Results 151 to 160 of about 95,782 (269)

Flow‐Adaptive Gas Sensing Enabled Using a Uniform Au Nanosheet Sensor Array and a Neural Network Inference

Advanced Materials Technologies, EarlyView.
Integrated Au nanosheet sensor array enables simultaneous inference of gas concentration and flow rate via deep neural network analysis, without external flow control. ABSTRACT Gas sensor responses are considerably affected by gas flow rates, thereby inhibiting the accurate detection of target gas concentrations in variable‐flow applications such as ...
Taro Kato, Takahisa Tanaka, Yusuke Hamanaka, Ryo Toyoshima, Ken Uchida   +4 more
wiley   +1 more source

Bridging binarization: causal inference with dichotomized continuous exposures. [PDF]

J Causal Inference
Lee K, Hubbard A, Schuler A.
europepmc   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source

Inference of Genetic Networks from Pseudo Time Series of Single-cell Gene Expression Data using Modified Random Forests. [PDF]

Bull Math Biol
Kimura S, Misaki R, Tokuhisa M, Iida K, Okada M.   +4 more
europepmc   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

CBMR: Coordinate-based meta-regression for group and covariate inference. [PDF]

Imaging Neurosci (Camb)
Yu Y, Hill-Bowen LD, Riedel MC, Bottenhorn K, Laird AR, Nichols TE.   +5 more
europepmc   +1 more source

Cap‐oPMN: Oral Inflammatory Load Quantification Using Capillary Microfluidics and Automated Image Processing

Advanced Materials Technologies, EarlyView.
ABSTRACT Quantifying oral polymorphonuclear neutrophils (oPMNs) is a clinically validated approach for assessing periodontal inflammation. However, current methods, such as manual hemocytometry and flow cytometry, are time‐consuming (>3 h), require invasive sampling, and depend on staining and complex instrumentation, making them unsuitable for point ...
Mohsen Hassani, Azam Zare, Fatemeh Soheili, S. M. Mahdi H. Daneshvar, Mahdi Amrollahi Biouki, Yasaman Tahernezhad, Chunxiang Sun, Michael Glogauer, Ebrahim Ghafar‐Zadeh, Amir Sanati‐Nezhad   +9 more
wiley   +1 more source

Justifying model complexity: Evaluating transfer learning against classical models for intraoperative nociception monitoring under anesthesia. [PDF]

PLoS One
Lee C, Lee J, Vogt KA, Munshi M.
europepmc   +1 more source

Soft Robotic Excretory Care Simulator for Nursing Education: Functional and Perceptual Biomimetics Approach

Advanced Robotics Research, EarlyView.
A soft robotic simulator is developed to replicate the digital removal of feces (DRF), a sensitive yet essential nursing procedure. Integrating soft actuators, sensors, and a realistic rectal model, the simulator balances functional fidelity with perceptual realism. Engineering evaluations and nurse feedback confirm its potential to enhance training in
Shoko Miyagawa, Sota Suzuki, Shizuka Watanabe, Zebing Mao, Young Ah Seong, Toshinori Fujie, Tatsuhiro Horii, Kenjiro Tadakuma, Koichi Suzumori, Hiroyuki Nabae, Shingo Maeda   +10 more
wiley   +1 more source

A practice-oriented guide to statistical inference in linear modeling for non-normal or heteroskedastic error distributions. [PDF]

Behav Res Methods
Rajh-Weber H, Huber SE, Arendasy M.
europepmc   +1 more source