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Abstract To investigate the current status of occupational calling among psychiatric nurses, identify its correlated factors, and explore the probabilistic dependencies and network structure using a Bayesian network mode...

Abstract Unmanned Aerial Vehicle (UAV) operations confront complex systemic risks that challenge traditional analytical methods. This paper develops a hierarchical Bayesian Network (BN) to quantitatively model these risk...

Abstract Recent advancements in single-cell DNA methylation (scDNAm) sequencing technologies have enabled the profiling of epigenetic landscapes at unprecedented resolution, offering insights into cellular heterogeneity,...

Abstract Cardiovascular disease (CVD) remains among the leading causes of death and disability worldwide. Although diet is a modifiable risk factor, the specific contribution of low vegetable intake to CVD burden has not...

Abstract Future flood dynamics in a lowland karst catchment draining into Galway Bay, Ireland, have been assessed under climate-change scenarios. Bayesian neural network model (BNN) was calibrated on 1980–2015 observat...

Abstract Klebsiella oxytoca is an under-recognised opportunistic pathogen increasingly associated with severe antimicrobial-resistant infections. Here, we report the first carbapenemase-producing K. oxytoca (CPKO) isolat...

Abstract Nonergodicity and Simpson’s paradox present significant, yet underappreciated challenges in cognitive neuroscience. Leveraging brain imaging and behavioral data from over 4000 individuals and a Bayesian comput...

Abstract Human social behaviors involve complex interactions between individuals, and understanding how interbrain neural activity reflects and predicts these interactions is critical for advancing social cognitive neuro...