Natural enzymes are highly efficient catalysts with strong substrate specificity, making them ideal for biomedical applications. However, they often face issues such as variability, high costs, challenging preparation processes, and difficulties in large-scale production. This has led to significant efforts in developing effective nanoenzymes and exploring their application potential. In recent years, carbon dots (CDs) have gained attention due to their strong fluorescence, excellent biocompatibility, and low cytotoxicity. Cationic CDs, which possess a positively charged surface, have shown the ability to mimic natural enzyme applications. The positive charge on the surfaces of these nanomaterials significantly influences their fluorescence, biological activity, and interactions with other biomolecules. Therefore, understanding how surface charge affects the performance of CDs is crucial for enhancing their usability. Considerable progress has been made in the design, synthesis, and mechanistic research of enzyme-like cationic CDs, as well as their advanced applications. This article reviews the latest research on the design structure, catalytic mechanisms, biosensing capabilities, and biomedical applications of enzyme-like cationic CDs. First, we review the synthesis strategies for cationic CDs and how surface charge influences their physical and chemical properties. Next, we highlight various applications of these cationic CDs, demonstrating their use in areas such as detection, biomedical applications (including antibacterial agents, gene carriers, and therapeutic agents), catalysis, and more. Finally, we discuss the challenges and obstacles faced in the development of cationic CDs and look forward to exploring new applications in the future.

Researchers from Sun Yat-sen University’s Shenzhen Campus, led by WenYuan Yang and Gege Jianga, have developed a decentralized federated learning framework, DFUN-KDF, to enhance UAV network efficiency. By leveraging federated knowledge distillation, it reduces data transmission by up to 99% while addressing model heterogeneity. A robust filtering mechanism ensures stability by eliminating faulty or malicious data. DFUN-KDF outperforms traditional methods in communication energy efficiency, adaptability, and resilience to node failures and attacks. This scalable solution offers significant potential for large-scale UAV deployments in urban management and logistics.

Accurate and efficient traffic speed prediction is crucial for improving road safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve this task with increasingly complicated structures and progressively deeper layers of neural networks. Despite the design of the models, they aim to optimize the overall average performance without discriminating against different traffic states. However, the fact is that predicting the traffic speed under congestion is normally more important than the one under free flow since the downstream tasks, such as traffic control and optimization, are more interested in congestion rather than free flow. Unfortunately, most of the state-of-the-art (SOTA) models do not differentiate the traffic states during training and evaluation. To this end, we first comprehensively study the performance of the SOTA models under different speed regimes to illustrate the low accuracy of low-speed prediction. We further propose and design a novel Congestion-Aware Sparse Attention transformer (CASAformer) to enhance the prediction performance under low-speed traffic conditions. Specifically, the CASA layer emphasizes the congestion data and reduces the impact of free-flow data. Moreover, we adopt a new congestion adaptive loss function for training to make the model learn more from the congestion data. Extensive experiments on real-world datasets show that our CASAformer outperforms the SOTA models for predicting speed under 40 mph in all prediction horizons.

Dr. Sung Mook Choi and his research team at the Energy & Environmental Materials Research Division of the Korea Institute of Materials Science (KIMS) have successfully developed a highly durable non-precious metal-based hydrogen evolution catalyst for use in a direct electrolysis system employing waste alkaline water and anion exchange membranes (AEM).