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This paper presents scientific analyses of the wall paintings in Wudang Lamasery, which is located in the Baotou area of Inner Mongolia, China. Raman spectroscopy was used to analyze the pigments of the mural of the Lam rim Hall. The results show that vermilion, red lead, chrome yellow, emerald green and synthetic ultramarine were used. The existence of synthetic pigments provides a clue for the date the mural was painted. [ABSTRACT FROM AUTHOR]

• A new modular k -out-of- n system model with functional dependency is developed. • Bayesian network is used to model the reliability of modular k -out-of- n systems. • An algorithm is designed to automatically generate the CPTs of the BN model. • DBN model is developed to update the system reliability using observations. • A real-world radar transmitter system in the space launch site is studied. The k -out-of- n systems are among the most important redundancy structures in engineering practices, and their reliability assessment has been extensively studied in the past decades. However, components in a k -out-of- n structure are often subject to functional dependency (FDEP), in which component states are affected by other components' states in the system. In this article, we study a new system structure, namely modular k -out-of- n system with FDEP. In such a system, the failure of some specific components will disable some components in the k -out-of- n structure. Bayesian network (BN) models are used to construct the structure function of modular k -out-of- n systems. The parameters encoded in the graphical structure of the modular k -out-of- n system are automatically generated by a customized algorithm. Furthermore, a dynamic BN (DBN) is developed to update the reliability of modular k -out-of- n system dynamically when observation data are collected from either component or system level. The Birnbaum importance measure of the different types of components in the modular k -out-of- n system is also evaluated by the DBN model via inserting evidence of the components' states overtime. A real-world case of a radar transmitter system in the space launch site is studied to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Structure function, which quantitatively represents the relation between system states and unit states, is essential for system reliability assessment and oftentimes may not be known in advance due to complicated interactions among units. In this article, a dynamic Bayesian network (DBN) model is put forth to leverage incomplete observation sequences of hierarchical multi-state systems for structure function learning. To achieve a consistent structure function at different time instants, a customized Expectation-Maximization (EM) algorithm with parameter modularization is proposed and executed by two steps: (1) filling the missing values in the incomplete observation sequences with their expectations to break the dependencies among nodes; (2) decomposing the graphical network into V-shape structures, and then integrating the identical V-shape structures at different time slices to learn the parameters in the DBN model. Based on the learned DBN model, system state distribution and reliability function over time can be readily assessed. Two illustrative examples are presented and the results demonstrate that the structure function of a hierarchical multi-state system can be accurately learned despite the incompleteness of observation sequences. [ABSTRACT FROM AUTHOR]

• The cumulative performance of multi-state systems is evaluated. • The mission success probability is derived for infinite and finite time horizons. • Load optimization models are formulated from a cumulative performance perspective. • The computational burden is alleviated by an approximation algorithm. Multi-state is a characteristic of advanced manufacturing systems and complicated engineering systems. Multi-state systems (MSSs) have gained considerable popularity in the last few decades due to their reliability. In this study, the load optimization problem for MSSs is investigated from the perspective of cumulative performance. The cumulative performance of MSSs and the corresponding mission success probability (MSP) are formulated for both infinite and finite time horizons. The distribution of the cumulative performance of a system at failure or a particular time is evaluated using a set of multiple integrals. Correspondingly, two load optimization models are formulated to identify the optimal loading strategy for each state of an MSS to achieve the maximum MSP. As an example, a set of comparative studies are performed to demonstrate the advantages of the proposed method. The results show that (1) the proposed method can effectively evaluate the MSP from a cumulative performance perspective in a computationally efficient manner, and (2) the optimal loading strategy of an MSS can be determined by the proposed method, while varying with respect to the set amount of work to be completed and the maximum allowable mission time. [ABSTRACT FROM AUTHOR]

Abstract: A three dimensional (3D) chemical feature based pharmacophore model was developed for selective histone deacetylase 1 (HDAC1) inhibitors, which provides an efficient way to discuss the isoform selectivity of HDAC inhibitors. In contrast to the classical pan-HDAC pharmacophore, two hydrophobic features (HY and HYAr2) were found in the chemical feature based pharmacophore model, which might be responsible for the selectivity of HDAC1 inhibitions. Molecular docking also highlighted the two hydrophobic features, which are located in the internal cavity adjacent to the active site. The results contribute to our understanding of the molecular mechanism underlying the selectivity of HDAC1 inhibitors and suggest a possible target region to design novel selective HDAC1 inhibitors. [Copyright &y& Elsevier]