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The development and generation of affordable and highly efficient energy, particularly hydrogen, are one of the best approaches to address the challenges posed by the depletion of non-renewable energy sources. Hydrogen energy, as a green and ecosystem-friendly source with zero carbon emission, can be generated through various methods, including water splitting (HER/OER) either photo- or electrocatalytic reactions. To implement these reactions effectively in practical applications, it is highly desirable to develop extremely efficient and cost-effective catalytic materials that are comparable to contemporary catalysts.

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Secure artificial intelligence at the edge.

Philos Trans A Math Phys Eng Sci

January 2025

Electrical and Computer Engineering Department, UCLA, Los Angeles, CA, USA.

Sensors for the perception of multimodal stimuli-ranging from the five senses humans possess and beyond-have reached an unprecedented level of sophistication and miniaturization, raising the prospect of making man-made large-scale complex systems that can rival nature a reality. Artificial intelligence (AI) at the edge aims to integrate such sensors with real-time cognitive abilities enabled by recent advances in AI. Such AI progress has only been achieved by using massive computing power which, however, would not be available in most distributed systems of interest.

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The rapid acceleration of urbanization and the surge in car ownership necessitate efficient automatic parking solutions in constricted spaces to address the escalating urban parking issue. To optimize space utilization, enhance traffic efficiency, and mitigate accident risks, a method is proposed for smooth, comfortable, and adaptable automatic parking trajectory planning. This study initially employs a hybrid A* algorithm to generate a preliminary path, then fits the velocity and acceleration based on a cubic polynomial.

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Artificial intelligence (AI) technology is a scientific and technological field that focuses on the research and development of systems that simulate, extend, and expand human intelligence activities. This field encompasses various applications such as image recognition, language processing, expert systems, and robotics. The advancement of AI has greatly improved the quality and efficiency of medical work, particularly in areas like medical imaging, clinical decision support, precision medicine, and healthcare management.

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Applications and Future Prospects of Medical LLMs: A Survey Based on the M-KAT Conceptual Framework.

J Med Syst

December 2024

School of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou, 310053, China.

The success of large language models (LLMs) in general areas have sparked a wave of research into their applications in the medical field. However, enhancing the medical professionalism of these models remains a major challenge. This study proposed a novel model training theoretical framework, the M-KAT framework, which integrated domain-specific training methods for LLMs with the unique characteristics of the medical discipline.

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