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One of the key enabler to the productivity and efficiency shift foreseen by SESAR will be the business-trajectory concept. The path to a deep understanding of how this new concept impacts on the future SESAR Air Traffic Management scenario goes through a better understanding of the actual air traffic network, and this will be done in the present paper by analyzing traffic data within the framework of complex network analysis. In this paper we will consider flights trajectory data from the Data Demand Repository database. In a first investigation, we perform a network study of the air traffic infrastructure starting from the airports and then refining our analysis at the level of navigation points in order to understand what are the main features that may help explaining why some nodes of the network happen to be found in the same community, i.e. cluster. In a second investigation we perform a study at the level of flight trajectories with the aim of identify statistical regularities in the spatio-temporal deviations of flights between their planned and actual 4D trajectories.

Reconstruction problems are always included in a computer vision system as one of the key step toward the recognition of a scene. There are hierarchies of reconstruction that go from pixels to regions in the lower level, and from patches to objects in higher steps. The success of the reconstruction phase de-pends on several factors: the selection of "suitable" features, the matching function, the linking strategy. I this paper we show some of problems and solutions in two different contexts: the 3D reconstruction from cloud of points representing fragments of archeological finding, and the mosaicing from sea bed video frames. Preliminary results show that in both cases the accuracy is satisfactory

SARS-CoV-2, the causative agent of COVID-19, has been responsible for over 42 million infections and 1 million deaths since its emergence in December 2019. There are few therapeutic options and no approved vaccines. Here, we examine the properties of highly potent human monoclonal antibodies (hu-mAbs) in a Syrian hamster model of SARS-CoV-2 and in a mouse-adapted model of SARS-CoV-2 infection (SARS-CoV-2 MA). Antibody combinations were effective for prevention and in therapy when administered early. However, in vitro antibody neutralization potency did not uniformly correlate with in vivo protection, and some hu-mAbs were more protective in combination in vivo. Analysis of antibody Fc regions revealed that binding to activating Fc receptors contributes to optimal protection against SARS-CoV-2 MA. The data indicate that intact effector function can affect hu-mAb protective activity and that in vivo testing is required to establish optimal hu-mAb combinations for COVID-19 prevention.

The introduction of alkoxy chains in the molecular architecture of meso push-pull porphyrins is of paramount importance aiming at high performing dye-sensitized solar cells (DSSCs) based on these specific sensitizers. Recently, we have demonstrated that the same approach is fruitful even if it is applied to tetraarylporphyrins with an acceptor/anchoring substituent in the [small beta]-pyrrolic position. In particular, among the ortho-ortho, the ortho-para and the ortho-functionalization of the aryl rings with an octyloxy chain, we identified the latter as the most performing in the series, showing a good balance between the dye loading and the reduction of [small pi]-[small pi] aggregation. Herein, focusing our attention on the mono-ortho-functionalized molecular structure, we have investigated the effect of the alkoxy chain length and nature on the reduction of dye-to-dye aggregation as well as on the enhancement of light harvesting capabilities, finding an almost linear relationship between the device photon conversion efficiency (PCE) and the alkoxy chain length both in the presence and in the absence of a co-disaggregating agent.

An automatic system for pre-segmented IIF images analysis was developed.A non-standard pipeline for supervised image classification was adopted.The system uses a two-level pyramid to retain some spatial information.From each cell image 216 features are extracted.15 SVM classifiers one-against-one have been implemented. This study addresses the classification problem of the HEp-2 cells using indirect immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. In this paper we describe a system able to classify pre-segmented immunofluorescence images of HEp-2 cells into six classes. For this study we used the dataset provided for the participation to the contest on performance evaluation on indirect immunofluorescence image analysis systems, hosted by the ICPR 2014. This system is based on multiple types of class-process and uses a two-level pyramid to retain some spatial information. We extract a large number (216) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based on the one-against-one (OAO) scheme. To do this, an ensemble of 15 support vector machines is used to classify each cell image. Leave-one-specimen-out cross validation method was used for the system optimization. The developed system was evaluated on a blind Hep-2 cells dataset performing a mean class accuracy (MCA) equal to 80.12%. Display Omitted

In this paper we present a parallel island model memetic algorithm for binary discrete tomography reconstruction that uses only four projections without any further a priori information. The underlying combination strategy consists in separated populations of agents that evolve by means of different processes. Agents progress towards a possible solution by using genetic operators, switch and a particular compactness operator. A guided migration scheme is applied to select suitable migrants by considering both their own and their sub-population fitness. That is, from time to time, we allow some individuals to transfer to different subpopulations. The benefits of this paradigm were tested in terms of correctness, robustness and time of the reconstruction by considering publicly available datasets of images. To tackle the so-called stability problem, we considered the case of noisy projections along four directions to simulate an instrumental error. Results show that the proposed method decreases the reconstruction error for all classes of images with respect to a serial implementation recently proposed by the authors, and that such reconstruction error is almost invariant with respect to the number of demes. Moreover, the computation time of the proposed parallel memetic algorithm scales in a quasi-linear manner with respect to the demes number, and is invariant with respect to the used number of migrations.

In this paper, are presented the preliminary results of the A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer) project which is developed in the frame of the cross-border cooperation Italy-Tunisia. According to the main objectives of this project, a database of interpreted Indirect ImmunoFluorescence (IIF) images on HEp 2 cells is being collected thanks to the contribution of Italian and Tunisian experts involved in routine diagnosis of autoimmune diseases. Through exchanging images and double reporting; a Gold Standard database, containing around 1000 double reported IIF images with different patterns including negative tests, has been settled. This Gold Standard database has been used for optimization of a computing solution (CADComputer Aided Detection) and for assessment of its added value in order to be used along with an immunologist as a second reader in detection of auto antibodies for autoimmune disease diagnosis. From the preliminary results obtained, the CAD appeared more powerful than junior immunologists used as second readers and may significantly improve their efficacy.

We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing,features extraction and classification. The choice of methods, features and parameters was performed automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based on two steps: the first step follows the one-against-all(OAA) scheme, while the second step follows the one-against-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO. Leave-one-out image cross validation method was used for the evaluation of the results.

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspaces and improve it by guiding the design of new ones. Specifically, we compare the performance of three community detection algorithms, also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace., 22 pages, 14 figures

This paper presents an exploratory analysis of the correlation between different network metrics and safety events. The objective is to develop analysis methods and indicators that relate the network structure with safety events. In particular by using data gathered with an automatic tool developed by EUROCONTROL and a database of traffic data, we measured the correlations between the classical metrics of the airspace network, especially the network of navigation points, and the occurrence of Short Term Conflicts Alerts (STCA). The results obtained are not univocal; correlation between STCA occurrences and classical network metrics are present but the correlation coefficient is around 0.7. This indicates that correlation between STCA occurrences and the airspace characteristics can be established but further analyses with more data are necessary to confirm this result. Moreover it could be useful to look for non-classical metrics that can better describe the relationship we are looking for.