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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1023397 | PMC |
| http://dx.doi.org/10.1136/jcp.4.2.200 | DOI Listing |
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CASTER: Direct species tree inference from whole-genome alignments.
Science
January 2025
Electrical and Computer Engineering Department, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, USA.
Genomes contain mosaics of discordant evolutionary histories, challenging the accurate inference of the tree of life. While genome-wide data are routinely used for discordance-aware phylogenomic analyses, due to modeling and scalability limitations, the current practice leaves out large chunks of genomes. As more high-quality genomes become available, we urgently need discordance-aware methods to infer the tree directly from a multiple genome alignment.
View Article and Find Full Text PDFEvaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.
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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 PDFPolarimetric Compressed Sensing with Hollow, Self-Assembled Diffractive Films.
ACS Nano
January 2025
Department of Mechanical Engineering, University of California at Riverside, Riverside, California 92521, United States.
Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder.
View Article and Find Full Text PDFPolymer material innovations for a green hydrogen economy.
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Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science, Technology and Research (A*STAR), 1 Pesek Road, Singapore 627833, Republic of Singapore.
Polymeric materials are ubiquitous in modern life. Similar to many other technological applications, polymer materials are essential in advancing the green hydrogen economy, offering solutions for hydrogen production, storage, transport, and utilization. In production, polymeric proton exchange membranes in water electrolysers enable efficient green hydrogen generation using renewable energy.
View Article and Find Full Text PDFAn In Vivo Biostability Evaluation of ALD and Parylene-ALD Multilayers as Micro-Packaging Solutions for Small Single-Chip Implants.
Small
January 2025
Department of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, 2628 CN, The Netherlands.
Miniaturization of next-generation active neural implants requires novel micro-packaging solutions that can maintain their long-term coating performance in the body. This work presents two thin-film coatings and evaluates their biostability and in vivo performance over a 7-month animal study. To evaluate the coatings on representative surfaces, two silicon microchips with different surface microtopography are used.
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