Results 141 to 150 of about 88,700 (323)

Robustness of the best linear unbiased estimator and predictor in linear regression models [PDF]

, 1990
František Štulajter
openalex   +1 more source

On The Calculation Of The Average Value Of The Strong Coupling Constant α_s, Using The Best Linear Unbiased Estimator (Blue) Method

, 2022
LAAMRI KALLOUCHE
openalex   +2 more sources

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Targeting the Membrane‐Embedded Rhomboid Protease GlpG: A Multimodal Strategy for Inhibitor Discovery and Mechanistic Insight

Angewandte Chemie, EarlyView.
Created in BioRender. Bohg, C. (2026) https://BioRender.Com/ vi9hi4f. Rhomboid proteases are a mechanistically unique and evolutionarily conserved protein family. Despite their pharmacological relevance, the development of selective inhibitors has lagged behind that of soluble proteases.
Claudia Bohg, Yurii Dubanych, Spyridon Kosteletos, Taoran Xiao, Martin Neuenschwander, Tillmann Utesch, Michael Lisurek, Carl Öster, Andreas Oder, Carola Seyffarth, Kathrin Bach, Denise‐Liù Gracias Leone, František Filandr, Marc Wegert, Sascha Lange, Henry Sawczyc, Jens Peter von Kries, Christian P. R. Hackenberger, Edgar Specker, Han Sun, Kvido Stříšovský, Adam Lange   +21 more
wiley   +2 more sources

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

A New Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators [PDF]

, 1980
Jerzy K. Baksalary, Radosław Kala
openalex   +1 more source

RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non‐Small Cell Lung Cancer

Advanced Intelligent Systems, EarlyView.
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Best linear unbiased estimators of population mean on current occasion in two-occasion rotation patterns [PDF]

, 2013
G. N. Singh, Shakti Prasad
openalex   +1 more source

Causal analysis of trade loss from pathogens: A global study of foot and mouth disease impacts on meat exports

American Journal of Agricultural Economics, EarlyView.
Abstract Our general interest is in global trade loss from livestock pathogens, specifically exports. We adopt a causal inference approach that considers animal disease outbreaks over time as non‐staggered binary treatments with the potential for switching in (infection) and out of treatment (recovery) within the sample period. The outcome evolution of
Mohammad Maksudur Rahman, Thomas L. Marsh   +1 more
wiley   +1 more source