Results 71 to 80 of about 422,144 (319)
Multi‐Scale Interface Engineering of MXenes for Multifunctional Sensory Systems
MXenes, as two‐dimensional transition metal carbides and nitrides, demonstrate remarkable capabilities for multifunctional sensing applications. This review systematically examines multi‐scale interface engineering approaches that enhance sensing performance, enable diverse detection functionalities, and improve system‐level compatibility in MXene ...
Jiaying Liao, Sin‐Yi Pang, Jianhua Hao
wiley +1 more source
Inherent Weight Normalization in Stochastic Neural Networks [PDF]
Multiplicative stochasticity such as Dropout improves the robustness and generalizability of deep neural networks. Here, we further demonstrate that always-on multiplicative stochasticity combined with simple threshold neurons are sufficient operations ...
Datta, Suman +5 more
core +2 more sources
Atomic Layer Deposition in Transistors and Monolithic 3D Integration
Transistors are fundamental building blocks of modern electronics. This review summarizes recent progress in atomic layer deposition (ALD) for the synthesis of two‐dimensional (2D) metal oxides and transition‐metal dichalcogenides (TMDCs), with particular emphasis on their enabling role in monolithic three‐dimensional (M3D) integration for next ...
Yue Liu +5 more
wiley +1 more source
Flexible piezoresistive pressure sensors underpin wearable and soft electronics. This review links sensing physics, including contact resistance modulation, quantum tunneling and percolation, to unified materials/structure design. We highlight composite and graded architectures, interfacial/porous engineering, and microstructured 3D conductive networks
Feng Luo +2 more
wiley +1 more source
Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on supervised (conversion, spike-based gradient descent) and unsupervised ...
Priyadarshini Panda +2 more
doaj +1 more source
Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling
Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee +7 more
wiley +1 more source
Artificial Neural Networks and Deep Learning
An introduction on Aritifical Neural Networks and Deep Learning: * Multilayer Perceptrons * Convolutional Neural Networks (CNN)
openaire +1 more source
Neural cell–derived small extracellular vesicles (sEVs) are emerging as pivotal mediators in neurodegenerative diseases, exerting both pathogenic and therapeutic functions. This review synthesizes current evidence on how sEVs from distinct neural cell types regulate neurodegeneration, neuroprotection, biomarker discovery, and targeted drug delivery ...
Muhammad Waqas Salim +4 more
wiley +1 more source
Practical recommendations for gradient-based training of deep architectures [PDF]
Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters.
Bengio, Yoshua
core
Implantable optoelectrical devices are an effective resource for the modulation and monitoring of neural activity with high spatiotemporal resolution. This review discusses current challenges faced by these devices and outlines future perspectives for the development of next‐generation neural interfaces targeting chronic, multisite, and multimodal ...
Stella Aslanoglou +4 more
wiley +1 more source

