Polymer-based flexible wireless sensors have demonstrated significant potential in health monitoring. However, existing studies have predominantly focused on material design, energy harvesting, and wireless communication, while research addressing signal attenuation, noise accumulation, and data distortion within wireless links remains relatively limited. Physiological signals are highly susceptible to multi-source interference during acquisition and transmission; without effective data processing and intelligent analysis strategies, monitoring outcomes may fail to meet the accuracy and stability requirements of clinical applications. To address this research gap, this review constructs a comprehensive framework encompassing signal sensing, link transmission, and intelligent processing, building on advances in flexible materials and wireless energy supply modes. Special attention is given to the critical roles of channel compensation, noise suppression, feature extraction, lightweight artificial intelligence models, and edge computing in enhancing data quality and reducing energy consumption. Furthermore, the review systematically analyzes the impact of wireless link stability and energy management on monitoring performance. By reinforcing the synergistic mechanisms between data processing and communication, this work aims to provide design principles and development directions for the next generation of high-reliability, low-power, and intelligent wireless flexible health monitoring systems.

High NOx emissions pose a critical challenge for ammonia engines. This study proposes ammonia post-injection as a strategy to achieve in-cylinder NOx active reduction in ammonia direct-injection engines, offering an innovative approach for NOx emission control. Computational fluid dynamics (CFD) simulation results elucidate the characteristics of ammonia combustion and NOx evolution under ammonia post-injection conditions. The post-injected ammonia can efficiently reduce the in-cylinder NOx it encounters, leading to a significant decrease in NOx concentration. Chemical kinetics analysis was conducted to reveal the underlying mechanisms and reaction pathways of the SNCR effect on NOx. The reduction of NOx primarily proceeds through the reactions between NO/NO2 and NH/NH2. NO is reduced via three pathways, yielding NNH (by NH2), N2O (by NH), and N2 (by NH and NH2), respectively. In contrast, NO2 is reduced via a single pathway that yields N2O under the action of NH and NH2. NH2 plays the overwhelmingly dominant role in reducing both NO and NO2. The effectiveness and feasibility of ammonia postinjection in reducing NOx emissions were evaluated through engine experiments. The experimental results demonstrate that the ammonia post-injection strategy enables significant NOx reduction for ammonia direct-injection engines. In the current work, a 14.4% NOx reduction was achieved without compromising combustion efficiency by appropriately delaying the ammonia post-injection timing, confirming the great potential of ammonia post-injection in NOx emission control.

The Fe–N–C single-atom catalyst represents a promising candidate for promoting the oxygen reduction reaction (ORR), which is crucial for fuel cell applications; yet, identifying optimal modification strategies to enhance its activity and stability remains challenging. Herein, the modulation of Fe–N–C catalysts via in-plane heteroatom doping and axial coordination is systematically investigated using integrated density functional theory (DFT) and machine learning (ML) approaches. The analysis reveals that axial ligands have a more profound influence on ORR performance than in-plane dopants, primarily by modulating the Fe dz2 orbital and weakening *OH adsorption. Through interpretable descriptors extracted from ML models, the key electronic and geometric properties governing catalyst behavior are identified, and several novel dual-modified candidates with enhanced activity relative to pristine Fe–N–C are subsequently predicted and validated by DFT calculations. This work provides a unified mechanistic and data-driven framework for accelerating the design of high-performance Fe–N–C ORR electrocatalysts.

Increasing atmospheric carbon dioxide (CO₂) levels pose a significant global challenge, making the assessment of natural carbon sinks, such as forests, ever more critical. New research conducted in the Garhwal region of the Indian Himalayas investigates how different forest types contribute to carbon storage across varying altitudes. The investigation by Arvind Singh, Vinod Prasad Khanduri, and colleagues identifies distinct carbon partitioning strategies, where high-altitude conifer forests excel at storing carbon in biomass, while lower-elevation mixed broadleaf forests are superior in stabilizing soil carbon. This understanding is vital for developing effective climate mitigation plans.

For more than two decades, the Global Positioning System (GPS) constellation has silently monitored high-energy electrons in Earth's radiation belts—but inconsistencies among its satellites have prevented scientists from fully trusting the combined data. 

A research team has proposed a new framework for understanding how traditional Chinese medicine (TCM) works at the tissue level by using artificial intelligence (AI), single-cell omics, and spatial omics to decode “cell niches”—local microenvironments where multiple cell types interact.