Revisiting the Late Jurassic-Early Cretaceous of the NW South Iberian Basin: new ages and sedimentary environments

The study of the Upper Jurassic-Lower Cretaceous deposits (Higueruelas, Villar del Arzobispo and Aldea de Cortés Formations) of the South Iberian Basin (NW Valencia, Spain) reveals new stratigraphic and sedimentological data, which have significant implications on the stratigraphic framework, depositional environments and age of these units. The Higueruelas Fm was deposited in a mid-inner carbonate platform where oncolitic bars migrated by the action of storms and where oncoid production progressively decreased towards the uppermost part of the unit. The overlying Villar del Arzobispo Fm has been traditionally interpreted as an inner platform-lagoon evolving into a tidal-flat. Here it is interpreted as an inner-carbonate platform affected by storms, where oolitic shoals protected a lagoon, which had siliciclastic inputs from the continent. The Aldea de Cortés Fm has been previously interpreted as a lagoon surrounded by tidal-flats and fluvial-deltaic plains. Here it is reinterpreted as a coastal wetland where siliciclastic muddy deposits interacted with shallow fresh to marine water bodies, aeolian dunes and continental siliciclastic inputs. The contact between the Higueruelas and Villar del Arzobispo Fms, classically defined as gradual, is also interpreted here as rapid. More importantly, the contact between the Villar del Arzobispo and Aldea de Cortés Fms, previously considered as unconformable, is here interpreted as gradual. The presence of Alveosepta in the Villar del Arzobispo Fm suggests that at least part of this unit is Kimmeridgian, unlike the previously assigned Late Tithonian-Middle Berriasian age. Consequently, the underlying Higueruelas Fm, previously considered Tithonian, should not be younger than Kimmeridgian. Accordingly, sedimentation of the Aldea de Cortés Fm, previously considered Valangian-Hauterivian, probably started during the Tithonian and it may be considered part of the regressive trend of the Late Jurassic-Early Cretaceous cycle. This is consistent with the dinosaur faunas, typically Jurassic, described in the Villar del Arzobispo and Aldea de Cortés Fms.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Updating of Finite Element Models using static and dynamic optical strains with application to damage assessment

In the recent years, vibration-based structural damage identification has been subject of significant research in structural engineering. The basic idea of vibration-based methods is that damage induces mechanical properties changes that cause anomalies in the dynamic response of the structure, which measures allow to localize damage and its extension. Vibration measured data, such as frequencies and mode shapes, can be used in the Finite Element Model Updating in order to adjust structural parameters sensible at damage (e.g. Young’s Modulus). The novel aspect of this thesis is the introduction into the objective function of accurate measures of strains mode shapes, evaluated through FBG sensors. After a review of the relevant literature, the case of study, i.e. an irregular prestressed concrete beam destined for roofing of industrial structures, will be presented. The mathematical model was built through FE models, studying static and dynamic behaviour of the element. Another analytical model was developed, based on the ‘Ritz method’, in order to investigate the possible interaction between the RC beam and the steel supporting table used for testing. Experimental data, recorded through the contemporary use of different measurement techniques (optical fibers, accelerometers, LVDTs) were compared whit theoretical data, allowing to detect the best model, for which have been outlined the settings for the updating procedure.

Neutron Scattering Studies on Complex Magnetic Structures in Magnetoelectric Materials

In this thesis, the magnetic properties of four transition-metal oxides are presented. Their multiferroic and magnetoelectric phases have been investigated by means of different neutron scattering techniques. The materials TbMnO3 and MnWO4 belong to the group of spin-induced multiferroics. Their ferroelectric polarization can be explained by the inverse DzyaloshinskiiMoriya interaction. Another common feature of both materials is the presence of subsequent magnetic transitions from a spin-density wave to a spin spiral. The features of the phase transitions have been studied in both materials and it could be shown that diffuse magnetic scattering from the spin spiral is present even in the ordered spin-density wave phase. The excitation spectrum in the multiferroic phase of TbMnO3 was investigated in detail and a comprehensive dataset was obtained using time-of-flight spectroscopy. A spin-wave model could be obtained which can quantitatively describe the full dispersion. Furthermore, the polarization of the zone-center excitations could be derived which fit well to data from inelastic neutron spectroscopy and infrared spectroscopy. With the combination of spherical neutron polarimetry and a poling of the sample by an electric field, it was possible to observe the chiral magnetic component of the magnetic excitations in TbMnO3 and MnWO4. The spin-wave model for TbMnO3 obtained in this thesis is able to correctly describe the dispersion of this component. The double tungstate NaFe(WO4)2 is isostructural to the multiferroic MnWO4 and develops a complex magnetic phase diagram. By the use of neutron diffraction techniques, the zero-field structure and high-field structures in magnetic field applied along the b-axis could be determined. The data reveal a direct transition into an incommensurate spin-spiral structure. The value of the incommensurability is driven by anharmonic modulations and shows strong hysteresis effects. The static and dynamic properties in the magnetoelectric spin-glass phase of Ni0.42Mn0.58TiO3 were studied in detail. The spin-glass phase is composed of short-ranged MnTiO3 and NiTiO3-type order. The antiferromagnetic domains could be controlled by crossed magnetic and electric fields, which was visualized using spherical neutron polarimetry. A comprehensive dataset of the magnetic excitations in the spin-glass phase was collected. The dataset revealed correlations in the hexagonal plane which are only weakly coupled along the c-axis. The excitation spectra could be simulated by taking into account the MnTiO3-type order.

Image classification accuracy from LANDSAT data for tropical rain forest in Sumatra: An evaluation of ERDAS imagine software

Abstract not available

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Detecting phase transitions in community structures using big data analysis of the engineering education research landscape: a European perspective

Comunicação apresentada na 44th SEFI Conference, 12-­15 September 2016, Tampere, Finland

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.

Assessing mackerel scad, Decapterus macarellus, in Cape Verde: using a Bayesian approach to biomass dynamic modelling in a data-limited situation

The fisheries for mackerel scad, Decapterus macarellus, are particularly important in Cape Verde, constituting almost 40% of total catches at the peak of the fishery in 1997 and 1998 ( 3700 tonnes). Catches have been stable at a much lower level of about 2 100 tonnes in recent years. Given the importance of mackerel scad in terms of catch weight and local food security, there is an urgent need for updated assessment. Stock assessment was carried out using a Bayesian approach to biomass dynamic modelling. In order to tackle the problem of a non-informative CPUE series, the intrinsic rate of increase, r, was estimated separately, and the ratio B-0/X, initial biomass relative to carrying capacity, was assumed based on available information. The results indicated that the current level of fishing is sustainable. The probability of collapse is low, particularly in the short-term, and it is likely that biomass may increase further above B-msy, indicating a healthy stock level. It would appear that it is relatively safe to increase catches even up to 4000 tonnes. However, the marginal posterior of r was almost identical to the prior, indicating that there is relatively low information content in CPUE. This was also the case in relation to B-0/X There have been substantial increases in fishing efficiency, which have not been adequately captured by the measure used for effort (days or trips), implying that the results may be overly optimistic and should be considered preliminary. (c) 2006 Elsevier B.V. All rights reserved.

Assessing landscape functional connectivity in a forest carnivore using path selection functions

Context Understanding connectivity patterns in relation to habitat fragmentation is essential to landscape management. However, connectivity is often judged from expert opinion or species occurrence patterns, with very few studies considering the actual movements of individuals. Path selection functions provide a promising tool to infer functional connectivity from animal movement data, but its practical application remains scanty. Objectives We aimed to describe functional connectivity patterns in a forest carnivore using path-level analysis, and to explore how connectivity is affected by land cover patterns and road networks. Methods We radiotracked 22 common genets in a mixed forest-agricultural landscape of southern Portugal. We developed path selection functions discriminating between observed and random paths in relation to landscape variables. These functions were used together with land cover information to map conductance surfaces. Results Genets moved preferentially within forest patches and close to riparian habitats. Functional connectivity declined with increasing road density, but increased with the proximity of culverts, viaducts and bridges. Functional connectivity was favoured by large forest patches, and by the presence of riparian areas providing corridors within open agricultural land. Roads reduced connectivity by dissecting forest patches, but had less effect on riparian corridors due to the presence of crossing structures. Conclusions Genet movements were jointly affected by the spatial distribution of suitable habitats, and the presence of a road network dissecting such habitats and creating obstacles in areas otherwise permeable to animal movement. Overall, the study showed the value of path-level analysis to assess functional connectivity patterns in human-modified landscapes.

Damage Identification of Structures through Vibration-Based Structural Monitoring Systems

The thesis has been carried out within the “SHAPE Project - Predicting Strength Changes in Bridges from Frequency Data Safety, Hazard, and Poly-harmonic Evaluation” (ERA-NET Plus Infravation Call 2014) which dealt with the structural assessment of existing bridges and laboratory structural reproductions through the use of vibration-based monitoring systems, for detecting changes in their natural frequencies and correlating them with the occurrence of damage. The main purpose of this PhD dissertation has been the detection of the variation of the main natural frequencies as a consequence of a previous-established damage configuration provided on a structure. Firstly, the effect of local damage on the modal feature has been discussed mainly concerning a steel frame and a composite steel-concrete bridge. Concerning the variation of the fundamental frequency of the small bridge, the increasing severity of two local damages has been investigated. Moreover, the comparison with a 3D FE model is even presented establishing a link between the dynamic properties and the damage features. Then, moving towards a diffused damage pattern, four concrete beams and a small concrete deck were loaded achieving the yielding of the steel reinforcement. The stiffness deterioration in terms of frequency shifts has been reconsidered by collecting a large set of dynamic experiments on simply supported R.C. beams discussed in the literature. The comparison of the load-frequency curves suggested a significant agreement among all the experiments. Thus, in the framework of damage mechanics, the “breathing cracks” phenomenon has been discussed leading to an analytical formula able to explain the frequency decay observed experimentally. Lastly, some dynamic investigations of two existing bridges and the corresponding FE Models are presented in Chapter 4. Moreover, concerning the bridge in Bologna, two prototypes of a network of accelerometers were installed and the data of a few months of monitoring have been discussed.

Data analysis and hydrological modelling of rainfall and discharge extremes along the Tione River: implications for the design of water structures

There are many natural events that can negatively affect the urban ecosystem, but weather-climate variations are certainly among the most significant. The history of settlements has been characterized by extreme events like earthquakes and floods, which repeat themselves at different times, causing extensive damage to the built heritage on a structural and urban scale. Changes in climate also alter various climatic subsystems, changing rainfall regimes and hydrological cycles, increasing the frequency and intensity of extreme precipitation events (heavy rainfall).  From an hydrological risk perspective, it is crucial to understand future events that could occur and their magnitude in order to design safer infrastructures. Unfortunately, it is not easy to understand future scenarios as the complexity of climate is enormous.  For this thesis, precipitation and discharge extremes were primarily used as data sources. It is important to underline that the two data sets are not separated: changes in rainfall regime, due to climate change, could significantly affect overflows into receiving water bodies. It is imperative that we understand and model climate change effects on water structures to support the development of adaptation strategies.   The main purpose of this thesis is to search for suitable water structures for a road located along the Tione River. Therefore, through the analysis of the area from a hydrological point of view, we aim to guarantee the safety of the infrastructure over time.   The observations made have the purpose to underline how models such as a stochastic one can improve the quality of an analysis for design purposes, and influence choices.

The Neuromuscular Activity Of Bothriopsis Bilineata Smaragdina (forest Viper) Venom And Its Toxin Bbil-tx (asp49 Phospholipase A2) On Isolated Mouse Nerve-muscle Preparations.

The presynaptic action of Bothriopsis bilineata smaragdina (forest viper) venom and Bbil-TX, an Asp49 PLA2 from this venom, was examined in detail in mouse phrenic nerve-muscle (PND) preparations in vitro and in a neuroblastoma cell line (SK-N-SH) in order to gain a better insight into the mechanism of action of the venom and associated Asp49 PLA2. In low Ca(2+) solution, venom (3μg/ml) caused a quadriphasic response in PND twitch height whilst at 10μg/ml the venom additionally induced an abrupt and marked initial contracture followed by neuromuscular facilitation, rhythmic oscillations of nerve-evoked twitches, alterations in baseline and progressive blockade. The venom slowed the relaxation phase of muscle twitches. In low Ca(2+), Bbil-TX [210nM (3μg/ml)] caused a progressive increase in PND twitch amplitude but no change in the decay time constant. Venom (10μg/ml) and Bbil-TX (210nM) caused minor changes in the compound action potential (CAP) amplitude recorded from sciatic nerve preparations, with no significant effect on rise time and latency; tetrodotoxin (3.1nM) blocked the CAP at the end of the experiments. In mouse triangularis sterni nerve-muscle (TSn-m) preparations, venom (10μg/ml) and Bbil-TX (210nM) significantly reduced the perineural waveform associated with the outward K(+) current while the amplitude of the inward Na(+) current was not significantly affected. Bbil-TX (210nM) caused a progressive increase in the quantal content of TSn-m preparations maintained in low Ca(2+) solution. Venom (3μg/ml) and toxin (210nM) increased the calcium fluorescence in SK-N-SH neuroblastoma cells loaded with Fluo3 AM and maintained in low or normal Ca(2+) solution. In normal Ca(2+), the increase in fluorescence amplitude was accompanied by irregular and frequent calcium transients. In TSn-m preparations loaded with Fluo4 AM, venom (10μg/ml) caused an immediate increase in intracellular Ca(2+) followed by oscillations in fluorescence and muscle contracture; Bbil-TX did not change the calcium fluorescence in TSn-m preparations. Immunohistochemical analysis of toxin-treated PND preparations revealed labeling of junctional ACh receptors but a loss of the presynaptic proteins synaptophysin and SNAP25. Together, these data confirm the presynaptic action of Bbil-TX and show that it involves modulation of K(+) channel activity and presynaptic protein expression.