Osaka Metropolitan University researchers developed a silica capsule–based technology that enables easy separation and reuse of colorants from plastics, overcoming limitations of conventional recycling. This approach allows repeated upcycling into high-quality, vividly colored materials with significantly lower energy consumption.

The ongoing evolution of software defect detection methodologies leveraging large language models is rapid; however, the current research landscape has not been adequately investigated. Existing reviews inclusively categorize smaller language models as LLMs, failing to concentrate specifically on the domain of software defect detection and omitting recent advancements in the field. This highlights a gap in the detailed analysis and assessment of cutting-edge applications of large language models in detecting software flaws.

The quality control issue has always been the focus of Crowdsourcing research. However, existing surveys predominantly concentrate on quality control in Boolean tasks, which are generally formulated as simple label classification, ranking, or numerical prediction. Ubiquitous open-ended tasks like question-answering, translation, and semantic segmentation have not been sufficiently discussed due to several challenges, like large to infinite answer spaces, complex task structure, dynamic and multidimensional worker abilities and non-unique acceptable answers.

SELinux (Security-Enhanced Linux) enforces mandatory access control (MAC) according to policies. However, due to the inherent complexity of systems, real-world SELinux policies often become intricate, comprising thousands of statements, making manual verification of their correctness and security challenging. In addition, specific environments with stringent security requirements demand the formal verification of policies. While existing policy tools primarily focus on particular aspects of policy analysis, such as integrity and consistency, there is a lack of a unified approach to satisfy diverse requirements.

Researchers have developed new algorithms to enhance the reliability of data center networks (DCNs) by constructing fault-tolerant paths and maximum disjoint paths in augmented cube-based architectures. These breakthroughs address critical challenges in handling node failures and improving data transmission efficiency, which are vital as cloud computing and big data continue to drive DCN expansion. The findings were published on 15 June 2026 in Frontiers of Computer Science, a journal co-published by Higher Education Press and Springer Nature.

Osaka Metropolitan University researchers used GPS trajectory data and quasi-experimental methods to evaluate how the opening of ONIKURU affected citizens’ stay behavior.