Results 201 to 210 of about 156,386 (347)

Broadband Near‐Field Computational Microwave Imaging Enabled by Nonperiodic Dispersive Unit‐Cell Loading in a Passive Metasurface

Advanced Electronic Materials, EarlyView.
A compact passive engineered surface converts changes in microwave frequency into many distinct illumination patterns, allowing nearby objects to be imaged without mechanical scanning, active tuning, or a large antenna array. Operating from 18 to 28 GHz, the single‐port system uses measured field patterns to reconstruct metallic targets and distinguish
Mohammed H. Arif, Firas Slewa Dawod, Henrik Jansen, Caner Kadıoğlu, Sayan Roy, Dirk Heberling, Renato Negra   +6 more
wiley   +1 more source

Identifying and investigating emergent behaviour in neural systems. [PDF]

Front Neurosci
Wedgwood KCA, McGivern P, Harris AR.
europepmc   +1 more source

Food Prices and Inflation Expectations in New Zealand

Agribusiness, EarlyView.
ABSTRACT Food prices are conspicuous, and spending on food constitutes a considerable share of household expenditure. In this study, we use partially identified Bayesian structural vector autoregression models to analyze the effects of food price shocks on core inflation and 1‐ and 5‐year inflation expectations in New Zealand.
Puneet Vatsa, Gabriel Pino, Alex Acheampong   +2 more
wiley   +1 more source

Attribution as a Conditional Reasoning Step in Multifactorial Risk Analysis and Decision-Making. [PDF]

Risk Anal
Ellenberg E, Katorza E.
europepmc   +1 more source

Machine learning driven many‐objective moving horizon scheduling optimization

AIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

A framework for comparative analysis of human and mouse cortical neuron dendrites in corresponding brain regions. [PDF]

Nat Neurosci
Yun Z, Ye W, Ji N, Wang Y, Rong J, Li T, Li Y, Shen Q, Wang R, Zhang H, Xu H, Mo N, Song B, Chen X, Wang Y, Mai Y, Ding M, Ding SL, Liu L, Zhang L, Peng H.   +20 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Separability of the Coupled-Cluster Excited State Equations: The Case of Excitonic Couplings. [PDF]

J Phys Chem A
Köhn A.
europepmc   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source