A generic method for estimating system reliability using Bayesian networks

This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples.     

Effect of filler content on the structure‐property behavior of poly(ethylene oxide) based polyurethaneurea‐silica nanocomposites

Poly(ethylene oxide) (PEO) based polyurethaneurea‐silica nanocomposites were prepared by solution blending and characterized by Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, Differential Scanning Calorimetry and tensile testing. The colloidal silica nanoparticles with an average size of 50 nm were synthesized by modified Stöber method in isopropanol. Silica particles were incorporated into three cycloaliphatic polyurethaneurea (PUs) copolymers based on PEO oligomers with molecular weights of 2,000, 4,600, and 8,000 g/mol. Hard segment content of PUs was constant at 30% by weight. Silica content of the PU nanocomposites varied between 1 and 20% by weight. Soft segment (SS) glass transition and melting temperatures slightly increased with increasing filler content for all the copolymers. Degree of SS crystallinity first increased with 1% silica incorporation and subsequently decreased by further silica addition. Elastic modulus and tensile strengths of PU copolymers gradually increased with increasing amount of the silica filler. Elongation at break values gradually decreased in PEO‐2000 based PU copolymer with increasing silica content, whereas no significant change was observed in PUs based on PEO‐4600 and PEO‐8000. Enhancement in tensile properties of the materials was mainly attributed to the homogeneous distribution of silica filler in polymer matrices and strong polymer‐filler interactions. POLYM. ENG. SCI., 58:1097–1107, 2018. © 2017 Society of Plastics Engineers     

A multiscale investigation on controlling bovine serum albumin adsorption onto polyurethane films

Our previous study on castor oil (CO) and poly(ethylene glycol) (PEG)‐based shape memory polyurethane (PU) films indicated that cell spreading on the polymer surface, cell morphology, and adhesion of fibroblast are closely related to the composition of the polymer that influences surface properties. This integrated experimental and computational study is designed to investigate the effect of important parameters such as surface roughness, crystallinity, hydrophilicity, distribution of hard/soft segments, and topology of the surface on protein adsorption for CO‐ and PEG‐based PUs. Analyses indicate that the crystallinity percentage highly promotes bovine serum albumin (BSA) adsorption. Roughness together with topological features determines BSA adsorption rate and concentration. Hydrophilicity and hard segment content seem to have less critical effect on adsorption. Distribution of hard segments into the soft segments emerges as another important factor for protein adsorption. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45669.     

The Use of Digital Image Processing Method to Estimate the Foam Characteristics of Polyurethane

Four air-bubbled polyurethane (PU) foams with different polyol:PMDI wt.% are produced, respectively. The chemical reaction mechanisms of polyurethane and bubble formation are proposed by performing standard Gibbs free energy calculations using the DFT M06-2X/6-31+G(d,p) method. The local minima, transition states, and intermediates in reaction mechanisms are detected. It is concluded that both reactions are exothermic. Then, raw images of the produced PU foams are taken with a 13 MP mobile phone camera, which can be considered inexpensive, and the mean radii of the pores are calculated by an image processing based method (IPBM) on a standard desktop computer with an i5 processor. It is determined that there is a close relationship between the calculated mean radius and instrumentally measured thermal conductivity coefficient of the foams. However, the thermal conductivity coefficients are independent of the calculated number and percentage of the pores. The mean radii of the samples calculated by the proposed IPBM are close to that of the SEM, with acceptable relative errors of less than 10%. Finally, it is concluded that IPBM, which is a more cost-effective, cleaner, and faster method than SEM, might replace SEM in the air bubble analysis of PU foams.     

Agent-based modelling and construction – reconstructing antiquity’s largest infrastructure project

Archaeological remains around the world are testament that large-scale construction projects have been successfully carried out for millennia. This success is particularly evident through the great infrastructural works of the Roman Empire. Yet, it was when the capital was moved from Rome to Constantinople that the largest of these projects was undertaken. This megaproject of the fourth- and fifth-century water supply was made of hundreds of kilometres aqueduct channels and bridges that brought fresh water to the city’s complex system of reservoirs and cisterns. Unlike projects of the previous centuries, we are left with no written record of how this titanic project was undertaken and existing archaeological and historical commentaries on structures of this period do not provide details of organization of construction. We explore the nature of building Constantinople’s water supply through diverse sources of knowledge and the application of agent-based modelling – a method for simulating the actions, interactions and behaviours of autonomous agents and the resulting emergent properties of the system in which they are a part. This paper demonstrates the ability of ABM to develop and test richer hypotheses about historical construction organization and management than physical and historical evidence on their own.