Results 111 to 120 of about 40,060 (297)

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

open access: yesAdvanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao   +8 more
wiley   +1 more source

Bayesian estimation of generalized exponential distribution under noninformative priors

open access: yes, 2012
The generalized exponential distribution, proposed by Gupta and Kundu (1999), is a good alternative to standard lifetime distributions as exponential, Weibull or gamma.
Damasceno Tomazella, Vera Lucia   +6 more
core   +2 more sources

Inference under progressively type II right censored sampling for certain lifetime distributions

open access: yes, 2009
In this paper, estimation of the parameters of a certain family of two-parameter lifetime distributions based on progressively Type II right censored samples (including ordinary Type II right censoring) is studied.
Yu, K, Wang, BX, Jones, MC
core  

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

open access: yesAdvanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung   +9 more
wiley   +1 more source

Estimation methods of theree parameters discrete generalized exponential distribution

open access: yes, 2010
This article presents the estimation methods of three parameters Discrete Generalized Exponential distribution. The two-parameter generalized exponential distribution was introduced by Gupta and Kundu (1999).
Khan, M. Shuaib   +2 more
core   +1 more source

A New Model of Exponentiated Generalized Transmuted G Family of Distributions With Real Data Applications

open access: yesJournal of Applied Mathematics
In this paper, a new family of distributions is introduced which generalized many families of distributions in literature. The distribution function of the new family of distributions is constructed using the exponentiated generalized transmuted family ...
Neama Salah Youssef Temraz
doaj   +1 more source

Skew generalized secant hyperbolic distributions: unconditional and conditional fit to asset returns [PDF]

open access: yes
A generalization of the hyperbolic secant distribution which allows both for skewness and for leptokurtosis was given by Morris (1982). Recently, Vaughan (2002) proposed another flexible generalization of the hyperbolic secant distribution which has a ...
Fischer, Matthias J.
core  

Machine Learning Accelerated Computational Design of Bio‐Inspired Catalysts in the Nitrogen Reduction Reaction

open access: yesAdvanced Materials, EarlyView.
We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano   +5 more
wiley   +1 more source

Bivariate generalized exponential distribution

open access: yes
Recently it has been observed that the generalized exponential distribution can be used quite effectively to analyze lifetime data in one dimension. The main aim of this paper is to define a bivariate generalized exponential distribution so that the ...
Kundu, Debasis, Gupta, Rameshwar D.
core  

Deep Learning Inverse Design of Phase‐Change Reconfigurable Terahertz Metadevices for Multidimensional Secure Communication

open access: yesAdvanced Materials, EarlyView.
A deep learning inverse‐design framework is established to create versatile reconfigurable terahertz metadevices. By synergizing deep learning with phase‐change materials, this approach enables on‐demand customization of multidimensional electromagnetic responses.
Yisheng Dong   +11 more
wiley   +1 more source

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