Analyse des déplacements permanents des barrages en remblai et en enrochement par des méthodes pseudo-dynamiques pour l’est du Canada

Le projet de recherche a été réalisé dans le but premier de mettre à niveau, d’adapter et de rendre opérationnelle la méthode de Newmark pour les types de barrages et les types de séismes habituellement rencontrés au Québec. Pour ce faire, une Banque de Signaux Sismiques pour l’Est du Canada (BDSSEC), contenant 451 accélérogrammes, a été produite de même qu’un code de calcul permettant de calculer les déplacements permanents, par double intégration numérique, selon la méthode de Newmark. À partir d’analyses statistiques, des modèles prédictifs applicables au contexte sismologique de l’Est canadien ont été développés en considérant un comportement rigide-plastique (méthode dite du « bloc rigide ») et un comportement viscoélastique linéaire équivalent (méthode dite « flexible »). La comparaison entre le nouveau modèle rigide-plastique pour l’Est avec ceux existants développés à partir de signaux de l’Ouest américain a montré que les déplacements permanents évalués pour l’Est peuvent être significativement inférieurs à ceux de l’Ouest pour des magnitudes et des distances épicentrales comparables compte tenu de leur contenu fréquentiel plus fort en hautes fréquences. Le modèle prédictif flexible a montré qu’il pouvait prédire des déplacements permanents du même ordre de grandeur que Makdisi et Seed (1977) et de Rathje et Antonakos (2011). Les déplacements permanents sont amplifiés par la méthode flexible comparativement à la méthode rigide principalement lorsque le ratio entre la période prédominante de l’ouvrage et la période moyenne de la sollicitation se situe autour de 1. Le second but consistait à tester l’applicabilité de la méthode du bruit de fond ambiant (H/V) comme outil expérimental pour estimer la période prédominante des barrages en remblai et en enrochement (Tp). Les études comparatives ont montré, de façon générale, une bonne concordance entre les calculs analytiques et les mesures expérimentales tant et aussi longtemps que les hypothèses à la base des modèles sont respectées.

Monoamniotic twins discordant for body stalk anomaly

Body stalk anomaly is a rare malformation. This anomaly in monozygotic twins is extremely unusual. We describe a case of monoamniotic pregnancy discordant for body stalk anomaly diagnosed at 11 weeks. Ultrasound showed a fetus with a large anterior abdominal wall defect, anomaly of the spine and no evidence of lower extremities and other with a normal morphology. As far as our concern, only three monoamniotic pregnancies discordant for this malformation were reported. Our case represents the fourth reported monoamniotic pregnancy discordant for body stalk anomaly with diagnosis made by ultrasound and the second diagnosed in the first trimester.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

Construcción de escenarios pseudo-perspectivos mediante la creación y modificación de gráficos tridimensionales utilizados en el trecento

Este estudio forma parte de una investigación más amplia que trata de la capacidad expresiva de los sistemas perspectivos1. Bajo esta premisa hemos reflexionado sobre los métodos utilizados por los artistas del trecento italiano para representar la tercera dimensión. Hemos analizado un amplio número de obras, con un doble objetivo: El primero, obtener datos que otorguen consistencia y fiabilidad a una clasificación que nos ayuda a tipificar el espacio perspectivo en esta etapa predecesora de la perspectiva «correcta» (lineal o cónica). El segundo, extrapolar sus características a la construcción de escenarios pseudo-perspectivos mediante la modificación y creación de gráficos tridimensionales por computadora. Este proceso se ha utilizado para extraer los esquemas que nos sirven como herramienta para explorar nuevos caminos en la representación perspectiva, alejados de la lógica euclidiana. Con este propósito se aportan esquemas formales de representaciones clasificadas en diferentes tipos, que posteriormente se transforman para efectuar la construcción volumétrica de los escenarios.

Low-Volatility Anomaly in Helsinki Stock Exchange

International research shows that low-volatility stocks have beaten high-volatility stocks in terms of returns for decades on multiple markets. This abbreviation from traditional risk-return framework is known as low-volatility anomaly. This study focuses on explaining the anomaly and finding how strongly it appears in NASDAQ OMX Helsinki stock exchange. Data consists of all listed companies starting from 2001 and ending close to 2015. Methodology follows closely Baker and Haugen (2012) by sorting companies into deciles according to 3-month volatility and then calculating monthly returns for these different volatility groups. Annualized return for the lowest volatility decile is 8.85 %, while highest volatility decile destroys wealth at rate of -19.96 % per annum. Results are parallel also in quintiles that represent larger amount of companies and thus dilute outliers. Observation period captures financial crisis of 2007-2008 and European debt crisis, which embodies as low main index annual return of 1 %, but at the same time proves the success of low-volatility strategy. Low-volatility anomaly is driven by multiple reasons such as leverage constrained trading and managerial incentives which both prompt to invest in risky assets, but behavioral matters also have major weight in maintaining the anomaly.

Magnetic Theory and Applications in the Naples Bay (Southern Tyrrhenian Sea, Italy): Magnetic Anomaly Fields and Relationships with Morpho-Structural Lineaments

Magnetic theory and application to a complex volcanic area located in Southern Italy are here discussed showing the example of the Gulf of Naples, located at Southern Italy Tyrrhenian margin. A magnetic anomaly map of the Gulf of Naples has been constructed aimed at highlighting new knowledge on geophysics and volcanology of this area of the Eastern Tyrrhenian margin, characterized by a complex geophysical setting, strongly depending on sea bottom topography. The theoretical aspects of marine magnetometry and multibeam bathymetry have been discussed. Magnetic data processing included the correction of the data for the diurnal variation, the correction of the data for the offset and the leveling of the data as a function of the correction at the cross-points of the navigation lines. Multibeam and single-beam bathymetric data processing has been considered. Magnetic anomaly fields in the Naples Bay have been discussed through a detailed geological interpretation and correlated with main morpho-structural features recognized through morphobathymetric interpretation. Details of magnetic anomalies have been selected, represented and correlated with significant seismic profiles, recorded on the same navigation lines of magnetometry. They include the continental shelf offshore the Somma-Vesuvius volcanic complex, the outer shelf of the Gulf of Pozzuoli offshore the Phlegrean Fields volcanic complex, the relict volcanic banks of Pentapalummo, Nisida and Miseno, the Gaia volcanic bank on the Naples slope, the western slope of the Dohrn canyon, the Magnaghi canyon’s head and the magnetic anomalies among the Ischia and Procida islands.

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is successful at detecting port scans.

Introducting Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound imnological concepts.

An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.

Dempster-Shafer for Anomaly Detection

In this paper, we implement an anomaly detection system using the Dempster-Shafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We further show that by applying this approach to a real-world email dataset the algorithm works for email worm detection. Dempster-Shafer can be a promising method for anomaly detection problems with multiple features (data sources), and two or more classes.

Dendritic Cells for Real-Time Anomaly Detection

Dendritic Cells (DCs) are innate immune system cells which have the power to activate or suppress the immune system. The behaviour of human DCs is abstracted to form an algorithm suitable for anomaly detection. We test this algorithm on the real-time problem of port scan detection. Our results show a significant difference in artificial DC behaviour for an outgoing portscan when compared to behaviour for normal processes.

Dendritic Cells for Anomaly Detection

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

Anomaly diagnosis in ceramic claddings by thermography - A review

With the increasing importance given to building rehabilitation comes the need to create simple, fast and non-destructive testing methods (NDT) to identify problems and for anomaly diagnosis. Ceramic tiles are one of the most typical kinds of exterior wall cladding in several countries; the earliest known examples are Egyptian dating from 4000 BC. This type of building facade coating, though being quite often used in due to its aesthetic and architectural characteristics, is one of the most complex that can be applied given the several parts from which it is composed; hence, it is also one of the most difficult to correctly diagnose with expeditious methods. The detachment of ceramic wall tiles is probably the most common and difficult to identify anomaly associated with this kind of cladding and it is also definitely the one that can compromise security the most. Thus, it is necessary to study a process of inspection more efficient and economic than the currently used which often consist in semi-destructive methods (the most common is the pull off test), that can only be used in a small part of the building at a time, allowing some assumptions of what can the rest of the cladding be like. Infrared thermography (IRT) is a NDT with a wide variety of applications in building inspection that is becoming commonly used to identify anomalies related with thermal variations in the inspected surfaces. Few authors have studied the application of IRT in anomalies associated with ceramic claddings claiming that the presence of air or water beneath the superficial layer will influence the heat transfer in a way that can be detected in both a qualitative and a quantitative way by the thermal camera, providing information about the state of the wall in a much broad area per trial than other methods commonly used nowadays. This article intends to present a review of the state of art of this NDT and its potentiality in becoming a more efficient way to diagnose anomalies in ceramic wall claddings.