Results 51 to 60 of about 8,828,165 (258)

AI‐Designed TREM1‐Targeted LYTAC Nanoparticles Reprogram the Neuroimmune Microenvironment in Traumatic Brain Injury

Advanced Science, EarlyView.
AI‐enabled de novo design of a TREM1 binder, integrated into a BBB‐crossing and macrophage‐targeted nanoplatform, enables TREM1 degradation and attenuates neuroinflammation, thereby promoting neuroprotection and tissue repair after traumatic brain injury. ABSTRACT Secondary neuroinflammation drives progressive damage after traumatic brain injury (TBI),
Yimin Huang, Yuan Liu, Huan Peng, Yu Ni, Zhengqiao Jiang, Chenxuan Yu, Xincheng Zhang, Huayu Kang, Yanchao Liu, Juan Chen, Kai Zhao, Lin Han, Xin Zou, Kai Shu, Xiaochuan Wang, Jian‐Zhi Wang, Ting Lei, Chao Gan, Jianfeng Liu, Huaqiu Zhang   +19 more
wiley   +1 more source

Total Least Squares Estimation in Hedonic House Price Models

ISPRS International Journal of Geo-Information
In real estate valuation using the Hedonic Price Model (HPM) estimated via Ordinary Least Squares (OLS) regression, subjectivity and measurement errors in the independent variables violate the Gauss–Markov theorem assumption of a non-random coefficient ...
Wenxi Zhan, Yu Hu, Wenxian Zeng, Xing Fang, Xionghua Kang, Dawei Li   +5 more
doaj   +1 more source

Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

AIChE Journal, EarlyView.
Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin, Jules Thibault, Clémence Fauteux‐Lefebvre   +2 more
wiley   +1 more source

Consistency of the structured total least squares estimator in a multivariate errors-in-variables model

, 2005
The structured total least squares estimator, defined via a constrained optimization problem, is a generalization of the total least squares estimator when the data matrix and the applied correction satisfy given structural constraints.
Van Huffel, S., Kukush, A., Markovsky, I.   +2 more
core   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Algorithms and statistical analysis for linear structured weighted total least squares problem

Geodesy and Geodynamics
Weighted total least squares (WTLS) have been regarded as the standard tool for the errors-in-variables (EIV) model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.
Jian Xie, Tianwei Qiu, Cui Zhou, Dongfang Lin, Sichun Long   +4 more
doaj   +1 more source

Götterdämmerung over total least squares

Journal of Geodetic Science, 2016
The traditional way of solving non-linear least squares (LS) problems in Geodesy includes a linearization of the functional model and iterative solution of a nonlinear equation system.
Malissiovas G., Neitzel F., Petrovic S.
doaj   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Least squares fitting the three-parameter inverse Weibull density [PDF]

, 2010
The inverse Weibull model was developed by Erto [10]. In practice, the unknown parameters of the appropriate inverse Weibull density are not known and must be estimated from a random sample.
Marušić, Miljenko, Darija Marković, Dragan Jukić, Miljenko Marušić, Marković, Darija, Jukić, Dragan   +5 more
core   +1 more source

Total least squares solution for compositional data using linear models [PDF]

, 2011
The restrictive properties of compositional data, that is multivariate data with positive parts that carry only relative information in their components, call for special care to be taken while performing standard statistical methods, for example ...
Fišerová, Eva, Hron, Karel, Karel Hron, Donevska, Sandra, Eva Fiserova   +4 more
core   +1 more source