The latest study in Engineering reveals a groundbreaking approach to greener ethylene manufacturing using a novel physically consistent machine learning (PCML)-based hybrid modeling framework for steam thermal cracking, a highly energy-intensive and carbon-emitting process. Conducted by a team from the University of Sheffield and Southeast University, this research has significant implications for global sustainable chemical production.

A research paper by scientists at Tianjin University introduces background electroencephalogram (EEG) mixing (BGMix), a novel data augmentation technique grounded in neural principles that enhances training samples by replacing background noise between different classes.

The new research paper, published on Oct. 07, 2025 in the journal Cyborg and Bionic Systems, introduced a novel EEG augmentation method and a new approach to designing deep learning models informed by the neural processes of EEG.

For many researchers, inspiration doesn’t just come from curiosity—it’s born out of personal struggle and lived experience. Professor Shoko Miyauchi of Kyushu University’s Faculty of Information Science and Electrical Engineering is one such researcher. Motivated by the frustrations and anxieties she encountered during her own parenting experience, she has developed systems to support families with childcare needs. We sat down with Miyauchi to hear about her current research and future goals.

To illustrate how generative AI breakthrough is driving real-world healthcare solutions, Alex Zhavoronkov PhD, founder and CEO of Insilico Medicine, will be speaking about the topic “Can Cell-Based lnnovation Keep Humanity in Peak Condition?” from 1:45 PM-2:10 PM local time, Oct 29. The next day, Alex Aliper, PhD, President of Insilico Medicine, will share the know-hows obtained in continued AI platform development and reproducible proof-of-concept achieved with highly efficient novel pipelines at Insilico, in the FII Innovators Pitch 2025 session.

To discover how machine learning (ML) is revolutionizing molecular crystal design and crystallization, a new review in Engineering explores ML’s role in accelerating solvate and co-crystal development, predicting crystal properties, and optimizing crystallization processes. Learn about the latest advancements and future prospects in this field.

The hidden Markov model (HMM), a statistical model widely applied in machine learning, has proven effective in addressing various problems in bioinformatics. Once primarily regarded as a mathematical framework for modeling stochastic processes, HMMs have become indispensable tools for solving a wide range of biological sequence problems, from gene prediction to protein structure analysis.