Results 61 to 70 of about 342,941 (283)

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Performance Evaluation of Long Short-Term Memory for Chili Price Prediction

JISKA (Jurnal Informatika Sunan Kalijaga)
Groceries prices often experience fluctuations in several regions in Indonesia, such as East Java Province and one of the commodities is chilies, both red chilies and rawit chilies.
Fata Nabil Fikri, Nurochman Nurochman
doaj   +1 more source

Source bearing and steering-vector estimation using partially calibrated arrays [PDF]

, 2009
The problem of source direction-of-arrival (DOA) estimation using a sensor array is addressed, where some of the sensors are perfectly calibrated, while others are uncalibrated. An algorithm is proposed for estimating the source directions in addition to
Li, M., Lu, Y.
core   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Digital Quantum Estimation

, 2017
Quantum Metrology calculates the ultimate precision of all estimation strategies, measuring what is their root mean-square error (RMSE) and their Fisher information. Here, instead, we ask how many bits of the parameter we can recover, namely we derive an
Hassani, Majid, Macchiavello, Chiara, Maccone, Lorenzo   +2 more
core   +1 more source

Application of HYDRUS (2D/3D) for Predicting the Influence of Subsurface Drainage on Soil Water Dynamics in a Rainfed-Canola Cropping System [PDF]

, 2018
The HYDRUS (2D/3D) model was applied to investigate the probable effects of different subsurface drainage systems on the soil water dynamics under a rainfed-canola cropping system in paddy fields.
Ajdari, Cameira, Cote, Crevoisier, Darzi-Naftchali, Darzi-Naftchali, Doltra, Feddes, Fernández, Garg, Genuchten, Jafari-Talukolaee, Janssen, Karandish, Karandish, Lazarovitch, Li, Li, Mubarak, Neitsch, Nishat, Ogino, Panigrahi, Parchami-Araghi, Phogat, Rahil, Ramos, Sander, Sander, Siyal, Skaggs, Tafteh, Tan, Tan, Tournebize, Wagenet, Wang, Wessolek, Šimůnek, Šimůnek   +39 more
core   +2 more sources

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Predicting Cryptocurrency Price Using RNN and LSTM Method

Jurnal Sisfokom, 2023
Cryptocurrency price prediction is a crucial task for financial investors as it helps determine appropriate investment strategies and mitigate risk. In recent years, deep learning methods have shown promise in predicting time-series data, making them a ...
Dzaki Mahadika Gunarto, Siti Sa'adah, Dody Qori Utama   +2 more
doaj   +1 more source

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source

THE COMPARISON OF EXTENDED AND ENSEMBLE KALMAN FILTERS IN MODELING ENVIRONMENTAL POLLUTION INFLUENCES ON ACUTE RESPIRATORY INFECTION DYNAMICS (ISPA)

Barekeng
Acute Respiratory Infections (ISPA) are a significant health issue. According to the World Health Organization (WHO), ISPA is the leading cause of death among children under five worldwide.
Yolanda Norasia, Dinni Rahma Oktaviani, Devi Marita Putri   +2 more
doaj   +1 more source