Cobalt-free Ni-rich layered cathodes are promising for high-energy lithium-ion batteries because they can reduce material cost and cobalt dependence. However, their practical use is limited by lattice oxygen release and structural degradation under high-voltage operation. Researchers have now developed a synergistic Fe/Zr codoping strategy for LiNi_0.8Mn_0.2O₂ cathodes. Fe helps regulate the local transition-metal–oxygen electronic environment, while Zr acts as a lattice anchor through strong Zr–O bonding. The codoped cathode shows improved cycling stability, rate capability, and lithium-ion transport kinetics.

Researchers have developed a low-cost route to high-performance sodium-ion battery anodes by transforming coal-tar-pitch-derived carbon into a host for ZnO/ZnSe heterostructures. The zinc-aluminum layered double hydroxide template acts both as a structural guide and a pore-forming agent, enabling closely coupled ZnO/ZnSe-carbon interfaces. The resulting anode delivered 637.5 mAh g⁻¹ at 100 mA g⁻¹ and retained 259.7 mAh g⁻¹ after 1000 cycles at 5 A g⁻¹.

As large models advance, there’s growing demand to use knowledge distillation to produce smaller, more portable models (student) that match higher-performing larger models (teacher). However, when a teacher’s capacity far exceeds the student’s, distillation often degrades, which is known as capacity mismatch. This mismatch caps the student model’s performance and has become a bottleneck in large-model distillation. Existing mitigation techniques remain ad hoc, and no study has yet systematically explained its root causes or proposed targeted methods to enable larger teachers to yield better distillation results.

In recent years, domain generalization (DG) has garnered significant attention for its goal of learning models from multiple source domains to mitigate domain shift and enable generalization to unseen target domains. Most existing methods e.g., domain-invariant representation, focus on learning a universal sample-to-label mapping (function) across domains, overlooking semantically intrinsic domain-specific information and failing to maintain invariance across diverse unseen target domains.

Skill-based reinforcement learning has become the mainstream approach to solve sparse-rewards decision making tasks. The skills extracted from the demonstration datasets provide the temporal abstraction. However, in previous skill-based RL methods, the skills are kept fixed during online learning, which brings in sub-optimal asymptotic performance when the dataset contains only sub-optimal behavior modes.

With the growing use of multiple social platforms, aligning user identities across networks, known as Social Network Alignment (SNA), has become crucial for personalized services, fraud detection, and online safety. However, existing SNA methods face significant challenges when dealing with sparse connections, heterogeneous structures, and dynamic changes in real-world social networks.

Artificial intelligence is beginning to transform not only drug discovery but also the design of advanced biomaterials. In a new study, researchers combined machine learning with laboratory validation to develop a data-driven strategy for identifying bioactive nucleoside hydrogels for periodontitis treatment. By integrating predictive modeling with innovative molecular scoring methods, the work aims to streamline biomaterial discovery and accelerate the development of targeted therapies for oral health applications.

The cyber-attack diagnosability (CA-diagnosability) of discrete event systems (DESs) assess the ability to diagnose issues when an attacker interferes with sensor-to-diagnostic communication. In real life, for example, in network systems, a network node (router or user) may be attacked or unable to correctly and promptly transmit messages due to other reasons; in power systems, a current or voltage detector may naturally age or be affected by external factors, thus failing to accurately obtain the current and voltage values of the power system; in automotive systems, damage to a component may cause the sensor readings to differ from the original values, and so on.

What do bee venom, frog skin secretions, and the body's own antimicrobial molecules have in common? According to a new review published in the Chinese Journal of Natural Medicines, they all contain naturally derived oncolytic peptides, small proteins that are emerging as promising tools in the fight against cancer. Unlike many conventional therapies that target specific molecular pathways, these peptides primarily attack the outer membranes of cancer cells, allowing them to destroy tumors through a fundamentally different mechanism. Many can also stimulate immune responses against cancer, raising interest in their use alongside modern immunotherapies. The review summarizes recent advances in peptide discovery, molecular engineering, drug delivery, and clinical development, highlighting both the opportunities and challenges involved in translating these natural molecules into future cancer treatments.

Chronic wounds and drug-resistant infections are becoming increasingly difficult to manage, creating an urgent need for new treatment options. Plant essential oils, such as tea tree oil, clove oil, and thyme oil, are known for their antimicrobial and anti-inflammatory properties, but their medical use has been limited because they evaporate easily and are difficult for the body to absorb effectively. A review published in the Chinese Journal of Natural Medicines explores how nano-emulsions, tiny droplets that improve the delivery of active ingredients, may help overcome these challenges. By enhancing stability, penetration, and bioavailability, nano-emulsions could strengthen the antimicrobial and wound-healing effects of essential oils. The review summarizes recent advances in this rapidly developing field and discusses how these formulations may help transform traditional plant-derived compounds into practical tools for infection control and wound management.