Schreier extensions of Steiner loops and extensions of Bol loops arising from Bol reflections

This dissertation explores two constructions of loop extensions: Schreier extensions of Steiner loops and a new extension formula for right Bol loops arising from Bol reflections.Steiner loops are a key tool in studying Steiner triple systems. We investigate extensions of Steiner loops, focusing in particular on the case of Schreier extensions, which provides a powerful method for constructing Steiner triple systems containing Veblen points. We determine the number of the Steiner triple systems of sizes 19, 27 and 31 with Veblen points, presenting some examples.Furthermore, we study a new extension formula for right Bol loops. We prove the necessary and sufficient conditions for the extension to be right Bol as well. We describe the most important invariants: right multiplication group, nuclei, center. We show that the core is an involutory quandle which is the disjoint union of two isomorphic involutory quandles. Lastly, we derive further results on the structure group of the core of the extension

Characterizing Algorithmic Performance in Machine Learning for Education

The integration of artificial intelligence (AI) in educational systems has revolutionized the field of education, offering numerous benefits such as personalized learning, intelligent tutoring, and data-driven insights. However, alongside this progress, concerns have arisen about potential algorithmic disparities and performance issues in AI applications for education. This doctoral thesis addresses these concerns and aims to foster the development of AI in educational contexts that emphasize performance analysis. The thesis begins by investigating the challenges and needs of the educational community in integrating responsible practices into AI-based educational systems. Through surveys and interviews with experts in the field, real-world needs and common areas for developing more responsible AI in education are identified. According to our findings, further research delves into the analysis of student behavior in both synchronous and asynchronous learning environments. By examining patterns of student engagement and predicting student success, the thesis uncovers potential performance issues (e.g., unknown unknowns: the model is really confident of its predictions but actually wrong), emphasizing the need for nuanced approaches that consider hidden factors impacting students’ learning outcomes. By providing an integrated view of the performance analyses conducted in different learning environments, the thesis offers a comprehensive understanding of the challenges and opportunities in developing responsible AI applications for education. Ultimately, this doctoral thesis contributes to the advancement of responsible AI in education, offering insights into the complexities of algorithmic disparities and their implications. The research work presented herein serves as a guiding framework for designing and deploying AI enabled educational systems that prioritize responsibility, and improved learning experiences

Local Market Mechanisms: how Local Markets can shape the Energy Transition

Europe has embarked on a journey towards a zero-emission system, with the power system at its core. From electricity generation to electric vehicles, the European power system must transform into an interconnected, intelligent network. To achieve this vision, active user participation is crucial, ensuring transparency, efficiency, and inclusivity. Thus, Europe has increasingly focused on the concept of markets in all their facets. This thesis seeks to answer the following questions: How can markets, often considered abstract and accessible only to high-level users, be integrated for end-users? How can market mechanisms be leveraged across various phases of the electrical system? Why is a market- driven approach essential for solving network congestions and even influencing planning? These questions shape the core of this research. The analysis unfolds in three layers, each aligned with milestones leading to 2050. The first explores how market mechanisms can be integrated into system operator development plans, enhancing system resilience in the face of changes. In this regard, this step addresses the question of how a market can be integrated into the development plans of a network and how network planning can account for uncertainties. Finally, the analysis highlights the importance of sector coupling in network planning, proposing a study in which various energy vectors lead to a multi-energy system. According to the roadmap to 2030, this layer demonstrates how markets can manage several components of the gas and electrical network. Finally, even though the robust optimisation increases the final cost in the market, it allows to cover the system operator from uncertainties. The second step delves into the concept of network congestion. While congestion management is primarily the domain of operators, it explores how technical and economic collaboration between operators and system users, via flexibility markets, can enhance resilience amid demand uncertainties and aggressive market behaviours. In addition to flexibility markets, other congestion markets are proposed, some radically different, like locational marginal pricing, and others more innovative, such as redispatching markets for distribution. Building upon the first analysis, this section addresses questions of how various energy vectors can be used not only to meet demand but also to manage the uncertainties associated with each resource. Consequently, this second part revisits the concept of sector coupling, demonstrating how various energy vectors can be managed through flexibility markets to resolve network congestion while simultaneously handling uncertainties related to different vectors. The results demonstrate the usefulness of the flexibility market in managing the sector coupling and the uncertainties related to several energy vectors. The third and most innovative step proposes energy and service markets for low-voltage users, employing distributed ledger technology. Since this step highlights topics that are currently too innovative to be realized, this third section offers a comparative study between centralised and decentralised markets using blockchain technology, highlighting which aspects of distributed ledger technology deserve attention and which aspects of low-voltage markets need revision. The results show that the blockchain technology is still in the early stage of its evolution, and several improvements are needed to fully apply this technology into real-world applications. To sum up, this thesis explores the evolving role of markets in the energy transition. Its insights are aimed at assisting system operators and network planners in effectively integrating market mechanisms at all levels of

Evaluation framework for context-aware speaker recognition in noisy smart living environments

The integration of voice control into connected devices is expected to improve the efficiency and comfort of our daily lives. However, the underlying biometric systems often impose constraints on the individual or the environment during interaction (e.g., quiet surroundings). Such constraints have to be surmounted in order to seamlessly recognize individuals. In this paper, we propose an evaluation framework for speaker recognition in noisy smart living environments. To this end, we designed a taxonomy of sounds (e.g., home-related, mechanical) that characterize representative indoor and outdoor environments where speaker recognition is adopted. Then, we devised an approach for off-line simulation of challenging noisy conditions in vocal audios originally collected under controlled environments, by leveraging our taxonomy. Our approach adds a (combination of) sound(s) belonging to the target environment into the current vocal example. Experiments on a large-scale public dataset and two state-of-the-art speaker recognition models show that adding certain background sounds to clean vocal audio leads to a substantial deterioration of recognition performance. In several noisy settings, our findings reveal that a speaker recognition model might end up to make unreliable decisions. Our framework is intended to help system designers evaluate performance deterioration and develop speaker recognition models more robust to smart living environments

Reproductive characteristics and differential response to seasonal temperatures of Blue and Great Tits (Cyanistes caeruleus & Parus major) in three neighbouring mediterranean habitats.

The breeding ecology of the Blue Tit (Cyanistes caeruleus) and Great Tit (Parus major) was studied for 18 years in three different neighbouring habitats in Sicily, comprising oakwoods, reforested pine and a reforested mix of pine and broad-leaved trees. Both Blue and Great Tits laid eggs up to two weeks earlier in oakwoods than in the reforested areas. Our results indicate a statistically greater breeding success for both species in the oakwoods compared to reforested habitats, with the mixed reforested habitat having a greater success than that of reforested pine habitat. We also correlated reproductive characteristics with local air temperature to verify if the laying date of tits advanced over a long period of years. Even though a variable egg-laying trend was recorded in the three habitats, an overall negative trendline was obtained indicating that the onset of nesting advanced through the 18-year study period. On the other hand, the air temperature trend was positive over the same period of time. The model of covariance analysis showed the relationship between egg-laying and March air temperatures remained consistent for both tit species, it was statistically different for each of the three habitats. Nestlings in the oak habitat fledged one day earlier than in reforested habitats and nestlings in the mixed habitat grew faster than nestlings in the pine habitat. Finally, clutch-size and number of fledglings remained consistent over the 18-year period in all three habitats, suggesting that prey availability may not have changed. Caterpillars comprised the primary prey in the oak and mixed habitats, less in the pine, where tits fed chicks with a more diverse food. The findings of this study indicate the importance of broad-leaved forests, whether natural or regenerated, for insectivorous species, and hence the potential conservation role of forestry management planning

Blockchain-Based Hardware-in-the-Loop Simulation of a Decentralized Controller for Local Energy Communities

The development of local energy communities observed in the last years requires the reorganization of energy consumption and production. In these newly considered energy systems, the commercial and technical decision processes should be decentralized in order to reduce their maintenance costs. This will be allowed by the progressive spreading of IoT systems capable of interacting with distributed energy resources, giving local sources the ability to be optimally coordinated in terms of network and energy management. In this context, this paper presents a decentralized controlling architecture that performs a wide spectrum of power system optimization procedures oriented to the local market management. The controller framework is based on a decentralized genetic algorithm. The manuscript describes the structure of the tool and its validation, considering an automated distributed resource scheduling for local energy markets. The simulation platform permits implementing the blockchain-based trading process and the automated distributed resource scheduling. The effectiveness of the tool proposed is discussed with a hardware-in-the-loop case study

Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices

Current trends in electrification of the final energy consumption and towards a massive electricity production from renewables are leading a revolution in the electric distribution system. Indeed, the traditional “fit & forget” planning approach used by Distributors would entail a huge amount of network investment. Therefore, for making these trends economically sustainable, the concept of Smart Distribution Network has been proposed, based on active management of the system and the exploitation of flexibility services provided by Distributed Energy Resources. However, the uncertainties associated to this innovation are holding its acceptance by utilities. For increasing their confidence, new risk-based planning tools are necessary, able to estimate the residual risk connected with each choice and identify solutions that can gradually lead to a full Smart Distribution Network implementation. Battery energy storage systems, owned and operated by Distributors, represent one of these solutions, since they can support the use of local flexibility services by covering part of the associated uncertainties. The paper presents a robust approach for the optimal exploitation of these flexibility services with a simultaneous optimal allocation of storage devices. For each solution, the residual risk is estimated, making this tool ready for its integration within a risk-based planning procedure

Improving cuff-less continuous blood pressure estimation with linear regression analysis

In this work, the authors investigate the cuff-less estimation of continuous BP through pulse transit time (PTT) and heart rate (HR) using regression techniques, which is intended as a first step towards continuous BP estimation with a low error, according to AAMI guidelines. Hypertension (the 'silent killer') is one of the main risk factors for cardiovascular diseases (CVDs), which are the main cause of death worldwide. Its continuous monitoring can offer a valid tool for patient care, as blood pressure (BP) is a significant indicator of health and, using it together with other parameters, such as heart and breath rates, could strongly improve prevention of CVDs. The novelties introduced in this work are represented by the implementation of pre-processing and by the innovative method for features research and features processing to continuously monitor blood pressure in a non-invasive way. Currently, invasive methods are the only reliable methods for continuous monitoring, while non-invasive techniques measure the values every few minutes. The proposed approach can be considered the first step for the integration of these types of algorithms on wearable devices, in particular on those developed for the SINTEC project

Energy Blockchain for Public Energy Communities

This paper suggests an application of blockchain as an energy open data ledger, designed to save and track data regarding the energy footprint of public buildings and public energy communities. The developed platform permits writing energy production and consumption of public buildings using blockchain-enabled smart meters. Once authenticated on the blockchain, this data can be made available to the public domain for techno-economic analyses for either research studies and internal or third parties audits, increasing, in this way, the perceived transparency of the public institutions. A further feature of the platform, starting on the previously disclosed raw data, allows calculating, validating, and sharing sustainability indicators of public buildings and facilities, allowing the tracking of their improvements in sustainability goals. The paper also provides the preliminary results of a field-test experimentation of the proposed platform on a group of public buildings, highlighting the possible benefits of its widespread exploitation

Clinical and Genotypical Features of False-Negative Patients in 26 Years of Cystic Fibrosis Neonatal Screening in Tuscany, Italy.

Cystic fibrosis (CF) is a life-threatening and common genetic disorder. Cystic fibrosis newborn screening (CF NBS) has been implemented in many countries over the last 30 years, becoming a widely accepted public health strategy in economically developed countries. False-negative (FN) cases can occur after CF NBS, with the number depending on the method. We evaluated the delayed diagnosis of CF, identifying the patients who had false-negative CF NBS results over 26 years (1992-2018) in Tuscany, Italy. The introduction of DNA analysis to the newborn screening protocol improved the sensitivity of the test and reduced the FNs. Our experience showed that, overall, at least 8.7% of cases of CF received FNs (18 cases) and were diagnosed later, with an average age of 6.6 years (range: 4 months to 22 years). Respiratory symptoms and salt-loss syndrome (metabolic hypochloremic alkalosis) are suggestive symptoms of CF and were commons events in FN patients. In Tuscany, a region with a high CFTR allelic heterogeneity, the salt-loss syndrome was a common event in FNs. Therefore, we provided evidence to support the claim that the FN patients had CFTR mutations rarer compared with the true-positive cases. We underline the importance of vigilance toward clinical manifestations suggestive of CF on the part of the primary care providers and hospital physicians in a region with an efficient newborn screening program