Nowadays, compute-intensive programs, like those for training artificial intelligence and machine learning models, are used extensively. Modern compilers use vectorization techniques to exploit parallel processing capabilities to improve the performance of such programs. A group of scientists from the University of Southern California, Cisco AI Research, and Intel Labs designed a data-driven, graph-based learning framework for automatic vectorization called autograph, which utilizes deep reinforcement learning to have an intelligent agent learn an optimal policy. Autograph greatly outperformed other approaches across different datasets. The work was published on June 2, 2025, in an article titled “A Graph-Based Learning Framework for Compiler Loop Auto-Vectorization” in Intelligent Computing, a Science Partner Journal.

Critical concerns regarding the security and privacy of information transmitted within Internet of Medical Things systems have increased greatly, since these systems manage and generate substantial amounts of sensitive private data. Current traditional security methods have not yet adapted to evolving cyber threats, making the need for data security in medical settings crucial. Recently, a security framework based on blockchain technology and distributed reinforcement learning has been presented to address these challenges. The new framework ensures that data are stored securely and transmitted reliably while minimizing resource usage, and it also enables security measures to adapt to changing threat patterns and enhance system resilience against attacks. In summary, the new methods demonstrated improved memory consumption and transaction latency compared to existing approaches, while maintaining high data throughput. This work was published in Intelligent Computing, a Science Partner Journal, under the title “Privacy-Preserving Strategies in the Internet of Medical Things Using Reinforcement Learning and Blockchain” by Dounia Doha and Ping Guo.

A review paper by scientists at the Harbin Institute of Technology proposed the signal-enhancing mechanisms of MNMs in single-modal imaging and explored multimodal applications through MNMs-probe design and discusses artificial intelligence-driven intelligent MNMs for precision imaging.

The new research paper, published on Sep. 10 in the journal Cyborg and Bionic Systems, presented recent advancements in their applications for multimodal imaging and integration with artificial intelligence (AI)-driven technologies.

A research paper by scientists at Nanyang Technological University presents wearable devices with headgear and abdominal buckle that address these challenges using hooking mechanisms, multimaterial 3-dimensional printing, and selective electroless plating.

The new research paper, published on Sep. 22, 2025 in the journal Cyborg and Bionic Systems, reported wearable devices, such as headgear and abdominal buckles with surface stimulators, designed for cyborg insect preparation and navigation.