AI is emerging as a key disruptor of local economies. The impact of AI technologies including machine learning, automation and precision farming in reshaping local business landscape, unlocking innovation, enhancing productivity and creating net new jobs, is yet to be fully appreciated. This paper provides an analysis of the role of AI in revitalising local economies: how AI technologies are enabling small and medium sized enterprises (SME) to improve productivity, how rural economies can overcome labour shortages and supply chain issues and how to realise AI’s transformative potential in the near term. It goes on to discuss the impact of AI on labour markets, the creation of net new jobs in data and drone related occupations and need for reskilling and upskilling. It also discusses the role of local governments in encouraging AI adoption through public-private partnerships, tax incentives and infrastructure improvements. The paper concludes with a discussion about how AI shapes the future of work, the need to address the digital divide and the role of AI in fostering sustainable economic development at the local level. This paper analyses the impact of AI on productivity, consumer engagement, supply chain optimisation and workforce transformation.

This study explores the potential of Federated Learning (FL) to facilitate the sharing and collaboration of medical data in drug development under the premise of privacy protection. This paper systematically describes the core mechanism of federated learning, including the key technologies such as model parameter updating, differential privacy and homomorphic encryption, and their applications in drug development and medical data processing. Examples, such as NVIDIA Clara's Federated learning application and COVID-19 resource prediction, show that federated learning improves the efficiency of multi-party collaboration and model performance while ensuring data privacy, especially in areas such as finance and insurance, where data privacy is critical.

Quality control strategy plays an important role in the field of industrial manufacturing, which is directly related to the reliability of products and the level of operation efficiency. To address this core issue, this study groundbreaking a new framework that cleverly combines statistical process control (SPC) with Six Sigma methodology and incorporates cutting-edge technologies such as predictive analytics, real-time monitoring, and machine learning. This comprehensive strategy addresses many of the key challenges in manufacturing quality control and provides a systematic path to strengthening quality management practices through in-depth analysis of process variability, active promotion of defect rate reduction, and continuous optimization of operational efficiency. In particular, practical case studies from the automotive and electronics industries have shown that the application of the framework has brought significant results, not only significantly reducing defect rates, but also effectively improving the overall stability of the process. These valuable findings not only provide highly operational practical guidance for industry practitioners, but also greatly promote the further development of data-driven quality management methods in the academic field, and lay a solid foundation for building more robust and flexible manufacturing systems.

Power electronic devices continuously evolve towards higher conversion efficiency and lower energy loss, promoting efficient energy use and sustainable development. However, the rising temperature of the working device usually leads to unavoidable energy loss. To address this issue, cryogenic power electronics have attracted increasing attention from researchers. The use of low temperatures in these devices minimizes thermal losses, improving their efficiency and performance. Additionally, the development of new technology, such as superconductivity, and complex application environments also intensify the demand for cryogenic power electronic devices. The purpose of this paper is to critically analyze the challenge of cryogenics power electronics and provide some solutions, especially for Gallium Nitride (GaN) devices. By reviewing published articles, this article believes that GaN has great potential to address the obstacles in developing cryogenic electronic power. In the first section, the development status of cryogenics power electronics and current research on GaN devices will be introduced, and some challenges will also be given. The second part of this article will explore the feasibility of developing GaN technology to solve these challenges. Finally, a conclusion will be drawn.

Programming language translation is essential in modern software development, facilitating cross-platform compatibility and the adaptation of legacy systems. This study examines the performance of large language models (LLMs), such as ChatGPT, in Python-to-Java code translation. Using a dataset of ten diverse algorithmic problems and advanced prompt engineering techniques, we evaluate the models’ effectiveness in maintaining computational accuracy (CA) and preserving method correctness (PMC). Results indicate that LLMs perform well on standard tasks but encounter challenges in complex scenarios involving advanced data structures and recursion. These findings uncover the potential of LLMs in code translation while highlighting the need for improved prompt strategies and domain-specific fine-tuning for complex tasks.

This paper proposes using the YOLO object detection algorithm to accurately and efficiently detect nuts and screws using a custom-built 6-DOF robotic arm equipped with a gripper and a depth camera running the YOLO v10 object detection model in real-time, addressing the challenges of traditional detection methods in fast-paced production environments. This capability enables subsequent robot control to dynamically adjust actions based on precise component localization. Dynamic control in robotic automation has traditionally depended on model-based approaches, where performance depends on accurate system modeling. However, as systems grow more complex, precise modeling becomes increasingly challenging, often leading to suboptimal control. To address these limitations, we propose an Adaptive Robust Learning (ARL) Control algorithm. By integrating a Disturbance Observer (DOB) within the ILC framework, the ARL Control algorithm enhances adaptability and robustness, compensating for real-time disturbances. This work highlights the potential of combining advanced control algorithms with state-of-the-art visual recognition in robotics, paving the way for robust learning solutions in dynamic environments.

Stock price fluctuations are influenced by numerous interrelated factors. Traditional stock price prediction models, including neural networks, often fail to account for these correlations effectively, leading to lower prediction accuracy. To improve prediction performance, this paper integrates Principal Component Analysis (PCA) with a Backpropagation (BP) neural network, proposing a dynamic PCA-BP model for stock price forecasting. Simulation experiments conducted on the stock prices of selected listed enterprises demonstrate that the PCA-BP model exhibits varying performance across different experimental groups. The findings indicate that while the combined model enhances prediction accuracy, its generalization capability requires further optimization for practical applications.

This research explores emotion classification in song lyrics using BERT models for multi-language datasets, focusing on English and Chinese lyrics. The study emphasizes the application of music therapy techniques by utilizing song lyrics to assist clients in expressing and processing emotions. Sentiment analysis is conducted through a combination of CNN, LSTM, and GRU models, with a GRU + CNN hybrid model demonstrating enhanced performance in multilingual contexts. A comprehensive preprocessing process, including translation processing and tokenization, enables the effective analysis of both English and Chinese lyrics. Experimental results indicate that the GRU + CNN model outperforms traditional models, particularly in cross-lingual sentiment analysis, achieving significant improvements in accuracy and emotional classification.

With recent developments in machine learning, bots have conquered many games that were previously believed to be too difficult for computers. We evaluated 4 machine-learning bots that mastered 4 different games: AlphaGo for Go, Stockfish for chess, AlphaStar for StarCraft, and Juewu for Honor of Kings. We summarized and compared their fundamental algorithms and discovered two potential patterns: an increase in generalizability causes an increase in the amount of computation needed, and an increase in the complexity of the game environment causes a decline in the neural networks’ performance. To conclude, we discuss the implications of machine learning game bots on neural networks aimed at handling real-life scenarios and artificial general intelligence (AGI).

Collision detection is one of the most important features of video games that involve models that might run into each other. Video game players want to see the two models moving against each other to crash but not go through each other; this is why collision detection has come about. For ordinary models, like a box or a ball, collision detection is quite straightforward: check if the bounding volume of the object has gotten into the bounding volume of another object. For deformable models, like a piece of cloth, there would be a great many of calculations for the collision, since it has a lot of bounding volumes in one model. As a result, a range of methods have been introduced to game developers to optimize players’ experience and computers’ performance. All of them have something to do with essential features like bounding volumes, but they use these basic things in different ways.