Nationwide integration of AI into the contemporary art sector has taken place since government AI regulations in 2023 to promote AI use. China’s AI integration into industry is ‘ahead’ of other countries, meaning that other countries can learn from these creative professionals. Consequently, contemporary visual artists have devised arts-led sustainable AI solutions to overcome global AI concerns. They are now putting these solutions into practice to maintain their jobs, arts forms, and industry. This paper draws on 30 interviews with contemporary visual artists, and a survey with 118 professional artists from across China between 2023 and 2024. Findings show that 87% use AI and 76% say AI is useful and they will continue to use AI into the future. Findings show professionals have had time to find DIY, bottom-up solutions to AI concerns, including (1) building strong authorship practices, identity, and brand, (2) showing human creativity and inner thinking, (3) gaining a balanced independent position with AI. They want AI regulations to liberalise and promote AI use so they can freely experiment and develop AI. These findings show how humans are directing the use of AI, altering current narratives on AI-led impacts on industry, jobs, and human creativity.

The COVID-19 pandemic has fundamentally transformed the global education landscape, compelling institutions to adopt e-learning as an essential tool to sustain academic activities. This research examines the critical impact of e-learning on arts and science college students in Coimbatore, with an emphasis on its influence on their readiness for campus recruitment. Using a survey of 300 students, this study investigates their perceptions of online education, highlighting both its advantages, such as flexibility and accessibility, and its challenges, including engagement barriers and technical limitations. Data was collected through structured questionnaires and analyzed using statistical methods to draw meaningful insights. The research also explores the efficacy of online assessments in recruitment processes and assesses students’ awareness of available e-learning platforms and courses. The urgency of this study lies in addressing the pressing need to optimize digital education models as institutions globally transition toward blended learning post-pandemic. The findings underline the dual potential and limitations of e-learning, concluding with actionable recommendations to enhance its effectiveness, particularly in preparing students for competitive employment opportunities.

This study explores the role of arts management in regional economic development within major Chinese cities, including Beijing, Shanghai, and Shenzhen. Cultural organizations—such as museums, theaters, and galleries—contribute significantly to local economies through tourism, job creation, and the enhancement of cultural branding. Using a qualitative approach, 18 semi-structured interviews with arts managers and policymakers selected based on their influential roles in cultural organizations across these cities. The interviews were analyzed using thematic analysis, which identified key themes including the economic impact of cultural organizations, the influence of government policies, challenges in arts management, and the role of cultural tourism in fostering regional growth. The findings reveal that while government policies play a pivotal role in supporting cultural organizations, providing crucial funding, tax incentives, and infrastructure development, concerns remain about the long-term sustainability of funding due to shifting political and economic priorities. Additionally, arts managers face challenges related to balancing artistic goals with financial viability, particularly as the sector becomes increasingly competitive and technology-dependent. Key challenges identified include securing stable funding sources, adapting to digital technologies, talent retention, and maintaining artistic integrity amid commercial pressures. The study highlights the need for diversified funding models such as public-private partnerships and alternative revenue streams and suggests further exploration into the role of smaller cultural organizations in rural regions to promote inclusive regional development. Practical recommendations include developing strategies to enhance financial sustainability, investing in digital capabilities, and formulating policies that provide long-term support for the cultural sector. Overall, the research contributes to a better understanding of how effective arts management can drive regional economic development and offers practical recommendations for strengthening the sustainability of China’s cultural sector.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The Moroccan economy has undergone significant structural changes since the 1980s. Attracting Foreign Direct Investment (FDI) has been a key strategy for the country’s economic growth and development, particularly in some specific high value-added sectors, such as the automotive supply industry. This paper uses the results of a survey to examine the reasons why multinational enterprises (MNEs) in the automotive supply sector set up in Morocco. Our findings show that proximity to Europe and labor costs and skills are the most important considerations for investing in this sector in Morocco. However, some institutional issues are still of concern to these MNEs.