Review on the decontaminative capabilities of halophilic microorganisms in saline wastewater and soil. a r t i c l e i n f o a b s t r a c tEnvironments with high-salt concentrations are often populated by dense microbial communities. Halophilic microorganisms can be isolated from different saline environments and different strains even belonging to the same genus have various applications. Wastewater and soil rich in both organic matter and salt are difficult to treat using conventional microorganisms typically found in wastewater treatment and soil bioremediation facilities. Studies on decontaminative capabilities and decontamination pathways of organic contaminants (i.e., aromatic compounds benzoate, cinnamate, 3-phenylpropionate, 4-hydroxybenzoic acid), heavy metals (i.e., tellurium, vanadium), and nutrients in the biological treatment of saline wastewater and soil by halophilic microorganisms are discussed in this review.

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A Bayesian inference method for refining crystallographic structures is presented. The distribution of model parameters is stochastically sampled using Markov chain Monte Carlo. Posterior probability distributions are constructed for all model parameters to properly quantify uncertainty by appropriately modeling the heteroskedasticity and correlation of the error structure. The proposed method is demonstrated by analyzing a National Institute of Standards and Technology silicon standard reference material. The results obtained by Bayesian inference are compared with those determined by Rietveld refinement. Posterior probability distributions of model parameters provide both estimates and uncertainties. The new method better estimates the true uncertainties in the model as compared to the Rietveld method.

For evaluation of the rheological and mechanical properties of highly filled wood plastic composites (WPCs), polypropylene/polyethylene (PP/PE) blends were grafted with maleic anhydride (MAH) to enhance the interfacial adhesion between wood fiber and matrix. WPCs were prepared from wood fiber up to 60 wt.% and modified PP/PE was blended by extrusion. The rheological properties were studied by using dynamic measurement. According to the strain sweep test, the linear viscoelastic region of composites in the melt was determined. The result showed that the storage modulus was independent of the strain at low strain region (<0.1%). The frequency sweep results indicated that all composites exhibited shear thinning behavior, and both the storage modulus and complex viscosity of MAH modified composites were decreased comparing to those unmodified. Flexural properties and impact strength of the prepared WPCs were measured according to the relevant standard specifications. The flexural and impact strength of the manufactured composites significantly increased and reached a maximum when MAH dosage was 1.0 wt.%, whereas the flexural modulus after an initial decreased, also increased with MAH dosage. The increase in mechanical properties indicated that the presence of anhydride groups enhanced the interfacial adhesion between wood fiber and PP/PE blends.

Machine learning models have been widely used for studying thermal sensations.However, the black-box properties of machine learning models lead to the lack of model transparency, and existing explanations for the thermal sensation models are generally flawed in terms of the perspectives of interpretable methods. In this study, we perform an interpretability analysis using the "SHapley Additive exPlanation" (SHAP) from game theory for thermal sensation machine learning models. The effects of different features on thermal sensations and typical decision routes in the models are investigated from both local and global perspectives, and the properties of correlation between features and thermal sensations and decision routes within machine learning models are summarized. The differences in the effects of features across samples reflect the effects of features on thermal sensations not only can be demonstrated by significant magnitudes but also by differentiation. The effects of features on thermal sensations often appear in the form of combinations of two to four features, which determine the final thermal sensation in most cases. Therefore, the neutral environment may actually be a dynamic high-dimensional space consisting of certain combinations of features in certain ranges with changing shapes.

To avoid specification of the error distribution in a regression model, we propose a general nonparametric scale mixture model for the error distribution. For fitting such mixtures, the predictive recursion method is a simple and computationally efficient alternative to existing methods. We define a predictive recursion-based marginal likelihood function, and estimation of the regression parameters proceeds by maximizing this function. A hybrid predictive recursion-EM algorithm is proposed for this purpose. The method's performance is compared with that of existing methods in simulations and real data analyses.