Researchers have found a low-power, inexpensive way for large numbers of devices, such as machines in factories and equipment in labs, to share information by efficiently using signals at untapped high frequencies. The technology is an advanced version of a device that transmits data in a wireless system, commonly known as a tag. The new tag can support data transmission for large networks of devices using a technique called backscattering. This is where a central reader sends a signal to a sensor tag to gather information, and the tag reflects the signal directly back to the reader. The new tag is the first of its kind to use backscattering in a high-frequency range known as sub-terahertz. The technology could immediately enable low-cost, efficient real-time monitoring in industrial settings, such as tracking the condition of manufacturing robots or detecting gas leaks in refineries, by eliminating the need for power-hungry signal transmitters.

Summary: Pensoft Publishers and the Southern eDNA Society (SeDNA) have signed a Memorandum of Understanding to collaborate and advance the field of environmental DNA (eDNA) research. The partnership aims to accelerate the development and application of eDNA technology, increase visibility and impact of eDNA research, and promote SeDNA's conferences and research outputs. Pensoft will also offer a special discount on article processing charges in its journal Metabarcoding and Metagenomics for SeDNA members.

Recently researchers from SEOULTECH have pioneered a graphene-based laser lift-off technique that prevents damage while separating ultrathin OLED displays. By utilizing graphene's ability to absorb UV light and distribute heat, they have achieved pristine, flexible displays. This advancement opens doors for ultra-thin, stretchable devices that fit comfortably against human skin, revolutionizing wearable device technology.

In a paper published in National Science Review, an international team of researchers introduced the one-core-neuron system (OCNS), a “small model” framework designed to tackle the inefficiency of “large models”. By utilizing a single core neuron, OCNS achieves comparable or superior performance in time-series forecasting while reducing parameter requirements to a fraction of those in existing “large models”. This approach highlights the potential of small models in efficient deep learning and more sustainable AI applications.