Researchers at the University of Windsor developed SH17, a large open-source dataset with 8,099 images and 75,994 labeled instances to improve detection of personal protective equipment (PPE) in manufacturing. Using advanced AI models like YOLOv9, the study achieved over 70% accuracy, offering industries a scalable tool to enhance worker safety and compliance.

Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI. It is crucial for advancing next-generation intelligent robots and has garnered significant interest recently. Unlike data-driven machine learning methods, embodied learning focuses on robot learning through physical interaction with the environment and perceptual feedback, making it especially suitable for robotic manipulation. In this paper, researchers provide a comprehensive survey of the latest advancements in this field and categorize the existing work into three main branches: 1) Embodied perceptual learning, which aims to predict object pose and affordance through various data representations; 2) Embodied policy learning, which focuses on generating optimal robotic decisions using methods such as reinforcement learning and imitation learning; 3) Embodied task-oriented learning, designed to optimize the robot′s performance based on the characteristics of different tasks in object grasping and manipulation. In addition, researchers offer an overview and discussion of public datasets, evaluation metrics, representative applications, current challenges, and potential future research directions. A project associated with this survey has been established at https://github.com/RayYoh/OCRM_survey.

This study examines the impact of corporate digital mergers and acquisitions (M&As) on the development of New Quality Productive Forces (NQPF). Using a multi-period difference-in-differences (DID) methodology with data from Chinese listed firms (2011-2021), we demonstrate that digital M&As significantly enhance NQPF. We identify two key mechanisms driving this effect: enhanced firm innovation capability and accelerated data asset accumulation. Furthermore, our findings reveal that external factors including advanced industrial structure, higher urban human capital, and lower economic policy uncertainty positively moderate this relationship. This research introduces a novel NQPF measurement index and provides actionable insights for firms and policymakers seeking to leverage digital transformation for high-quality economic development.

A groundbreaking study from New Zealand demonstrates that central bank "forward guidance" significantly strengthens the transmission of monetary policy. Analyzing New Zealand's banking data, the research finds that providing clear communication about the future path of interest rates enhances the pass-through from the official policy rate to bank deposit and lending rates. The results show improved long-term pass-through, especially for time deposits and fixed mortgages, alongside a slight acceleration in short-term adjustments. These findings offer critical evidence for central banks worldwide on the power of communication as a policy tool.

Avenas has won first place in the Rhodium Ventures 2025 startup competition, organized by the Hebrew University of Jerusalem School of Business and Rhodium Ventures, in partnership with Earth & Beyond Ventures, Kyocera and the MAAYAN Student Foundation. The startup secured an investment commitment of up to NIS 6.5 million from the Earth & Beyond Ventures fund, subject to a due diligence process.