Mass preserving distributed langrage multiplier approach to immersed boundary method

This research is devoted to mass conservation and CFL properties of the Finite Elements Immersed Boundary Method. We first explore an enhanced higher order scheme applied to the Finite Element Immersed Boundary Method technique introduced by Boffi and Gastaldi. This technique is based on a Pointwise (PW) formulation of the kinematic condition, and higher order elements show better conservation properties than the original scheme. A further improvement with respect to the classical PW formulation is achieved introducing a fully variational Distributed Lagrange Multiplier (DLM) formulation. Numerical experiments show that DLM is not affected by any CFL condition. Furthermore the mass conservation properties of this method are extremely competitive

Immersed boundary method: performance analysis of popular finite element spaces

The aim of this paper is to understand the performances of different finite elements in the space discretization of the Finite Element Immersed Boundary Method. In this exploration we will analyze two popular solution spaces: Hood-Taylor and Bercovier- Pironneau (P1-iso-P2). Immersed boundary solution is characterized by pressure discontinuities at fluid structure interface. Due to such a discontinuity a natural enrichment choice is to add piecewise constant functions to the pressure space. Results show that P1 + P0 pressure spaces are a significant cure for the well known “boundary leakage” affecting IBM. Convergence analysis is performed, showing how the discontinuity in the pressure is affecting the convergence rate for our finite element approximation

Fused Adjacency Matrices to enhance information extraction: the beer benchmark

Multivariate exploratory data analysis allows revealing patterns and extracting information from complex multivariate data sets. However, highly complex data may not show evident groupings or trends in the principal component space, e.g. because the variation of the variables are not grouped but rather continuous. In these cases, classical exploratory methods may not provide satisfactory results when the aim is to find distinct groupings in the data. To enhance information extraction in such situations, we propose a novel approach inspired by the concept of combining weak classifiers, but in the unsupervised context. The approach is based on the fusion of several adjacency matrices obtained by different distance measures on data from different analytical platforms. This paper is intended to present and discuss the potential of the approach through a benchmark data set of beer samples. The beer data were acquired using three spectroscopic techniques: Visible, near-Infrared and Nuclear Magnetic Resonance. The results of fusing the three data sets via the proposed approach are compared with those from the single data blocks (Visible, NIR and NMR) and from a standard mid-level data fusion methodology. It is shown that, with the suggested approach, groupings related to beer style and other features are efficiently recovered, and generally more evident

A Metabolomic Approach to Beer Characterization

The consumers’ interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of “beeromics” has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. 1H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved

The effects of chronic lifelong activation of the AHR pathway by industrial chemical pollutants on female human reproduction

Environmental chemicals, such as heavy metals, affect female reproductive function. A biological sensor of the signals of many toxic chemical compounds seems to be the aryl hydrocarbon receptor (AHR). Previous studies demonstrated the environmental of heavy metals in Taranto city (Italy), an area that has been influenced by anthropogenic factors such as industrial activities and waste treatments since 1986. However, the impact of these elements on female fertility in this geographic area has never been analyzed. Thus, in the present study, we evaluated the AHR pathway, sex steroid receptor pattern and apoptotic process in granulosa cells (GCs) retrieved from 30 women, born and living in Taranto, and 30 women who are living in non-contaminated areas (control group), who were undergoing in vitro fertilization (IVF) protocol. In follicular fluids (FFs) of both groups the toxic and essential heavy metals, such as chromiun (Cr), Manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), cadmium (Cd) and lead (Pb), were also analyzed. Higher levels of Cr, Fe, Zn and Pb were found in the FFs of the women from Taranto as compared to the control group, as were the levels of AHR and AHR-dependent cytochrome P450 1A1 and 1B1; while CYP19A1 expression was decreased. The anti-apoptotic process found in the GCs of women fromTaranto was associated with the highest levels of progesterone receptor membrane component 1(PGRMC1), a novel progesterone receptor, the expression of which is subjected to AHR activated by its highest affinity ligands (e.g., dioxins) or indirectly by other environmental pollutants, such as heavy metals. In conclusion, decreased production of estradiol and decreased number of retrieved mature oocytes found in women from Taranto could be due to chronic exposure to heavy metals, in particular to Cr and Pb

Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis

The sale of frozen–thawed fish and fish products, labeled as fresh, is currently one of the most common and insidious commercial food frauds. For this reason, the demand of reliable tools to identify the storage conditions is increasing. The present study was performed on two species, commonly sold in large-scale distribution: Cuttlefish (Sepia officinalis) and musky octopus (Eledone spp.). Fifty fresh cephalopod specimens were analyzed at refrigeration temperature (2 ± 2°C), then frozen at −20°C for 10 days and finally thawed and analyzed again. The performance of three near-infrared (NIR) instruments in identifying storage conditions were compared: The benchtop NIR Multi Purpose Analyzer (MPA) by Bruker, the portable MicroNIR by VIAVI and the handheld NIR SCiO by Consumer Physics. All collected spectra were processed and analyzed with chemometric methods. The SCiO data were also analyzed using the analytical tools available in the online application provided by the manufacturer to evaluate its performance. NIR spectroscopy, coupled with chemometrics, allowed discriminating between fresh and thawed samples with high accuracy: Cuttlefish between 82.3–94.1%, musky octopus between 91.2–97.1%, global model between 86.8–95.6%. Results show how food frauds could be detected directly in the marketplace, through small, ultra-fast and simplified handheld devices, whereas official control laboratories could use benchtop analytical instruments, coupled with chemometric approaches, to develop accurate and validated methods, suitable for regulatory purposes

Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a “grey area” in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans