Cranfield University researchers have developed a new method for 3D printing rubber gloves that is set to reshape the glove manufacturing industry.

Precision nutrient management in high-density orchards is critical for yield and fruit quality, yet accurately gauging the nitrogen status of individual trees remains a challenge. 

University of Waterloo engineers have developed a low-cost, customizable 3D-printed filter that removes up to 90 per cent of microplastics from water, offering an inexpensive solution for both residential and industrial uses. 

With the exponential increase in the number of objects in low Earth orbit (LEO), collision risks have risen correspondingly, making high-precision orbit prediction a core requirement for ensuring satellite safety and space environment management. Two-Line Elements (TLEs) are the only publicly available orbital data source and are widely used in conjunction with the Simplified General Perturbations 4 (SGP4) model for LEO object orbit prediction. However, LEO objects are significantly affected by complex time-varying perturbations such as atmospheric drag, causing model prediction errors to grow rapidly over time—short-term errors can reach hundreds to thousands of meters, while medium- and long-term prediction errors may exceed 10 km. Although traditional dynamical models can achieve relatively high accuracy through numerical integration, they incur substantial computational costs and suffer from limited atmospheric density modeling accuracy (only about 5%–15%). Analytical methods, while computationally efficient, struggle to handle complex perturbations. In recent years, bidirectional long short-term memory (BiLSTM) networks have demonstrated strong capabilities in time-series prediction; nevertheless, an effective approach for systematically modeling and compensating for TLE-based prediction errors using solely publicly available TLE data has remained lacking.

Forests store carbon and support biodiversity, but management that benefits one goal may not benefit the other. A study of 150 forest plots found that increasing management intensity reduced total and aboveground carbon while increasing belowground carbon. Aboveground insect diversity and biomass also increased, whereas belowground fauna showed no overall response. Relationships varied among arthropod groups, habitats and years, revealing no single optimal management strategy. The authors suggest combining generally lower-intensity management with a mosaic of forest types to better balance carbon storage, arthropod habitat, climate mitigation and biodiversity conservation.