New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

The present study is designed to analyse how the Public-Private Partnership (PPP) model is helping to create sustainable livelihood opportunities for women. It draws an inference from ‘Marudhara Rangsaaz’, a producer company operating in the textile sector in Rajasthan, India. It explains how this woman-based organisation operates in a PPP model to create economic value for women. It also tries to understand the specific role of the Rajasthan Grameen Aajeevika Vikas Parishad (RAJEEVIKA), The Rajasthan Government partner and ‘Rang Sutra’, the private partner, and the women members of ‘Marudhara Rangsaaz’ in the PPP model. The paper adopted a case study research design. The data was collected using in-depth interviews with all stakeholders and analysis of the documents. The findings indicate that in the said PPP model, Government took the role of mobilizer, financer, mentor, and private player, took the responsibility of building up capacity and arranging market links, and the women members worked together to help themselves sustain the project.

The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.

Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

With the rapid development of modern AI painting, Chinese university fine arts education is facing numerous challenges and opportunities. This paper analyzes the impact of modern AI painting on traditional art creation and its implications for student skill development. Additionally, it explores the key areas where Chinese university fine arts education needs to transform, including curriculum, teaching methods, and teacher training, while proposing corresponding strategies.