Scientific Reports volume 15, Article number: 43,291 (2025) Cite this article.
The platform can also hint at the type of respiratory issue involved, classifying cases as normal, obstructive, restrictive, or mixed. Obstructive patterns commonly appear in asthma and chronic obstructive pulmonary disease (COPD), while restrictive patterns are often linked to conditions such as pulmonary fibrosis.
The technology draws on the idea that cough sounds carry meaningful diagnostic clues. The researchers used a platform to classify the cases as ‘risk yes’ or ‘risk no’. When compared with physicians’ assessments, the model achieved a sensitivity of 97.27%. There was also strong agreement between the patterns identified by pulmonologists and the findings generated by the new tool.
Advances in AI have renewed interest in cough sound analysis as an accessible pre-screening method. Machine-learning models trained on large datasets can detect patterns associated with tuberculosis, Covid-19, asthma, and COPD, and can be built into portable devices or mobile apps for use in community settings.
A research team led by Professor Kanghyun Nam from the Department of Robotics and Mechanical Engineering at DGIST has developed a physical AI-based vehicle state estimation technology that accurately estimates the driving state of electric vehicles in real time.
This technology is viewed as a key advancement that can improve the core control performance of electric vehicles and greatly enhance the safety of autonomous vehicles. The work was conducted through international joint research with Shanghai Jiao Tong University in China and the University of Tokyo in Japan.
The work is published in the journal IEEE Transactions on Industrial Electronics.
A cross-institutional team led by researchers from the Department of Electrical and Electronic Engineering (EEE), under the Faculty of Engineering at The University of Hong Kong (HKU), have achieved a major breakthrough in the field of artificial intelligence (AI) hardware by developing a new type of analog-to-digital converter (ADC) that uses innovative memristor technology. The work is published in Nature Communications.
Challenges with conventional AI hardware Conventional AI accelerators face challenges because the essential components that convert analog signals into digital form are often bulky and power-consuming. Led by Professor Ngai Wong, Professor Can Li and Dr. Zhengwu Liu of HKU EEE, in collaboration with researchers from Xidian University and the Hong Kong University of Science and Technology, the cross-disciplinary research team developed a new type of ADC that uses innovative memristor technology. This new converter can process signals more efficiently and accurately, paving the way for faster, more energy-efficient AI chips.
Adaptive system and efficiency gains The research team created an adaptive system that automatically adjusts its settings based on the data it receives, i.e., dynamically fine-tuning how signals are converted. This results in a 15.1× improvement in energy efficiency and a 12.9× reduction in circuit area compared with state-of-the-art solutions.
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npj Computational Materials, Article number: (2025) Cite this article
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Automation and robotics, particularly with the integration of AI, are transforming industries and poised to significantly impact the workforce, but are likely to lead to a reduction in work hours and increased productivity rather than total job destruction.
## Questions to inspire discussion.
Investment & Market Opportunity.
🤖 Q: What is the revenue potential for robotics by 2025? A: ARK Invest projects a $26 trillion global revenue opportunity across household and manufacturing robotics by 2025, driven by convergence of humanoid robots, AI, and computer vision technologies.
💰 Q: How should companies evaluate robot ROI for deployment? A: Robots are worth paying for based on task-specific capabilities delivering 2–10% productivity gains, unlike autonomous vehicles requiring full job performance—Roomba succeeded despite early limitations by being novel and time-saving for specific tasks.
Implementation Strategy.