Deep brain stimulation, a method where specific brain regions are activated using implanted electrodes, is a well-established approach for treating movement disorders such as Parkinson’s disease. Researchers led by Christian Kell from Frankfurt University Medicine as well as Nils Warneke and Katrin Neumann from Münster University Hospital have now successfully alleviated severe stuttering in a person with developmental stuttering using this method for the first time. The researchers are now preparing a study to test the therapy on additional individuals who experience severe stuttering.

A Michigan State University philosophy scholar has added clarity to a messy philosophical debate.

Interpretable deep learning network significantly improves tropical cyclone intensity forecast accuracy

Researchers explore the human immune system by looking at the active components, namely the various genes and cells involved. But there is a broad range of these, and observations necessarily produce vast amounts of data. For the first time, researchers including those from the University of Tokyo built a software tool which leverages artificial intelligence to not only offer a more consistent analysis of these cells at speed but also categorizes them and aims to spot novel patterns people have not yet seen.

Organic photovoltaics (OPVs) have achieved remarkable progress, with laboratory-scale single-junction devices now demonstrating power conversion efficiencies (PCEs) exceeding 20%. However, these efficiencies are highly dependent on the thickness of the photoactive layer, which is typically around 100 nm. This sensitivity poses a challenge for industrial-scale fabrication. Achieving high PCEs in thick-film OPVs is therefore essential. This review systematically examines recent advancements in thick-film OPVs, focusing on the fundamental mechanisms that lead to efficiency loss and strategies to enhance performance. We provide a comprehensive analysis spanning the complete photovoltaic process chain: from initial exciton generation and diffusion dynamics, through dissociation mechanisms, to subsequent charge-carrier transport, balance optimization, and final collection efficiency. Particular emphasis is placed on cutting-edge solutions in molecular engineering and device architecture optimization. By synthesizing these interdisciplinary approaches and investigating the potential contributions in stability, cost, and machine learning aspects, this work establishes comprehensive guidelines for designing high-performance OPVs devices with minimal thickness dependence, ultimately aiming to bridge the gap between laboratory achievements and industrial manufacturing requirements.

Identifying and interpreting vacancies in patent maps is a promising approach to discover technological opportunities. However, it remains a challenging task. Recently, scientists from Seoul National University of Science and Technology have developed an innovative machine learning approach based on text-embedding inversion, which translates patent vacancies into human-readable formats, helping to uncover technological opportunities for corporate growth.