AIMC Topic: Computer Simulation

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Machine learning-driven prediction of eye irritation toxicity: Integration of in silico and in vitro study.

Toxicology and applied pharmacology
Eye irritation (EI) toxicity poses critical challenges in chemical safety assessment, demanding alternatives to ethically contentious animal testing. We present the first integrative framework combining computational prediction with experimental vali...

Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control.

IEEE transactions on cybernetics
Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, t...

DRL-driven padel players: Simulating padel matches through deep reinforcement learning in real and hypothetical scenarios.

Journal of sports sciences
Recent advances in Deep Reinforcement Learning (DRL) have opened new avenues for sport research. DRL allows virtual agents to learn and solve complex tasks with minimal input, which means that models can be trained with little or no data collection. ...

Hepatitis B In Silico Trials Capture Functional Cure, Indicate Mechanistic Pathways, and Suggest Prognostic Biomarker Signatures.

Clinical pharmacology and therapeutics
In silico trials, utilizing mathematical models calibrated with clinical data, present a transformative approach to expedite drug development. We propose a virtual trial framework for chronic Hepatitis B, accurately simulating clinical protocols, pat...

Internal sensory models allow for balance control using muscle spindle acceleration feedback.

Neural networks : the official journal of the International Neural Network Society
Motor control requires sensory feedback, and the nature of this feedback has implications for the tasks of the central nervous system (CNS): for an approximately linear mechanical system (e.g., a freely standing person, a rider on a bicycle), if the ...

Consensus synchronization via quantized iterative learning for coupled fractional-order time-delayed competitive neural networks with input sharing.

Neural networks : the official journal of the International Neural Network Society
This paper presents the D-type distributed iterative learning control protocol to synchronize fractional-order competitive neural networks with time delay within a finite time frame. Firstly, the input sharing strategy of such desired competitive neu...

Predicting in vitro assays related to liver function using probabilistic machine learning.

Toxicology
While machine learning has gained traction in toxicological assessments, the limited data availability requires the quantification of uncertainty of in silico predictions for reliable decision-making. This study addresses the challenge of predicting ...

Decomposition method-based global Mittag-Leffler synchronization for fractional-order Clifford-valued neural networks with transmission delays and impulses.

Neural networks : the official journal of the International Neural Network Society
This study examines the global Mittag-Leffler synchronization (GMLS) problem for fractional-order Clifford-valued neural networks (FOCLVNNs) including transmission delays and impulses. Firstly, a novel kind of FOCLVNNs is developed that incorporates ...

Multiple-input and multiple-output encoders with DNA-based winner-take-all neural Networks.

Neural networks : the official journal of the International Neural Network Society
DNA logic circuits are essential building blocks for molecular computers. Traditional molecular logic circuits primarily use basic gate circuits as computational units, achieving complex functions via multiple cascades. However, even simple logical f...

Fixed-time adaptive neural network compensation control for uncertain nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
Uncertainties are the main obstacle to improving the control performance of nonlinear systems. To address this challenge, this paper proposes a fixed-time adaptive neural network compensation control method for a class of high-order nonlinear systems...