Researchers from the University of South Florida have used artificial intelligence and citizen science to identify what may be the first specimen of Anopheles stephensi — an invasive and deadly malaria-carrying mosquito — ever detected in Madagascar.

University of Tennessee, Knoxville researchers Jindong Tan and Dr. Gregory Mancini developed a wireless robotic imaging device for minimally invasive surgery, integrating AI for enhanced optics and precision. Funded by a $1 million grant from the National Science Foundation, the device reduces incisions, improves outcomes, and expands surgical possibilities in tight spaces like joints and the brain, fostering collaborative innovation in medical technology.

The balance between electricity supply and demand requires advanced technologies and precise management, especially given the growing presence of renewable sources. Researchers are working on new energy storage and control strategies to ensure a stable and secure energy supply, preventing blackouts like the one that occurred on April 28, 2025.

The project “Management of Renewable Systems with Storage and Converter Control to Contribute to the Operation of the Future Power System”, led by Professors Emilio Pérez Soler and Ignacio Peñarrocha Alós, members of the Electricity, Electronics and Automation Research Group, is progressing toward its goal of integrating renewable energies through the development of advanced control strategies and their experimental validation on a platform that allows real-time testing of the combined operation of batteries, converters and controllers.

So far, the research team has developed predictive models related to the electricity market, such as daily market prices and services aimed at regulating the power system frequency. These models have been used to define a strategy based on deep reinforcement learning, enabling optimal participation of grid-connected storage systems in various electricity markets. Regarding lithium-ion batteries, new techniques have been developed to estimate their state of charge and health, improving both performance and lifespan.

Fashion trend prediction has traditionally relied on experts’ intuition and creativity, as well as big-data analysis for incorporating insights on consumer behaviors. However, this method remains inaccessible to fashion students and small brands. Now, researchers show how AI can supplement expert knowledge with data-based insights, enabling individuals to predict trends with more accuracy and nuance. To this end, the researchers developed a novel prompting technique to enable ChatGPT to provide more accurate and specific responses.

A study led by Aalto University reveals that when it comes to AI, specifically, LLMs, the Dunning-Kruger Effect doesn’t hold. All users show a significant inability to assess their performance accurately when using ChatGPT, with people overestimating their performance across the board. On top of this, the researchers identified a reversal of the Dunning-Kruger Effect ––   an identifiable tendency for those users who considered themselves more AI literate to assume their abilities were greater than they really were.

Researchers at Seoul National University have compiled the latest technologies in the field of fabricating stretchable synaptic transistors, a neuromorphic device capable of learning even under deformation. This review introduces materials, manufacturing processes (photopatterning, printing, lamination-and-transfer), and structures (vertical, wave-like, textile) that maintain low-voltage stable operation even under deformation. By clearly identifying remaining technical challenges, such as n-type/bipolar semiconductors, CMOS-compatible patterning, and biocompatibility, it provides a roadmap for developing scalable wearable and neuroprosthetic systems.