Modelling and studies of the spectral response of some optoelectronic components

Solar radiation takes in today's world, an increasing importance. Different devices are used to carry out spectral and integrated measurements of solar radiation. Thus the sensors can be divided into the fallow types: Calorimetric, Thermomechanical, Thermoelectric and Photoelectric. The first three categories are based on components converting the radiation to temperature (or heat) and then into electrical quantity. On the other hand, the photoelectric sensors are based on semiconductor or optoelectronic elements that when irradiated change their impedance or generate a measurable electric signal. The response function of the sensor element depends not only on the intensity of the radiation but also on its wavelengths. The radiation sensors most widely used fit in the first categories, but thanks to the reduction in manufacturing costs and to the increased integration of electronic systems, the use of the photoelectric-type sensors became more interesting. In this work we present a study of the behavior of different optoelectronic sensor elements. It is intended to verify the static response of the elements to the incident radiation. We study the optoelectronic elements using mathematical models that best fit their response as a function of wavelength. As an input to the model, the solar radiation values are generated with a radiative transfer model. We present a modeling of the spectral response sensors of other types in order to compare the behavior of optoelectronic elements with other sensors currently in use.

Contribution to the diffuse radiation modelling in Évora, Portugal

Solar radiation data is crucial for the design of energy systems based on the solar resource. Since diffuse radiation measurements are not always available in the archive data series, either due to the inexistence of measuring equipment, shading device misplacement or missing data, models to generate these data are needed. In this work, one year of hourly and daily horizontal solar global and diffuse irradiation measurements in Évora are used to establish a new relation between the diffuse radiation and the clearness index. The proposed model includes a fitting parameter, which was adjusted through a simple optimization procedure to minimize the Least Square Error as compared to measurements. A comparison against several other fitting models presented in the literature was also carried out using the Root Mean Square Error as statistical indicator, and it was found that the present model is more accurate than the previous fitting models for the diffuse radiation data in Évora.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Modelling spillover effects in spatial stochastic frontier analysis

In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

New perspectives in statistical mechanics and high-dimensional inference

The main purpose of this thesis is to go beyond two usual assumptions that accompany theoretical analysis in spin-glasses and inference: the i.i.d. (independently and identically distributed) hypothesis on the noise elements and the finite rank regime. The first one appears since the early birth of spin-glasses. The second one instead concerns the inference viewpoint. Disordered systems and Bayesian inference have a well-established relation, evidenced by their continuous cross-fertilization. The thesis makes use of techniques coming both from the rigorous mathematical machinery of spin-glasses, such as the interpolation scheme, and from Statistical Physics, such as the replica method. The first chapter contains an introduction to the Sherrington-Kirkpatrick and spiked Wigner models. The first is a mean field spin-glass where the couplings are i.i.d. Gaussian random variables. The second instead amounts to establish the information theoretical limits in the reconstruction of a fixed low rank matrix, the “spike”, blurred by additive Gaussian noise. In chapters 2 and 3 the i.i.d. hypothesis on the noise is broken by assuming a noise with inhomogeneous variance profile. In spin-glasses this leads to multi-species models. The inferential counterpart is called spatial coupling. All the previous models are usually studied in the Bayes-optimal setting, where everything is known about the generating process of the data. In chapter 4 instead we study the spiked Wigner model where the prior on the signal to reconstruct is ignored. In chapter 5 we analyze the statistical limits of a spiked Wigner model where the noise is no longer Gaussian, but drawn from a random matrix ensemble, which makes its elements dependent. The thesis ends with chapter 6, where the challenging problem of high-rank probabilistic matrix factorization is tackled. Here we introduce a new procedure called "decimation" and we show that it is theoretically to perform matrix factorization through it.

Numerical modelling of rock masses interacting with dams: the case of Ridracoli

A three-dimensional Direct Finite Element procedure is here presented which takes into account most of the factors affecting the interaction problem of the dam-water-foundation system, whilst keeping the computational cost at a reasonable level by introducing some simplified hypotheses. A truncated domain is defined, and the dynamic behaviour of the system is treated as a wave-scattering problem where the presence of the dam perturbs an original free-field system. The rock foundation truncated boundaries are enclosed by a set of free-field one-dimensional and two-dimensional systems which transmit the effective forces to the main model and apply adsorbing viscous boundaries to ensure radiation damping. The water domain is treated as an added mass moving with the dam. A strategy is proposed to keep the viscous dampers at the boundaries unloaded during the initial phases of analysis, when the static loads are initialised, and thus avoid spurious displacements. A focus is given to the nonlinear behaviour of the rock foundation, with concentrated plasticity along the natural discontinuities of the rock mass, immersed in an otherwise linear elastic medium with Rayleigh damping. The entire procedure is implemented in the commercial software Abaqus®, whose base code is enriched with specific user subroutines when needed. All the extra coding is attached to the Thesis and tested against analytical results and simple examples. Possible rock wedge instabilities induced by intense ground motion, which are not easily investigated within a comprehensive model of the dam-water-foundation system, are treated separately with a simplified decoupled dynamic approach derived from the classical Newmark method, integrated with FE calculation of dam thrust on the wedges during the earthquake. Both the described approaches are applied to the case study of the Ridracoli arch-gravity dam (Italy) in order to investigate its seismic response to the Maximum Credible Earthquake (MCE) in a full reservoir condition.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.

Optimizing fused deposition modelling: the impact of geometrical slicing parameters on mechanical properties

This comprehensive study explores the intricate world of 3D printing, with a focus on Fused Deposition Modelling (FDM). It sheds light on the critical factors that influence the quality and mechanical properties of 3D printed objects. Using an optical microscope with 40X magnification, the shapes of the printed beads is correlated to specific slicing parameters, resulting in a 2D parametric model. This mathematical model, derived from real samples, serves as a tool to predict general mechanical behaviour, bridging the gap between theory and practice in FDM printing. The study begins by emphasising the importance of geometric parameters such as layer height, line width and filament tolerance on the final printed bead geometry and the resulting theoretical effect on mechanical properties. The introduction of VPratio parameter (ratio between the area of the voids and the area occupied by printed material) allows the quantification of the variation of geometric slicing parameters on the improvement or reduction of mechanical properties. The study also addresses the effect of overhang and the role of filament diameter tolerances. The research continues with the introduction of 3D FEM (Finite Element Analysis) models based on the RVE (Representative Volume Element) to verify the results obtained from the 2D model and to analyse other aspects that affect mechanical properties and not directly observable with the 2D model. The study also proposes a model for the examination of 3D printed infill structures, introducing also an innovative methodology called “double RVE” which speeds up the calculation of mechanical properties and is also more computationally efficient. Finally, the limitations of the RVE model are shown and a so-called Hybrid RVE-based model is created to overcome the limitations and inaccuracy of the conventional RVE model and homogenization procedure on some printed geometries.

The Basic Reproduction Number Obtained From Jacobian And Next Generation Matrices - A Case Study Of Dengue Transmission Modelling.

The basic reproduction number is a key parameter in mathematical modelling of transmissible diseases. From the stability analysis of the disease free equilibrium, by applying Routh-Hurwitz criteria, a threshold is obtained, which is called the basic reproduction number. However, the application of spectral radius theory on the next generation matrix provides a different expression for the basic reproduction number, that is, the square root of the previously found formula. If the spectral radius of the next generation matrix is defined as the geometric mean of partial reproduction numbers, however the product of these partial numbers is the basic reproduction number, then both methods provide the same expression. In order to show this statement, dengue transmission modelling incorporating or not the transovarian transmission is considered as a case study. Also tuberculosis transmission and sexually transmitted infection modellings are taken as further examples.

Photoelastic And Finite Element Analyses Of Occlusal Loads In Mandibular Body.

This study proposed to evaluate the mandibular biomechanics in the posterior dentition based on experimental and computational analyses. The analyses were performed on a model of human mandible, which was modeled by epoxy resin for photoelastic analysis and by computer-aided design for finite element analysis. To standardize the evaluation, specific areas were determined at the lateral surface of mandibular body. The photoelastic analysis was configured through a vertical load on the first upper molar and fixed support at the ramus of mandible. The same configuration was used in the computer simulation. Force magnitudes of 50, 100, 150, and 200 N were applied to evaluate the bone stress. The stress results presented similar distribution in both analyses, with the more intense stress being at retromolar area and oblique line and alveolar process at molar level. This study presented the similarity of results in the experimental and computational analyses and, thus, showed the high importance of morphology biomechanical characterization at posterior dentition.

Metacommunity Versus Biogeography: A Case Study Of Two Groups Of Neotropical Vegetation-dwelling Arthropods.

Biogeography and metacommunity ecology provide two different perspectives on species diversity. Both are spatial in nature but their spatial scales do not necessarily match. With recent boom of metacommunity studies, we see an increasing need for clear discrimination of spatial scales relevant for both perspectives. This discrimination is a necessary prerequisite for improved understanding of ecological phenomena across scales. Here we provide a case study to illustrate some spatial scale-dependent concepts in recent metacommunity studies and identify potential pitfalls. We presented here the diversity patterns of Neotropical lepidopterans and spiders viewed both from metacommunity and biogeographical perspectives. Specifically, we investigated how the relative importance of niche- and dispersal-based processes for community assembly change at two spatial scales: metacommunity scale, i.e. within a locality, and biogeographical scale, i.e. among localities widely scattered along a macroclimatic gradient. As expected, niche-based processes dominated the community assembly at metacommunity scale, while dispersal-based processes played a major role at biogeographical scale for both taxonomical groups. However, we also observed small but significant spatial effects at metacommunity scale and environmental effects at biogeographical scale. We also observed differences in diversity patterns between the two taxonomical groups corresponding to differences in their dispersal modes. Our results thus support the idea of continuity of processes interactively shaping diversity patterns across scales and emphasize the necessity of integration of metacommunity and biogeographical perspectives.

Finite-element Analysis Of 3 Situations Of Trauma In The Human Edentulous Mandible.

Maxillofacial trauma resulting from falls in elderly patients is a major social and health care concern. Most of these traumatic events involve mandibular fractures. The aim of this study was to analyze stress distributions from traumatic loads applied on the symphyseal, parasymphyseal, and mandibular body regions in the elderly edentulous mandible using finite-element analysis (FEA). Computerized tomographic analysis of an edentulous macerated human mandible of a patient approximately 65 years old was performed. The bone structure was converted into a 3-dimensional stereolithographic model, which was used to construct the computer-aided design (CAD) geometry for FEA. The mechanical properties of cortical and cancellous bone were characterized as isotropic and elastic structures, respectively, in the CAD model. The condyles were constrained to prevent free movement in the x-, y-, and z-axes during simulation. This enabled the simulation to include the presence of masticatory muscles during trauma. Three different simulations were performed. Loads of 700 N were applied perpendicular to the surface of the cortical bone in the symphyseal, parasymphyseal, and mandibular body regions. The simulation results were evaluated according to equivalent von Mises stress distributions. Traumatic load at the symphyseal region generated low stress levels in the mental region and high stress levels in the mandibular neck. Traumatic load at the parasymphyseal region concentrated the resulting stress close to the mental foramen. Traumatic load in the mandibular body generated extensive stress in the mandibular body, angle, and ramus. FEA enabled precise mapping of the stress distribution in a human elderly edentulous mandible (neck and mandibular angle) in response to 3 different traumatic load conditions. This knowledge can help guide emergency responders as they evaluate patients after a traumatic event.

O modelamento estatístico de misturas: experimento tutorial usando voltametria de redissolução anódica

This work illustrates the modeling procedure for a solvent mixture using the simplex- centroid approach. The selected experiment was the optimization of the peak current observed in the direct determination of nickel by anodic stripping voltammetry (ASV) in a solvent mixture composed of N,N-dimethylformamide, ethanol and water. The text is presented in a tutorial way, showing in detail the several steps which must be followed in such a process. Since not all possible mixtures lead to a measurable instrumental response, pseudocomponents had to be used to rescale the experimental design. This also allows to show how to apply this tool, usually troublesome for non-specialists in mixture modeling procedures.

Efeito da adição de cinza da casca de arroz em misturas cimento-casca de arroz

The rice husk and its ash are abundant and renewable and can be used to obtain alternative building materials. An increase in the consumption of such waste could help minimize the environmental problems from their improper disposal. This study aimed to evaluate the use of ashes as a cargo mineral (filler). However, the rice husk chemically interferes in the conduct of the based cement mixtures. Thus, different mixes cement-rice husk with and without the addition of ash were evaluated in order to highlight the influence of its components (husk; ash), which could otherwise be excluded or be underestimated. Cylindrical samples (test of simple compression and traction by diametrical compression) and samples extracted from manufactured pressed board (test of bending and parallel compression to the surface), were used to evaluate the behavior of different mixtures of components (rice hush; RHA - rice husk ahs). The results of the mechanical tests showed, in general, there is not a statistical difference between the mixtures, which are associated with the chemical suppressive effect of the rice husk ash. The mixture of rice husk of 10 mm, with an addition of 35% of the rice husk ash, is notable for allowing the highest consumption of rice husk and rice husk ash, to reduce 25% the consumption of cement and to allow the storage (without emissions to the atmosphere), around 1.9 ton of CO2 per ton of cement consumed, thus contributing to the reduction of CO2 emissions, which can stimulate rural constructions under an ecological point of view.