Results 201 to 210 of about 7,371,142 (245)

Nanostructured Deep Eutectic Systems in Healthcare: From Bioactive Solvents to Intelligent Biointerfaces, Wearables, and AI‐Driven Design

Advanced NanoBiomed Research, EarlyView.
Beyond the green solvent paradigm, this review redefines Deep Eutectic Systems (DES) as programmable supramolecular nanoassemblies. We survey their biomedical convergence: stabilizing thermolabile mRNA to enable cold chain‐free logistics, reshaping transdermal microneedle delivery, enabling long‐term wearables via eutectogels, and utilizing Generative ...
Jeesu Moon, Min Seo Kim, Jae‐Seung Lee
wiley   +1 more source

Radiative Hybrid Nanofluid Flow Over a Porous Riga Surface: A Fuzzy–ANN Modeling Approach

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT This study proposes a fuzzy–ANN model to investigate the nonlinear thermal transport in a tangent hyperbolic (Tanh) hybrid nanofluid flow past a porous Riga surface, considering the effects of Rosseland diffusion, chemical reactions, and internal volumetric heating.
Azad Hussain, Rabia Zetoon, Reeha Iqbal
wiley   +1 more source

Dynamic Adaptive Bayesian Networks

This paper introduces a dynamic adaptive Bayesian network structure designed to enhance model generalization and prediction accuracy through adaptive parameter adjustment. Traditional Bayesian networks often suffer from static parameterization, limiting their effectiveness in dynamic environments.
openaire   +1 more source

Context‐centric proactive information delivery for Knowledge Work support: Opportunities, challenges, and directions. An Annual Review of Information Science and Technology (ARIST) paper

Journal of the Association for Information Science and Technology, EarlyView.
Abstract Context‐centric proactive information delivery (PID) is a relatively underexplored domain within recommender systems (RS) aimed at enhancing Knowledge Workers' productivity by proactively providing relevant information during digital tasks.
Mahta Bakhshizadeh, Christian Jilek, Markus Schröder, Heiko Maus, Andreas Dengel   +4 more
wiley   +1 more source

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Bayesian compression for dynamically expandable networks

Pattern Recognition, 2022
Abstract This paper develops Bayesian Compression for Dynamically Expandable Network (BCDEN), which can learn a compact model structure with preserving the accuracy in a continual learning scenarios. Dynamically Expandable Network (DEN) is efficiently trained by performing selective retraining, dynamically expands network capacity with only the ...
Yang Yang 0072, Bo Chen 0001, Hongwei Liu 0001   +2 more
openaire   +2 more sources

Dynamic Bayesian Networks

2021
Underground transportation systems are in great demand in many large cities all over the world. Tunnel construction has presented a powerful momentum for rapid economic development worldwide. However, owing to various risk factors in complex project environments, safety violations occur frequently in tunnel construction, leading to large problems on ...
Limao Zhang, Yue Pan, Xianguo Wu, Mirosław J. Skibniewski   +3 more
openaire   +1 more source

Simulation metamodeling with dynamic Bayesian networks

European Journal of Operational Research, 2011
This paper presents a novel approach to simulation metamodeling using dynamic Bayesian networks (DBNs) in the context of discrete event simulation. A DBN is a probabilistic model that represents the joint distribution of a sequence of random variables and enables the efficient calculation of their marginal and conditional distributions.
Virtanen, Kai, Poropudas, Jirka
openaire   +5 more sources

Topological Dynamic Bayesian Networks

2010 20th International Conference on Pattern Recognition, 2010
The objective of this research is to embed topology within the dynamic Bayesian network (DBN) formalism. This extension of a DBN (that encodes statistical or causal relationships) to a topological DBN (TDBN) allows continuous mappings (e.g., topological homeomorphisms), topological relations (e.g., homotopy equivalences) and invariance properties (e.g.,
openaire   +2 more sources

An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks

International Journal of Intelligent Systems, 2004
Summary: We extend Darwiche's differential approach to inference in Bayesian Networks (BNs) to handle specific problems that arise in the context of Dynamic Bayesian Networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial.
Boris Brandherm, Anthony Jameson
openaire   +2 more sources

Dynamic Bayesian Networks for Student Modeling

IEEE Transactions on Learning Technologies, 2017
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling.
Tanja Käser, Severin Klingler, Alexander G. Schwing, Markus H. Gross   +3 more
openaire   +2 more sources