Results 171 to 180 of about 2,418,705 (282)

Editorial: AI and inverse methods for building digital twins in neuroscience. [PDF]

Front Comput Neurosci
Nogaret A, Mirallave-Pescador A, Kschischo M.   +2 more
europepmc   +1 more source

A Spatially Resolved View on the Aging Substantia nigra: An Exploratory Proteomic Study

Advanced Biology, EarlyView.
Although aging is the most important risk factor for several neurodegenerative diseases, the molecular effects of physiological aging are still understudied. By applying spatially‐resolved proteomic analyses of the human substantia nigra pars compacta, alterations in vesicular trafficking and mitochondrial proteins are observed, as well as reduced ...
Britta Eggers, Maximilian Hausherr, Michel Lim, Karin Schork, Bilhan Karacora, Robin Grugel, Martin Eisenacher, Isabel Gil Aldea, Peter Riederer, Manfred Gerlach, Katrin Marcus   +10 more
wiley   +1 more source

Therapeutic effects of Poncirus fructus on colonic dysfunction and visceral pain in a zymosan-induced irritable bowel syndrome mouse model through interstitial cells of Cajal and ion channel modulation. [PDF]

Front Pharmacol
Choi NR, Ko SJ, Choi WG, Jung D, Kim SC, Lim DW, Kim YT, Woo JH, Park JW, Kim BJ.   +9 more
europepmc   +1 more source

Soft‐Layered Composites with Wrinkling‐Activated Multi‐Linear Elastic Behavior, Stress Mitigation, and Enhanced Strain Energy Storage

Advanced Engineering Materials, EarlyView.
In this study, exciting new bi‐/multi‐linear elastic behavior of soft elastic composites that accompany the activation of wrinkling in the embedded interfacial layers is analyzed. The new features and performance of these composite materials, including dramatic enhancements in energy storage, can be tailored by the concentration of interfacial layers ...
Narges Kaynia, Nicholas Xuanlai Fang, Mary C. Boyce   +2 more
wiley   +1 more source

Fully memristive spiking neural network for energy-efficient graph learning. [PDF]

Sci Adv
Shi T, Gao L, Zhou R, Tian Y, Chen P, Ding Y, Tang S, Ma H, Lu J, Zhang H, Wang Z, Lyu B, Zhang X, Yan X, Liu Q.   +14 more
europepmc   +1 more source

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Computing the bridge length: the key ingredient in a continuous isometry classification of periodic point sets. [PDF]

Acta Crystallogr A Found Adv
McManus J, Kurlin V.
europepmc   +1 more source

Comparative Wear and Friction Analysis of Sliding Surface Materials for Hydrostatic Bearing under Oil Supply Failure Conditions

Advanced Engineering Materials, EarlyView.
Hydrostatic bearings excel in high‐precision applications, but their performance hinges on a continuous external supply. This study evaluates various material combinations for sliding surfaces to mitigate damage during supply failures or misalignment and to discover the most effective materials identified for enhancing the reliability and efficiency of
Michal Michalec, Lukáš Daněk, Jan Foltýn, Petr Svoboda, Carsten Gachot, Martin Hartl, Ivan Křupka   +6 more
wiley   +1 more source

Analysis of three-phase asymmetrical currents in the secondary voltage of signal change sensors in the power supply system using graph models [PDF]

Azizjan Abubakirov, Timur Kurbaniyazov, Muratbay Bekimbetov   +2 more
openalex   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source