity effect's role to enhance economic development in the modern tourism industry catalyzed by this phenomenon.

This study, based on panel data from 30 provinces in China from 2007 to 2020, employs the Super Efficiency SBM model to measure the green total factor productivity of each province. By constructing mediating effect models and spatial econometric models, it conducts an empirical investigation into the mechanisms and spatial characteristics of how green investments promote China's green development. The results reveal that, at an overall level, green investments play a positive role in influencing China's green development. Regarding the influence pathways, innovative green technology advancements, both in terms of inventions and improvements, are important mediating channels through which green investments promote green development, with inventions having a stronger mediating effect. In terms of regional heterogeneity, green investments significantly promote green development in the eastern and central regions, while their promotion effect in the western regions is less pronounced. Concerning spatial spillover characteristics, green investments not only drive local green development levels but also stimulate the green development in surrounding areas through spatial spillover effects.

The accurate prediction of inflation is of utmost importance in informing economic policy formulation and guiding private investment choices. Enhancing the precision of inflation prediction through the utilization of univariate models continues to present difficulties. Inspired by the hybrid methology that has emerged in the field of time series forecasting, which seeks to enhance forecasting accuracy by integrating linear and nonlinear approaches, the author employs a hybrid model that combines Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) techniques. The objective is to forecast the non-seasonally adjusted monthly US Consumer Price Index (CPI) inflation. The hybrid model employed in this study incorporates the utilization of wavelet transform, namely the discrete wavelet transforms (DWT), for the purpose of breaking the time series data into its constituent components of details and approximation. The subsequent application of LSTM and SARIMA models aims to capture both nonlinear and linear patterns present in the data. The findings of this research indicate that the DWT-based LSTM-SARIMA model outperforms both the solo SARIMA and LSTM models in a one-step rolling window forecast over a period of five years. This superiority is particularly evident during periods characterized by severe levels of inflation.