Results 81 to 90 of about 90,690 (297)

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

From Materials to Systems: Challenges and Solutions for Fast‐Charge/Discharge Na‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review systematically analyzes the key characteristics limiting the fast‐charge/discharge capability of Na‐ion batteries (SIBs) from a multi‐scale perspective encompassing electrode materials, the electrode‐electrolyte interface, and the system. Furthermore, it presents practical solution strategies for the fundamental issues arising at each scale,
Bonyoung Ku, Sangbin Park, Sunha Hwang, Sunghyun Lim, Won‐Sub Yoon, Jongsoon Kim   +5 more
wiley   +1 more source

Temporal evolution of insecticide resistance and bionomics in Anopheles funestus, a key malaria vector in Uganda

Scientific Reports
Insecticide resistance escalation is decreasing the efficacy of vector control tools. Monitoring vector resistance is paramount in order to understand its evolution and devise effective counter-solutions.
Ambrose Oruni, Magellan Tchouakui, Carlos S. Djoko Tagne, Jack Hearn, Jonathan Kayondo, Charles S. Wondji   +5 more
doaj   +1 more source

A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features [PDF]

, 2022
Pallabi Saikia, Dhwani Dholaria, Priyanka Yadav, Vaidehi Patel, Mohendra Roy   +4 more
openalex   +1 more source

Probabilistic Modeling for Prediction Errors to Enhance Balancing Market Participation of Photovoltaic Systems: Error Threshold Estimation, Multisite Aggregation, and Overloading Effects

Advanced Energy and Sustainability Research, EarlyView.
This study proposes a method to increase the value of solar power in balancing markets by managing prediction errors. The approach models prediction uncertainties and quantifies reserve requirements based on a probabilistic model. This enables the more reliable participation of photovoltaic plants in balancing markets across multiple sites, especially ...
Jindan Cui, Xue Fang, Takashi Oozeki, Yuzuru Ueda   +3 more
wiley   +1 more source

Volatility analysis and forecasting of vegetable prices using an ARMA‐GARCH model: An application of the CF filter and seasonal adjustment method to Korean green onions

Agribusiness, EarlyView.
Abstract The vegetable market experiences significant price fluctuations due to the complex interplay of trend, cyclical, seasonal, and irregular factors. This study takes Korean green onions as an example and employs the Christiano–Fitzgerald filter and the CensusX‐13 seasonal adjustment methods to decompose its price into four components: trend ...
Yiyang Qiao, Byeong‐il Ahn
wiley   +1 more source

Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

BMC Plant Biology, 2018
Background Among semi-aquatic species of the legume genus Aeschynomene, some have the unique property of being root and stem-nodulated by photosynthetic Bradyrhizobium lacking the nodABC genes necessary for the production of Nod factors.
Clémence Chaintreuil, Xavier Perrier, Guillaume Martin, Joël Fardoux, Gwilym P. Lewis, Laurent Brottier, Ronan Rivallan, Mario Gomez-Pacheco, Mickaël Bourges, Léo Lamy, Béatrice Thibaud, Heriniaina Ramanankierana, Herizo Randriambanona, Hervé Vandrot, Pierre Mournet, Eric Giraud, Jean-François Arrighi   +16 more
doaj   +1 more source

Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector [PDF]

, 2019
Ayu Purwarianti, Ida Ayu Putu Ari Crisdayanti   +1 more
openalex   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source