Dynamic Latent Space Model With Position Clusters and Its Application in International Trade Network
Discrete Dynamics in Nature and SocietyThe dynamic latent space model is widely used in analysing network data. It can provide useful visualization and interpretation of networks, as well as represent the inherent reciprocity and transitivity.
Jiajia Wang
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Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment. [PDF]
J Cloud Comput (Heidelb), 2023 Behbehani D +3 more
europepmc +1 more source
Advanced Science, EarlyView.ABSTRACT The muscle capsule of Trichinella is a critical structure that impedes immune attacks and drug penetration, yet the molecular mechanisms underlying its formation remain poorly understood. Using a high‐quality super‐pangenome comprising 12 Trichinella species, we compared extensive genomic variations between encapsulating and non‐encapsulating ...
Qingbo Lv +8 more
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Prediction of electricity price intervals using dynamic bayesian networks
Energy InformaticsThe increasing volatility of electricity prices, driven by the growing share of renewable energy, calls for new approaches. This paper proposes a dynamic Bayesian network (DBN) method for electricity price interval forecasting.
Hongtao Wang
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Qualitative Prediction of End-to-End Delay in 5G Networks
IEEE AccessAccurate end-to-end (E2E) delay prediction is critical for optimizing network performance and ensuring quality of service in 5G networks. This paper investigates two complementary qualitative E2E delay prediction methodologies: a Bayesian approach based ...
Diyar Fadhil, Rodolfo Oliveira
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ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
Advanced Science, EarlyView.The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray +3 more
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Considerations for Fitting Dynamic Bayesian Networks With Latent Variables: A Monte Carlo Study. [PDF]
Appl Psychol Meas, 2022 Reichenberg RE, Levy R, Clark A.
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Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model. [PDF]
Ecol Evol, 2022 Hatum PS, McMahon K, Mengersen K, Wu PP.
europepmc +1 more source
Advanced Science, EarlyView.By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin +12 more
wiley +1 more source