In recent years, with the rapid development of the new energy vehicle industry, the endurance and energy density of lithium batteries have been significantly improved. However, this advancement has correspondingly increased the risks associated with battery failures. Consequently, implementing safety failure analysis and early warning mechanisms for lithium batteries has become critically important. Conducting dynamic analysis of the entire lifecycle process – including encapsulation, electrolyte filling, charging/discharging, and damage – can effectively guide battery manufacturing and usage practices, thereby advancing next-generation battery development.

Current battery health monitoring methodologies encompass solutions such as radiography, thermal imaging, ultrasonic testing, and internal stress detection. Among these, ultrasound technology stands out as an exceptionally suitable "CT" imaging tool for lithium batteries due to its superior penetration capability, rapid response speed, high spatial resolution, and real-time monitoring capacity, which enable distinct responses to various internal evolution processes. Notably, fiber-optic ultrasound solutions, in particular, offer miniaturization, high sensitivity, resolution, and penetration depth, positioning them as a promising technology for battery diagnostics.

A research team from the Institute of Modern Physics (IMP), Chinese Academy of Sciences (CAS), has developed a machine learning (ML) model enhanced with expert knowledge to classify eight types of faults in superconducting radio-frequency (SRF) cavities at the China Accelerator Facility for superheavy Elements (CAFE2) facility. This model helps operators quickly identify fault patterns after incidents, speeding recovery and supporting long-term stable SRF accelerator operation.

A recent study reveals that dimethyl fumarate (DMF), a compound already approved for other inflammatory diseases, can prevent and alleviate periodontal tissue damage. 

Large language models (LLMs) are at the forefront of artificial intelligence (AI) and have been widely used for conversational interactions. However, assessing the personality of a given LLM remains a significant challenge. A research team at The Hong Kong Polytechnic University (PolyU) has developed an AI-driven assessment system, the Language Model Linguistic Personality Assessment (LMLPA), with capabilities to quantitatively measure the personality traits of LLMs through linguistic analysis.

The popularization and diffusion of compound-eye array camera technology faces formidable challenges. On the one hand, the high-resolution realization of compound-eye array camera systems usually relies on a large-scale number of cameras and high-pixel-density image sensors, with high system complexity and limited imaging real-time. Zoom imaging technology is expected to reduce the number of cameras and the need for sensor pixel density and improve imaging adaptability while taking into account the large field of view and high-resolution imaging capability of the compound eye. However, the traditional mechanical zoom method is slow and lacks dynamic responsiveness, and the introduction of compound-eye array cameras will cause a drastic increase in the size, weight, and power consumption, which makes it difficult to apply to compound-eye array cameras. On the other hand, the compound-eye array camera is susceptible to the interference of the imaging environment during the actual imaging, resulting in the degradation of the imaging quality and difficulty in giving full play to its resolution advantage, and due to the variability of the environmental interference factors and the inherent manufacturing tolerances caused by the variability between the sub-camera units, the traditional image processing algorithms are often difficult to complete the image information demodulation and enhancement of the compound-eye array camera. Therefore, the realization of fast optical zoom and high-fidelity resolution enhancement in compound-eye array cameras remains a key challenge to be solved.

A new study in Engineering presents GlycoPro, a high-throughput sample-processing platform for multi-glycosylation-omics analysis. It overcomes limitations of existing methods, efficiently processes a large number of samples, and shows promise in breast cancer biomarker discovery, with potential for broader applications in glycosylation-related diseases research.

A pioneering study has demonstrated the remarkable potential of visual feedback technology to assist profoundly deaf individuals in developing oral speech. By translating speech sounds into visual patterns, the technology enables users to "see" their vocal efforts and adjust them to match reference models. Early trials with 72 participants have shown significant progress, with many learning up to 18 phonetic sounds within six months. This groundbreaking approach could revolutionize speech rehabilitation, offering a viable alternative to traditional methods like sign language and cochlear implants, particularly for those without early auditory interventions.

In a research paper, scientists from the Tsinghua University proposed a novel enhanced Digital Light Processing (DLP) 3D printing technology, capable of printing composite magnetic structures with different material sin a single step. Furthermore, a soft robot with a hard magnetic material-superparamagnetic material composite was designed and printed.

Wastewater treatment plants (WWTPs) are significant contributors to greenhouse gas (GHG) emissions, including methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O).