A review from Xidian University shows that advanced computational algorithms—from neural networks and matrix methods to recommendation engines and text-mining—can reliably predict novel uses for existing drugs, offering a faster, lower-cost, and lower-risk path to discovering new therapies.

Researchers from Jinan University, Huawei, and ByteDance have developed LTAA-FGAC, an innovative authentication system that balances user privacy with public traceability and fine-grained access control to enhance digital security and accountability.

An international team of researchers has developed LLaVA-Endo, a powerful new AI tool that helps doctors more accurately diagnose digestive diseases by combining visual and language understanding during gastrointestinal endoscopies.

Researchers at Nanjing University have developed a new training framework and benchmark that dramatically enhance how AI communicates and coordinates with humans in unpredictable, real-world scenarios, paving the way for safer, more efficient collaboration across various industries.

Researchers have developed an innovative AI approach that embeds causal reasoning into offline reinforcement learning, enabling autonomous systems like driverless cars and medical support devices to make safer, more reliable decisions by accurately discerning true cause-and-effect relationships in historical data.

Researchers have developed a drone system that uses blockchain sharding to significantly enhance the speed, security, and real-time coordination of search and rescue missions.

Researchers have developed an innovative AI-driven system that automates and optimizes penetration testing, significantly reducing testing steps while expanding vulnerability assessments for large-scale networks.

Researchers at Shanghai Jiao Tong University have developed CHASER, a blockchain-based incentive system that transforms mobile crowdsensing by leveraging automated smart contracts and advanced encryption to deliver fair compensation, robust data security, and high efficiency, dramatically boosting user participation and reliability.

Researchers at Wuhan University have introduced Fair Adversarial Training (FairAT), a technique that enhances AI security and fairness by identifying and strengthening vulnerable data points, thereby reducing cyber-attack risks and mitigating ethical biases.