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New indicators of star cluster dynamical evolution
Get PDFThis thesis presents a study of globular clusters (GCs), based on analysis of Monte Carlo simulations of globular clusters (GCs)
with the aim to define new empirical parameters measurable from observations and able to trace the different phases of their dynamical evolution history. During their long term dynamical evolution, due to mass segregation and and dynamical friction, massive stars transfer kinetic energy to lower-mass objects, causing them to sink toward the cluster center. This continuous transfer of kinetic energy from the core to the outskirts triggers the runaway contraction of the core, known as "core collapse" (CC), followed by episodes of expansion and contraction called gravothermal oscillations. Clearly, such an internal dynamical
evolution corresponds to significant variations also of the structure of the system. Determining the dynamical age of a cluster can be challenging as it depends on various internal and external properties. The traditional classification of GCs as CC or post-CC systems relies on detecting a steep power-law cusp in the central density profile, which may not always be reliable due to post-CC oscillations or other processes.
In this thesis, based on the normalized cumulative radial distribution (nCRD) within a fraction of the half-mass radius is analyzed, and three diagnostics (A5, P5, and S2.5) are defined. These diagnostics show sensitivity to dynamical evolution and can distinguish pre-CC clusters from post-CC clusters.The analysis performed using multiple simulations with different initial conditions, including varying binary fractions and the presence of dark remnants showed the time variations of the diagnostics follow distinct patterns depending on the binary fraction and the retention or ejection of black holes. This analysis is extended to a larger set of simulations matching the observed properties of Galactic GCs, and the parameters show a potential to distinguish the dynamical
stages of the observed clusters as well
Red flesh fruit in european pear (Pyrus communis): genetic sources, QTLs, candidate genes and tools for breeding
Get PDFRed flesh fruit is a character which interest is increasing in several commercial species. Following a review of the research on the biosynthesis and accumulation of anthocyanin in pears (Chapter 1) the general aim of the project is reported in Chapter 2.
Chapter 3 reports the results of a molecular analysis of 33 red-fleshed pear accessions, genotyped with 18 SSR markers with the aim of improving germplasm conservation strategies to support ongoing breeding programs. The molecular profiles revealed both cases of synonymy and homonymy and 6 unique genotypes were identified. The S-allele were established to highlight the genetic relationships among these landraces. Four of the unique genotypes have been clustered based on pomological data.
In the Chapter 4, the work was directed to identify the putative genomic regions involved in the appearance of this character in pear fruit. A crossing population (‘Carmen’ x ‘Cocomerina Precoce’) segregating for the trait was phenotyped for 2 consecutive years and used for QTL analysis. A strong QTL was identified in a small genomic region related to the red flesh fruit trait at 27 Mb from the start of LG5. Two candidate genes were detected in this genomic region: ‘PcMYB114’ and ‘PcABCC2’. SSR marker SSR114 was found able to detect the red flesh phenotype segregation in all the red-fleshed pear accessions and segregating progenies tested.
Chapter 5 focuses on examining the trend of anthocyanin synthesis and accumulation during the fruit development, from fruit set to ripening time. Three different trials were planned: qPCR and HPLC methods were performed to correlate the genes expression with the anthocyanin accumulation in ‘Cocomerina Precoce’ and six progenies. Total transcriptome sequencing was used to compare the differential genes expression between red and white-fleshed fruit.
Chapter 6 reviews and analyses all the earlier study findings while providing new potential future perspectives
Distributed optimization and games over networks: a system theoretical perspective
Get PDFSeveral decision and control tasks involve networks of cyber-physical systems that need to be coordinated and controlled according to a fully-distributed paradigm involving only local communications without any central unit. This thesis focuses on distributed optimization and games over networks from a system theoretical perspective. In the addressed frameworks, we consider agents communicating only with neighbors and running distributed algorithms with optimization-oriented goals. The distinctive feature of this thesis is to interpret these algorithms as dynamical systems and, thus, to resort to powerful system theoretical tools for both their analysis and design. We first address the so-called consensus optimization setup. In this context, we provide an original system theoretical analysis of the well-known Gradient Tracking algorithm in the general case of nonconvex objective functions. Then, inspired by this method, we provide and study a series of extensions to improve the performance and to deal with more challenging settings like, e.g., the derivative-free framework or the online one. Subsequently, we tackle the recently emerged framework named distributed aggregative optimization. For this setup, we develop and analyze novel schemes to handle (i) online instances of the problem, (ii) ``personalized'' optimization frameworks, and (iii) feedback optimization settings. Finally, we adopt a system theoretical approach to address aggregative games over networks both in the presence or absence of linear coupling constraints among the decision variables of the players. In this context, we design and inspect novel fully-distributed algorithms, based on tracking mechanisms, that outperform state-of-the-art methods in finding the Nash equilibrium of the game
Data sensitivity detection in chat interactions for privacy protection
Get PDFIn recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’).
This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model?
RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a
benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential
of hybrid architectures in solving automatic tasks
La sostenibilità nel settore delle costruzioni e dei rifiuti: metriche, sfide e opportunità
Get PDFNowadays, the scientific community has devoted a consistent effort to the sustainable development of the waste management sector and resource efficiency in building
infrastructures. Waste is the fourth largest source sector of emissions and the municipal solid waste management system is considered as the most complex system to manage, due to its diverse composition and fragmentation of producers and responsibilities. Nevertheless, given the deep complexity that characterize the waste management sector, sustainability is still a challenging task. Interestingly, open issues arise when dealing with the sustainability of the waste sector. In this thesis, some recent advances in the waste management sector have been presented. Specifically, through the analysis of four author publications this thesis attempted to fill the gap in the following open issues: (i) the waste collection and generation of waste considering the pillars of sustainability; (ii) the environmental and social analysis in designing building infrastructures; (iv) the role of the waste collection in boosting sustainable systems of waste
management; (v) the ergonomics impacts of waste collection. For this purpose, four author publications in international peer – reviewed journals were selected among the wholly
author's contributions (i.e., final publication stage).Al giorno d'oggi, la comunità scientifica ha dedicato uno sforzo costante ed intenso improntato allo sviluppo sostenibile del settore dei rifiuti e all'efficienza delle risorse nella costruzione di infrastrutture. I rifiuti sono il quarto settore più grande in termini di emissioni inquinanti e il sistema di gestione dei rifiuti solidi urbani è considerato il sistema più complesso da gestire, a causa della sua diversa composizione e frammentazione dei produttori e delle responsabilità estese del produttore stesso. Tuttavia, data la profonda complessità che caratterizza la gestione dei rifiuti, il tema della sostenibilità è ancora una sfida aperta. È interessante notare che quando si parla di sostenibilità del settore dei rifiuti sorgono questioni non ancora risolte.
In questa tesi sono stati presentati alcuni recenti progressi nel settore della gestione dei rifiuti. Nello specifico, attraverso l'analisi di quattro pubblicazioni dell'autore, questa tesi risponde alle seguenti questioni aperte: (i) raccolta e generazione dei rifiuti considerando i pilastri della sostenibilità; (ii) analisi ambientale e sociale nella progettazione delle infrastrutture edilizie; (iv) ruolo della raccolta differenziata nel promuovere sistemi sostenibili di gestione dei rifiuti; (v) quantificazione degli impatti ergonomici della raccolta dei rifiuti. A tale scopo, sono state selezionate quattro pubblicazioni dell'autore su riviste internazionali in fase di pubblicazione finale tra la totalità dei contributi dell'autore stesso
Unsupervised reinforcement learning via state entropy maximization
Get PDFReinforcement Learning (RL) provides a powerful framework to address sequential decision-making problems in which the transition dynamics is unknown or too complex to be represented. The RL approach is based on speculating what is the best decision to make given sample estimates obtained from previous interactions, a recipe that led to several breakthroughs in various domains, ranging from game playing to robotics. Despite their success, current RL methods hardly generalize from one task to another, and achieving the kind of generalization obtained through unsupervised pre-training in non-sequential problems seems unthinkable. Unsupervised RL has recently emerged as a way to improve generalization of RL methods. Just as its non-sequential counterpart, the unsupervised RL framework comprises two phases: An unsupervised pre-training phase, in which the agent interacts with the environment without external feedback, and a supervised fine-tuning phase, in which the agent aims to efficiently solve a task in the same environment by exploiting the knowledge acquired during pre-training. In this thesis, we study unsupervised RL via state entropy maximization, in which the agent makes use of the unsupervised interactions to pre-train a policy that maximizes the entropy of its induced state distribution. First, we provide a theoretical characterization of the learning problem by considering a convex RL formulation that subsumes state entropy maximization. Our analysis shows that maximizing the state entropy in finite trials is inherently harder than RL. Then, we study the state entropy maximization problem from an optimization perspective. Especially, we show that the primal formulation of the corresponding optimization problem can be (approximately) addressed through tractable linear programs. Finally, we provide the first practical methodologies for state entropy maximization in complex domains, both when the pre-training takes place in a single environment as well as multiple environments
Selection of candidate probiotic strains from human microbial niches, for the development of tailored foods designed for specific consumers
Get PDFMy PhD project was intended, throughout the selection of probiotics from human milk and healthy vaginal environment, for the development of tailored fermented foods. According to this aim, several activities were carried out. The first one, concerning the isolation of Lactobacillus and Bifidobacterium strains from human milk to find new probiotic candidates to be included in food products showed promising results. Probiotics have been also proposed to improve female genital health and microbial strains isolated and connected with healthy vaginal ecosystem could be used to prevent or treat vaginal dysbiosis. In this context vaginal lactobacilli previously characterized for their technological features and antagonistic activity against several female uro-genital pathogens were investigated for their metabolic aptitude and additional probiotic features, showing interesting results hypothesizing their inclusion in foods. In addition, in order to preserve vaginal strains viability during food processing/digestion it was also evaluated the potential of microencapsulation by spray-drying. In this framework the results obtained were highly promising from the perspective of using encapsulated powders in food formulations. Another activity connected with the main idea to develop a food strategy for the administration of these vaginal strains was carried out. Lactobacillus crispatus BC4, was supplemented in a Squacquerone cheese, and its digestive fate was evaluated adopting SHIME® system. The results showed that during colonic fermentation, L. crispatus BC4 was metabolically active. Additionally, although probiotic delivery to humans has traditionally been associated with fermented dairy foods, recently the demand for non-dairy-alternatives as potential probiotics carrier is increasing. In this framework, my latest activity was connected with the development of fermented soy milks with encapsulated and non-encapsulated L. crispatus BC4 and L. gasseri BC9. The same fermented soy milks were also investigated for their nutritional qualities and after in vitro digestion for their specific functionality on post-menopausal fecal microbiota and protein bioaccessibility
Validation of a new method for human nerve decellularization: toward a new tool in peripheral nerve reconstructive surgery.
Get PDFDefects of the peripheral nervous system are extremely frequent in trauma and surgeries and have high socioeconomic costs. In case of peripheral nerve injury, the first approach is primary neurorrhaphy, which is direct nerve repair with epineural microsutures of the two stumps. However, this is not feasible in case of stump retraction or in case of tissue loss (gap > 2 cm), where the main surgical options are autologous grafts, allogenic grafts, or nerve conduits. While the gold standard is the autograft, it has disadvantages related to its harvesting, with an inevitable donor site morbidity and functional deficit. Fresh nerve allografts have therefore become a viable alternative option, but they require immunosuppression, which is often contraindicated. Acellular Nerve Allografts (ANA) represent a valid alternative, they do not need immunosuppression and appear to be safe and effective based on recent studies.
The purpose of this study is to propose and develop an innovative method of nerve decellularization (Rizzoli method), conforming to cleanroom requirements in order to perform the direct tissue manipulation step and the nerve decellularization process within five hours, so as to accelerate the detachment of myelin and cellular debris, without detrimental effects on nerve architecture.
In this study, the safety and the efficacy of the new method are evaluated in vitro and in vivo by histological, immunohistochemical, and histomorphometric studies in rabbits and humans.
The new method is rapid, safe, and cheaper if compared with available commercial ANAs. The present study shows that the method, previously optimized in vitro and in vivo on animal model presented by our group, can be applied on human nerve samples.
This work represents the first step in providing a novel, safe, and inexpensive tool for use by European tissue banks to democratize the use of nerve tissue transplantation for nerve injury reconstruction
Late onset spinal cord ischemia after thoracoabdominal aortic aneurysm open repair
Get PDFThe aim of this study is to evaluate if spinal cord ischemia (SCI), especially its late presentation, and can be correlated to the results of intraoperative evoked potential monitoring (IOM).
Methods.
This study is a physician-initiated, retrospective, single-center, non-randomized study. Data from all patients undergoing a thoracoabdominal aortic aneurysm surgical repair (TAAA SR) between January 2016 and March 2020 IOM was collected and analyzed.
Results.
During the study period, 261 patients underwent TAAA SR with MEP/SSEPs monitoring [190 males, 73%; median age 65 (57-71)]. Thirty-seven patients suffered from SCI, for an overall rate of 14% (permanent 9%). When stratifying patients according to the SCI onset, 18 patients presented with an early (11 permanent) and 19 with a late SCI (28 (p=.048) and TAAA extent II (p=.009). The irreversible MEP change without peripheral showed a trend of significance (p=.052).
Conclusions.
Evoked potential intraoperative monitoring is an important adjunct during thoracoabdominal aortic open repair to predict and possibly prevent spinal cord ischemia. Irreversible IOM loss without peripheral changes was predictive of late SCI, therefore more attention should be paid to the postoperative management of this subgroup of patients
Analytics and optimization for emergency healthcare processes
Get PDFThis thesis deals with the analysis and management of emergency healthcare processes through the use of advanced analytics and optimization approaches. Emergency processes are among the most complex within healthcare. This is due to their non-elective nature and their high variability.
This thesis is divided into two topics. The first one concerns the core of emergency healthcare processes, the emergency department (ED). In the second chapter, we describe the ED that is the case study. This is a real case study with data derived from a large ED located in northern Italy. In the next two chapters, we introduce two tools for supporting ED activities. The first one is a new type of analytics model. Its aim is to overcome the traditional methods of analyzing the activities provided in the ED by means of an algorithm that analyses the ED pathway (organized as event log) as a whole. The second tool is a decision-support system, which integrates a deep neural network for the prediction of patient pathways, and an online simulator to evaluate the evolution of the ED over time. Its purpose is to provide a set of solutions to prevent and solve the problem of the ED overcrowding.
The second part of the thesis focuses on the COVID-19 pandemic emergency. In the fifth chapter, we describe a tool that was used by the Bologna local health authority in the first part of the pandemic. Its purpose is to analyze the clinical pathway of a patient and from this automatically assign them a state. Physicians used the state for routing the patients to the correct clinical pathways. The last chapter is dedicated to the description of a MIP model, which was used for the organization of the COVID-19 vaccination campaign in the city of Bologna, Italy