In the field of Large Language Models(LLMs) vertical applications, the innovative direction of deeply integrating foundational LLMs with industrial scenarios has emerged. Researchers have developed an intelligent decision support system for CNC system fault diagnosis, utilizing LLMs and domain knowledge graph(KG) technologies. This advancement effectively overcomes the limitations of typical expert systems in symbolic reasoning efficiency and accuracy. The study, published in Engineering, provides insights and methods for the practical application of industrial large models.

Mystery infections—persistent and unexplained illnesses—are a rising global health challenge, often delaying critical treatment as patients and clinicians search for answers. Today, advanced genetic testing technologies are transforming the landscape, rapidly identifying hidden pathogens and unlocking new pathways for accurate diagnosis and effective intervention.

A new national guideline provides evidence-based recommendations for diagnosing, treating, and preventing pediatric respiratory syncytial virus (RSV) infections in China. Addressing 12 clinical questions, it guides clinicians on diagnostic methods, oxygen therapy, use of interferon-alpha, and nirsevimab prophylaxis. The guideline discourages unnecessary medications and emphasizes supportive care and long-term monitoring. Developed by leading experts and published in Pediatric Investigation, it aims to standardize care, reduce hospitalizations, and reflect China’s RSV burden and seasonal trends.

Photothermoelectric (PTE) photodetectors with self-powered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields. However, traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument. Herein, we present a flexible PTE photodetector capable of dual-mode output, combining electrical and optical signal generation for enhanced functionality. Using solution processing, high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer. The geometrically asymmetric electrode design significantly enhances the responsivity, achieving 0.33 mA W^-1 under infrared illumination, twice that of the symmetrical configuration. This improvement stems from optimized photothermal conversion and an expanded temperature gradient. The PTE device maintains stable performance after 300 bending cycles, demonstrating excellent flexibility. A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials, leading to a real-time visualization of invisible infrared radiation. Leveraging this functionality, we demonstrate the first human–machine collaborative infrared imaging system, wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern. Our study not only provides a new solution for functional integration of flexible photodetectors, but also sets a new benchmark for human–machine collaborative optoelectronics.

Conjugated polymers (CPs) have emerged as an interesting class of materials in modern electronics and photonics, characterized by their unique delocalized π-electron systems that confer high flexibility, tunable electronic properties, and solution processability. These organic polymers present a compelling alternative to traditional inorganic semiconductors, offering the potential for a new generation of optoelectronic devices. This review explores the evolving role of CPs, exploring the molecular design strategies and innovative approaches that enhance their optoelectronic properties. We highlight notable progress toward developing faster, more efficient, and environmentally friendly devices by analyzing recent advancements in CP-based devices, including organic photovoltaics, field-effect transistors, and nonvolatile memories. The integration of CPs in flexible sustainable technologies underscores their potential to revolutionize future electronic and photonic systems. As ongoing research pushes the frontiers of molecular engineering and device architecture, CPs are poised to play an essential role in shaping next-generation technologies that prioritize performance, sustainability, and adaptability.

Could the future of clean energy hinge on the spin of a single electron? A new scientific review suggests it might. Researchers are turning to the quantum world—specifically, electron spin—to unlock new possibilities for high-performance electrocatalysts that drive green energy reactions. By fine-tuning how electrons spin within catalyst materials, scientists are finding ways to accelerate reactions such as oxygen reduction, oxygen evolution, carbon dioxide conversion, and nitrogen fixation. The study introduces emerging strategies—from atomic doping to magnetic field modulation—that allow precise control over catalytic behavior. This marks a bold step toward engineering smarter, faster, and more sustainable catalysts for tomorrow’s energy solutions.

This study is pleased to present a seminal study examining reserve management strategies across 45197 company-line-year observations from 2000-2012. The research pioneers line-of-business-level analysis to disentangle insurers' multidimensional incentives—tax optimization versus solvency management—through structural reserve adjustments. Employing two-way clustered fixed-effects models and regulatory policy shocks, the study establishes causal evidence of strategic reserve allocation patterns previously undocumented in actuarial literature.

Recently, a research team led by Prof. LI Xianfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS) developed a novel interfacial polymer cross-linking strategy to fabricate ultra-thin polymeric membranes with nanoscale separation layers.

Researchers from Nanjing University of Science and Technology have developed a novel computational method called BlastGraphNet, which uses graph neural networks to predict the distribution of blast loads on complex 3D buildings. This data-driven approach offers significant improvements in accuracy and efficiency compared to traditional methods, with potential applications in structural design, civil defense, and safety assessments. The study, published in Engineering, demonstrates the model’s ability to provide rapid and precise predictions of blast loads, supporting more effective risk management and engineering solutions.