On building physics-based AI models for the design and SHM of mooring systems

Expert systems in industrial processes are modelled using physics-based approaches, data-driven models or hybrid approaches in which however the underlying physical models generally constitute a separate block with respect to the Artificial Intelligence (AI) technique(s). This work applies the novel concept of “imbrication”-a physics-based AI approach-to the mooring system of offshore renewable energy devices to achieve a complete integration of both perspectives. This approach can reduce the size of the training dataset and computational time while delivering algorithms with higher generalization capability and explicability. We first undertake the design of the mooring system by developing a surrogate model coupled with a Bayesian optimiser. Then, we analyse the structural health monitoring of the mooring system by designing a supervised Deep Neural Network architecture. Herein, we describe the characteristics of the imbrication process, analyse preliminary results of our investigation and provide considerations for orienting further research work and sector applicability

A Study of the Near-Ultraviolet Spectrum of Vega

UV, optical, and near-IR spectra of Vega have been combined to test our understanding of stellar atmospheric opacities, and to examine the possibility of constraining chemical abundances from low-resolution UV fluxes. We have carried out a detailed analysis assuming Local Thermodynamic Equilibrium (LTE) to identify the most important contributors to the UV continuous opacity: H, H$^{-}$, C I, and Si II. Our analysis also assumes that Vega is spherically symmetric and its atmosphere is well described with the plane parallel approximation. Comparing observations and computed fluxes we have been able to discriminate between two different flux scales that have been proposed, the IUE-INES and the HST scales, favoring the latter. The effective temperature and angular diameter derived from the analysis of observed optical and near-UV spectra are in very good agreement with previous determinations based on different techniques. The silicon abundance is poorly constrained by the UV observations of the continuum and strong lines, but the situation is more favorable for carbon and the abundances inferred from the UV continuum and optical absorption lines are in good agreement. Some spectral intervals in the UV spectrum of Vega that the calculations do not reproduce well are likely affected by deviations from LTE, but we conclude that our understanding of UV atmospheric opacities is fairly complete for early A-type stars.Comment: 13 pages, 9 figures, to be published in Ap

Effectiveness of steroids versus placebo in preventing upper airway obstruction after extubation in critically ill children: rationale and design of a multicentric, double-blind, randomized study

BACKGROUND: Post-extubation upper airway obstruction (UAO) is a frequent complication causing stridor and respiratory distress, which occasionally require reintubation, thereby increasing morbidity and mortality rates. Contradictory results have been obtained in studies assessing the effectiveness of steroids in preventing post-extubation UAO, and the available evidence is limited. We designed a multicentric randomized, placebo-controlled study to explore the effectiveness of dexamethasone in preventing post-extubation UAO in children. METHODS: A multicentric, prospective, double-blind, randomized, placebo-controlled, phase IV clinical trial has been designed. The sample will include pediatric patients who are between 1 month and 16 years of age and who have been intubated for more than 48 h. Patients who have airway disorders or who have received steroids within the previous seven days will be excluded. Patients will be randomly assigned to receive either placebo or a therapy with dexamethasone 0.25 mg/kg every 6 h to be started 6 to 12 h prior to extubation (to a total of four doses). Randomization will be performed at a 1:1 ratio. Follow-up of patients will be carried out for 48 h after extubation. The main objective of this study is to access the reduction in the incidence of moderate to severe UAO symptoms following extubation. Secondary objectives include assessing the decrease in the incidence of reintubation, evaluating the use of additional therapies for UAO, and monitoring potential side effects of dexamethasone. DISCUSSION: The results of this study will contribute to the existing evidence on prophylaxis for post-extubation airway obstruction. TRIAL REGISTRATION: EudraCT identifier: 2009-016596-30. Registered on May 11, 2010

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability

Suppressing dimension-5 operators in general SU(5) models

We discuss dimension-5 operators in supersymmetric models containing extra hypercharge 1/3 color-triplets. We derive a general formula relating dimension-5 operator to the color-triplet mass matrix. We show that certain zeros in the triplet mass-matrix together with some triplet coupling selection rules can lead to elimination of dimension-5 operators. In particular we focus on SU(5) models and show that (a) Dimension-5 operators can be eliminated in the standard SU(5) model by the introduction of an extra pair of 5+5b Higgses with specific couplings (b) Flipped SU(5) models with extra 10+10b Higgses are free of dimension-5 operators (c) Flipped SU(5) models with extra 5+5b and/or extra 10+10b Higgses can be made free of dimension-5 operators for a textured form of the triplet mass-matrix accompanied by constraints on the 5-plet couplings to matter. Our analysis is motivated by the recently put forward M-theory phenomenological framework that requires a strong string coupling and reintroduces the problem of eliminating dimension-5 operators.Comment: 10 pages, Latex2e, minor changes, references adde

Polycyclic aromatic hydrocarbons in the dwarf galaxy IC 10

Infrared observations from the Spitzer Space Telescope archive are used to study the dust component of the interstellar medium in the IC~10 irregular galaxy. Dust distribution in the galaxy is compared to the distributions of H$\alpha$ and [SII] emission, neutral hydrogen and CO clouds, and ionizing radiation sources. The distribution of polycyclic aromatic hydrocarbons (PAH) in the galaxy is shown to be highly non-uniform with the mass fraction of these particles in the total dust mass reaching 4%. PAHs tend to avoid bright HII regions and correlate well with atomic and molecular gas. This pattern suggests that PAHs form in the dense interstellar gas. We propose that the significant decrease of the PAH abundance at low metallicity is observed not only globally (at the level of entire galaxies), but also locally (at least, at the level of individual HII regions). We compare the distribution of the PAH mass fraction to the distribution of high-velocity features, that we have detected earlier in wings of H$\alpha$ and SII lines, over the entire available galaxy area. No conclusive evidence for shock destruction of PAHs in the IC~10 galaxy could be found.Comment: Accepted for publication in Astronomy Report

On the dissipative non-minimal braneworld inflation

We study the effects of the non-minimal coupling on the dissipative dynamics of the warm inflation in a braneworld setup, where the inflaton field is non-minimally coupled to induced gravity on the warped DGP brane. We study with details the effects of the non-minimal coupling and dissipation on the inflationary dynamics on the normal DGP branch of this scenario in the high-dissipation and high-energy regime. We show that incorporation of the non-minimal coupling in this setup decreases the number of e-folds relative to the minimal case. We also compare our model parameters with recent observational data.Comment: 32 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1001.044

Naturalness and Fine Tuning in the NMSSM: Implications of Early LHC Results

We study the fine tuning in the parameter space of the semi-constrained NMSSM, where most soft Susy breaking parameters are universal at the GUT scale. We discuss the dependence of the fine tuning on the soft Susy breaking parameters M_1/2 and m0, and on the Higgs masses in NMSSM specific scenarios involving large singlet-doublet Higgs mixing or dominant Higgs-to-Higgs decays. Whereas these latter scenarios allow a priori for considerably less fine tuning than the constrained MSSM, the early LHC results rule out a large part of the parameter space of the semi-constrained NMSSM corresponding to low values of the fine tuning.Comment: 19 pages, 10 figures, bounds from Susy searches with ~1/fb include