Results 101 to 110 of about 81,662 (309)
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Identifiability of the Stochastic Frontier Models [PDF]
This paper examines the identifiability of the standard single-equation stochastic frontier models with uncorrelated and correlated error components giving, inter alia, mathematical content to the notion of “near-identifiability” of a statistical model ...
Bandyopadhyay, Debdas, Das, Arabinda
core +1 more source
Effects of Foreign Direct Investment on Firm-level Technical Efficiency: Stochastic Frontier Model Evidence from Chinese Manufacturing Firms [PDF]
, 2016 It has been recognized that multinational corporations can spill over to non-affiliated firms in host economies. Existing studies of foreign direct investment (FDI) and productivity growth often assume firms are perfectly efficient.
Wang, Miao Grace, Wong, Sunny
core +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Stochastic Frontier Models With Correlated Error Components [PDF]
In the productivity modelling literature, the disturbances U (representing technical inefficiency) and V (representing noise) of the composite error W=V-U of the stochastic frontier model are assumed to be independent random variables.
Murray D Smith
core
Deep Learning‐Assisted Design of Mechanical Metamaterials
Advanced Intelligent Discovery, EarlyView.This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang +3 more
wiley +1 more source
Lahore Journal of EconomicsThis paper investigates the technical efficiency of selected manufacturing industries of Bangladesh using a stochastic frontier production function approach suggested by Battese and Coelli (1992) applied to panel data. A feasible Cobb-Douglas stochastic
Md. Azizul Baten +3 more
doaj
Regional Tourism Competition in the Baltic States: a Spatial Stochastic Frontier Approach [PDF]
This paper aimed at a statistical analysis of competition for tourists between regions within Baltic states (Estonia, Latvia, Lithuania) and estimation relative efficiency levels of regions.
Pavlyuk, Dmitry
core +1 more source
Advanced Intelligent Discovery, EarlyView.This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri +6 more
wiley +1 more source