Andrea Bosin

Theoretical evidence for efficient p-type doping of GaN using beryllium

Ab initio calculations predict that Be is a shallow acceptor in GaN. Its thermal ionization energy is 0.06 eV in wurtzite GaN; the level is valence resonant in the zincblende phase. Be incorporation is severely limited by the formation of Be3N2. It is shown, however, that co-incorporation with reactive species can enhance the solubility. H-assisted incorporation should lead to high doping levels in metal-organic-chemical-vapor deposition growth after post-growth annealing at about 850 K. Be-O co-incorporation produces high Be and O concentrations at molecular beam epitaxy growth temperatures.

Anomalous relaxations and chemical trends at III-V semiconductor nitride nonpolar surfaces

Relaxations at nonpolar surfaces of semiconductor III-V compounds result from a competition between dehybridization and charge transfer. First-principles calculations for the (110) and (10

A Database of Force-Field Parameters, Dynamics, and Properties of Antimicrobial Compounds.

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Hydrogen, acceptors, and H-acceptor complexes in GaN

0) faces of zinc-blende and wurtzite AlN, GaN, and InN reveal an anomalous behavior as compared with ordinary III-V semiconductors. Additional calculations for GaAs and ZnO suggest close analogies with the latter. We interpret our results in terms of the larger ionicity (charge asymmetry) and bonding strength (cohesive energy) in the nitrides with respect to other III-V compounds, both essentially due to the strong valence potential and absence of p core states in the lighter anion. The same interpretation applies to Zn II-VI compounds.

Learning bayesian classifiers from gene-expression microarray data

We present an on-line database of all-atom force-field parameters and molecular properties of compounds with antimicrobial activity (mostly antibiotics and some beta-lactamase inhibitors). For each compound, we provide the General Amber Force Field parameters for the major species at physiological pH, together with an analysis of properties of interest as extracted from μs-long molecular dynamics simulations in explicit water solution. The properties include number and population of structural clusters, molecular flexibility, hydrophobic and hydrophilic molecular surfaces, the statistics of intra- and inter-molecular H-bonds, as well as structural and dynamical properties of solvent molecules within first and second solvation shells. In addition, the database contains several key molecular parameters, such as energy of the frontier molecular orbitals, vibrational properties, rotational constants, atomic partial charges and electric dipole moment, computed by Density Functional Theory. The present database (to our knowledge the first extensive one including dynamical properties) is part of a wider project aiming to build-up a database containing structural, physico-chemical and dynamical properties of medicinal compounds using different force-field parameters with increasing level of complexity and reliability. The database is freely accessible at http://www.dsf.unica.it/translocation/db/.

Capturing heuristics and intelligent methods for improving micro-array data classification

The authors present ab-initio calculations on energetics and geometries of atomic hydrogen, of several candidate acceptors, and of H-acceptor complexes in wurtzite GaN. For the H-Mg complex in Mg-doped GaN, they calculate the vibrational frequencies of H. Hydrogen is found to be a negative-U center. H-acceptor complex formation is always exothermic. Substitutional Be has a low formation energy and a shallow impurity level, which makes it a good candidate for p-doping in MBE growth. C{sub N} appears not to be shallow. Atomic hydrogen incorporation in undoped GaN is disfavored in an H{sub 2} atmosphere; it becomes favorable in p and n-type conditions in atomic H environments.

Intelligent Bayesian classifiers in network intrusion detection

Computing methods that allow the efficient and accurate processing of experimentally gathered data play a crucial role in biological research. The aim of this paper is to present a supervised learning strategy which combines concepts stemming from coding theory and Bayesian networks for classifying and predicting pathological conditions based on gene expression data collected from micro-arrays. Specifically, we propose the adoption of the Minimum Description Length (MDL) principle as a useful heuristic for ranking and selecting relevant features. Our approach has been successfully applied to the Acute Leukemia dataset and compared with different methods proposed by other researchers.

Applying enterprise models to design cooperative scientific environments

Classification of micro-array data has been studied extensively but only a small amount of research work has been done on classification of microarray data involving more than two classes. This paper proposes a learning strategy that deals with building a multi-target classifier and takes advantage from well known data mining techniques. To address the intrinsic difficulty of selecting features in order to promote the classification accuracy, the paper considers the use of a set of binary classifiers each of ones is devoted to predict a single class of the multi-classification problem. These classifiers are similar to local experts whose knowledge (about the features that are most correlated to each class value) is taken into account by the learning strategy for selecting an optimal set of features. Results of the experiments performed on a publicly available dataset demonstrate the feasibility of the proposed approach.

Cooperative E-Organizations for Distributed Bioinformatics Experiments

The aim of this paper is to explore the effectiveness of Bayesian classifiers in intrusion detection (ID). Specifically, we provide an experimental study that focuses on comparing the accuracy of different classification models showing that the Bayesian classification approach is reasonably effective and efficient in predicting attacks and in exploiting the knowledge required by a computational intelligent ID process.

A Cost-Sensitive Approach to Feature Selection in Micro-Array Data Classification

Scientific experiments are supported by activities that create, use, communicate and distribute information whose organizational dynamics is similar to processes performed by distributed cooperative enterprise units. On this premise, the aim of this paper is to apply existing enterprise models and processes for designing cooperative scientific experiments. The presented approach assumes the Service Oriented Architecture as the enacting paradigm to formalize experiments as cooperative services on various computational nodes of a network. Specifically, a framework is proposed that defines the responsibility of e-nodes in offering services, and the set of rules under which each service can be accessed by e-nodes through service invocation. By discussing a representative case study, the paper details how specific classes of experiments can be mapped into a service-oriented model whose implementation is carried out in a prototypical scientific environment.