CS/Ti₃C₂T_x/Co@CNTs composite could obtain the minimum reflection loss (RL_min) of -55.01 dB at the thickness of 3.4 mm and the effective absorption bandwidth (EAB) could reach 9.07 GHz. It should be emphasized that the average density of CS/Ti₃C₂T_x/Co@CNTs is only 0.03 g/cm³. The electrostatic self-assembled CS/Ti₃C₂T_x/Co@CNTs composites behaved the excellent conductive loss, polar loss and the impedance matching.

Researchers have introduced a statistical method that allows accurate forest monitoring using satellite images with missing data. The hybrid estimator works directly with flawed data, bypassing the need for complex and uncertain data repair processes. This approach achieved over 90% sampling precision, meeting national forest inventory standards, and performed as well as techniques requiring complete satellite imagery. This provides a cost-effective way to leverage decades of archived satellite data for reliable forest and carbon stock assessment, supporting vital climate and conservation efforts.

Autonomous driving systems increasingly rely on data-driven approaches, yet many still struggle with reasoning, handling rare scenarios, and transparently explaining their actions. A new study introduces DriveMLM, a multi-modal large language model framework that aligns language-based reasoning with structured behavioral planning states, enabling full closed-loop driving in realistic simulators. By integrating multi-view images, LiDAR inputs, traffic rules, and natural-language instructions, DriveMLM generates both driving decisions and human-readable explanations that map directly to vehicle control. The system significantly improves safety, adaptability, and interpretability, demonstrating how large language models (LLMs) can advance the next generation of autonomous driving technology.

Understanding how sound travels through the middle ear is essential for designing reliable hearing implants. Traditional measurements of the middle ear transfer function (METF) can be affected by inner ear impedance and surgical manipulation, limiting their accuracy. This study introduces a new technique that eliminates inner ear interference while precisely capturing stapes footplate vibrations at multiple points. By accessing the vestibular side of the stapes through a trans-petrous approach, the researchers generated a stable and reproducible METF reference range using human temporal bone specimens. This refined method offers more reliable data for evaluating implant performance and enhances the biomechanical understanding of auditory transmission.

Eustachian tube dysfunction often determines whether a routine ear infection clears quickly or develops into a persistent condition. This study reveals that the mitochondrial enzyme SIRT3 plays a crucial protective role during acute inflammation. Using a mouse model of otitis media, the researchers show that loss of SIRT3 transforms a typical inflammatory reaction into a more severe pathological process—characterized by excessive mucus buildup, ciliary damage, increased resistance to tube opening, and impaired mucociliary transport. These findings highlight SIRT3 as an unrecognized stabilizer of middle-ear physiology and help explain why some infections resolve smoothly while others progress toward chronic disease.

Organoid research has rapidly advanced as a transformative platform for modeling development, disease, and regeneration, yet inconsistent reporting has hindered reproducibility and limited data integration across laboratories. The newly introduced Minimum Information about Organoid Research (MIOR) framework establishes a comprehensive, modular reporting system designed to address these challenges. MIOR defines clear requirements for project metadata, biological sources, organoid characterization, culture conditions, engineering strategies, and assay parameters. By distinguishing essential from recommended fields, the framework balances rigor with practical usability. MIOR aims to turn organoid datasets into reusable, comparable resources and strengthen the reliability and translational potential of organoid-based research.

Simultaneous localization and mapping (SLAM) is widely used in autonomous driving, augmented reality, and embodied intelligence. In real-world settings, sensor measurements often suffer from substantial clutter (false alarms) and missed detections, which complicate SLAM data association. This complexity manifests as uncertainty in associating observations to landmarks, the possibility of erroneous associations between clutter and landmarks, and the potential absence of landmark observations. Random Finite Set (RFS) theory offers a Bayesian estimation framework well suited to SLAM with uncertain data association and an unknown, time-varying number of landmarks, and has spurred extensive research on RFS-based SLAM methods. Particle-filter-based Probability Hypothesis Density (PHD)-SLAM can effectively estimate the joint probability density of the pose and the map under clutter and missed detections, yielding robust SLAM performance. However, improving the estimation accuracy of particle-filter PHD-SLAM typically requires increasing the number of particles, which rapidly scales the computational cost.

SARS-CoV-2 evolves rapidly, creating challenges for traditional broad antibody development strategies that rely on conserved epitopes. By surveying 7,116 published receptor-binding domain（RBD）-targeting monoclonal antibodies, we identify three single monoclonal antibodies (mAbs)—SA55, VIR-7229, and BD55-1205—and one broadly neutralizing antibodies (bsAb) Dia-19, that retain ng (in the ng/mL range) neutralization activity even when their binding footprints overlap RBD residues with mutation rates up to 39%. Notably, the three mAbs above carry ~2× more VH somatic hypermutations than the dataset median. Guided by these observations, we outline two complementary strategies: (1) an immune trajectory strategy that prioritizes higher-maturity candidates, and (2) a viral fitness-constraint strategy suited to upgrading lower-maturity antibodies. Together, these provide practical paths for discovering and improving antibodies against fast-evolving SARS-CoV-2.

Developing high-efficiency sintering technologies with mild conditions is crucial for energy reducing and performances manipulating. However, sintering ceramics at low temperatures in short times without pressure is challenging. Inspired by microwave resonance and dissolution-precipitation phenomena, microwave cold sintering process (MW-CSP) is proposed here to densify high-performance ceramics with significantly reduced sintering times and temperatures under pressureless conditions. A range of ceramics including chlorides, oxides, phosphates and molybdates with various applications are shown to be well sintered by MW-CSP. The mechanical and dielectric properties of the selected materials are improved by 50-95%, while the energy consumption of MW-CSP is dramatically reduced by over 97% compared to other pressureless sintering technologies.

Aluminum Oxynitride (AlON) transparent ceramics are recognized as one of the most promising transparent ceramic materials in the 21st century, combining high optical transmittance with excellent mechanical properties. However, producing high-transmittance AlON ceramics via pressureless sintering (also known as conventional sintering, CS) has consistently faced the challenge of excessively long dwell durations at high temperatures (6-30 h). Prolonged sintering not only leads to high risks, high energy consumption, low efficiency, and elevated costs, but also results in excessive grain growth, degrading mechanical performance. In this study, based on the CS route and incorporating the emerging technique of ultra-fast high-temperature sintering (UHS), we propose a novel strategy-UHS combined with CS (UHS+CS)-for efficiently fabricating highly transparent AlON ceramics. This approach achieves remarkable technical outcomes, and the underlying mechanisms are clarified.