Since Wilhelm Röntgen discovered X-rays in 1895, X-ray imaging has become an indispensable tool in modern medical diagnosis, industrial non-destructive testing, security screening, and even cultural heritage preservation. In X-ray imaging systems, detection methods fall into two main categories: direct and indirect. Direct detection uses semiconductor materials to convert X-ray photons directly into electrical signals, offering fast response and high spatial resolution, but it is often expensive and complex to manufacture. Indirect detection employs a “scintillator + photodetector” structure: X-rays are first absorbed by a scintillator material and converted into visible or ultraviolet light, which is then captured by a photodiode or CCD/CMOS sensor and transformed into electrical signals. Indirect detection dominates the current X-ray imaging market because of its mature technology, controllable cost, and excellent compatibility with existing sensors.

A growing body of research suggests that the effects of periodontitis extend far beyond the mouth. In a large population-based study from Germany, researchers found that severe gum disease was associated with reduced kidney function and higher levels of kidney damage markers, even in the early stages of chronic kidney disease. The findings indicate that oral health may play a broader role in overall health than previously recognized.

Conference Announcement: 2026 International Symposium on Energy Frontiers

Degraded face videos have long plagued digital communication—from glitchy video calls to blurry archival footage. 

A nationwide study in China has shown that serious lower respiratory infections are not driven by the same pathogens at every age. Analyzing 695,142 bronchoalveolar lavage fluid samples from hospitalized patients, researchers identified eight life-stage pathogen profiles, 124 significant co-detecting pathogen pairs grouped into three networks, and marked sex disparities. The findings could help refine vaccination strategies, diagnostic panels, and clinical risk assessment.

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.

The high annotation costs in medical detection tasks make semi-supervised object detection (SSOD) methods particularly promising. However, current research has paid little attention to medical image SSOD, and conventional SSOD approaches struggle with the unique challenges of medical imaging—scarce annotations and difficult-to-learn features.

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.

Sequential recommendation is an important research task in the field of recommendation systems. The goal of the sequential recommendation task can be defined as follows: given a user’s interaction sequence and the associated timestamp sequence, predict the next item that the user is likely to interact with at the next time step. The existing methods have limitations in capturing the diversity of long-term interests, the dynamics of short-term user interest, and the hierarchical relationship between them.