The Kore University of Enna, in Italian Università Kore di Enna, is a university founded in 1995 in Enna, the capital city of the Province of Enna, in the center of Sicily and has been visited by two Italian Presidents . The university has very modern facilities; it also has a strong relationships with governments and universities of the Mediterranean Sea countries and with some of European and U.S. areas. The President of the University and the President of the University Foundation is Cataldo Salerno, an Sicilian professor who is also the President of the Province of Enna. The university has laboratories, auditoriums and a conference and concert hall where important musicians play; other Kore facilities include a sports centre and a residential building for students. Kore is planned to be a large campus in the American style with two Student Houses and many facilities for students. The international relationship of Kore University involves many countries of the Mediterranean Area and also American, European and Asian universities. The Kore University of Enna is the newest Sicilian university: in fact, it's the first university founded in Sicily after the unification of Italy, and it was founded 200 years after the foundation of the last Sicilian university, the University of Palermo. Wikipedia.
Milazzo A.,University of Palermo |
Oliveri V.,Kore University of Enna
AIAA Journal | Year: 2017
A Ritz approach for the analysis of buckling and postbuckling of stiffened composite panels with through-thethickness cracks and/or delaminations is presented. The structure is modeled as the assembly of plate elements of which the behavior is described by the first-order shear deformation theory and von Kármán's geometric nonlinearities. Penalty techniques ensure continuity along the edges of contiguous plate elements and the enforcement of the restraints on the external boundaries. They are also used to avoid interpenetration problems. General symmetric and unsymmetric stacking sequences are considered. A computer code has been developed and used to validate the proposed method, comparing the results with literature and finite-elements solutions. Results are also presented for postbuckling of multilayered stiffened plates with through-the-thickness cracks and delaminations. The analyses carried out show the efficiency and potential of the method, which provides accuracy comparable to those of other techniques in conjunction with a reduction in the number of degrees of freedom and simplification in data preparation. © 2016 by the American Institute of Aeronautics and Astronautics, Inc.
Marvuglia A.,CRP Henri Tudor |
Messineo A.,Kore University of Enna
Applied Energy | Year: 2012
The estimation of a wind farm's power curve, which links the wind speed to the power that is produced by the whole wind farm, is a challenging task because this relationship is nonlinear and bounded, in addition to being non-stationary due for example to changes in the site environment and seasonality. Even for a single wind turbine the measured power at different wind speeds is generally different than the rated power, since the operating conditions on site are generally different than the conditions under which the turbine was calibrated (the wind speed on site is not uniform horizontally across the face of the turbine; the vertical wind profile and the air density are different than during the calibration; the wind data available on site are not always measured at the height of the turbine's hub).The paper presents a data-driven approach for building an equivalent steady state model of a wind farm under normal operating conditions and shows its utilization for the creation of quality control charts at the aim of detecting anomalous functioning conditions of the wind farm. We use and compare three different machine learning models - viz. a self-supervised neural network called GMR (Generalized Mapping Regressor), a feed-forward Multi Layer Perceptron (MLP) and a General Regression Neural Network (GRNN) - to estimate the relationship between the wind speed and the generated power in a wind farm. GMR is a novel incremental self-supervised neural network which can approximate every multidimensional function or relation presenting any kind of discontinuity; MLPs are the most widely used state-of-the-art neural network models and GRNNs belong to the family of kernel neural networks. The methodology allows the creation of a non-parametric model of the power curve that can be used as a reference profile for on-line monitoring of the power generation process, as well as for power forecasts. The results obtained show that the non-parametric approach provides fair performances, provided that a suitable pre-processing of the input data is accomplished. © 2012 Elsevier Ltd.
Freni G.,Kore University of Enna |
Mannina G.,University of Palermo
Journal of Hydrology | Year: 2010
Mathematical models are of common use in urban drainage, and they are increasingly being applied to support decisions about design and alternative management strategies. In this context, uncertainty analysis is of undoubted necessity in urban drainage modelling. However, despite the crucial role played by uncertainty quantification, several methodological aspects need to be clarified and deserve further investigation, especially in water quality modelling. One of them is related to the " a priori" hypotheses involved in the uncertainty analysis. Such hypotheses are usually condensed in " a priori" distributions assessing the most likely values for model parameters. This paper explores Bayesian uncertainty estimation methods investigating the influence of the choice of these prior distributions. The research aims at gaining insights in the selection of the prior distribution and the effect the user-defined choice has on the reliability of the uncertainty analysis results. To accomplish this, an urban stormwater quality model developed in previous studies has been employed. The model has been applied to the Fossolo catchment (Italy), for which both quantity and quality data were available. The results show that a uniform distribution should be applied whenever no information is available for specific parameters describing the case study. The use of weak information (mostly coming from literature or other model applications) should be avoided because it can lead to wrong estimations of uncertainty in modelling results. Model parameter related hypotheses would be better dropped in these cases. © 2010 Elsevier B.V.
La Mantia F.P.,University of Palermo |
Morreale M.,Kore University of Enna
Composites Part A: Applied Science and Manufacturing | Year: 2011
The rising concern towards environmental issues and, on the other hand, the need for more versatile polymer-based materials has led to increasing interest about polymer composites filled with natural-organic fillers, i.e. fillers coming from renewable sources and biodegradable. The composites, usually referred to as "green", can find several industrial applications. On the other hand, some problems exist, such as worse processability and reduction of the ductility. The use of adhesion promoters, additives or chemical modification of the filler can help in overcoming many of these limitations. These composites can be further environment-friendly when the polymer matrix is biodegradable and comes from renewable sources as well. This short review briefly illustrates the main paths and results of research (both academic and industrial) on this topical subject, providing a quick overview (with no pretence of exhaustiveness over such a vast topic), as well as appropriate references for further in-depth studies. © 2011 Elsevier Ltd. All rights reserved.
Schimmenti A.,Kore University of Enna |
Bifulco A.,Middlesex University
Child and Adolescent Mental Health | Year: 2015
Background: Emotional neglect can be characterized as cold or critical parenting and denotes a parent intentionally or unintentionally overlooking the signs that a child needs comfort or attention and ignoring its emotional needs. Parental emotional neglect is widely posited as an antecedent of anxiety disorder, with attachment researchers arguing for anxious-ambivalent attachment style as a mediating factor. Method: Childhood experience of neglect and abuse, including antipathy (cold, critical parenting), attachment styles, and anxiety disorders were assessed in a high-risk sample of 160 adolescents and young adults by means of interview measures. Results: Antipathy was associated with 12-month prevalence of anxiety disorders in the sample. Anxious-ambivalent attachment scores statistically mediated the relationship between antipathy and anxiety disorders. Conclusions: Clinicians treating anxiety disorders in youths need to consider that emotional neglect in childhood in the form of antipathy could lead to anxious-ambivalent internal working models operating around fear of rejection and fear of separation. © 2013 Association for Child and Adolescent Mental Health.
Lee C.-H.,Georgia Institute of Technology |
Siniscalchi S.M.,Kore University of Enna
Proceedings of the IEEE | Year: 2013
The field of automatic speech recognition (ASR) has enjoyed more than 30 years of technology advances due to the extensive utilization of the hidden Markov model (HMM) framework and a concentrated effort by the speech community to make available a vast amount of speech and language resources, known today as the Big Data Paradigm. State-of-the-art ASR systems achieve a high recognition accuracy for well-formed utterances of a variety of languages by decoding speech into the most likely sequence of words among all possible sentences represented by a finite-state network (FSN) approximation of all the knowledge sources required by the ASR task. However, the ASR problem is still far from being solved because not all information available in the speech knowledge hierarchy can be directly integrated into the FSN to improve the ASR performance and enhance system robustness. It is believed that some of the current issues of integrating various knowledge sources in top-down integrated search can be partially addressed by processing techniques that take advantage of the full set of acoustic and language information in speech. It has long been postulated that human speech recognition (HSR) determines the linguistic identity of a sound based on detected evidence that exists at various levels of the speech knowledge hierarchy, ranging from acoustic phonetics to syntax and semantics. This calls for a bottom-up attribute detection and knowledge integration framework that links speech processing with information extraction, by spotting speech cues with a bank of attribute detectors, weighting and combining acoustic evidence to form cognitive hypotheses, and verifying these theories until a consistent recognition decision can be reached. The recently proposed automatic speech attribute transcription (ASAT) framework is an attempt to mimic some HSR capabilities with asynchronous speech event detection followed by bottom-up knowledge integration and verification. In the last few years, ASAT has demonstrated good potential and has been applied to a variety of existing applications in speech processing and information extraction. © 1963-2012 IEEE.
Craparo G.,Kore University of Enna
Clinical Neuropsychiatry | Year: 2014
The term addiction applies to a morbid form characterized by substance abuse, an object or a behavior; it defines a dysfunctional mental state characterized by a feeling of irrepressible desire and an uncontrollable need to repeat this behavior in a compulsive manner; it is an invasive condition marked by the phenomena of craving in a framework of uncontrollable habit that causes clinically significant distress. The author proposes a new interpretation of an sexual addiction as a dissociative mechanism to regulate non modulated emotions which were not mentalized (traumatic emotions) in early relationships with primary caregivers. To start from this theoretical model, this article suggests a treatment of sexual addiction focused on the identifying and regulation of traumatic emotions implicated in sexual compulsion. © 2014 Giovanni Fioriti Editore s.r.l.
Siniscalchi S.M.,Kore University of Enna
Neurocomputing | Year: 2012
Over the past few years, there has been a resurgence of interest in designing high-accuracy automatic speech recognition (ASR) systems due to the key rule they can play in many real-world applications, such as voice print for biometric identification, language identification, and call-scanning. Improving current state-of-the-art technology is therefore vital for the success of those aforementioned applications, yet this is not simple with the standard technology based on hidden Markov models (HMMs) trained on short-term spectral features. This paper offers an innovative prospective on how two novel prominent approaches to ASR, namely speech attribute detection and discriminative training, can be combined into a unified framework with beneficial effects on the overall speech recognition performance. This goal is achieved by embedding phonetic feature detection into a penalized logistic regression machine (PLRM). The proposed approach is evaluated on both isolated and continuous phoneme recognition tasks. Experimental evidence indicate that the proposed framework is able to achieve state-of-the-art performance in the isolated speech recognition task and to outperform current technology in the continuous speech recognition task. © 2012 Elsevier B.V..
Minafo G.,Kore University of Enna
Engineering Structures | Year: 2015
Reinforced concrete (RC) jacketing is nowadays one of the most common techniques adopted for seismic retrofitting of existing RC columns. It is used to increase load-carrying capacity and ductility of weak existing members by means of a simple and cheap method. The structural efficiency is related to two main effects: - the enlargement of the transverse cross section; - the confinement action provided by the external jacket to the inner core. Several theoretical and experimental studies were addressed in the past to investigate on how it is possible to calculate the strength enhancement due to these effects and to highlight the main key parameters influencing the structural behavior of jacketed columns. Most of theoretical studies analyzed members subjected to axial compression while the case of axial force and bending moment was adapted only with complex formulations based on numerical approaches, which require the use of a suitable algorithm (e.g. non-linear finite element analyses, sectional fiber models). This paper presents a simplified approach, able to calculate the strength domains for jacketed columns subjected to axial force and uniaxial bending moment. The model takes into account the effects of confinement with proper stress-block parameters, the latter adapted for confined concrete, and of the composite action of jacket and core; buckling of longitudinal bars is considered and discussed with an appropriate stress-strain law for steel in compression. Results are compared with numerical analyses carried-out with the fiber model approach implemented in a commercial software (SAP2000), showing the accuracy of proposed method. Comparisons are also made with experimental results available in the literature in order to validate the model. Finally parametric considerations are made on the basis of adopted model, useful for design/verification purposes. © 2014 Elsevier Ltd.
Messineo A.,Kore University of Enna
Energy Procedia | Year: 2012
In this study is presented a thermodynamic analysis of a cascade refrigeration system using as refrigerant carbon dioxide in low-temperature circuit and ammonia in high-temperature circuit. The operating parameters considered in this paper include condensing, evaporating, superheating and subcooling temperatures in the ammonia (R717) hightemperature circuit and in the carbon dioxide (R744) low-temperature circuit. Diagrams of COP versus operating parameters have been obtained. In addition, values for R744-R717 cascade refrigeration system are compared with the values obtained for a partial injection two-stage refrigeration system using the synthetic refrigerant R404A, a nearly azeotropic blend, specially used for commercial refrigeration. Results show that a carbon dioxide-ammonia cascade refrigeration system is an interesting alternative to R404A two-stage refrigeration system for low evaporating temperatures (-30°C ÷ -50°C) in commercial refrigeration for energy, security and environmental reasons. © 2011 Published by Elsevier Ltd.