Flow Field–Flow Fractionation for size analysis and characterization of nanoparticles for applications in Life Sciences

Nanotechnologies are rapidly expanding because of the opportunities that the new materials offer in many areas such as the manufacturing industry, food production, processing and preservation, and in the pharmaceutical and cosmetic industry. Size distribution of the nanoparticles determines their properties and is a fundamental parameter that needs to be monitored from the small-scale synthesis up to the bulk production and quality control of nanotech products on the market. A consequence of the increasing number of applications of nanomaterial is that the EU regulatory authorities are introducing the obligation for companies that make use of nanomaterials to acquire analytical platforms for the assessment of the size parameters of the nanomaterials. In this work, Asymmetrical Flow Field-Flow Fractionation (AF4) and Hollow Fiber F4 (HF5), hyphenated with Multiangle Light Scattering (MALS) are presented as tools for a deep functional characterization of nanoparticles. In particular, it is demonstrated the applicability of AF4-MALS for the characterization of liposomes in a wide series of mediums. Afterwards the technique is used to explore the functional features of a liposomal drug vector in terms of its biological and physical interaction with blood serum components: a comprehensive approach to understand the behavior of lipid vesicles in terms of drug release and fusion/interaction with other biological species is described, together with weaknesses and strength of the method. Afterwards the size characterization, size stability, and conjugation of azidothymidine drug molecules with a new generation of metastable drug vectors, the Metal Organic Frameworks, is discussed. Lastly, it is shown the applicability of HF5-ICP-MS for the rapid screening of samples of relevant nanorisk: rather than a deep and comprehensive characterization it this time shown a quick and smart methodology that within few steps provides qualitative information on the content of metallic nanoparticles in tattoo ink samples.

Advanced polymeric materials for applications in technical equipment for snow sports

The thesis is divided in three chapters, each one covering one topic. Initially, the thermo-mechanical and impact properties of materials used for back protectors have been analysed. Dynamical mechanical analysis (DMTA) has shown that materials used for soft-shell protectors present frequency-sensitive properties. Furthermore, through impact tests, the shock absorbing characteristics of the materials have been investigated proving the differences between soft and hard-shell protectors; moreover it has been demonstrated that the materials used for soft-shell protectors maintain their protective properties after multi-impacts. The second chapter covers the effect of the visco-elastic properties of the thermoplastic polymers on the flexural and rebound behaviours of ski boots. DMTA analysis on the materials and flexural and rebound testing on the boots have been performed. A comparison of the results highlighted a correlation between the visco-elastic properties and the flexural and rebound behaviour of ski boots. The same experimental methods have been used to investigate the influence of the design on the flexural and rebound behaviours. Finally in the third chapter the thermoplastic materials employed for the construction of ski boots soles have been characterized in terms of chemical composition, hardness, crystallinity, surface roughness and coefficient of friction (COF). The results showed a relation between material hardness and grip, in particular softer materials provide more grip with respect to harder materials. On the contrary, the surface roughness has a negative effect on friction because of the decrease in contact area. The measure of grip on inclined wet surfaces showed again a relation between hardness and grip. The performance ranking of the different materials has been the same for the COF and for the slip angle tests, indicating that COF can be used as a parameter for the choice of the optimal material to be used for the soles of ski boots.

Predictability in Social Science, The statistical mechanics approach.

The subject of this work concerns the study of the immigration phenomenon, with emphasis on the aspects related to the integration of an immigrant population in a hosting one. Aim of this work is to show the forecasting ability of a recent finding where the behavior of integration quantifiers was analyzed and investigated with a mathematical model of statistical physics origins (a generalization of the monomer dimer model). After providing a detailed literature review of the model, we show that not only such a model is able to identify the social mechanism that drives a particular integration process, but it also provides correct forecast. The research reported here proves that the proposed model of integration and its forecast framework are simple and effective tools to reduce uncertainties about how integration phenomena emerge and how they are likely to develop in response to increased migration levels in the future.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.

Novel hydroxystearoyl-, heterocyclic and carbocyclic derivatives: synthesis and applications in biological field

This PhD project has been mainly focused on the synthesis of novel organic compounds containing heterocyclic and/or carbocyclic scaffold and on the study of stearic acid derivatives and their applications in biological field. The synthesis of novel derivatives of 9-hydroxystearic acid (9-HSA) evidenced how the presence of substituents on C9, able to make hydrogen bonds is of crucial importance for the biological activity. Also the position of the hydroxy group along the chain of hydroxystearic acids was investigated: regioisomers with the hydroxy group bound to odd carbons resulted more active than those bearing the hydroxy group on even carbons. Further, the insertion of (R)-9-HSA in magnetic nanoparticles gave a novel material which characterization remarked its suitability for drug delivery. Structural hybrids between amino aza-heterocycles and azelaic acid have been synthesized and some of them showed a selective activity towards osteosarcoma cell line U2OS. Several Apcin analogues bearing indole, benzothiazole, benzofurazan moieties connected to tryptaminyl-, amino pyridinyl-, pyrimidinyl- and pyrazinyl ring through a 1,1,1-trichloroethyl group were synthesized. Biological tests showed the importance of both the tryptaminyl and the pyrimidinyl moieties, confirming the effectiveness against acute leukemia models. The SNAr between 2-aminothiazole derivatives and 7-chlorodinitrobenzofuroxan revealed different behaviour depending from amino substituent of the thiazole. The reaction with 2-N-piperidinyl-, 2-N-morpholinyl-, or 2-N-pyrrolidinyl thiazole gave two isomeric species derived from the attack on C-5 of thiazole ring. Thiazoles substituted with primary- or not-cyclic secondary amines reacted with the exocyclic amino nitrogen atom giving a series of compounds whose biological activity have highlighted as they might be promising candidates for further development of antitumor agents. A series of 9-fluorenylidene derivatives, of interest in medical and optoelectronic field as organic scintillators, was synthesized through Wittig or Suzuky reaction and will be analyzed to test their potential scintillatory properties.

Development, mechanical characterization and qualification in liquid lead of stainless steels for structural applications in the future fission and fusion nuclear reactors

The research activity carried out in the Brasimone Research Center of ENEA concerns the development and mechanical characterization of steels conceived as structural materials for future fission reactors (Heavy Liquid Metal IV Generation reactors: MYRRHA and ALFRED) and for the future fusion reactor DEMO. Within this framework, two parallel lines of research have been carried out: (i) characterization in liquid lead of steels and weldings for the components of the IV Generation fission reactors (GIV) by means of creep and SSRT (Slow Strain Rate Tensile) tests; (ii) development and screening on mechanical properties of RAFM (Reduced Activation Ferritic Martensitic) steels to be employed as structural materials of the future DEMO fusion reactor. The doctoral work represents therefore a comprehensive report of the research carried out on nuclear materials both from the point of view of the qualification of existing (commercial) materials for their application in the typical environmental conditions of 4th generation fission reactors operating with lead as coolant, and from the point of view of the metallurgical study (with annexed microstructural and mechanical characterization of the selected compositions / Thermo Mechanical Treatment (TMT) options) of new compositional variants to be proposed for the “Breeding Blanket” of the future DEMO Fusion Reactor.

Methods and tools for analysis and management of risks and regulatory compliance in the healthcare sector: the hospital at home – HaH

Changing or creating an organisation means creating a new process. Each process involves many risks that need to be identified and managed. The main risks considered here are procedural and legal risks. The former are related to the risks of errors that may occur during processes, while the latter are related to the compliance of processes with regulations. Managing the risks implies proposing changes to the processes that allow the desired result: an optimised process. In order to manage a company and optimise it in the best possible way, not only should the organisational aspect, risk management and legal compliance be taken into account, but it is important that they are all analysed simultaneously with the aim of finding the right balance that satisfies them all. This is the aim of this thesis, to provide methods and tools to balance these three characteristics, and to enable this type of optimisation, ICT support is used. This work isn’t a thesis in computer science or law, but rather an interdisciplinary thesis. Most of the work done so far is vertical and in a specific domain. The particularity and aim of this thesis is not to carry out an in-depth analysis of a particular aspect, but rather to combine several important aspects, normally analysed separately, which however have an impact and influence each other. In order to carry out this kind of interdisciplinary analysis, the knowledge base of both areas was involved and the combination and collaboration of different experts in the various fields was necessary. Although the methodology described is generic and can be applied to all sectors, the case study considered is a new type of healthcare service that allows patients in acute disease to be hospitalised to their home. This provide the possibility to perform experiments using real hospital database.

A distributed middleware for IT/OT convergence in modern industrial environments

The modern industrial environment is populated by a myriad of intelligent devices that collaborate for the accomplishment of the numerous business processes in place at the production sites. The close collaboration between humans and work machines poses new interesting challenges that industry must overcome in order to implement the new digital policies demanded by the industrial transition. The Industry 5.0 movement is a companion revolution of the previous Industry 4.0, and it relies on three characteristics that any industrial sector should have and pursue: human centrality, resilience, and sustainability. The application of the fifth industrial revolution cannot be completed without moving from the implementation of Industry 4.0-enabled platforms. The common feature found in the development of this kind of platform is the need to integrate the Information and Operational layers. Our thesis work focuses on the implementation of a platform addressing all the digitization features foreseen by the fourth industrial revolution, making the IT/OT convergence inside production plants an improvement and not a risk. Furthermore, we added modular features to our platform enabling the Industry 5.0 vision. We favored the human centrality using the mobile crowdsensing techniques and the reliability and sustainability using pluggable cloud computing services, combined with data coming from the crowd support. We achieved important and encouraging results in all the domains in which we conducted our experiments. Our IT/OT convergence-enabled platform exhibits the right performance needed to satisfy the strict requirements of production sites. The multi-layer capability of the framework enables the exploitation of data not strictly coming from work machines, allowing a more strict interaction between the company, its employees, and customers.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Evaluation of a 2D multileader training system for improving sustainability and precision orchard management applications in italian apple orchards

The presented study aimed to evaluate the productive and physiological behavior of a 2D multileader apple training systems in the Italian environment both investigating the possibility to increase yield and precision crop load management resolution. Another objective was to find valuable thinning thresholds guaranteeing high yields and matching fruit market requirements. The thesis consists in three studies carried out in a Pink Lady®- Rosy Glow apple orchard trained as a planar multileader training system (double guyot). Fruiting leaders (uprights) dimension, crop load, fruit quality, flower and physiological (leaf gas exchanges and fruit growth rate) data were collected and analysed. The obtained results found that uprights present dependence among each other and as well as a mutual support during fruit development. However, individual upright fruit load and upright’s fruit load distribution on the tree (~ plant crop load) seems to define both upright independence from the other, and single upright crop load effects on the final fruit quality production. Correlations between fruit load and harvest fruit size were found and thanks to that valuable thinning thresholds, based on different vegetative parameters, were obtained. Moreover, it comes out that an upright’s fruit load random distribution presents a widening of those thinning thresholds, keeping un-altered fruit quality. For this reason, uprights resulted a partially physiologically-dependent plant unit. Therefore, if considered and managed as independent, then no major problems on final fruit quality and production occurred. This partly confirmed the possibility to shift crop load management to single upright. The finding of the presented studies together with the benefits coming from multileader planar training systems suggest a high potentiality of the 2D multileader training systems to increase apple production sustainability and profitability for Italian apple orchard, while easing the advent of automation in fruit production.