Physical soil attributes of Conilon coffee (Coffea canephora) under organic and conventional management systems.

Coffea sp. is cultivated in large areas, using both conventional and organic management. However, information about the sustainability of these two management systems is still deficient. The objective of the present study was to evaluate the physical properties of soil cultivated with Conilon coffee (C. canephora) under organic and conventional management. Two areas cultivated with Conilon coffee (under organic and conventional management) and a fragment of Atlantic forest, used as a reference, were selected for the experiment. Soil granulometry, hydraulic conductivity, water retention curve, resistance to penetration, porosity, optimal hydric interval, and other physical characteristics were measured at depths of 0 to 10 and 10 to 20 cm. The data was submitted to multivariate and descriptive statistical analyses. Higher similarity was observed between the soil cultivated with Conilon coffee under organic management and the Atlantic forest soil. Soil resistance to penetration at 10, 30, 100, 500 and 1500 kPa, macro porosity, density and total porosity were the main physical properties that differentiated both management systems studied. The non-use of agricultural machinery and the addition of organic matter may be the main reasons for higher soil sustainability observed under organic management when compared with the conventional system.

Understanding, defining and assessing food systems’ social and economic sustainability indicators: implementation of mixed methodologies for policy insights

Sustainability encompasses the presence of three dimensions that must coexist simultaneously, namely the environmental, social, and economic ones. The economic and social dimensions are gaining the spotlight in recent years, especially within food systems. To assess social and economic impacts, indicators and tools play a fundamental role in contributing to the achievements of sustainability targets, although few of them have deepen the focus on social and economic impacts. Moreover, in a framework of citizen science and bottom-up approach for improving food systems, citizen play a key role in defying their priorities in terms of social and economic interventions. This research expands the knowledge of social and economic sustainability indicators within the food systems for robust policy insights and interventions. This work accomplishes the following objectives: 1) to define social and economic indicators within the supply chain with a stakeholder perspective, 2) to test social and economic sustainability indicators for future food systems engaging young generations. The first objective was accomplished through the development of a systematic literature review of 34 social sustainability tools, based on five food supply chain stages, namely production, processing, wholesale, retail, and consumer considering farmers, workers, consumers, and society as stakeholders. The second objective was achieved by defining and testing new food systems social and economic sustainability indicators through youth engagement for informed and robust policy insights, to provide policymakers suggestions that would incorporate young generations ones. Future food systems scenarios were evaluated by youth through focus groups, whose results were analyzed through NVivo and then through a survey with a wider platform. Conclusion addressed the main areas of policy interventions in terms of social and economic aspects of sustainable food systems youth pointed out as in need of interventions, spanning from food labelling reporting sustainable origins to better access to online food services.

Design and evaluation of aerosol and liquid separation systems

Conventional chromatographic columns are packed with porous beads by the universally employed slurry-packing method. The lack of precise control of the particle size distribution, shape and position inside the column have dramatic effects on the separation efficiency. In the first part the thesis an ordered, three-dimensional, pillar-array structure was designed by a CAD software. Several columns, characterized by different fluid distributors and bed length, were produced by a stereolithographic 3D printer and compared in terms of pressure drop and height equivalent to a theroretical plate (HETP). To prevent the release of unwanted substances and to provide a surface for immobilizing a ligand, pillars were coated with one or more of the following materials: titanium dioxide, nanofibrillated cellulose (NFC) and polystyrene. The external NFC layer was functionalized with Cibacron Blue and the dynamic binding capacity of the column was measured by performing three chromatographic cycles, using bovine serum albumin (BSA) as target molecule. The second part of the thesis deals with Covid-19 pandemic related research activities. In early 2020, due to the pandemic outbreak, surgical face masks became an essential non-pharmaceutical intervention to limit the spread. To address the consequent shortage and to support the reconversion of the Italian industry, in late March 2020 a multidisciplinary group of the University of Bologna created the first Italian laboratory able to perform all the tests required for the evaluation and certification of surgical masks. More than 1200 tests were performed on about 350 prototypes, according to the standard EN 14683:2019. The results were analyzed to define the best material properties and masks composition for the production of masks with excellent efficiency. To optimize the usage of surgical masks and to reduce their environmental burden, the variation of their performance over time of usage were investigated as to determine the maximum lifetime.

Exploring domain-informed and physics-guided learning in image-to-image translation

Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.

Impacts of trace element pollution on sewage sludge and soil-plant systems: developing remediation techniques and assessing plant uptake prevention measures

Trace Elements (TEs) pollution is a significant environmental concern due to its toxic effects on human and ecosystem health and its potential to bioaccumulate in the food chain and to threaten species survival, leading to a decline in biodiversity. Urban areas, industrial and mining activities, agricultural practices, all contribute to the release of TEs into the environment posing a significant risk to human health and ecosystems. Several techniques have been developed to control TEs into the environment. This work presents the findings of three-year PhD program that focused on research on TEs pollution. The study discusses three fundamental aspects related to this topic from the perspective of sustainable development, environmental and human health. (1) High levels of TEs contamination prevent the use of sewage sludge (SS) as a fertilizer in agriculture, despite its potential as a soil amendment. Developing effective techniques to manage TEs contamination in SS is critical to ensure its safe use in agriculture and promote resource efficiency through sludge reuse. Another purpose of the study was to evaluate different strategies to limit the TEs uptake by horticultural crops (specifically, Cucumis Melo L.). This study addressed the effect of seasonality, Trichoderma inoculation and clinoptilolite application on chromium (Cr), copper (Cu) and lead (Pb) content of early- and late-ripening cultivars of Cucumis Melo L.. Finally, the accumulation of copper and the effect of its bioavailable fraction on bacterial and fungal communities in the rhizosphere soil of two vineyards, featuring two different varieties of Vitis vinifera grown for varying lengths of time, were evaluated.

Crystal engineering of co-crystals and host-guest systems for environmentally friendly applications: sunscreens stabilization, pollutant sequestering and herbicide reformulation.

Solid state engineered materials have proven to be useful and suitable tools in the quest of new materials. In this thesis different crystalline compounds were synthesized to provide more sustainable products for different applications, as in cosmetics or in agrochemistry, to propose pollutants removal strategy or to obtain materials for electrocatalysis. Therefore, the research projects presented here can be divided into three main topics: (i) sustainable preparation of solid materials of widely used active ingredients aimed at the reduction of their occurrence in the natural environment. The systems studied in this section are cyclodextrins host-guest compounds, obtained via mechanochemical and slurry synthesis. The first chemicals studied are sunscreens inclusion complexes, that proved to have enhanced photostability and desired photoprotection. The same synthetic methods were applied to obtain inclusion complexes of bentazon, a herbicide often found to leach in groundwaters. The resulting products showed to have desired water solubility properties. The same herbicide was also adsorbed on amorphous calcium phosphate nanoparticles, to obtain a biocompatible formulation of this agrochemical. This herbicide could benefit by the adsorption on nanoparticles for what concerns its kinetic release in different media as well as its photostability. (ii) Sustainable synthesis of co-crystals based on polycyclic aromatic hydrocarbons, for the proposal of a sequestering method with a resulting material with enhanced properties. The co-crystallization via mechanochemical means proved that these pollutants can be sequestered via simple solvent-free synthesis and the obtained materials present better photochemical properties when compared to the starting co-formers. (iii) Crystallization from mild solvents of nanosized materials useful for the application in electrocatalysis. The study of compounds based on nickel and cobalt metal ions resulted in the obtainment of 2D and 1D coordination polymers. Moreover, solid solutions were obtained. These crystals showed layered structures and, according to preliminary results, they can be exfoliated.

Governing algorithms in the big data era for balancing new digital rights - designing GDPR compliant and trustworthy XAI systems

This thesis investigates the legal, ethical, technical, and psychological issues of general data processing and artificial intelligence practices and the explainability of AI systems. It consists of two main parts. In the initial section, we provide a comprehensive overview of the big data processing ecosystem and the main challenges we face today. We then evaluate the GDPR’s data privacy framework in the European Union. The Trustworthy AI Framework proposed by the EU’s High-Level Expert Group on AI (AI HLEG) is examined in detail. The ethical principles for the foundation and realization of Trustworthy AI are analyzed along with the assessment list prepared by the AI HLEG. Then, we list the main big data challenges the European researchers and institutions identified and provide a literature review on the technical and organizational measures to address these challenges. A quantitative analysis is conducted on the identified big data challenges and the measures to address them, which leads to practical recommendations for better data processing and AI practices in the EU. In the subsequent part, we concentrate on the explainability of AI systems. We clarify the terminology and list the goals aimed at the explainability of AI systems. We identify the reasons for the explainability-accuracy trade-off and how we can address it. We conduct a comparative cognitive analysis between human reasoning and machine-generated explanations with the aim of understanding how explainable AI can contribute to human reasoning. We then focus on the technical and legal responses to remedy the explainability problem. In this part, GDPR’s right to explanation framework and safeguards are analyzed in-depth with their contribution to the realization of Trustworthy AI. Then, we analyze the explanation techniques applicable at different stages of machine learning and propose several recommendations in chronological order to develop GDPR-compliant and Trustworthy XAI systems.

Continual learning on nano drones

Miniaturized flying robotic platforms, called nano-drones, have the potential to revolutionize the autonomous robots industry sector thanks to their very small form factor. The nano-drones’ limited payload only allows for a sub-100mW microcontroller unit for the on-board computations. Therefore, traditional computer vision and control algorithms are too computationally expensive to be executed on board these palm-sized robots, and we are forced to rely on artificial intelligence to trade off accuracy in favor of lightweight pipelines for autonomous tasks. However, relying on deep learning exposes us to the problem of generalization since the deployment scenario of a convolutional neural network (CNN) is often composed by different visual cues and different features from those learned during training, leading to poor inference performances. Our objective is to develop and deploy and adaptation algorithm, based on the concept of latent replays, that would allow us to fine-tune a CNN to work in new and diverse deployment scenarios. To do so we start from an existing model for visual human pose estimation, called PULPFrontnet, which is used to identify the pose of a human subject in space through its 4 output variables, and we present the design of our novel adaptation algorithm, which features automatic data gathering and labeling and on-device deployment. We therefore showcase the ability of our algorithm to adapt PULP-Frontnet to new deployment scenarios, improving the R2 scores of the four network outputs, with respect to an unknown environment, from approximately [−0.2, 0.4, 0.0,−0.7] to [0.25, 0.45, 0.2, 0.1]. Finally we demonstrate how it is possible to fine-tune our neural network in real time (i.e., under 76 seconds), using the target parallel ultra-low power GAP 8 System-on-Chip on board the nano-drone, and we show how all adaptation operations can take place using less than 2mWh of energy, a small fraction of the available battery power.

Dominant set approach to ECG biometrics

Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new ECG acquisition paradigms that use fingers or hand palms, which lead to signals with lower signal to noise ratio, and more prone to noise artifacts. Preliminary results show the potential of the approach, helping to further validate the highly usable setups and ECG signals as a complementary biometric modality.

A survey addressing the fundamental matrix estimation problem

Epipolar geometry is a key point in computer vision and the fundamental matrix estimation is the only way to compute it. This article surveys several methods of fundamental matrix estimation which have been classified into linear methods, iterative methods and robust methods. All of these methods have been programmed and their accuracy analysed using real images. A summary, accompanied with experimental results, is given

Shape complexity based on mutual information

Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others

Segmentació de punts 3D rígids i no rígids obtinguts a partir d’un sistema estèreo

L’estudi consta de dues grans parts que serien la part de dissenyar, desenvolupar i implementar els mètodes de segmentació que ens serviran per separar els punts rígids dels punts no rígids/deformables. I l’altra part seria la d’obtenir reconstruccions 3D a partir d’un sistema estèreo, passant per la calibració de les càmeres del sistema, la realització de captures d’experiments reals, la generació de reconstruccions 3D per finalment posar a prova els mètodes desenvolupats en la part anterior

Desenvolupament d’un sistema de visió estereoscòpica per a la reconstrucció tridimensional d’objectes

El projecte consisteix en analitzar, dissenyar i desenvolupar un sistemaestèreo binocular (format per dues càmeres) sobre un suport que ofereixi la mobilitat iportabilitat necessària per utilitzar-lo de forma independent, és a dir, sense necessitat deconnexió a un ordinador, ja que normalment, els sistemes de visió per computador solenincorporar un ordinador amb un frame grabber (placa de captura d’imatges). Per a dur a terme elsistema estèreo més adient, s’analitzaran els requeriments necessaris, s’estudiaran diferentsalternatives, i finalment, es desenvoluparà i es demostrarà el funcionament del sistema en qüestió

Aplicació de visió per dispositius mòbils

En aquest projecte es presenta l’aplicació per a dispositius mòbils Doppelganger. La seva funció és, a partir d’una fotografia, detectar la cara i mostrar la persona famosa de la nostra base de dades que més s’assembla a la persona en la fotografia. Per la implementació s’han utilitzat algoritmes de visió per computador i d’aprenentatge automàtic com per exemple el PCA i el K-Nearest Neighbor, tot utilitzant llibreries gratuïtes com són les OpenCV.

Sistema d'estereovisió amb una càmera

Els sistemes de visió estèreo es basen en la reconstrucció per triangulació a partir de dues càmeres, permetent la representació d’objectes del món real en tres dimensions. L’objectiu d’aquest projecte consisteix a dissenyar i implementar un sistema estèreo amb una sola càmera amb dos petits vidres d’alta transmissivitat davant de la lent, utilitzant la teoria clàssica desenvolupada a partir de dues càmeres. D’aquesta forma obtindrem un sistema molt més compacte que en el cas de tenir dues càmeres, que serà apte per entorns molt reduïts i per escenes molt properes