Cathodoluminescence of Recent biogenic carbonates: environmental and ontogenetic fingerprint

Cathodoluminescence (CL) examination of Recent biogenic carbonates shows that they are often luminescent regardless of their mineralogical composition (calcite v. aragonite), habitat (marine v. fresh water), way of life (sessile v. vagile) or environment (hyper- v. hyposaline water). Thus, the presence of luminescence in biogenic particles is not a reliable indicator of diagenetic alteration as some authors have suggested. In addition, CL can reveal variations in the mineralogy of shell material (e.g. regenerated calcitic v. primary aragonitic) and can highlight growth-related structures. Manganese (Mn2+) is the most likely activator of this luminescence, and its content in the shells of benthic organisms seems to be linked to growth rate, ontogeny, open sea conditions, bathymetry and salinity. In neritic environments the Mn2+ content and the CL of molluscs and foraminifera appear to increase with decreasing salinity. This study indicates that CL may be an important tool for the determination of environmental and ontogenetic parameters in biogenic carbonates in addition to its current use indiagenetic studie

Effect of the anisotropy of monocrystalline silicon mechanical properties on the dynamic characteristics of a micromechanical gyroscope

The aim of the research was to determine the effect of temperature on mechanical properties of a micromechanical gyroscope with the sensing element mounted on a silicon wafer, with the crystallographic orientation of (100) (110) (111). The research is of current relevancy since the metrological characteristics that depend on the eigenfrequencies over the full temperature range are to be controlled. The temperature-modal analysis of the micromechanical gyroscope model was performed with ANSYS program. The temperature dependence for eigenfrequencies was obtained. The dependence of the scale factor on temperature for the most temperature-independent variant of sensor positioning on the wafer was determined. The developed mathematical model was used to find the forms of the output oscillations of the gyroscope

Analyzing capacitor-based reconfigurable antennas using graph models

This paper discusses reconfigurable antennas using variable capacitors or varactors to achieve reconfiguration. We propose graph models as tools to understand reconfigurable antenna topologies and their configurations. Guidelines are set to model this type of reconfigurable antennas using graphs and examples are studied to investigate their optimal performance

PIT telemetry as a method to study the habitat requirements of fish populations: application to native and stocked trout movements

Passive integrated transponder (PIT) technology was used to study the behaviour of fishes during the summer season in two headwater streams of northeastern Portugal. A total of 71 PIT tags (12 mm long x 2.1 mm diameter) were surgically implanted in 1+ stocked (39) and native (32) brown trout of two size classes (< 20.0 and ≥ 20.0 cm). Eight independent antennae, connected to a multi-point decoder (MPD reader) unit, were placed in different microhabitats, selected randomly every three days during the observation period (29 August to 9 September in Baceiro stream and 19 September to 4 October in Sabor stream). The results confirmed this method as a suitable labour efficient tool to assess the movement and habitat use of sympatric stocked and native trout populations. About 76.9% of stocked and 59.4% of native PIT tagged trouts were detected. Multivariate techniques (CCA, DFA and classification tree) showed a separation in habitat use between the two sympatric populations. Stocked trout mainly used the microhabitats located in the middle of the channel with higher depths and without cover. Furthermore, these fishes displayed a greater mobility and a diel activity pattern different to native trout populations

Data reduction by randomization subsampling for the study of large hyperspectral datasets

Large amount of information in hyperspectral images (HSI) generally makes their analysis (e.g., principal component analysis, PCA) time consuming and often requires a lot of random access memory (RAM) and high computing power. This is particularly problematic for analysis of large images, containing millions of pixels, which can be created by augmenting series of single images (e.g., in time series analysis). This tutorial explores how data reduction can be used to analyze time series hyperspectral images much faster without losing crucial analytical information. Two of the most common data reduction methods have been chosen from the recent research. The first one uses a simple randomization method called randomized sub-sampling PCA (RSPCA). The second implies a more robust randomization method based on local-rank approximations (rPCA). This manuscript exposes the major benefits and drawbacks of both methods with the spirit of being as didactical as possible for a reader. A comprehensive comparison is made considering the amount of information retained by the PCA models at different compression degrees and the performance time. Extrapolation is also made to the case where the effect of time and any other factor are to be studied simultaneously.J.P Cruz-Tirado acknowledges scholarship funding from FAPESP, grant number 2020/09198–1

Impact of volatile phenols and their precursors on wine quality and control measures of Brettanomyces/Dekkera yeasts

Volatile phenols are aromatic compounds and one of the key molecules responsible for olfactory defects in wine. The yeast genus Brettanomyces is the only major microorganism that has the ability to covert hydroxycinnamic acids into important levels of these compounds, especially 4-ethylphenol and 4-ethylguaiacol, in red wine. When 4-ethylphenols reach concentrations greater than the sensory threshold, all wine’s organoleptic characteristics might be influenced or damaged. The aim of this literature review is to provide a better understanding of the physicochemical, biochemical, and metabolic factors that are related to the levels of p-coumaric acid and volatile phenols in wine. Then, this work summarizes the different methods used for controlling the presence of Brettanomyces in wine and the production of ethylphenols

Deep computer vision system for cocoa classification

Cocoa hybridisation generates new varieties which are resistant to several plant diseases, but has individual chemical characteristics that affect chocolate production. Image analysis is a useful method for visual discrimination of cocoa beans, while deep learning (DL) has emerged as the de facto technique for image processing. However, these algorithms require a large amount of data and careful tuning of hyperparameters. Since it is necessary to acquire a large number of images to encompass the wide range of agricultural products, in this paper, we compare a Deep Computer Vision System (DCVS) and a traditional Computer Vision System (CVS) to classify cocoa beans into different varieties. For DCVS, we used a Resnet18 and Resnet50 as backbone, while for CVS, we experimented traditional machine learning algorithms, Support Vector Machine (SVM), and Random Forest (RF). All the algorithms were selected since they provide good classification performance and their potential application for food classification A dataset with 1,239 samples was used to evaluate both systems. The best accuracy was 96.82% for DCVS (ResNet 18), compared to 85.71% obtained by the CVS using SVM. The essential handcrafted features were reported and discussed regarding their influence on cocoa bean classification. Class Activation Maps was applied to DCVS’s predictions, providing a meaningful visualisation of the most important regions of the images in the model

Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy

Near Infrared (NIR) spectroscopy is an analytical technology widely used for the non-destructive characterisation of organic samples, considering both qualitative and quantitative attributes. In the present study, the combination of Multi-target (MT) prediction approaches and Machine Learning algorithms has been evaluated as an effective strategy to improve prediction performances of NIR data from wheat flour samples. Three different Multi-target approaches have been tested: Multi-target Regressor Stacking (MTRS), Ensemble of Regressor Chains (ERC) and Deep Structure for Tracking Asynchronous Regressor Stack (DSTARS). Each one of these techniques has been tested with different regression methods: Support Vector Machine (SVM), Random Forest (RF) and Linear Regression (LR), on a dataset composed of NIR spectra of bread wheat flours for the prediction of quality-related parameters. By combining all MT techniques and predictors, we obtained an improvement up to 7% in predictive performance, compared with the corresponding Single-target (ST) approaches. The results support the potential advantage of MT techniques over ST techniques for analysing NIR spectra