Strumenti per la valutazione dello stato conservativo e per la manutenzione programmata dei ponti della Provincia di Bologna

La tesi tratta di strumenti finalizzati alla valutazione dello stato conservativo e di supporto all'attività di manutenzione dei ponti, dai più generali Bridge Management Systems ai Sistemi di Valutazione Numerica della Condizione strutturale. Viene proposto uno strumento originale con cui classificare i ponti attraverso un Indice di Valutazione Complessiva e grazie ad esso stabilire le priorità d'intervento. Si tara lo strumento sul caso pratico di alcuni ponti della Provincia di Bologna. Su un ponte in particolare viene realizzato un approfondimento specifico sulla determinazione approssimata dei periodi propri delle strutture da ponte. Si effettua un confronto dei risultati di alcune modellazioni semplificate in riferimento a modellazioni dettagliate e risultati sperimentali.

Nonlinear/electromagnetic co-simulation of microwaves and millimeter-waves links

The Ph.D. thesis describes the simulations of different microwave links from the transmitter to the receiver intermediate-frequency ports, by means of a rigorous circuit-level nonlinear analysis approach coupled with the electromagnetic characterization of the transmitter and receiver front ends. This includes a full electromagnetic computation of the radiated far field which is used to establish the connection between transmitter and receiver. Digitally modulated radio-frequency drive is treated by a modulation-oriented harmonic-balance method based on Krylov-subspace model-order reduction to allow the handling of large-size front ends. Different examples of links have been presented: an End-to-End link simulated by making use of an artificial neural network model; the latter allows a fast computation of the link itself when driven by long sequences of the order of millions of samples. In this way a meaningful evaluation of such link performance aspects as the bit error rate becomes possible at the circuit level. Subsequently, a work focused on the co-simulation an entire link including a realistic simulation of the radio channel has been presented. The channel has been characterized by means of a deterministic approach, such as Ray Tracing technique. Then, a 2x2 multiple-input multiple-output antenna link has been simulated; in this work near-field and far-field coupling between radiating elements, as well as the environment factors, has been rigorously taken into account. Finally, within the scope to simulate an entire ultra-wideband link, the transmitting side of an ultrawideband link has been designed, and an interesting Front-End co-design technique application has been setup.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

A New Prediction Model for Slope Stability Analysis

The instability of river bank can result in considerable human and land losses. The Po river is the most important in Italy, characterized by main banks of significant and constantly increasing height. This study presents multilayer perceptron of artificial neural network (ANN) to construct prediction models for the stability analysis of river banks along the Po River, under various river and groundwater boundary conditions. For this aim, a number of networks of threshold logic unit are tested using different combinations of the input parameters. Factor of safety (FS), as an index of slope stability, is formulated in terms of several influencing geometrical and geotechnical parameters. In order to obtain a comprehensive geotechnical database, several cone penetration tests from the study site have been interpreted. The proposed models are developed upon stability analyses using finite element code over different representative sections of river embankments. For the validity verification, the ANN models are employed to predict the FS values of a part of the database beyond the calibration data domain. The results indicate that the proposed ANN models are effective tools for evaluating the slope stability. The ANN models notably outperform the derived multiple linear regression models.

Global and regional sea level variations in the recent past and future: insight from observations and modeling

This work is focused on the analysis of sea–level change (last century), based mainly on instrumental observations. During this period, individual components of sea–level change are investigated, both at global and regional scales. Some of the geophysical processes responsible for current sea-level change such as glacial isostatic adjustments and current melting terrestrial ice sources, have been modeled and compared with observations. A new value of global mean sea level change based of tide gauges observations has been independently assessed in 1.5 mm/year, using corrections for glacial isostatic adjustment obtained with different models as a criterion for the tide gauge selection. The long wavelength spatial variability of the main components of sea–level change has been investigated by means of traditional and new spectral methods. Complex non–linear trends and abrupt sea–level variations shown by tide gauges records have been addressed applying different approaches to regional case studies. The Ensemble Empirical Mode Decomposition technique has been used to analyse tide gauges records from the Adriatic Sea to ascertain the existence of cyclic sea-level variations. An Early Warning approach have been adopted to detect tipping points in sea–level records of North East Pacific and their relationship with oceanic modes. Global sea–level projections to year 2100 have been obtained by a semi-empirical approach based on the artificial neural network method. In addition, a model-based approach has been applied to the case of the Mediterranean Sea, obtaining sea-level projection to year 2050.

Teaching Cognitive Modeling Using PDP++

PDP++ is a freely available, open source software package designed to support the development, simulation, and analysis of research-grade connectionist models of cognitive processes. It supports most popular parallel distributed processing paradigms and artificial neural network architectures, and it also provides an implementation of the LEABRA computational cognitive neuroscience framework. Models are typically constructed and examined using the PDP++ graphical user interface, but the system may also be extended through the incorporation of user-written C++ code. This article briefly reviews the features of PDP++, focusing on its utility for teaching cognitive modeling concepts and skills to university undergraduate and graduate students. An informal evaluation of the software as a pedagogical tool is provided, based on the author’s classroom experiences at three research universities and several conference-hosted tutorials.

Investigation on the evolution of an indoor robotic localization system based on wireless networks

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.

Oxygen isotopes, IRD and SST reconstruction for the interval MIS31-19 of IODP Site 306-U1314

Early and Mid-Pleistocene climate, ocean hydrography and ice sheet dynamics have been reconstructed using a high-resolution data set (planktonic and benthic d18O time series, faunal-based sea surface temperature (SST) reconstructions and ice-rafted debris (IRD)) record from a high-deposition-rate sedimentary succession recovered at the Gardar Drift formation in the subpolar North Atlantic (Integrated Ocean Drilling Program Leg 306, Site U1314). Our sedimentary record spans from late in Marine Isotope Stage (MIS) 31 to MIS 19 (1069-779 ka). Different trends of the benthic and planktonic oxygen isotopes, SST and IRD records before and after MIS 25 (~940 ka) evidence the large increase in Northern Hemisphere ice-volume, linked to the cyclicity change from the 41-kyr to the 100-kyr that occurred during the Mid-Pleistocene Transition (MPT). Beside longer glacial-interglacial (G-IG) variability, millennial-scale fluctuations were a pervasive feature across our study. Negative excursions in the benthic d18O time series observed at the times of IRD events may be related to glacio-eustatic changes due to ice sheets retreats and/or to changes in deep hydrography. Time series analysis on surface water proxies (IRD, SST and planktonic d18O) of the interval between MIS 31 to MIS 26 shows that the timing of these millennial-scale climate changes are related to half-precessional (10 kyr) components of the insolation forcing, which are interpreted as cross-equatorial heat transport toward high latitudes during both equinox insolation maxima at the equator.

Paleoceanographic record since 13kyr BP of sediment core LaPAS-KF02

In this paper we present a paleoceanographic reconstruction of the southwestern South Atlantic for the past 13 kyr based on faunal and isotopic analysis of planktonic foraminifera from a high-resolution core retrieved at the South Brazil Bight continental slope. Our record indicates that oceanographic changes in the southwestern South Atlantic during the onset of the Holocene were comparable in strength to those that occurred during the Younger Dryas. Full interglacial conditions started abruptly after 8.2 kyr BP with a sharp change in faunal composition and surface hydrography (SST and SSS). Part of the observed events may be explained in terms of changes in thermohaline circulation while the other part suggests a dominant role of winds. Our data indicate that during the Early Holocene upwelling was significantly strengthened in the South Brazil Bight promoting high productivity and preventing the establishment of the typically interglacial menardiiform species. In general terms, oceanographic changes recorded by core KF02 occurred in synchrony with Antarctica's climate.

Distribution of planktonic foraminifera during the Mid-Pleistocene of ODP Site 181-1123

Planktonic foraminiferal assemblages and artificial neural network estimates of sea-surface temperature (SST) at ODP Site 1123 (41°47.2'S, 171°29.9'W; 3290 m deep), east of New Zealand, reveal a high-resolution history of glacial-interglacial (G-I) variability at the Subtropical Front (STF) for the last 1.2 million years, including the Mid-Pleistocene climate transition (MPT). Most G-I cycles of ~100 kyr duration have short periods of cold glacial and warm deglacial climate centred on glacial terminations, followed by long temperate interglacial periods. During glacial-deglacial transitions, maximum abundances of subantarctic and subtropical taxa coincide with SST minima and maxima, and lead ice volume by up to 8 kyrs. Such relationships reflect the competing influence of subantarctic and subtropical surface inflows during glacial and deglacial periods, respectively, suggesting alternate polar and tropical forcing of southern mid-latitude ocean climate. The lead of SSTs and subtropical inflow over ice volume points to tropical forcing of southern mid-latitude ocean-climate during deglacial warming. This contrasts with the established hypothesis that southern hemisphere ocean climate is driven by the influence of continental glaciations. Based on wholesale changes in subantarctic and subtropical faunas, the last 1.2 million years are subdivided into 4-distinct periods of ocean climate. 1) The pre-MPT (1185-870 ka) has high amplitude 41-kyr fluctuations in SST, superimposed on a general cooling trend and heightened productivity, reflecting long-term strengthening of subantarctic inflow under an invigorated Antarctic Circumpolar Current. 2) The early MPT (870-620 ka) is marked by abrupt warming during MIS 21, followed by a period of unstable periodicities within the 40-100 kyr orbital bands, decreasing SST amplitudes, and long intervals of temperate interglacial climate punctuated by short glacial and deglacial phases, reflecting lower meridional temperature gradients. 3) The late MPT (620-435 ka) encompasses an abrupt decrease in the subantarctic inflow during MIS 15, followed by a period of warm equable climate. Poorly defined, low amplitude G-I variations in SSTs during this interval are consistent with a relatively stable STF and evenly balanced subantarctic and subtropical inflows, possibly in response to smaller, less dynamic polar icesheets. 4) The post-MPT (435-0 ka) is marked by a major climatic deterioration during MIS 12, and a return to higher amplitude 100 kyr-frequency SST variations, superimposed on a long term trend towards cooler SSTs and increased mixed-layer productivity as the subantarctic inflow strengthened and polar icesheets expanded.

Global macrozoobenthos production and energy budget data base V150731, with link to database

I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.