Uncertainty management in multidisciplinary design of critical safety systems

Managing the uncertainty in multidisciplinary design of safety-critical systems requires not only the availability of a single approach or methodology to deal with uncertainty but a set of different strategies and scalable computational tools (that is, by making use of the computational power of a cluster and grid computing). The availability of multiple tools and approaches for dealing with uncertainties allows cross validation of the results and increases the confidence in the performed analysis. This paper presents a unified theory and an integrated and open general-purpose computational framework to deal with scarce data, and aleatory and epistemic uncertainties. It allows solving of the different tasks necessary to manage the uncertainty, such as uncertainty characterization, sensitivity analysis, uncertainty quantification, and robust design. The proposed computational framework is generally applicable to solve different problems in different fields and be numerically efficient and scalable, allowing for a significant reduction of the computational time required for uncertainty management and robust design. The applicability of the proposed approach is demonstrated by solving a multidisciplinary design of a critical system proposed by NASA Langley Research Center in the multidisciplinary uncertainty quantification challenge problem

Coordinatively unsaturated ruthenium complexes as efficient alkyne-azide cycloaddition catalysts

The performance of 16-electron ruthenium complexes with the general formula Cp*Ru(L)X (in which L = phosphine or N-heterocyclic carbene ligand; X = Cl or OCH2CF3) was explored in azide−alkyne cycloaddition reactions that afford the 1,2,3- triazole products. The scope of the Cp*Ru(PiPr3)Cl precatalyst was investigated for terminal alkynes leading to new 1,5-disubstituted 1,2,3-triazoles in high yields. Mechanistic studies were conducted and revealed a number of proposed intermediates. Cp*Ru- (PiPr3)(η2-HCCPh)Cl was observed and characterized by 1H, 13C, and 31P NMR at temperatures between 273 and 213 K. A rare example of N,N-κ2-phosphazide complex, Cp*Ru(κ2-iPr3PN3Bn)Cl, was fully characterized, and a single-crystal X-ray diffraction structure was obtained. DFT calculations describe a complete map of the catalytic reactivity with phenylacetylene and/or benzylazide.Publisher PDFPeer reviewe

Surgical outcome and indicators of postoperative worsening in intra-axial thalamic and posterior fossa pediatric tumors: Preliminary results from a single tertiary referral center cohort

Background: Shared indications about the best management of intra-axial thalamic (IAT) and posterior fossa (PF) pediatric tumors are still lacking. The aim of this study was to analyze neurosurgical outcome in these tumors and to investigate factors associated with postoperative worsening. Methods: A retrospective single-center study on IAT and PF pediatric tumor patients treated surgically over a 7-year period was conducted. The Lansky Scale (LS) was used to assess patients' functional status. Surgical complexity was graded with the Milan Complexity Scale (MCS). The following analyses were performed: a longitudinal analysis of the preoperative, discharge, and 3 months' follow-up (FU) LS, a comparison between improved/unchanged and worsened patients, and an analysis of the predictive value of single MCS items. Results: 37 cases were collected: 20 PF and 17 thalamic. Mean MCS score was 6 ± 1.7. Mean preoperative, discharge and FU LS were 80.8, 74.6 and 80.3 respectively. Surgical mortality was 0%.The longitudinal analysis showed a neurological worsening at discharge compared to preoperative status (p = 0.011) and an improvement at FU compared to discharge (p < 0.004), both statistically significant. None of the variables analyzed showed a significant predictive value of early postoperative change; however, higher MCS scores were associated with a greater risk of worsening. Conclusions: The surgical management of IAT and PF pediatric brain tumors remains challenging; early postoperative worsening is possible, but most deficits tend to improve at FU. The MCS seems to be a valuable tool to estimate the risk of early postoperative worsening and to facilitate parents' informed consent

L'exili i el silenci dels farmacèutics catalans i de les institucions científiques de la II República Espanyola

L'objectiu d'aquesta comunicació és donar a conèixer l'exili i el silenci dels farmacèutics catalans i de les Institucions Científiques de la II República espanyola, en reconeixement i homenatge a la recerca pionera feta pel Dr. Ramon Jordi i publicada en el llibre "Cien años de vida farmacéutica barcelonesa (1830-1939)", l'any 1960. Malgrat la destrucció, inexplicable, dels expedients de guerra pel Col·legi de Farmacèutics de Barcelona i de la desapareguda en temps de guerra i postguerra, s'han trobat fonts de seguiment per complementar i continuar aquesta investigació tal com volia el col·lega Ramon Jordi i a enriquir el patrimoni científic i cultural de la nostra Nació Catalana

Efficient posterior estimation for stochastic SHM using transport maps

Accurate parameter estimation is a challenging task that demands realistic models and algorithms to obtain the parameter’s probability distributions. The Bayesian theorem in conjunction with sampling methods proved to be invaluable here since it allows for the formulation of the problem in a probabilistic framework. This opens up the possibilities of using prior information and knowledge about parameter distributions as well as the natural incorporation of aleatory and epistemic uncertainties. Traditionally, Markov Chain Monte Carlo (MCMC) methods are used to approximate the posterior distribution of samples given some data. However, these methods usually need a large amount of samples and therefore a large amount of model evaluations. Recent advances in transport theory and its application in the context of Bayesian model updating (BMU) make it possible to approximate the posterior distribution analytically and hence eliminate the need for sampling methods. This paves the way for the usage in real-time applications and for fast parameter estimation. We investigate here the application of transport maps to a simple analytical model as well as a structural dynamics model. The performance is compared to an MCMC approach to assess the accuracy and efficiency of transport maps. A discussion about requirements for the implementation of transport maps as well as details on the implementation are also given

Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF

One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images

Distribution-free stochastic model updating with staircase density functions

In stochastic model updating, hybrid uncertainties are typically characterized by the distributional p-box. It assigns a certain probability distribution to model parameters and assumes its hyper-parameters as interval values. Thus, regardless of the updating method employed, the distribution family needs to be known a priori to parameterize the distribution. Meanwhile, a novel class of the random variable, called staircase random variable, can discretely approximate a wide range of distributions by solving moment-matching optimization problem. The first author and his co-workers have recently developed a distribution-free stochastic updating framework, in which model parameters are considered as staircase random variables and their hyper-parameters are inferred in a Bayesian fashion. This framework can explore an optimal distribution from a broad range of potential distributions according to the available data. This study aims to further demonstrate the capability of this framework through a simple numerical example with a parameter following various types of distributions