'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

Self-sealing of thermal fatigue and mechanical damage in fiber-reinforced composite materials

Fiber reinforced composite tanks provide a promising method of storage for liquid oxygen and hydrogen for aerospace applications. The inherent thermal fatigue of these vessels leads to the formation of microcracks, which allow gas phase leakage across the tank walls. In this dissertation, self-healing functionality is imparted to a structural composite to effectively seal microcracks induced by both mechanical and thermal loading cycles. Two different microencapsulated healing chemistries are investigated in woven glass fiber/epoxy and uni-weave carbon fiber/epoxy composites. Self-healing of mechanically induced damage was first studied in a room temperature cured plain weave E-glass/epoxy composite with encapsulated dicyclopentadiene (DCPD) monomer and wax protected Grubbs' catalyst healing components. A controlled amount of microcracking was introduced through cyclic indentation of opposing surfaces of the composite. The resulting damage zone was proportional to the indentation load. Healing was assessed through the use of a pressure cell apparatus to detect nitrogen flow through the thickness direction of the damaged composite. Successful healing resulted in a perfect seal, with no measurable gas flow. The effect of DCPD microcapsule size (51 um and 18 um) and concentration (0 - 12.2 wt%) on the self-sealing ability was investigated. Composite specimens with 6.5 wt% 51 um capsules sealed 67% of the time, compared to 13% for the control panels without healing components. A thermally stable, dual microcapsule healing chemistry comprised of silanol terminated poly(dimethyl siloxane) plus a crosslinking agent and a tin catalyst was employed to allow higher composite processing temperatures. The microcapsules were incorporated into a satin weave E-glass fiber/epoxy composite processed at 120C to yield a glass transition temperature of 127C. Self-sealing ability after mechanical damage was assessed for different microcapsule sizes (25 um and 42 um) and concentrations (0 - 11 vol%). Incorporating 9 vol% 42 um capsules or 11 vol% 25 um capsules into the composite matrix leads to 100% of the samples sealing. The effect of microcapsule concentration on the short beam strength, storage modulus, and glass transition temperature of the composite specimens was also investigated. The thermally stable tin catalyzed poly(dimethyl siloxane) healing chemistry was then integrated into a [0/90]s uniweave carbon fiber/epoxy composite. Thermal cycling (-196C to 35C) of these specimens lead to the formation of microcracks, over time, formed a percolating crack network from one side of the composite to the other, resulting in a gas permeable specimen. Crack damage accumulation and sample permeability was monitored with number of cycles for both self-healing and traditional non-healing composites. Crack accumulation occurred at a similar rate for all sample types tested. A 63% increase in lifetime extension was achieved for the self-healing specimens over traditional non-healing composites.

Monitoramento e identificação numérico e experimental de danos em vigas e pontes de aço e concreto utilizando transformadas de wavelet

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

Exchange rate dynamics, structural breaks, and central bank interventions in Colombia

We evaluate the effectiveness of the Colombian Central Bank´s interventions in the foreign exchange market during the period 2000 to 2014 -- We examine the stochastic process that describes the exchange rate, with a focus on the detection of structural breaks or unit roots in the data to determine whether the Central Bank´s interventions were effective -- We find that the exchange rate can be described either by a random walk or by a trend-stationary model with multiple breaks -- In neither cases do we find any evidence that the exchange rate was affected by the Central Bank interventions

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Optical sensing of the fatigue damage state of CFRP under realistic aeronautical load sequences

Abstract: We present an optical sensing methodology to estimate the fatigue damage stateof structures made of carbon fiber reinforced polymer (CFRP), by measuring variations on the surface roughness. Variable amplitude loads (VAL), which represent realistic loads during aeronautical missions of fighter aircraft (FALSTAFF) have been applied to coupons until failure. Stiffness degradation and surface roughness variations have been measured during the life of the coupons obtaining a Pearson correlation of 0.75 between both variables. The data were compared with a previous study for Constant Amplitude Load (CAL) obtaining similar results. Conclusions suggest that the surface roughness measured in strategic zones is a useful technique for structural health monitoring of CFRP structures, and that it is independent of the type of load applied. Surface roughness can be measured in the field by optical techniques such as speckle, confocal perfilometers and interferometry, among others.

Hilbert Space Valued Signal Plus Noise Models: Analysis of Structural Breaks under High Dimensionality and Temporal Dependence

This thesis is concerned with change point analysis for time series, i.e. with detection of structural breaks in time-ordered, random data. This long-standing research field regained popularity over the last few years and is still undergoing, as statistical analysis in general, a transformation to high-dimensional problems. We focus on the fundamental »change in the mean« problem and provide extensions of the classical non-parametric Darling-Erdős-type cumulative sum (CUSUM) testing and estimation theory within highdimensional Hilbert space settings. In the first part we contribute to (long run) principal component based testing methods for Hilbert space valued time series under a rather broad (abrupt, epidemic, gradual, multiple) change setting and under dependence. For the dependence structure we consider either traditional m-dependence assumptions or more recently developed m-approximability conditions which cover, e.g., MA, AR and ARCH models. We derive Gumbel and Brownian bridge type approximations of the distribution of the test statistic under the null hypothesis of no change and consistency conditions under the alternative. A new formulation of the test statistic using projections on subspaces allows us to simplify the standard proof techniques and to weaken common assumptions on the covariance structure. Furthermore, we propose to adjust the principal components by an implicit estimation of a (possible) change direction. This approach adds flexibility to projection based methods, weakens typical technical conditions and provides better consistency properties under the alternative. In the second part we contribute to estimation methods for common changes in the means of panels of Hilbert space valued time series. We analyze weighted CUSUM estimates within a recently proposed »high-dimensional low sample size (HDLSS)« framework, where the sample size is fixed but the number of panels increases. We derive sharp conditions on »pointwise asymptotic accuracy« or »uniform asymptotic accuracy« of those estimates in terms of the weighting function. Particularly, we prove that a covariance-based correction of Darling-Erdős-type CUSUM estimates is required to guarantee uniform asymptotic accuracy under moderate dependence conditions within panels and that these conditions are fulfilled, e.g., by any MA(1) time series. As a counterexample we show that for AR(1) time series, close to the non-stationary case, the dependence is too strong and uniform asymptotic accuracy cannot be ensured. Finally, we conduct simulations to demonstrate that our results are practically applicable and that our methodological suggestions are advantageous.

Innovative Al Alloys for High Performance Automotive Pistons: Enhancement of Specific Strength at High Temperature and Resistance to Knock Damage

The increasingly strict regulations on greenhouse gas emissions make the fuel economy a pressing factor for automotive manufacturers. Lightweighting and engine downsizing are two strategies pursued to achieve the target. In this context, materials play a key role since these limit the engine efficiency and components weight, due to their acceptable thermo-mechanical loads. Piston is one of the most stressed engine components and it is traditionally made of Al alloys, whose weakness is to maintain adequate mechanical properties at high temperature due to overaging and softening. The enhancement in strength-to-weight ratio at high temperature of Al alloys had been investigated through two approaches: increase of strength at high temperature or reduction of the alloy density. Several conventional and high performance Al-Si and Al-Cu alloys have been characterized from a microstructural and mechanical point of view, investigating the effects of chemical composition, addition of transition elements and heat treatment optimization, in the specific temperature range for pistons operations. Among the Al-Cu alloys, the research outlines the potentialities of two innovative Al-Cu-Li(-Ag) alloys, typically adopted for structural aerospace components. Moreover, due to the increased probability of abnormal combustions in high performance spark-ignition engines, the second part of the dissertation deals with the study of knocking damages on Al pistons. Thanks to the cooperation with Ferrari S.p.A. and Fluid Machinery Research Group - Unibo, several bench tests have been carried out under controlled knocking conditions. Knocking damage mechanisms were investigated through failure analyses techniques, starting from visual analysis up to detailed SEM investigations. These activities allowed to relate piston knocking damage to engine parameters, with the final aim to develop an on-board knocking controller able to increase engine efficiency, without compromising engine functionality. Finally, attempts have been made to quantify the knock-induced damages, to provide a numerical relation with engine working conditions.

Structural health monitoring of storage tanks

In this thesis, Ph.D candidate presents a compact sensor node (SN) designed for long-term and real-time acoustic emission (AE) monitoring of above ground storage tanks (ASTs). Each SN exploits up to three inexpensive low-frequency sensors based on piezoelectric diaphragms for effective leakage detection, and it is capable by means of built-in Digital Signal Processing functionalities to process the acquired time waveforms extracting the AE features usually required by testing protocols. Alternatively, capability to plug three high frequency AE sensors to a SN for corrosion simulated phenomena detection is envisaged and demonstrated. Another innovative aspect that the Ph.D candidate presents in this work is an alternative mathematical model of corrosion location on the bottom of the AST. This approach implies considering the three-dimensional localization model versus the two-dimensional commonly used according to the literature. This approach is aimed at significant optimization in the number of sensors in relation to the standard approach for solving localization problems as well as to allow filtering the false AE events related to the condensate droplets from AST ceiling. The technological implementation of this concept required the solution of a number of technical problems, such as the precise time of arrival (ToA) signal estimation, vertical localization of the AE source and multilaration solution that were discussed in detail in this work. To validate the developed prototype, several experimental campaigns were organized that included the simulation of target phenomena both in laboratory conditions and on a real water storage tank. The presented test results demonstrate the successful application of the developed AE system both for simulated leaks and for corrosion processes on the tank bottom. Mathematical and technological algorithms for localization and characterization of AE signals implemented during the development of the prototype are also confirmed by the test results.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Structural and stratigraphic control on hypogene dissolution in carbonate rocks: case studies of cave analogues and implications for karst reservoirs

The aim of this research is to improve the understanding of the factors that control the formation of karst porosity in hypogene settings and its associated patterns of void-conduit networks. Subsurface voids created by hypogene dissolution may span from few microns to decametric tubes providing interconnected conduit systems and forming highly anisotropic permeability domains in many reservoirs. Characterizing the spatial-morphological organization of hypogene karst is a challenging task that has dramatic implications for the applied industry, given that only partial data can be acquired from the subsurface by indirect techniques. Therefore, two outcropping cave analogues are examined: the Cavallone-Bove Cave in the Majella Massif (Italy), and the karst systems of the Salitre Formation (Brazil). In the latter, a peculiar example of hypogene speleogenesis associated with silicification has been studied, providing an analogue of many karstified reservoirs hosted in cherts or cherty-carbonates within mixed sedimentary sequences. The first part of the thesis is focused on the relationships between fracture patterns and flow pathways in deformed units in: 1) a fold-and-thrust setting (Majella Massif); 2) a cratonic block (Brazil). These settings represent potential playgrounds for the migration and accumulation of geofluids, where hypogene conduits may affect flow pathways, fluid storage, and reservoir properties. The results indicate that localized deformation producing cross-formational fracture zones associated with anticline hinges or fault damage zones is critical for hypogene fluid migration and karstification. The second part of the thesis deals with the multidisciplinary study of hydrothermal silicification and hypogene dissolution in Calixto Cave (Brazil). Petrophysical analyses and a geochemical characterization of silica deposits are used to unravel the spatial-morphological organization of the conduit system and its speleogenesis. The novel results obtained from this cave shed new light on the relationship between hydrothermal silicification, hypogene dissolution and the development of multistorey cave systems in layered carbonate-siliciclastic sequences.

Seismic vulnerability of buildings and non-structural elements: insights from the 2011 Lorca earthquake

This PhD dissertation presents a profound study of the vulnerability of buildings and non-structural elements stemming from the investigation of the Mw 5.2 Lorca 2011 earthquake; which constitutes one of the most significant earthquakes in Spain. It left nine fatalities due to falling debris from reinforced concrete buildings, 394 injured and material damage valued at 800 million euros. Within this framework, the most relevant initiatives concerning the vulnerability of buildings and the exposure of Lorca are studied. This work revealed two lines of research: the elaboration of a rational method to determine the adequacy of a specific fragility curve for the particular seismic risk study of a region; and the relevance of researching the seismic performance of non-structural elements. As a consequence, firstly, a method to assess and select fragility curves for seismic risk studies from the catalogue of those available in the literature is elaborated and calibrated by means of a case study. The said methodology is based on a multidimensional index and provides a ranking that classifies the curves in terms of adequacy. Its results for the case of Lorca led to the elaboration of new fragility curves for unreinforced masonry buildings. Moreover, a simplified method to account for the unpredictable directionality of the seism in the creation of fragility curves is contributed. Secondly, the characterisation of the seismic capacity and demand of the non-structural elements that caused most of the human losses is studied. Concerning the capacity, an analytical approach derived from theoretical considerations to characterise the complete out-of-plane seismic response curve of unreinforced masonry cantilever walls is provided; as well as a simplified and more practical trilinear version of it. Concerning the demand, several methods for characterising the Floor Response Spectra of reinforced concrete buildings are tested through case studies.

Integrating structural variant calling, annotation and prioritization into whole genome analysis workflows: a practical application in the molecular diagnosis of neurodevelopmental disorders

Background: WGS is increasingly used as a first-line diagnostic test for patients with rare genetic diseases such as neurodevelopmental disorders (NDD). Clinical applications require a robust infrastructure to support processing, storage and analysis of WGS data. The identification and interpretation of SVs from WGS data also needs to be improved. Finally, there is a need for a prioritization system that enables downstream clinical analysis and facilitates data interpretation. Here, we present the results of a clinical application of WGS in a cohort of patients with NDD. Methods: We developed highly portable workflows for processing WGS data, including alignment, quality control, and variant calling of SNVs and SVs. A benchmark analysis of state-of-the-art SV detection tools was performed to select the most accurate combination for SV calling. A gene-based prioritization system was also implemented to support variant interpretation. Results: Using a benchmark analysis, we selected the most accurate combination of tools to improve SV detection from WGS data and build a dedicated pipeline. Our workflows were used to process WGS data from 77 NDD patient-parent families. The prioritization system supported downstream analysis and enabled molecular diagnosis in 32% of patients, 25% of which were SVs and suggested a potential diagnosis in 20% of patients, requiring further investigation to achieve diagnostic certainty. Conclusion: Our data suggest that the integration of SNVs and SVs is a main factor that increases diagnostic yield by WGS and show that the adoption of a dedicated pipeline improves the process of variant detection and interpretation.

Lung Age Is Related To Carotid Structural Alterations In Hypertensive Subjects.

Hypertensive patients exhibit higher cardiovascular risk and reduced lung function compared with the general population. Whether this association stems from the coexistence of two highly prevalent diseases or from direct or indirect links of pathophysiological mechanisms is presently unclear. This study investigated the association between lung function and carotid features in non-smoking hypertensive subjects with supposed normal lung function. Hypertensive patients (n = 67) were cross-sectionally evaluated by clinical, hemodynamic, laboratory, and carotid ultrasound analysis. Forced vital capacity, forced expired volume in 1 second and in 6 seconds, and lung age were estimated by spirometry. Subjects with ventilatory abnormalities according to current guidelines were excluded. Regression analysis adjusted for age and prior smoking history showed that lung age and the percentage of predicted spirometric parameters associated with common carotid intima-media thickness, diameter, and stiffness. Further analyses, adjusted for additional potential confounders, revealed that lung age was the spirometric parameter exhibiting the most significant regression coefficients with carotid features. Conversely, plasma C-reactive protein and matrix-metalloproteinases-2/9 levels did not influence this relationship. The present findings point toward lung age as a potential marker of vascular remodeling and indicate that lung and vascular remodeling might share common pathophysiological mechanisms in hypertensive subjects.