Results 111 to 120 of about 2,478,343 (382)

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

open access: yesAdvanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva   +9 more
wiley   +1 more source

Modulation of neural oscillations during working memory update, maintenance, and readout: An hdEEG study

open access: yesbioRxiv, 2020
Working memory (WM) performance is very often measured using the n-back task, in which the participant is presented with a sequence of stimuli, and required to indicate whether the current stimulus matches the one presented n steps earlier. In this study,
M. Semprini   +7 more
semanticscholar   +1 more source

Golden‐Ratio–Guided Aperiodic Architected Metamaterials with Simultaneously Enhanced Strength and Toughness

open access: yesAdvanced Functional Materials, EarlyView.
Guided by the golden ratio, a class of aperiodic architected metamaterials is introduced to address the intrinsic trade‐off between strength and toughness. By unifying local geometric heterogeneity with global order, the golden‐ratio‐guided aperiodic architecture promotes spatial delocalization of damage tolerence regions, leading to more tortuous ...
Junjie Deng   +9 more
wiley   +1 more source

Decoding the Narcissistic Brain

open access: yesNeuroImage
There is a substantial knowledge gap in the narcissism literature:
Zhiwei Zhou   +5 more
doaj   +1 more source

Subthreshold dynamics of the neural membrane potential driven by stochastic synaptic input [PDF]

open access: yes, 2002
In the cerebral cortex, neurons are subject to a continuous bombardment of synaptic inputs originating from the network's background activity. This leads to ongoing, mostly subthreshold membrane dynamics that depends on the statistics of the background ...
A. Destexhe   +24 more
core   +2 more sources

Reliability of layered neural oscillator networks [PDF]

open access: yesCommunications in Mathematical Sciences, 2009
We study the reliability of large networks of coupled neural oscillators in response to fluctuating stimuli. Reliability means that a stimulus elicits essentially identical responses upon repeated presentations. We view the problem on two scales: neuronal reliability, which concerns the repeatability of spike times of individual neurons embedded within
Lin, Kevin. K.   +2 more
openaire   +3 more sources

Electrosynthesis of Bioactive Chemicals, From Ions to Pharmaceuticals

open access: yesAdvanced Functional Materials, EarlyView.
This review discusses recent advances in electrosynthesis for biomedical and pharmaceutical applications. It covers key electrochemical materials enabling precise delivery of ions and small molecules for cellular modulation and disease treatment, alongside catalytic systems for pharmaceutical synthesis.
Gwangbin Lee   +4 more
wiley   +1 more source

Inertial Neural Networks with Unpredictable Oscillations

open access: yesMathematics, 2020
In this paper, inertial neural networks are under investigation, that is, the second order differential equations. The recently introduced new type of motions, unpredictable oscillations, are considered for the models.
Marat Akhmet   +2 more
doaj   +1 more source

Emergence of Synchronous Oscillations in Neural Networks Excited by Noise

open access: yes, 2005
The presence of noise in non linear dynamical systems can play a constructive role, increasing the degree of order and coherence or evoking improvements in the performance of the system. An example of this positive influence in a biological system is the
FitzHugh   +17 more
core   +1 more source

Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows

open access: yes, 2020
In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the likelihood ...
Gaitan, Vicens   +3 more
core   +2 more sources

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