Results 61 to 70 of about 192,869 (306)

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Unique Performance Considerations for Printable Organic Semiconductor and Perovskite Radiation Detectors: Toward Consensus on Best Practice Evaluation

Advanced Functional Materials, EarlyView.
A lack of standard approaches for testing and reporting the performance of metal halide perovskites and organic semiconductor radiation detectors has resulted in inconsistent interpretation of performance parameters, impeding progress in the field. This Perspective recommends key metrics and experimental details, which are suggested for reporting in ...
Jessie A. Posar, Matthew Large, Andrea Ciavatti, Laura Basiricò, Beatrice Fraboni, Paul J. Sellin, Attila J. Mozer, Marco Petasecca, Matthew J. Griffith   +8 more
wiley   +1 more source

Cholesky-based model averaging for covariance matrix estimation

Statistical Theory and Related Fields, 2017
Estimation of large covariance matrices is of great importance in multivariate analysis. The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables.
Hao Zheng, Kam-Wah Tsui, Xiaoning Kang, Xinwei Deng   +3 more
doaj   +1 more source

Penalized maximum likelihood for multivariate Gaussian mixture

, 2001
In this paper, we first consider the parameter estimation of a multivariate random process distribution using multivariate Gaussian mixture law. The labels of the mixture are allowed to have a general probability law which gives the possibility to ...
Mohammad-Djafari, Ali, Snoussi, Hichem
core   +3 more sources

PiP‐Plex: A Particle‐in‐Particle System for Multiplexed Quantification of Proteins Secreted by Single Cells

Advanced Materials, EarlyView.
Detecting proteins secreted by a single cell while retaining its viability remains challenging. A particles‐in‐particle (PiPs) system made by co‐encapsulating barcoded microparticles (BMPs) with a single cell inside an alginate hydrogel particle is introduced.
Félix Lussier, Byeong‐Ui Moon, Mojra Janta‐Polczynski, Yonatan Morocz, Fabian Svahn, Molly Shen, Ljuboje Lukic, Lidija Malic, Teodor Veres, Andy Ng, David Juncker   +10 more
wiley   +1 more source

Covariance Estimation: The GLM and Regularization Perspectives [PDF]

, 2011
Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in covariance estimation, particularly in the recent high-dimensional data ...
Pourahmadi, Mohsen
core   +5 more sources

Mixed‐Metal Promotion in a Manganese‐Molybdenum Oxynitride as Catalyst to Integrate C─C and C─N Coupling Reactions for the Direct Synthesis of Acetonitrile from Syngas and Ammonia

Advanced Materials, EarlyView.
Transition metal oxy/carbo‐nitrides show great promise as catalysts for sustainable processes. A Mn‐Mo mixed‐metal oxynitride attains remarkable performance for the direct synthesis of acetonitrile, an important commodity chemical, via sequential C─N and C─C coupling from syngas (C1) and ammonia (N1) feedstocks.
M. Elena Martínez‐Monje, Wilson Henao, Tania Rodenas, Patricia Concepción, Virginia Pérez Dieste, Alexander Missyul, Giovanni Agostini, Gonzalo Prieto   +7 more
wiley   +1 more source

Element Aggregation for Estimation of High-Dimensional Covariance Matrices

Mathematics
This study addresses the challenge of estimating high-dimensional covariance matrices in financial markets, where traditional sparsity assumptions often fail due to the interdependence of stock returns across sectors.
Jingying Yang
doaj   +1 more source

Modeling Heterogeneity of the Level-1 Error Covariance Matrix in Multilevel Models for Single-Case Data

Methodology, 2020
Previous research applying multilevel models to single-case data has made a critical assumption that the level-1 error covariance matrix is constant across all participants.
Eunkyeng Baek, John J. M. Ferron
doaj   +1 more source

An interval Kalman filter enhanced by lowering the covariance matrix upper bound

International Journal of Applied Mathematics and Computer Science, 2021
This paper proposes a variance upper bound based interval Kalman filter that enhances the interval Kalman filter based on the same principle proposed by Tran et al. (2017) for uncertain discrete time linear models.
Tran Tuan Anh, Jauberthie Carine, Trave-Massuyés Louise, Lu Quoc Hung   +3 more
doaj   +1 more source