Advanced Mission Synthesis Algorithms for Vibration-based Accelerated Life-testing

In the field of vibration qualification testing, with the popular Random Control mode of shakers, the specimen is excited by random vibrations typically set in the form of a Power Spectral Density (PSD). The corresponding signals are stationary and Gaussian, i.e. featuring a normal distribution. Conversely, real-life excitations are frequently non-Gaussian, exhibiting high peaks and/or burst signals and/or deterministic harmonic components. The so-called kurtosis is a parameter often used to statistically describe the occurrence and significance of high peak values in a random process. Since the similarity between test input profiles and real-life excitations is fundamental for qualification test reliability, some methods of kurtosis-control can be implemented to synthesize realistic (non-Gaussian) input signals. Durability tests are performed to check the resistance of a component to vibration-based fatigue damage. A procedure to synthesize test excitations which starts from measured data and preserves both the damage potential and the characteristics of the reference signals is desirable. The Fatigue Damage Spectrum (FDS) is generally used to quantify the fatigue damage potential associated with the excitation. The signal synthesized for accelerated durability tests (i.e. with a limited duration) must feature the same FDS as the reference vibration computed for the component’s expected lifetime. Current standard procedures are efficient in synthesizing signals in the form of a PSD, but prove inaccurate if reference data are non-Gaussian. This work presents novel algorithms for the synthesis of accelerated durability test profiles with prescribed FDS and a non-Gaussian distribution. An experimental campaign is conducted to validate the algorithms, by testing their accuracy, robustness, and practical effectiveness. Moreover, an original procedure is proposed for the estimation of the fatigue damage potential, aiming to minimize the computational time. The research is thus supposed to improve both the effectiveness and the efficiency of excitation profile synthesis for accelerated durability tests.

Nanostructured piezoelectric materials for the design and development of self-sensing composite materials and energy harvesting devices

The work activities reported in this PhD thesis regard the functionalization of composite materials and the realization of energy harvesting devices by using nanostructured piezoelectric materials, which can be integrated in the composite without affecting its mechanical properties. The self-sensing composite materials were fabricated by interleaving between the plies of the laminate the piezoelectric elements. The problem of negatively impacting on the mechanical properties of the hosting structure was addressed by shaping the piezoelectric materials in appropriate ways. In the case of polymeric piezoelectric materials, the electrospinning technique allowed to produce highly-porous nanofibrous membranes which can be immerged in the hosting matrix without inducing delamination risk. The flexibility of the polymers was exploited also for the production of flexible tactile sensors. The sensing performances of the specimens were evaluated also in terms of lifetime with fatigue tests. In the case of ceramic piezo-materials, the production and the interleaving of nanometric piezoelectric powder limitedly affected the impact resistance of the laminate, which showed enhanced sensing properties. In addition to this, a model was proposed to predict the piezoelectric response of the self-sensing composite materials as function of the amount of the piezo-phase within the laminate and to adapt its sensing functionalities also for quasi-static loads. Indeed, one final application of the work was to integrate the piezoelectric nanofibers in the sole of a prosthetic foot in order to detect the walking cycle, which has a period in the order of 1 second. In the end, the energy harvesting capabilities of the piezoelectric materials were investigated, with the aim to design wearable devices able to collect energy from the environment and from the body movements. The research activities focused both on the power transfer capability to an external load and the charging of an energy storage unit, like, e.g., a supercapacitor.

Unveiling the unknown of cool stars with high-resolution spectroscopy

Cool giant and supergiant stars are among the brightest populations in any stellar system and they are easily observable out to large distances, especially at infrared wavelengths. These stars also dominate the integrated light of star clusters in a wide range of ages, making them powerful tracers of stellar populations in more distant galaxies. High-resolution near-IR spectroscopy is a key tool for quantitatively investigating their kinematic, evolutionary and chemical properties. However, the systematic exploration and calibration of the NIR spectral diagnostics to study these cool stellar populations based on high-resolution spectroscopy is still in its pioneering stage. Any effort to make progress in the field is innovative and of impact on stellar archaeology and stellar evolution. This PhD project takes the challenge of exploring that new parameter space and characterizing the physical properties, the chemical content and the kinematics of cool giants and supergiants in selected disc fields and clusters of our Galaxy, with the ultimate goal of tracing their past and recent star formation and chemical enrichment history. By using optical HARPS-N and near-infrared GIANO-B high-resolution stellar spectra in the context of the large program SPA-Stellar Population Astrophysics: the detailed, age-resolved chemistry of the Milky Way disk” (PI L. Origlia), an extensive study of Arcturus, a standard calibrator for red giant stars, has been performed. New diagnostics of stellar parameters as well as optimal linelists for chemical analysis have been provided. Then, such diagnostics have been used to determine evolutionary properties, detailed chemical abundances of almost 30 different elements and mixing processes of a homogeneous sample of red supergiant stars in the Perseus complex.

Integration of by-products and waste from agri-food supply chains into the «water-energy-food» nexus (RECOW-ER): the role of fungi

The agricultural sector is undoubtedly one of the sectors that has the greatest impact on the use of water and energy to produce food. The circular economy allows to reduce waste, obtaining maximum value from products and materials, through the extraction of all possible by-products from resources. Circular economy principles for agriculture include recycling, processing, and reusing agricultural waste in order to produce bioenergy, nutrients, and biofertilizers. Since agro-industrial wastes are principally composed of lignin, cellulose, and hemicellulose they can represent a suitable substrate for mushroom growth and cultivation. Mushrooms are also considered healthy foods with several medicinal properties. The thesis is structured in seven chapters. In the first chapter an introduction on the water, energy, food nexus, on agro-industrial wastes and on how they can be used for mushroom cultivation is given. Chapter 2 details the aims of this dissertation thesis. In chapters three and four, corn digestate and hazelnut shells were successfully used for mushroom cultivation and their lignocellulosic degradation capacity were assessed by using ATR-FTIR spectroscopy. In chapter five, through the use of the Surface-enhanced Raman Scattering (SERS) spectroscopy was possible to set-up a new method for studying mushroom composition and for identifying different mushroom species based on their spectrum. In chapter six, the isolation of different strains of fungi from plastic residues collected in the fields and the ability of these strains to growth and colonizing the Low-density Polyethylene (LDPE) were explored. The structural modifications of the LDPE, by the most efficient fungal strain, Cladosporium cladosporioides Clc/1 strain were monitored by using the Scanning Electron Microscope (SEM) and ATR-FTIR spectroscopy. Finally, chapter seven outlines the conclusions and some hints for future works and applications are provided.

Hetero-bifunctional molecules as possible therapeutics for the treatment of Alzheimer’s disease.

Alzheimer’s disease (AD) is the most common form of dementia, currently affecting more than 50 million people worldwide. In recent years attention towards this disease has risen in search for discovery and development of a drug that can stop it. Indeed, therapies for AD provide only temporary symptomatic relief. The cause for the high attrition rate for AD drug discovery has been attributed to several factors, including the fact that the AD pathogenesis is not yet fully understood. Nevertheless, what is increasingly recognized is that AD is a multifactorial syndrome, characterized by many conditions which may lead to neuronal death. Given this, it is widely accepted that a molecule able to modulate more than one target would bring benefit to the therapy of AD. In the first chapter of this thesis, there are reported two projects regarding the design and synthesis of new series of GSK-3/HDAC dual inhibitors, two of the main enzymes involved in AD. Two different series of compounds were synthesized and evaluated for their inhibitory activity towards the target enzymes. The best compounds of the series were selected for further biologic investigation to evaluate their properties. The second project focused on the design of non ATP-competitive GSK-3 inhibitors combined with HDAC inhibition properties. Also in this case, the best compounds of the series were selected for biologic investigation to further evaluate their properties. In chapter 2, the design and synthesis of a GSK-3-directed Proteolysis Targeting Chimeras (PROTAC), a new technology in drug discovery that act through degradation rather than inhibition, is reported. The design and synthesis of a small series of GSK-3-directed PROTACs was achieved. In vitro assays were performed to evaluate the GSK-3-degradation ability, the effective involvement of E3 ubiquitine ligase in the process and their neuroprotective abilities.

On the motion planning & control of nonlinear robotic systems

In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance.

Unveiling the expansion history of the universe with cosmic chronometers and gravitational waves

This Thesis explores two novel and independent cosmological probes, Cosmic Chronometers (CCs) and Gravitational Waves (GWs), to measure the expansion history of the Universe. CCs provide direct and cosmology-independent measurements of the Hubble parameter H(z) up to z∼2. In parallel, GWs provide a direct measurement of the luminosity distance without requiring additional calibration, thus yielding a direct measurement of the Hubble constant H0=H(z=0). This Thesis extends the methodologies of both of these probes to maximize their scientific yield. This is achieved by accounting for the interplay of cosmological and astrophysical parameters to derive them jointly, study possible degeneracies, and eventually minimize potential systematic effects. As a legacy value, this work also provides interesting insights into galaxy evolution and compact binary population properties. The first part presents a detailed study of intermediate-redshift passive galaxies as CCs, with a focus on the selection process and the study of their stellar population properties using specific spectral features. From their differential aging, we derive a new measurement of the Hubble parameter H(z) and thoroughly assess potential systematics. In the second part, we develop a novel methodology and pipeline to obtain joint cosmological and astrophysical population constraints using GWs in combination with galaxy catalogs. This is applied to GW170817 to obtain a measurement of H0. We then perform realistic forecasts to predict joint cosmological and astrophysical constraints from black hole binary mergers for upcoming gravitational wave observatories and galaxy surveys. Using these two probes we provide an independent reconstruction of H(z) with direct measurements of H0 from GWs and H(z) up to z∼2 from CCs and demonstrate that they can be powerful independent probes to unveil the expansion history of the Universe.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.

Numerical modeling on the short-term response of fiber-reinforced concrete round panels

Fiber-reinforced concrete is a composite material consisting of discrete, discontinuous, and uniformly distributed fibers in plain concrete primarily used to enhance the tensile properties of the concrete. FRC performance depends upon the fiber, interface, and matrix properties. The use of fiber-reinforced concrete has been increasing substantially in the past few years in different fields of the construction industry such as ground-level application in sidewalks and building floors, tunnel lining, aircraft parking, runways, slope stabilization, etc. Many experiments have been performed to observe the short-term and long-term mechanical behavior of fiber-reinforced concrete in the last decade and numerous numerical models have been formulated to accurately capture the response of fiber-reinforced concrete. The main purpose of this dissertation is to numerically calibrate the short-term response of the concrete and fiber parameters in mesoscale for the three-point bending test and cube compression test in the MARS framework which is based on the lattice discrete particle model (LDPM) and later validate the same parameters for the round panels. LDPM is the most validated theory in mesoscale theories for concrete. Different seeds representing the different orientations of concrete and fiber particles are simulated to produce the mean numerical response. The result of numerical simulation shows that the lattice discrete particle model for fiber-reinforced concrete can capture results of experimental tests on the behavior of fiber-reinforced concrete to a great extent.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

Brazilian Natural Fiber (jute) As Raw Material For Activated Carbon Production.

Jute fiber is the second most common natural cellulose fiber worldwide, especially in recent years, due to its excellent physical, chemical and structural properties. The objective of this paper was to investigate: the thermal degradation of in natura jute fiber, and the production and characterization of the generated activated carbon. The production consisted of carbonization of the jute fiber and activation with steam. During the activation step the amorphous carbon produced in the initial carbonization step reacted with oxidizing gas, forming new pores and opening closed pores, which enhanced the adsorptive capacity of the activated carbon. N2 gas adsorption at 77K was used in order to evaluate the effect of the carbonization and activation steps. The results of the adsorption indicate the possibility of producing a porous material with a combination of microporous and mesoporous structure, depending on the parameters used in the processes, with resulting specific surface area around 470 m2.g-1. The thermal analysis indicates that above 600°C there is no significant mass loss.

Removal Of Glyphosate Herbicide From Water Using Biopolymer Membranes.

Enormous amounts of pesticides are manufactured and used worldwide, some of which reach soils and aquatic systems. Glyphosate is a non-selective herbicide that is effective against all types of weeds and has been used for many years. It can therefore be found as a contaminant in water, and procedures are required for its removal. This work investigates the use of biopolymeric membranes prepared with chitosan (CS), alginate (AG), and a chitosan/alginate combination (CS/AG) for the adsorption of glyphosate present in water samples. The adsorption of glyphosate by the different membranes was investigated using the pseudo-first order and pseudo-second order kinetic models, as well as the Langmuir and Freundlich isotherm models. The membranes were characterized regarding membrane solubility, swelling, mechanical, chemical and morphological properties. The results of kinetics experiments showed that adsorption equilibrium was reached within 4 h and that the CS membrane presented the best adsorption (10.88 mg of glyphosate/g of membrane), followed by the CS/AG bilayer (8.70 mg of glyphosate/g of membrane). The AG membrane did not show any adsorption capacity for this herbicide. The pseudo-second order model provided good fits to the glyphosate adsorption data on CS and CS/AG membranes, with high correlation coefficient values. Glyphosate adsorption by the membranes could be fitted by the Freundlich isotherm model. There was a high affinity between glyphosate and the CS membrane and moderate affinity in the case of the CS/AG membrane. Physico-chemical characterization of the membranes showed low values of solubility in water, indicating that the membranes are stable and not soluble in water. The SEM and AFM analysis showed evidence of the presence of glyphosate on CS membranes and on chitosan face on CS/AG membranes. The results showed that the glyphosate herbicide can be adsorbed by chitosan membranes and the proposed membrane-based methodology was successfully used to treat a water sample contaminated with glyphosate. Biopolymer membranes therefore potentially offer a versatile method to eliminate agricultural chemicals from water supplies.

Caracterização físico-química de filmes comestíveis de amido adicionado de acerola (Malphigia emarginata D.C.)

Edibles films are an alternative to synthetic materials used for packing food products. Barbados cherry is rich in vitamin C and carotenoids. The aim of this study was to characterize and develop films by casting from cassava starch, lyophilized Barbados cherry pulp and glycerol. The films were characterized with respect to thickness, water vapor permeability (WVP), water solubility, vitamin C, carotene and mechanical properties. The interaction of pulp and glycerol reduced film thickness. An increase in pulp concentration up to 60% increased WVP but beyond this concentration reduced both WVP and solubility leading to an increased level of vitamin C and β carotene in the films.

Atividade de extratos de Arrabidaea chica (Humb. & Bonpl.) Verlot obtidos por processos biotecnológicos sobre a proliferação de fibroblastos e células tumorais humanas

Arrabidaea chica (H&B) Verlot is a plant popularly known as Pariri and this species is a known source of anthocyanins, flavonoids and tannins. This report describes an approach involving enzymatic treatment prior to extraction procedures to enhance A chica crude extract anticancer activity. Anticancer activity in human cancer cell lines in vitro using a 48 h SRB cell viability assay was performed to determine growth inhibition and cytotoxic properties. The final extraction yield without enzyme treatment was higher (24.28%) compared to the enzyme-treated material (19.03%), with an enhanced aglycones anthocyanin ratio as determined by HPLC- DAD and LC-MS with direct infusion.

PREDIÇÃO DA ATIVIDADE DE ÁGUA E RELAÇÃO ENTRE A ATIVIDADE DE ÁGUA E A DEPRESSÃO DO PONTO DE CONGELAMENTO DE SUCOS DE FRUTAS

This work proposes to determine the water activity and the freezing point depression of tangerine, pineapple and lemon juices at various concentrations (10-55oBrix) and to achieve a correlation between these properties. The freezing point depression was determined with a LAKTRON cryoscope and common laboratory materials. The water activity was determined with a DECAGON CX-2 hygrometer in the temperature range of 15 to 30oC. With the results, the adjustment to CHEN (1987) water activity prediction equation to non-electrolyte mixtures was verified, through the calculation of the variation coefficient (CV). Being CV smaller than 3% for the proposed model, it can be said that the experimental data have adjusted well to the prediction equation. The water activity and the freezing point depression was correlated for tangerine, pineapple and lemon juices and r2 values were higher than 99%. Therefore, it is possible to obtain the water activity by knowing the freezing point depression of studied juices.