Journal of Machine Learning for Modeling and Computing

Journal of Machine Learning for Modeling and Computing JMLMC publishes the latest research in deep learning, engineering, AI, neural networks and more!

🔬 New from Thermopedia: Supercritical Fluids and New Phase Diagrams reexamines one of science's most foundational tools,...
06/12/2026

🔬 New from Thermopedia: Supercritical Fluids and New Phase Diagrams reexamines one of science's most foundational tools, the Gibbs phase diagram, proposing its first major extension in 150 years, complete with a newly discovered thermodynamic state and a redefined map of matter at extreme conditions.

Authored by Raad Shahmat Haque (University of North Texas), Laura M. Almara (Texas State University), Guo-Xiang Wang (University of Akron / Xi'an Jiaotong University), and Vish Prasad (University of North Texas), this work identifies three supercritical pseudo-phases: gas-like, liquid-like, and solid-like. It also introduces the "Dwij Point," a newly discovered state analogous to the triple point, beyond which only gas and solid phases exist.

Read the full article to explore the new phase diagrams and their implications for energy systems, pipeline transport, and beyond:

🔗 https://thermopedia.com/content/10480/?utm_medium=email&utm_source=ctct

✨ As conversations about equity in STEM education grow louder (and in many places, more contested) this 2001 article ...
05/29/2026

✨ As conversations about equity in STEM education grow louder (and in many places, more contested) this 2001 article from our journal reminds us that the data has been speaking for decades. Research published in the Journal of Women and Minorities in Science and Engineering explored how gender and race/ethnicity shape the science motivation and self-efficacy beliefs of middle school students, revealing meaningful differences across groups at a pivotal stage in academic development. The study found that self-efficacy, a student's belief in their own ability to succeed, emerged as a critical predictor of science achievement, but the factors shaping that belief were not the same for every student. At a time when DEI programs in K–12 education are being rolled back across the country, findings like these raise urgent questions: what happens to students whose paths to science confidence are already less supported when the structural investments designed to reach them disappear? The full picture is more nuanced,and more important, than a headline can capture.

Read the full article here: http://dl.begellhouse.com/journals/00551c876cc2f027,2615af3e3226be3e,6fffc62e1bcd9212.html

🫁 New from Thermopedia: AI-Driven Digital Twins for Real-Time Lung Mechanics charts a path from computationally intensiv...
05/15/2026

🫁 New from Thermopedia: AI-Driven Digital Twins for Real-Time Lung Mechanics charts a path from computationally intensive fluid-structure interaction (FSI) simulations to patient-ready clinical tools, using AI and reduced-order models to deliver instantaneous, personalized predictions of respiratory function at the bedside.

Authored by Syed Anas Nisar and Debjyoti Banerjee of Texas A&M University, this work bridges computational fluid dynamics, machine learning, and clinical medicine. By training neural networks on high-fidelity FSI data, including convolutional models and physics-consistent neural FSI frameworks, the authors create digital twins capable of mapping patient anatomy directly to airway mechanics in seconds, not hours.

A standout example of the interdisciplinary research Begell House champions: forging connections across engineering, AI, and biomedicine to open new pathways toward transdisciplinary discoveries and next-generation practices in personalized respiratory care and precision medicine.

Read the full article to explore the computational strategies enabling bedside decision support: 🔗 https://thermopedia.com/content/10477/?utm_medium=email&utm_source=ctct

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