Software reusability encourages developers to heavily rely on a variety of third-party libraries and packages, resulting in dependent software products. Often ignored by developers due to the risk of breakage but dependent software have to adopt security and performance updates in their external dependencies. Existing work advocates a shift towards Automatic updation of dependent software code to implement update dependencies. Emerging automatic dependency management tools notify the availability of new updates, detect their impacts on dependent software and identify potential breakages or other vulnerabilities. However, support for automatic source code refactoring to fix potential breaking changes (to the best of my current knowledge) is missing from these tools. This paper presents a prototyping tool, DepRefactor, that assist in the programmed refactoring of software code caused by automatic updating of their dependencies. To measure the accuracy and effectiveness of DepRefactor, we test it on various students project developed in C#.

Time series analysis and decomposition are crucial in examining economic data as they uncover elements such as trends, and seasonal influences, within the data. However, some approaches have difficulty in accommodating complex, high-dimensional data. In this research, we investigate the possibilities of utilizing artificial intelligence (AI) tools, specifically, machine learning (ML) and deep learning (DL) for better timeliness and accuracy of economic forecasting. In some instances, it was shown how recent AI models can improve the data analysis of economic indicators (GDP, inflation, stock indices) through the accurate depiction of non-linear trends and changing seasonals. Model enhancements using AI also result in significant improvement in the accuracy of economic forecasts and provide more detailed and useful time series decomposition for economists and policymakers. This paper is a step towards more extensive use of artificial intelligence in econometric analysis and provides evidence on the feasibility of such in practical econometric studies.

The emerging US-China rivalry over Artificial Intelligence (AI) and cutting-edge technologies has become a fundamental aspect of contemporary global politics. This research explores how the mission for technological dominance between these two major powers is redesigning the geopolitical landscape, transforming long-standing coalitions, and introducing new frontiers of competition such as innovation, data manipulation, and semiconductor supply chains. The research explores critical developments such as the rise of strategic tech-driven coalitions like QUAD and AUKUS in setting global narratives around AI governance. This research applies the theoretical lens of Defensive Realism, which argues that states act to preserve their security in an anarchic international system by countering potential threats. This qualitative study examines how AI leadership and control over innovative technologies are becoming strategic instruments in the restructuring of geopolitical power dynamics.