Towards a Base UML Profile for Architecture Description

This paper discusses a base UML profile for architecture description as supported by existing Architecture Description Languages (ADLs). The profile may be extended so as to enable architecture modeling both as expressed in conventional ADLs and according to existing runtime infrastructures (e.g., system based on middleware architectures).

The effect of gamma irradiation on selected growth factors and receptors mRNA in glycerol cryopreserved human amniotic membrane

Human amniotic membrane (HAM), due to its high biocompatibility, low immunogenicity, anti-microbial, anti-viral properties as well as the presence of its growth factors, has been used in various clinical applications. These growth factors are key factors in regulating many cellular processes such as cellular growth, proliferation and cellular differentiation. The current study aimed to explore the effect of glycerol cryopreservation and gamma irradiation on the selected growth factors and receptors mRNA present in HAM. Eight growth factors, namely, EGF, HGF, KGF, TGF-α, TGF-β1, TGF-β2, TGF-β3 and bFGF and two growth factor receptors, HGFR and KGFR were evaluated in this study. The total RNA was extracted and converted to complimentary DNA using commercial kits. Subsequently, the mRNA expressions of these growth factors were evaluated using quantitative PCR and the results were statistically analyzed using REST-MCS software. This study indicated the presence of these growth factors and receptors mRNA in fresh, glycerol cryopreserved and irradiated glycerol cryopreserved HAM. In glycerol cryopreserved HAM, the mRNA expression showed up-regulation of HGF and bFGF and down-regulation of the rest of 8 genes which were EGF, HGFR, KGF, KGFR, TGF-α, TGF-β1, TGF-β2 and TGF-β3. Interestingly, the glycerol cryopreserved HAM radiated with 15 kGy showed up-regulation in the mRNA expression of 7 genes, namely, EGF, HGF, KGF, KGFR, TGF-β1, TGF-β2 and TGF-β3 and down-regulated mRNA expression of HGFR, TGF-α and bFGF. However, these mRNA expressions did not show a statistically significant difference compared to control groups. Thus, it can be concluded that the glycerol cryopreservation did not have an effect on the growth factors’ and receptors’ mRNA expression levels in HAM. Similarly, 15 kGy gamma irradiation did not have an effect on the growth factors’ and receptors’ mRNA expression in glycerol cryopreserved HAM. This finding provides a useful information to clinicians and surgeons to choose the best method for HAM preservation that could benefit patients in their treatment

Processing, Adhesion and Corrosion-inhibiting Properties of Poly[2-methoxy-5-(2’-ethylhexyloxy)-1,4-phenylene vinylene], (MEH-PPV) on Aerospace Aluminum Alloys

Researchers at the Naval Air Warfare Center Weapons Division (NAWCWD) and Wright-Patterson Air Force Base (WPAFB) investigated poly[2-methoxy-5-(2’-ethylhexyloxy)-1,4-phenylene vinylene], (MEH-PPV) for its potential corrosion-inhibition properties on aerospace aluminum alloy AA2024-T3. Solution processing of the polymer, as well as adhesion testing and accelerated weathering tests were performed on MEH-PPV full military aerospace coatings. Wet and dry tape adhesion testing, as well as pencil hardness, impact flexibility and pneumatic adhesion tensile test instrument (PATTI) testing were used to demonstrate the adhesion performance of MEH-PPV on aluminum substrates. The results showed that MEH-PPV had acceptable adhesion characteristics when compared to hexavalent chromium (Cr(VI)) based coatings in all of these tests. Accelerated weathering analysis was performed on MEH-PPV coatings to determine their corrosion protection and weathering resistance capabilities. These tests included neutral salt spray (NSS) exposure and xenon-arc lamp testing. The results showed that while MEH-PPV does not exhibit significant color change after 500 hours of xenon arc lamp exposure, the polymer has poor corrosion protection performance under aggressive salt environments

Software Architecture and Dependability

International audienceDependable systems are characterized by a number of attributes including: reliability, availability, safety and security. For some attributes (namely for reliability, availability, safety), there exist probability- based theoretic foundations, enabling the application of dependability analysis techniques. The goal of dependability analysis is to forecast the values of dependability attributes, based on certain properties (e.g. failure rate, MTBF, etc.) that characterize the system's constituent elements. Nowadays, architects, designers and developers build systems based on an architecture-driven approach. They specify the system's software architecture using Architecture Description Languages or other standard modeling notations like UML. Given the previous, we examine what we need to specify at the architectural level to enable the automated generation of models for dependability analysis. In this paper, we further present a prototype implementation of the proposed approach, which relies on UML specifications of dependable systems' software architectures. Moreover, we exemplify our approach using a case study system

Robust open cellular porous polymer monoliths made from cured colloidal gels of latex particles

The coagulation of oppositely charged latexes, prepared from the soap-free emulsion polymerisation of styrene using water as the reaction medium, resulted in the obtainment of colloidal gels that were porous in nature and held together by electrostatic interactions. Chemical crosslinking, involving the introduction of a water-soluble crosslinker, resulted in the obtainment of stronger chemical bonds between particles affording a rigid porous material known as a monolith. It was found that, in a simpler approach, these materials could be prepared using a single latex where the addition of ammonium persulfate both resulted in the formation of the colloidal gel and initiated the crosslinking process. The pore size of the resulting monoliths was predictable as this was observed to directly correlate to the particle diameter, with larger pores achieved using particles of increased size. All gels obtained in this work were highly mouldable and retained their shape, which allowed for a range of formats to be easily prepared without the requirement of a mould

Do Bayesian Variational Autoencoders Know What They Don't Know?

The problem of detecting the Out-of-Distribution (OoD) inputs is of paramount importance for Deep Neural Networks. It has been previously shown that even Deep Generative Models that allow estimating the density of the inputs may not be reliable and often tend to make over-confident predictions for OoDs, assigning to them a higher density than to the in-distribution data. This over-confidence in a single model can be potentially mitigated with Bayesian inference over the model parameters that take into account epistemic uncertainty. This paper investigates three approaches to Bayesian inference: stochastic gradient Markov chain Monte Carlo, Bayes by Backpropagation, and Stochastic Weight Averaging-Gaussian. The inference is implemented over the weights of the deep neural networks that parameterize the likelihood of the Variational Autoencoder. We empirically evaluate the approaches against several benchmarks that are often used for OoD detection: estimation of the marginal likelihood utilizing sampled model ensemble, typicality test, disagreement score, and Watanabe-Akaike Information Criterion. Finally, we introduce two simple scores that demonstrate the state-of-the-art performance.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

Vacant Holes for Unsupervised Detection of the Outliers in Compact Latent Representation

Detection of the outliers is pivotal for any machine learning model deployed and operated in real-world. It is essential for the Deep Neural Networks that were shown to be overconfident with such inputs. Moreover, even deep generative models that allow estimation of the probability density of the input fail in achieving this task. In this work, we concentrate on the specific type of these models: Variational Autoencoders (VAEs). First, we unveil a significant theoretical flaw in the assumption of the classical VAE model. Second, we enforce an accommodating topological property to the image of the deep neural mapping to the latent space: compactness to alleviate the flaw and obtain the means to provably bound the image within the determined limits by squeezing both inliers and outliers together. We enforce compactness using two approaches: (i) Alexandroff extension and (ii) fixed Lipschitz continuity constant on the mapping of the encoder of the VAEs. Finally and most importantly, we discover that the anomalous inputs predominantly tend to land on the vacant latent holes within the compact space, enabling their successful identification. For that reason, we introduce a specifically devised score for hole detection and evaluate the solution against several baseline benchmarks achieving promising results.Cyber Securit