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| http://dx.doi.org/10.1093/pastj/gtt029 | DOI Listing |
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Retraction Note: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection.
Soft comput
August 2024
Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
[This retracts the article DOI: 10.1007/s00500-022-07798-y.].
View Article and Find Full Text PDFNanomachine Networks: Functional All-Enzyme Hydrogels from Photochemical Cross-Linking of Glucose Oxidase.
Biomacromolecules
January 2025
School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, U.K.
Enzymes are attractive as catalysts due to their specificity and biocompatibility; however, their use in industrial and biomedical applications is limited by stability. Here, we present a facile approach for enzyme immobilization within "all-enzyme" hydrogels by forming photochemical covalent cross-links between the enzyme glucose oxidase. We demonstrate that the mechanical properties of the enzyme hydrogel can be tuned with enzyme concentration and the data suggests that the dimeric nature of glucose oxidase results in unusual gel formation behavior which suggests a degree of forced induced dimer dissociation and unfolding.
View Article and Find Full Text PDFEvaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.
PLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFAdvanced low-power filter architecture for biomedical signals with adaptive tuning.
PLoS One
January 2025
Computer Engineering, CCSIT, King Faisal University, Al Hufuf, Kingdom of Saudi Arabia.
This paper presents a low-power, second-order composite source-follower-based filter architecture optimized for biomedical signal processing, particularly ECG and EEG applications. Source-follower-based filters are recommended in the literature for high-frequency applications due to their lower power consumption when compared to filters with alternative topologies. However, they are not suitable for biomedical applications requiring low cutoff frequencies as they are designed to operate in the saturation region.
View Article and Find Full Text PDFPerceptual learning of modulation filtered speech.
J Exp Psychol Hum Percept Perform
January 2025
School of Psychology, University of Sussex.
Human listeners have a remarkable capacity to adapt to severe distortions of the speech signal. Previous work indicates that perceptual learning of degraded speech reflects changes to sublexical representations, though the precise format of these representations has not yet been established. Inspired by the neurophysiology of auditory cortex, we hypothesized that perceptual learning involves changes to perceptual representations that are tuned to acoustic modulations of the speech signal.
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