Innovative seismic design optimization with reliability constraints

Performance-Based Design (PBD) methodologies is the contemporary trend in designing better and more economic earthquake-resistant structures where the main objective is to achieve more predictable and reliable levels of safety and operability against natural hazards. On the other hand, reliability-based optimization (RBO) methods directly account for the variability of the design parameters into the formulation of the optimization problem. The objective of this work is to incorporate PBD methodologies under seismic loading into the framework of RBO in conjunction with innovative tools for treating computational intensive problems of real-world structural systems. Two types of random variables are considered: Those which influence the level of seismic demand and those that affect the structural capacity. Reliability analysis is required for the assessment of the probabilistic constraints within the RBO formulation. The Monte Carlo Simulation (MCS) method is considered as the most reliable method for estimating the probabilities of exceedance or other statistical quantities albeit with excessive, in many cases, computational cost. First or Second Order Reliability Methods (FORM, SORM) constitute alternative approaches which require an explicit limit-state function. This type of limit-state function is not available for complex problems. In this study, in order to find the most efficient methodology for performing reliability analysis in conjunction with performance-based optimum design under seismic loading, a Neural Network approximation of the limit-state function is proposed and is combined with either MCS or with FORM approaches for handling the uncertainties. These two methodologies are applied in RBO problems with sizing and topology design variables resulting in two orders of magnitude reduction of the computational effort.     

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.     

An efficient generation method of embedded reinforcement in hexahedral elements for reinforced concrete simulations

In this paper, an automatic procedure for the generation of embedded steel reinforcement inside hexahedral finite elements is presented. The automatic mapping of the entire reinforcement network inside the concrete hexahedral finite elements is performed using the end-point coordinates of the rebar reinforcement macro-elements. By introducing a geometrical constraint, this procedure decreases significantly the computational effort for generating the input data of the embedded rebar elements in three-dimensional finite-element analysis, particularly when dealing with relatively large-scale reinforced concrete models. The computational robustness and efficiency of the proposed mesh generation method are demonstrated through numerical experiments.     

Between the Local and the Global: History of Science in the European Periphery Meets Post‐Colonial Studies

The aim of this paper is to discuss two historiographical issues pertaining to the history of science in the European periphery. The first issue concerns the wide use of the centre‐periphery dichotomy in historical accounts discussing the diffusion and institutionalization of science across the world. The second issue concerns the use of appropriation (instead of transfer, or adaptation) as a means to overcome the diffusionist model in history of science. Recent work at the intersection of history of science with post‐colonial studies will provide the framework for reassessing these matters. As it will be shown, theoretical discussions about the history of science in post‐colonial context can help historians overcome the centre‐periphery dichotomy and turn European periphery into a privileged standpoint for showing the actual diversity of ‘European science.’ At the same time, the experience of post‐colonial studies can also help sharpen the historiographical tool of appropriation. The assumption that will be made is that by focusing on appropriation rather than on discovery and innovation (the favourite categories of much of mainstream historiography), or on transfer and adaptation (the favourite categories of the diffusionist model), historians of science can not only set aside the artificial distinctions of the diffusionist model, but also bring forward the re‐inventions, the conceptual shifts and the cultural adjustments, which are responsible for the emergence of science as a global phenomenon in the periphery. Especially concerning European periphery, the use of appropriation may bring forward the particular historical circumstances under which certain knowledge patterns gained universal epistemic authority as constitutive elements of an imagined European intellectual identity.     

MeteoBayes: Effective Plan Recognition in a Weather Dialogue System

When you've called a voice portal for any kind of information retrieval, chances are that an automated system guided the entire interaction. It might have correctly identified your goal, but probably only after asking too many questions. MeteoBayes is a meteorological information dialogue system that lets you use natural language to direct the interaction. Based on Bayesian networks, MeteoBayes' inference engine attempts to identify user intentions by consulting its past dialogue repository. For unfamiliar words, MeteoBayes has an unknown-term disambiguation module that learns word similarities from texts to avoid unnecessary system inquiries, thus speeding up the understanding process     

Solution-processable graphene linked to 3,5-dinitrobenzoyl as an electron acceptor in organic bulk heterojunction photovoltaic devices

3,5-Dinitrobenzoyl chloride was covalently linked to graphene oxide (GO) nanosheets prepared by a modified Hummers’ method, using ethylenediamine as a spacer. The linkage of the GO with the small molecule was confirmed by spectroscopic (e.g., Fourier transform infrared, Raman) and microscopic analyses. The resultant GO-ethylene-dinitro-benzoyl (GO-EDNB) consists of a controlled scale of different graphene structures and is highly dispersable in common organic solvents. The GO-EDNB was used as the electron acceptor material in poly-(3-hexylthiophene) (P3HT) bulk heterojunction photovoltaic devices to significantly improve the performance, yielding a power conversion efficiency improvement of two orders and one order of magnitude compared with the pristine P3HT and the P3HT-GO devices respectively.