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Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques

Anwer Shees, Mohd Tariq, Arif I. Sarwat • Energies (2024)

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement...

DOAJ Open Access (DOAJ) DOI: 10.3390/en17235870

Precision water quality indices forecasting through an optimized hybrid SMW-LSSVM-R model enhanced by SATLDE and uncertainty analysis

Heming Jia, Marjan Kordani, Iman Ahmadianfar, Arvin Samadi Koucheksaraee et al. • Engineering Applications of Computational Fluid Mechanics (2026)

Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices usin...

DOAJ Open Access (DOAJ) DOI: 10.1080/19942060.2025.2602582

Revealing the dynamic evolution of interfacial water during Mg anodic dissolution

Xinchen Xu, Xingpeng Liao, Gaoning Shi, Yuyang Chen et al. • Journal of Magnesium and Alloys (2026)

Though magnesium alloys possess attractive mechanical properties, their applications are severely limited by galvanic corrosion, due to the rapid anodic dissolution and anomalous hydrogen evolution. In this work, well-tempered metadynamics ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jma.2026.102217

Physics-Informed Machine Learning

Steven L. Brunton, J. Nathan Kutz • Cambridge University Press eBooks (2022)

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering...

OPENALEX DOI: https://doi.org/10.1017/9781009089517.019

Machine learning and applications in microbiology

Stephen J. Goodswen, Joel Barratt, Paul J Kennedy, Alexa Kaufer et al. • FEMS Microbiology Reviews (2021)

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence applica...

OPENALEX cc-by-nc DOI: https://doi.org/10.1093/femsre/fuab015 PMC: 8498514

Machine learning and medical education

Vijaya B. Kolachalama, Priya S. Garg • npj Digital Medicine (2018)

Artificial intelligence (AI) driven by machine learning (ML) algorithms is a branch in computer science that is rapidly gaining popularity within the healthcare sector. Recent regulatory approvals of AI-driven companion diagnostics and othe...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41746-018-0061-1 PMC: 6550167

Machine Learning in Enzyme Engineering

Stanislav Mazurenko, Zbyněk Prokop, Jiřı́ Damborský • ACS Catalysis (2019)

Abstract Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have b...

OPENALEX other-oa DOI: https://doi.org/10.1021/acscatal.9b04321

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia

Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis, Lesley-Ann Sutton et al. • BMC Bioinformatics (2018)

Abstract Background Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D stru...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12859-018-2381-1

High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli

Tomoya Maeda, Junichiro Iwasawa, Hazuki Kotani, Natsue Sakata et al. • Nature Communications (2020)

Understanding evolutionary constraints in antibiotic resistance is crucial for prediction and control. Here, the authors use high-throughput laboratory evolution of Escherichia coli alongside machine learning to identify trade-off relations...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41467-020-19713-w

Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening

Seonghyeon An, Yeongjoo Suh, Payam Kelich, Dakyeon Lee et al. • Nanomaterials (2024)

In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimiz...

DOAJ Open Access (DOAJ) DOI: 10.3390/nano14030247

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Sebastian Raschka, Joshua M. Patterson, Corey Nolet • Information (2020)

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the...

OPENALEX cc-by DOI: https://doi.org/10.3390/info11040193

Inferring Markovian quantum master equations of few-body observables in interacting spin chains

Francesco Carnazza, Federico Carollo, Dominik Zietlow, Sabine Andergassen et al. • New Journal of Physics (2022)

Full information about a many-body quantum system is usually out-of-reach due to the exponential growth—with the size of the system—of the number of parameters needed to encode its state. Nonetheless, in order to understand the complex phen...

DOAJ Open Access (DOAJ) DOI: 10.1088/1367-2630/ac7df6