Nonequilibrium dynamics of the three-dimensional Edwards-Anderson spin-glass model with Gaussian couplings: Strong heterogeneities and the backbone picture

We numerically study the three-dimensional Edwards-Anderson model with Gaussian couplings, focusing on the heterogeneities arising in its nonequilibrium dynamics. Results are analyzed in terms of the backbone picture, which links strong dynamical heterogeneities to spatial heterogeneities emerging from the correlation of local rigidity of the bond network. Different two-times quantities as the flipping time distribution and the correlation and response functions, are evaluated over the full system and over high- and low-rigidity regions. We find that the nonequilibrium dynamics of the model is highly correlated to spatial heterogeneities. Also, we observe a similar physical behavior to that previously found in the Edwards-Anderson model with a bimodal (discrete) bond distribution. Namely, the backbone behaves as the main structure that supports the spin-glass phase, within which a sort of domain-growth process develops, while the complement remains in a paramagnetic phase, even below the critical temperature

Reducing the Learning Domain by Using Image Processing to Diagnose COVID-19 from X-Ray Image

Over the last months, dozens of artificial intelligence (AI) solutions for COVID-19 diagnosis based on chest X-ray image analysis have been proposed. All of them with very impressive sensitivity and specificity results. However, its generalization and translation to the clinical practice are rather challenging due to the discrepancies between domain distributions when training and test data come from different sources. Consequently, applying a trained model on a new data set may have a problem with domain adaptation leading to performance degradation. This research aims to study the impact of image pre-processing on pre-trained deep learning models to reduce the learning domain. The dataset used in this research consists of 5,000 X-ray images obtained from different sources under two categories: negative and positive COVID-19 detection. We implemented transfer learning in 3 popular convolutional neural networks (CNNs), including VGG16, VGG19, and DenseNet169. We repeated the study following the same structure for original and pre-processed images. The pre-processing method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) filter application and image registration. After evaluating the models, the CNNs that have been trained with pre-processed images obtained an accuracy score up to 1.2% better than the unprocessed ones. Furthermore, we can observe that in the 3 CNN models, the repeated misclassified images represent 40.9% (207/506) of the original image dataset with the erroneous result. In pre-processed ones, this percentage is 48.9% (249/509). In conclusion, image processing techniques can help to reduce the learning domain for deep learning applications

A proposal for compiling quantitative hydrogeological maps

An innovative approach to hydrogeological mapping based on quantitative analysis is shown in this paper. It gives some cartographical solutions for an immediate evaluation of the groundwater resources and their spatial distribution. All relevant aquifers, springs and their regime, geological and structural setting and their hydraulic role should be shown in several understandable and clear hydrogeological maps where all hydrogeological information is reported in detail in the “Hydrogeological experimental Map” composed by a. “Hydrogeological Complexes and Natural Springs Map”, b. “Surface Hydrology Map”, c. “Conceptual Hydrogeological Model” and d. “Hydrogeological sections”. The cartographical solutions adopted for representing all these documents are proposed in this paper. Some graphical solutions have been proposed for improving the Italian official guidelines of hydrogeological mapping at scale 1:50.000, explain the legends symbols and illustrate the structure of a hydrogeological GIS database. An application of this approach has been carried out in north-western sector of Sibillini Mts. (Marche, Italy)

Chromosome numbers for the Italian flora: 2.

In this contribution new chromosome numbers for Italian endemic taxa are presented. It includes 13 chromosome counts for Ornithogalum (Asparagaceae), Anthemis, Carduus, Centaurea, Cirsium, Hieracium, Taraxacum (Asteraceae), Asyneuma (Campanulaceae), Knautia (Caprifoliaceae), Gypsophila (Caryophyllaceae), Linum (Linaceae), Helleborus (Ranunculaceae)

Chromosome numbers for the Italian flora: 2.

In this contribution new chromosome numbers for Italian endemic taxa are presented. It includes 13 chromosome counts for Ornithogalum (Asparagaceae), Anthemis, Carduus, Centaurea, Cirsium, Hieracium, Taraxacum (Asteraceae), Asyneuma (Campanulaceae), Knautia (Caprifoliaceae), Gypsophila (Caryophyllaceae), Linum (Linaceae), Helleborus (Ranunculaceae)

Chromosome numbers for the Italian flora: 1.

In this contribution new chromosome data obtained on material collected in Italy are presented. It includes 15 chromosome counts for Carduus, Crepis, Picris, Taraxacum (Asteraceae), Ceratonia, Lathyrus (Fabaceae), Colchicum (Colchicaceae), Fritillaria (Liliaceae), Petrorhagia (Caryophyllaceae), Potentilla (Rosaceae), Quercus (Fagaceae), Reseda (Resedaceae), and Thymus (Lamiaceae)