This paper describes a Bayesian network model for a candidate assessment design that had four proficiency variables and 48 tasks with 3–12 observable outcome variables per task and scale anchors to identify the location of the subscales. The domain experts’ view of the relationship among proficiencies and tasks established a complex prior distribution over 585 parameters. Markov Chain Monte Carlo (MCMC) estimation recovered the parameters of data simulated from the expert model. The sample size and the strength of the prior had only a modest effect on parameter recovery, but did affect the standard error of estimated parameters. Finally, an identifiability issue involving relabeling of proficiency states and permutations of the matrixes is addressed in the context of this study.
Conductive hydrogels as potential soft materials have attracted tremendous attention in wearable electronic devices. Nonetheless, manufacturing intelligent materials that integrate mouldability, stretchability, responsive ability, fast self‐healing ability, as well as mechanical and electrochemical properties is still a challenge. Here, multifunctional conductive hydrogels composed of poly(vinyl alcohol) (PVA) and polypyrrole (PPy) nanotube are prepared using borax as cross‐linker. The existence of multicomplexation, entangled PVA chains, and interconnected PPy nanotubes, as well as extensive hydrogen bonding results in the fabrication of hierarchical network of PVA‐PPy hydrogels. PVA‐PPy hydrogels exhibit high stretchability (more than 1000%), multiresponsiveness, low density (0.95 g cm?3), high water content (96%), and 15 s self‐healing features. Furthermore, the self‐healing supercapacitor electrode and motion sensor based on PVA‐PPy hydrogels demonstrate ideal performances. This facile strategy in this work would be promising to construct an excellent multifunctional soft material for various flexible electrode and biosensor. 相似文献