Results 211 to 220 of about 70,095 (287)

Application of Principal Component Analysis and Probabilistic Neural Networks in Ferralsols Recovery Evaluation Through Planting of Mabea Fistulifera and Eucalyptus Urograndis

Land Degradation &Development, EarlyView.
ABSTRACT This study presents an innovative assessment model for analyzing the evolution of degraded soils subjected to different reclamation strategies. The proposal combines statistical and artificial intelligence tools to jointly integrate multiple physical and chemical soil properties, allowing for a more synthetic view of the processes.
Melissa Alexandre Santos, Borja Velázquez‐Martí, Jorge João Delfim, Laura Silva Nantes, Jose Augusto Liberato de Souza, Gabriel Augusto da Silva Lunardelli, Dayara Vivian Alvares, Carolina dos Santos Batista Bonini   +7 more
wiley   +1 more source

Global optimization tailored for graphics processing units: Complete and rigorous search for large-scale nonlinear minimization. [PDF]

PNAS Nexus
Zhang G, Shan Q, Cagan J.
europepmc   +1 more source

Static and Dynamic Analysis of Porous Gas Bearings Lubricated With Air, Refrigerant R‐134a, Helium and Hydrogen

Lubrication Science, EarlyView.
ABSTRACT Porous gas bearings (PGBs) enable high‐speed, oil‐free operation in modern rotating machinery. This study develops a coupled thermo‐hydrodynamic (THD) model for a cylindrical porous gas bearing lubricated with four working fluids—air, R‐134a, helium and hydrogen.
S. Bechiri, B. Bouchehit, B. Bou‐Saïd
wiley   +1 more source

Organic photovoltaic prediction model based on Bayesian optimization and explainable AI. [PDF]

Sci Rep
Abdelghafar S, Alshater H, Abouelmagd LM, Darwish A, Hassanien AE.   +4 more
europepmc   +1 more source

Development and Validation of a Questionnaire‐Based Model for Households With Multiple Helicobacter pylori Infections: A Family‐Based Screening Strategy

Med Research, EarlyView.
This study developed and externally validated a questionnaire‐based household risk model to identify families with multiple Helicobacter pylori infections. Using only noninvasive, readily available household information, the model showed good discrimination and enabled a family‐based screening strategy that substantially improved detection efficiency ...
Yifan Qiu, Xianzhu Zhou, Yi Hu, Liting Shen, Jiayue Wang, Shihan Xu, Huiyun Zhu, Huabing Huang, Zhaoshen Li, Yiqi Du   +9 more
wiley   +1 more source

Optimized low power hybrid adder architecture for OFDM in wireless communication. [PDF]

Sci Rep
Devi TK, Priyanka EB, Anand R, Bhavana GB, Sakthivel P, Thangavel S, Khan B, Jeyanthi K.   +7 more
europepmc   +1 more source

A Synergistic Strategy for Data‐Constrained Deep Learning in Materials Science

Materials Genome Engineering Advances, EarlyView.
This work develops a three‐stage machine learning framework for materials property prediction, integrating data preparation, graph‐based model training, and final property inference. By synergistically integrating attention pooling, multi‐task learning, auxiliary tasks, and classification‐corrected regression, this hybrid framework provide a ...
Chun Ting Shao, Yi Chen, Shan Man Song, Jian Xu, Peipei Yang, Qing Bo Yan, Gang Su   +6 more
wiley   +1 more source

A scalable automated framework for multiply-accumulate unit design in high-performance computing applications. [PDF]

Sci Rep
Venkatachalam A, Seerengasamy U, Ponraj AS.   +2 more
europepmc   +1 more source

Self‐Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi‐b Prostate Diffusion MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Convolutional neural networks (CNNs) are evaluated for improved and accelerated denoising and Rician bias correction in multi‐b DW images with simultaneous signal modeling. Methods Prostate diffusion images from 46 individuals acquired at 20 linearly distributed b‐values (bmax=2000s/mm2)$$ {b}_{\mathrm{max}}=2000\kern0.3em \mathrm{s}/{\
Mustafa Abbas, Wenyin Zhou, Stephan E. Maier, Fredrik Langkilde, Stefan Kuczera   +4 more
wiley   +1 more source

GABA+‐Edited Magnetic Resonance Spectroscopy Deep Learning Quality Assessment Framework

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Motivated by the need to improve GABA+‐edited magnetic resonance spectroscopy (MRS) quality, we developed a three‐module framework to improve transient averaging based on quality. We hypothesized that training a deep learning (DL) model to differentiate spectrum quality could improve transient averaging compared to traditional ...
Hanna Bugler, Roberto Souza, Ashley D. Harris   +2 more
wiley   +1 more source