Learning from errors: Psychological, relational, and cultural aspects

Although errors might foster learning, they can also be perceived as something to avoid if they are associated with negative consequences (e.g., receiving a bad grade or being mocked by classmates). Such adverse perceptions may trigger negative emotions and error-avoidance attitudes, limiting the possibility to use errors for learning. These students’ reactions may be influenced by relational and cultural aspects of errors that characterise the learning environment. Accordingly, the main aim of this research was to investigate whether relational and cultural characteristics associated with errors affect psychological mechanisms triggered by making mistakes. In the theoretical part, we described the role of errors in learning using an integrated multilevel (i.e., psychological, relational, and cultural levels of analysis) approach. Then, we presented three studies that analysed how cultural and relational error-related variables affect psychological aspects. The studies adopted a specific empirical methodology (i.e., qualitative, experimental, and correlational) and investigated different samples (i.e., teachers, primary school pupils and middle school students). Findings of study one (cultural level) highlighted errors acquire different meanings that are associated with different teachers’ error-handling strategies (e.g., supporting or penalising errors). Study two (relational level) demonstrated that teachers’ supportive error-handling strategies promote students’ perceptions of being in a positive error climate. Findings of study three (relational and psychological level) showed that positive error climate foster students’ adaptive reactions towards errors and learning outcomes. Overall, our findings indicated that different variables influence students’ learning from errors process and teachers play an important role in conveying specific meanings of errors during learning activities, dealing with students’ mistakes supportively, and establishing an error-friendly classroom environment.

Vegetables biofortified with iodine: chemical, agronomic and qualitative aspects of raw and fried potatoes

Iodine is an essential microelement for human health because it is a constituent of the thyroid hormones that regulate growth and development of the organism. Iodine Deficiency Disorders (IDDs) are believed to be one of the commonest preventable human health problems in the world today, according to the World Health Organization: that diseases include endemic goiter, cretinism and fetal abnormalities, among others, and they are caused by lack of iodine in the diet, that is the main source of iodine. Since iodine intake from food is not enough respect to human needs, this can be remedied through dietary diversification, mineral supplementation, food fortification, or increasing the concentration and/or bioavailability of mineral elements in the edible portions of crops through agricultural intervention or genetic selection (biofortification). The introduction of iodized salt is a strategy widely used and accepted to eradicate iodine deficiency, because it is an inexpensive source of stable iodine. Since the intake of salt, though iodized, must still be limited according to the risk of cardiovascular disease, so the increase of iodine content in plants for the production of functional foods is representing a field of study of particular interest and a potential market. In Italy potatoes enriched with iodine are produced by a patented procedure of agronomic biofortification for the fresh market since several years, furthermore they are recently accepted and recommended by Italian Thyroid Association, as an alternative source of iodine. Researches performed during the PhD course intended to characterize this innovative vegetables products, focusing the attention on different aspects, such as chemistry, agriculture, and quality of fresh and fried potatoes. For this purpose, lipid fraction of raw material was firstly investigated, in order to assess whether the presence of iodine in plant metabolism can affect fatty acid or sterol biosynthesis, according to the hypothesis that iodine can be bounded to polyunsaturated fatty acids of cell membranes, protecting them from peroxydation; phytosterols of plant sterol are also studied because their importance in reducing serum cholesterol, especially in potato plant sterols are also involved in synthesis of glycoalkaloid, a family of steroidal toxic secondary metabolites present in plants of the Solanaceae family. To achieve this goal chromatographic analytical techniques were employed to identify and quantify fatty acids and sterols profile of common and iodine enriched row potatoes. Another aim of the project was to evaluate the effects of frying on the quality of iodine-enriched and common potatoes. Since iodine-enriched potatoes are nowadays produced only for the fresh market, preliminary trials of cultivation under controlled environment were carried out to verify if potato varieties suitable for processing were able to absorb and accumulate iodine in the tuber. In a successive phase, these varieties were grown in the field, to evaluate their potential productivity and quality at harvest and after storage. The best potato variety to be destined for processing purposes, was finally subjected to repeated frying cycles; the effects of lipid oxidation on the composition and quality of both potatoes and frying oil bath were evaluated by chromatographic and spectrophotometric analytical techniques. Special attention were paid on volatile compounds of fried potatoes.

Physiological and structural aspects of fruit and vegetable mild processing

Over the past years fruit and vegetable industry has become interested in the application of both osmotic dehydration and vacuum impregnation as mild technologies because of their low temperature and energy requirements. Osmotic dehydration is a partial dewatering process by immersion of cellular tissue in hypertonic solution. The diffusion of water from the vegetable tissue to the solution is usually accompanied by the simultaneous solutes counter-diffusion into the tissue. Vacuum impregnation is a unit operation in which porous products are immersed in a solution and subjected to a two-steps pressure change. The first step (vacuum increase) consists of the reduction of the pressure in a solid-liquid system and the gas in the product pores is expanded, partially flowing out. When the atmospheric pressure is restored (second step), the residual gas in the pores compresses and the external liquid flows into the pores. This unit operation allows introducing specific solutes in the tissue, e.g. antioxidants, pH regulators, preservatives, cryoprotectancts. Fruit and vegetable interact dynamically with the environment and the present study attempts to enhance our understanding on the structural, physico-chemical and metabolic changes of plant tissues upon the application of technological processes (osmotic dehydration and vacuum impregnation), by following a multianalytical approach. Macro (low-frequency nuclear magnetic resonance), micro (light microscopy) and ultrastructural (transmission electron microscopy) measurements combined with textural and differential scanning calorimetry analysis allowed evaluating the effects of individual osmotic dehydration or vacuum impregnation processes on (i) the interaction between air and liquid in real plant tissues, (ii) the plant tissue water state and (iii) the cell compartments. Isothermal calorimetry, respiration and photosynthesis determinations led to investigate the metabolic changes upon the application of osmotic dehydration or vacuum impregnation. The proposed multianalytical approach should enable both better designs of processing technologies and estimations of their effects on tissue.

In defence of modeling simultaneity for a correct approximation of cultural aspects: implications for food consumers studies with latent variables

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

Homological and Hodge-theoretic aspects of matroids and polymatroids

In the first part of this thesis, we study the action of the automorphism group of a matroid on the homology space of the co-independent complex. This representation turns out to be isomorphic, up to tensoring with the sign representation, with that on the homology space associated with the lattice of flats. In the case of the cographic matroid of the complete graph, this result has application in algebraic geometry: indeed De Cataldo, Heinloth and Migliorini use this outcome to study the Hitchin fibration. In the second part, on the other hand, we use ideas from algebraic geometry to prove a purely combinatorial result. We construct a Leray model for a discrete polymatroid with arbitrary building set and we prove a generalized Goresky-MacPherson formula. The first row of the model is the Chow ring of the polymatroid; we prove Poincaré duality, Hard-Lefschetz theorem and Hodge-Riemann relations for the Chow ring.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.