Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica +38 more
wiley +1 more source
Meat hygiene and safety practices among food establishments in Debre Markos Town, Ethiopia: a One Health approach, 2025. [PDF]
Adane B +7 more
europepmc +1 more source
Designing Wire Mazes for Replicating Natural Echoes to Study Bat Biosonar Function
A validated framework combining efficient physical modeling (multiple scattering model) and deep learning is presented to guide wire‐maze design for bat biosonar studies. This approach rapidly generates large datasets to test acoustic distinguishability among wire arrangements.
Chunlin Jia +3 more
wiley +1 more source
Nano-selenium, organic selenium, and novel selenium sources: Which is best for clean and sustainable poultry production? [PDF]
Fouad AM +6 more
europepmc +1 more source
Single‐Step Analog In‐Memory Matrix Computation in Three‐Dimensional Circuits
A wooden model illustrates a 3D memristive array designed for single‐step kernel convolutions. It shows how two‐dimensional input data are mapped into the 3D array and convolved in parallel with stored kernels. Color‐coded connection points represent programmed memory‐element values, with each color corresponding to a specific kernel weight.
Alireza Jaberi Rad +8 more
wiley +1 more source
Prevalence, antimicrobial resistance, and risk factors of Salmonella in Cattle, environment, and workers at gombe township abattoir, Nigeria, with an assessment of workers' knowledge, attitudes, and practices. [PDF]
Boyi N, Bitrus AA, Audu BJ, James WB.
europepmc +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
Effects of Tamarind (<i>Tamarindus indica</i> L.) Pulp-Based Marination on Physicochemical Quality, Oxidative Stability, Microbiological Characteristics, and Sensory Attributes of Eye of Round Beef. [PDF]
Barreto BG +9 more
europepmc +1 more source
Motion artifacts in electrocardiographic signals, conventionally dismissed as noise, are shown to encode discriminative activity information. A smart t‐shirt fusing textile strain sensors with textile ECG electrodes, processed through a complex‐valued residual network, recognizes 12 sport activities.
Nada Al‐azzawi +4 more
wiley +1 more source
Transcriptomic analysis identifies candidate genes associated with striated muscle contraction during breast muscle development in meat-type ducks. [PDF]
Chang H +8 more
europepmc +1 more source

