Results 41 to 50 of about 369 (174)
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
This article proposes NIRGB‐GS, a multimodal 3DGS variant that enables reliable 3D reconstruction and normal‐light novel‐view synthesis for extremely low‐light scenes by fusing paired near‐infrared and noisy RGB captures. High‐SNR near‐infrared modality and modality‐specific appearance encoding together resolve the issues of unstable pose/geometry ...
Chengyun Yang +3 more
wiley +1 more source
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng +4 more
wiley +1 more source
Mechanochemistry Meets Catalysis: Metal Complexes for Greener Organic Transformations
Mechanochemistry is redefining metal catalysis by controlling catalyst formulation, speciation, and deployment. This Review shows how milling, LAG, RAM, and TSE enable rapid metal‐complex assembly, distinctive catalytic manifolds, and scalable synthesis beyond solution chemistry.
Sourav Behera +2 more
wiley +2 more sources
Application of the FFLUX Force Field to Molecular Crystals: A Study of Formamide. [PDF]
Brown ML, Skelton JM, Popelier PLA.
europepmc +1 more source
Complex dynamics, often avoided in electromechanical design, can enhance soft robotics. We develop durable magnetic soft actuators operating in tunable dynamic regimes, enabling random number generation, stochastic computing, and time‐series prediction.
Eduardo Sergio Oliveros‐Mata +14 more
wiley +1 more source
A Two‐Stage Characterization Pipeline and Open‐Source Framework for Reproducible Tactile Sensing
The same soft tactile sensor returns different numbers when embodied in different robots. This is an Embodiment Gap that no shared framework currently captures transparently. A two‐stage characterization pipeline, paired with a FAIR open‐source digital datasheet, decouples intrinsic sensor behavior from embodiment effects and condenses cross‐laboratory
Matteo Lo Preti +6 more
wiley +1 more source
ABSTRACT Open‐source artificial intelligence is widely promoted as a democratising pathway to digital sovereignty for African states, offering access to frontier architectures without prohibitive capital investment. This paper investigates whether open‐source AI represents a credible route to autonomy or generates a new form of structural dependency ...
Ololade A. Shonubi
wiley +1 more source

