Results 101 to 110 of about 1,612,043 (238)
This work demonstrates the use of reactive laser annealing to selectively tune the epsilon‐near‐zero condition of tin‐doped indium oxide patches integrated in silicon waveguides. This postgrowth annealing technique enables flexible control over the optical constants of transparent conducting oxides (TCOs) at critical telecom wavelengths, paving the way
Juan Navarro‐Arenas+6 more
wiley +1 more source
Multiple incoherent nonlinear deconvolutions (MIND) has extended dimension for optimization that provides a better solution than Wiener deconvolution and nonlinear reconstruction. MIND minimizes deconvolution noise by averaging over multiple deconvolutions each with different system aperture that effectively enhances the resolution.
Jawahar Prabhakar Desai+2 more
wiley +1 more source
Digitized Phase‐Change Material Heterostack for Transmissive Diffractive Optical Neural Network
A phase‐change‐material‐based digitized heterostack is experimentally demonstrated and theoretically analyzed for future energy‐efficient, fast reconfigured, and compact transmissive diffractive optical neural networks. All‐optical and fully reconfigurable transmissive diffractive optical neural network (DONN) architectures emerge as high‐throughput ...
Ruiyang Chen+3 more
wiley +1 more source
Graphical Abstract This study deals with a timely topic, COVID‐19, whose impacts on aquaculture have never been quantified in Benin. Direct discussions were held with aquaculture producers to understand how they are feeling the impact of the pandemic on their business.
Toundji Olivier Amoussou+7 more
wiley +1 more source
Mathematical tags of group theory
I present a collection of mathematical results regarding group theory organized by tags.
openaire +1 more source
This work presents a systematic review of atmospheric turbulence fundamentals, including theoretical formulations and adaptive optics‐based mitigation strategies. This includes an in‐depth examination of the devices, theories, and methodologies associated with traditional correction approaches.
Qinghui Liu+5 more
wiley +1 more source
Hierarchical Learning for Robotic Assembly Tasks Leveraging Learning from Demonstration
This paper presents a hierarchical learning approach for robotic assembly by integrating Learning from Demonstration with Deep Reinforcement Learning. The method enables long‐horizon assembly tasks with minimal demonstrations and without explicit task descriptions, improving adaptability and efficiency. It reduces reliance on predefined task sequences,
Siddharth Singh, Qing Chang, Tian Yu
wiley +1 more source
Abstract Recent global events and the rise of sustainable investing have made clear that the chemical and energy industry must consider sustainability goals beyond profit maximization to remain competitive. Multiobjective optimization provides an ideal framework for analyzing sustainability tradeoffs, but when four or more objectives are considered ...
Justin M. Russell, Andrew Allman
wiley +1 more source
An Analysis of Elusive Relationships in Floating Zone Growth Using Data Mining Techniques
Ultra‐pure silicon single‐crystals can be grown by the Floating Zone (FZ) method. This study investigates intricate relationships between process stability measures and multiple growth parameters by applying data mining techniques on FZ simulations. Regression Trees identified multivariate relationships that help explaining complex interactions between
Lucas Vieira+3 more
wiley +1 more source
Crystal Structure Prediction of Cs–Te with Supervised Machine Learning
High‐throughput density functional theory calculations combined with machine learning models are employed to predict stable Cs– Te binary crystals. By systematically evaluating various structural descriptors and learning algorithms, the superiority of models based on atomic coordination environments is revealed.
Holger‐Dietrich Saßnick+1 more
wiley +1 more source