The advent of the fifth-generation (5G) technology has revolutionized the way we think about communication networks, enabling a plethora of new use cases and scenarios that were not possible with previous generations of wireless technology. The existing 5G communication networks predominantly rely on terrestrial communication infrastructure, which, however, exhibits several notable limitations such as capacity constraints, latency issues, and energy inefficiencies, necessitating this next-generation leap. The 6G aims to provide a comprehensive solution for a hyperconnected, intelligent, and immersive digital ecosystem, seamlessly integrating terrestrial, airborne, and spaceborne networks which is referred to as the space-air-ground-sea integrated network (SAGSIN). A traditional SAGSIN scenario mainly includes 3 segments: spaceborne network, airborne network, and terrestrial network. The classical SAGSIN architecture has shown remarkable capabilities in providing massive connectivity by integrating spaceborne, airborne, and terrestrial cellular networks, which, however, encounters inherent technical challenges against their respective practical implementation. On the other hand, the philosophy of near-space communications (NS-COM) is gradually emerging. The distinctive location of near space which is 20 ~ 100 km above Earth’s surface positiones at the core of the SAGSIN, thus allowing the NS-COM network to address the shortcomings of traditional spaceborne, terrestrial, and airborne networks. NS-COM present a promising addition to SAGSIN, making them the final piece of the SAGSIN puzzle.

With winter approaching and people spending more time indoors, the quality of the air they breathe becomes increasingly important. Especially during cold and flu season.

Researchers at UBC Okanagan are exploring an air-cleaning device that can remove airborne pathogens, offering a powerful new tool for reducing the spread of respiratory diseases in enclosed spaces.

 Landon Marston, associate professor of civil and environmental engineering at Virginia Tech, is part of a group of researchers that have completed the first comprehensive assessment of the Rio Grande–Bravo basin, using decades of data from 14 sub-basins across the United States and Mexico. This multi-institutional effort, led by Brian Richter at Sustainable Waters, offer the clearest look yet at how limited water supplies are being allocated and where the system is breaking down.

Dr. Jongkil Park and his team of the Semiconductor Technology Research Center at the Korea Institute of Science and Technology (KIST) have presented a new approach that mimics the brain's learning principles. The team engineered the principle of spike-timing-dependent plasticity (STDP), in which the brain adjusts the strength of connections based on the order of signal firing between neurons. This allows them to learn the connectivity in a brain's neural network in real-time without having to store the activity of all the neurons.

Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human–machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti–freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol–gelatin (PVA/GLE) matrix. Fabricated using a binary solvent system of water and ethylene glycol (EG), the CoN CNT/PVA/GLE organogel exhibits excellent flexibility, biocompatibility, and temperature tolerance with remarkable environmental stability. Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range (40%-95% RH). Freeze-tolerant conductivity under sub-zero conditions (−20 °C) is attributed to the synergistic role of CoN CNT and EG, preserving mobility and network integrity. The CoN CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 kPa⁻¹ in the detection range from 0 to 20 kPa, ideal for subtle biomechanical motion detection. A smart human–machine interface for English letter recognition using deep learning achieved 98% accuracy. The organogel sensor utility was extended to detect human gestures like finger bending, wrist motion, and throat vibration during speech.