Fungal and fungal-like plant pathogens of the Maltese Islands

The paper provides updated lists of plant pathogenic species belonging to the kingdoms Protozoa, Chromista and Fungi (one, 21, and 211 species entries, respectively) recorded in Malta. It is intended primarily for the use of plant pathologists and authorities involved in plant protection and quarantine issues. It is based on published papers and unpublished reports of several authors and on our original data. The latter were based on inspections in the field and at the Maltese fruit and vegetable market, on surveys requested by EC and on samples brought by farmers at the Għammieri, Marsa, laboratories of the Ministry for Rural Affairs and the Environment (MRAE). They include records or more than 30 species new for Malta and several new host and new location records. Major diseases observed during 2004-2006 include Verticillium wilt of olive, late blight of potato and tomato, powdery mildew on several hosts, crown and root rot (Forl) of tomato, Sclerotinia stem rot of vegetables, grey mould of several crops, leaf mould of tomato. Most of the pathogenic species reported at the beginning of the last century are still present. Several species, including Spongospora subterranea f. sp. subterranea, Colletotrichum acutatum, Fusarium oxysporum f. sp. radicis-lycopersici, probably have been introduced recently. Intensified plant trade, due to world trends and the accession of Malta into the EU, increases this risk and requires consolidating the national quarantine service and extending monitoring of the territory. The incidence and severity of some diseases could be traced back to inappropriate cultural practices or unsuitable seed or plant material. MRAE and private organisations have a key role to play in improving this situationpeer-reviewe

Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications

Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead

Energy Converter with Inside Two, Three, and Five Connected H2/Air Swirling Combustor Chambers: Solar and Combustion Mode Investigations

This work reports the performance of an energy converter characterized by an emitting parallelepiped element with inside two, three, or five swirling connected combustion chambers. In particular, the idea is to adopt the heat released by H2/air combustion, occurring in the connected swirling chambers, to heat up the emitting surfaces of the thermally-conductive emitting parallelepiped brick. The final goal consists in obtaining the highest emitting surface temperature and the highest power delivered to the ambient environment, with the simultaneous fulfillment of four design constraints: dimension of the emitting surface fixed to 30 30 mm2, solar mode thermal efficiency greater than 20%, emitting surface peak temperature T > 1000 K, and its relative DT 99.9%, and high peak temperature, but the emitting surface DT is strongly sensitive to the geometrical configuration. The present work is related to the “EU-FP7-HRC-Power” project, aiming at developing micro-meso hybrid sources of power, compatible with a thermal/electrical conversion by thermo-photovoltaic cells

Thermodynamic modeling of the NbeB system

In the present work, the Nb–B binary system was thermodynamically optimized. The stable phases in this system are BCC (niobium), Nb3B2, NbB, Nb3B4, Nb5B6, NbB2, B (boron) and liquid L. The borides Nb3B2, NbB, Nb3B4 and Nb5B6 and the B (boron) were modeled as stoichiometric phases and the liquid L, BCC (niobium) and NbB2 as solutions, using the sublattices model, with their excess terms described by the Redlich–Kister polynomials. The Gibbs energy coefficients were optimized based on the experimental values of enthalpy of formation, low temperature specific heat, liquidus temperatures and temperatures of invariant transformations. The calculated Nb–B diagram reproduces well the experimental values from the literature

Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers

Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effective classification are occlusions, variable lighting conditions, and image quality. In this work we present a solution based on a novel combination of deep neural networks and Bayesian classifiers. The neural network works on a bottom-up approach, analyzing emotions expressed by isolated faces. The Bayesian classifier estimates a global emotion integrating top-down features obtained through a scene descriptor. In order to validate the system we tested the framework on the dataset released for the Emotion Recognition in the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test set, significantly outperforming the 53.62% competition baseline.Comment: accepted by the Fifth Emotion Recognition in the Wild (EmotiW) Challenge 201

High-velocity stars from the interaction of a globular cluster and a massive black hole binary

High-velocity stars are usually thought to be the dynamical product of the interaction of binary systems with supermassive black holes. In this paper, we investigate a particular mechanism of production of high-velocity stars as due to the close interaction between a massive and orbitally decayed globular cluster and a supermassive black hole binary. The high velocity acquired by some stars of the cluster comes from combined effect of extraction of their gravitational binding energy and from the slingshot due to the interaction with the black hole binary. After the close interaction, stars could reach a velocity sufficient to travel in the halo and even overcome the galactic potential well, while some of them are just stripped from the globular cluster and start orbiting around the galactic centre

Ab initio Bogoliubov coupled cluster theory for open-shell nuclei

Ab initio many-body methods address closed-shell nuclei up to mass A ~ 130 on the basis of realistic two- and three-nucleon interactions. Several routes to address open-shell nuclei are currently under investigation, including ideas which exploit spontaneous symmetry breaking. Singly open-shell nuclei can be efficiently described via the sole breaking of $U(1)$ gauge symmetry associated with particle number conservation, to account for their superfluid character. The present work formulates and applies Bogoliubov coupled cluster (BCC) theory, which consists of representing the exact ground-state wavefunction of the system as the exponential of a quasiparticle excitation cluster operator acting on a Bogoliubov reference state. Equations for the ground-state energy and cluster amplitudes are derived at the singles and doubles level (BCCSD) both algebraically and diagrammatically. The formalism includes three-nucleon forces at the normal-ordered two-body level. The first BCC code is implemented in $m$-scheme, which will eventually permit the treatment of doubly open-shell nuclei. Proof-of-principle calculations in an $N_{\text{max}}=6$ spherical harmonic oscillator basis are performed for $^{16,18,20}$O, $^{18}$Ne, $^{20}$Mg in the BCCD approximation with a chiral two-nucleon interaction, comparing to results obtained in standard coupled cluster theory when applicable. The breaking of $U(1)$ symmetry is monitored by computing the variance associated with the particle-number operator. The newly developed many-body formalism increases the potential span of ab initio calculations based on single-reference coupled cluster techniques tremendously, i.e. potentially to reach several hundred additional mid-mass nuclei. The new formalism offers a wealth of potential applications and further extensions dedicated to the description of ground and excited states of open-shell nuclei.Comment: 22 pages, 13 figure

Sensitivities and correlations of nuclear structure observables emerging from chiral interactions

Starting from a set of different two- and three-nucleon interactions from chiral effective field theory, we use the importance-truncated no-core shell model for ab initio calculations of excitation energies as well as electric quadrupole (E2) and magnetic dipole (M1) moments and transition strengths for selected p-shell nuclei. We explore the sensitivity of the excitation energies to the chiral interactions as a first step towards and systematic uncertainty propagation from chiral inputs to nuclear structure observables. The uncertainty band spanned by the different chiral interactions is typically in agreement with experimental excitation energies, but we also identify observables with notable discrepancies beyond the theoretical uncertainty that reveal insufficiencies in the chiral interactions. For electromagnetic observables we identify correlations among pairs of E2 or M1 observables based on the ab initio calculations for the different interactions. We find extremely robust correlations for E2 observables and illustrate how these correlations can be used to predict one observable based on an experimental datum for the second observable. In this way we circumvent convergence issues and arrive at far more accurate results than any direct ab initio calculation. A prime example for this approach is the quadrupole moment of the first 2^+ state in C-12, which is predicted with an drastically improved accuracy.Comment: 11 pages, 8 figure