Relationships between plant diversity and environmental heterogeneity in rupicolous grasslands on gypsum. The case study of Alysso-Sedion albi (Habitat 6110).

Plant communities on weathered rock and outcrops are characterized by high values in species richness (Dengler 2006) and often persist on small and fragmented surfaces. Yet very few studies have examined the relationships between heterogeneity and plant diversity at small scales, in particular in poor-nutrient and low productive environment (Shmida and Wilson 1985, Lundholm 2003). In order to assess these relationships both in space and time in relationship, two different approaches were employed in the present study, in two gypsum outcrops of Northern Apennine. Diachronic and synchronic samplings from April 2012 to March 2013 were performed. A 50x50 cm plot was used in both samplings such as the sampling unit base. The diachronic survey aims to investigate seasonal patterning of plant diversity by the use of images analysis techniques integrated with field data and considering also seasonal climatic trend, the substrate quality and its variation in time. The purpose of the further, synchronic sampling was to describe plant diversity pattern as a function of the environmental heterogeneity meaning in substrate typologies, soil depth and topographic features. Results showed that responses of diversity pattern depend both on the resources availability, environmental heterogeneity and the manner in which the different taxonomic group access to them during the year. Species richness and Shannon diversity were positively affected by increasing in substrate heterogeneity. Furthermore a good turnover in seasonal species occurrence was detected. This vegetation may be described by the coexistence of three groups of species which created a gradient from early colonization stages, characterized by greater slope and predominance of bare rock, gradually to situation of more developed soil.

Essays on social networks, altruism and information diffusion

This dissertation consists of three standalone articles that contribute to the economics literature concerning technology adoption, information diffusion, and network economics in one way or another, using a couple of primary data sources from Ethiopia. The first empirical paper identifies the main behavioral factors affecting the adoption of brand new (radical) and upgraded (incremental) bioenergy innovations in Ethiopia. The results highlight the importance of targeting different instruments to increase the adoption rate of the two types of innovations. The second and the third empirical papers of this thesis, use primary data collected from 3,693 high school students in Ethiopia, and shed light on how we should select informants to effectively and equitably disseminate new information, mainly concerning environmental issues. There are different well-recognized standard centrality measures that are used to select informants. These standard centrality measures, however, are based on the network topology---shaped only by the number of connections---and fail to incorporate the intrinsic motivations of the informants. This thesis introduces an augmented centrality measure (ACM) by modifying the eigenvector centrality measure through weighting the adjacency matrix with the altruism levels of connected nodes. The results from the two papers suggest that targeting informants based on network position and behavioral attributes ensures more effective and equitable (gender perspective) transmission of information in social networks than selecting informants on network centrality measures alone. Notably, when the information is concerned with environmental issues.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

PCM-based in-memory computing: architectures, circuits and applications

Analog In-memory Computing (AIMC) has been proposed in the context of Beyond Von Neumann architectures as a valid strategy to reduce internal data transfers energy consumption and latency, and to improve compute efficiency. The aim of AIMC is to perform computations within the memory unit, typically leveraging the physical features of memory devices. Among resistive Non-volatile Memories (NVMs), Phase-change Memory (PCM) has become a promising technology due to its intrinsic capability to store multilevel data. Hence, PCM technology is currently investigated to enhance the possibilities and the applications of AIMC. This thesis aims at exploring the potential of new PCM-based architectures as in-memory computational accelerators. In a first step, a preliminar experimental characterization of PCM devices has been carried out in an AIMC perspective. PCM cells non-idealities, such as time-drift, noise, and non-linearity have been studied to develop a dedicated multilevel programming algorithm. Measurement-based simulations have been then employed to evaluate the feasibility of PCM-based operations in the fields of Deep Neural Networks (DNNs) and Structural Health Monitoring (SHM). Moreover, a first testchip has been designed and tested to evaluate the hardware implementation of Multiply-and-Accumulate (MAC) operations employing PCM cells. This prototype experimentally demonstrates the possibility to reach a 95% MAC accuracy with a circuit-level compensation of cells time drift and non-linearity. Finally, empirical circuit behavior models have been included in simulations to assess the use of this technology in specific DNN applications, and to enhance the potentiality of this innovative computation approach.

Spectrum shaping and its application: spread-spectrum clock generator and circuits for ultra wide band

Electromagnetic spectrum can be identified as a resource for the designer, as well as for the manufacturer, from two complementary points of view: first, because it is a good in great demand by many different kind of applications; second, because despite its scarce availability, it may be advantageous to use more spectrum than necessary. This is the case of Spread-Spectrum Systems, those systems in which the transmitted signal is spread over a wide frequency band, much wider, in fact, than the minimum bandwidth required to transmit the information being sent. Part I of this dissertation deals with Spread-Spectrum Clock Generators (SSCG) aiming at reducing Electro Magnetic Interference (EMI) of clock signals in integrated circuits (IC) design. In particular, the modulation of the clock and the consequent spreading of its spectrum are obtained through a random modulating signal outputted by a chaotic map, i.e. a discrete-time dynamical system showing chaotic behavior. The advantages offered by this kind of modulation are highlighted. Three different prototypes of chaos-based SSCG are presented in all their aspects: design, simulation, and post-fabrication measurements. The third one, operating at a frequency equal to 3GHz, aims at being applied to Serial ATA, standard de facto for fast data transmission to and from Hard Disk Drives. The most extreme example of spread-spectrum signalling is the emerging ultra-wideband (UWB) technology, which proposes the use of large sections of the radio spectrum at low amplitudes to transmit high-bandwidth digital data. In part II of the dissertation, two UWB applications are presented, both dealing with the advantages as well as with the challenges of a wide-band system, namely: a chaos-based sequence generation method for reducing Multiple Access Interference (MAI) in Direct Sequence UWB Wireless-Sensor-Networks (WSNs), and design and simulations of a Low-Noise Amplifier (LNA) for impulse radio UWB. This latter topic was studied during a study-abroad period in collaboration with Delft University of Technology, Delft, Netherlands.

Audiovisivi e Social Networks: il testo e i modi dell'esperienza 2007-2010

The doctoral research project "Audiovisuals and Social Networks: Text and Experiences 2007-2010" is mainly based on the analysis of the international audiovisuals landscape and of the promotional strategies of these products in Social Networks environment. The aim is to understand what kind of changes we can find about the concept of "text", users and marketing. The thesis is focused not just on Social Network marketing but also on new media development, such as Social TV and mobile.

Electrothermal characterization and nonlinear modelling of GaN transistors for telecommunication circuit design

This thesis starts showing the main characteristics and application fields of the AlGaN/GaN HEMT technology, focusing on reliability aspects essentially due to the presence of low frequency dispersive phenomena which limit in several ways the microwave performance of this kind of devices. Based on an equivalent voltage approach, a new low frequency device model is presented where the dynamic nonlinearity of the trapping effect is taken into account for the first time allowing considerable improvements in the prediction of very important quantities for the design of power amplifier such as power added efficiency, dissipated power and internal device temperature. An innovative and low-cost measurement setup for the characterization of the device under low-frequency large-amplitude sinusoidal excitation is also presented. This setup allows the identification of the new low frequency model through suitable procedures explained in detail. In this thesis a new non-invasive empirical method for compact electrothermal modeling and thermal resistance extraction is also described. The new contribution of the proposed approach concerns the non linear dependence of the channel temperature on the dissipated power. This is very important for GaN devices since they are capable of operating at relatively high temperatures with high power densities and the dependence of the thermal resistance on the temperature is quite relevant. Finally a novel method for the device thermal simulation is investigated: based on the analytical solution of the tree-dimensional heat equation, a Visual Basic program has been developed to estimate, in real time, the temperature distribution on the hottest surface of planar multilayer structures. The developed solver is particularly useful for peak temperature estimation at the design stage when critical decisions about circuit design and packaging have to be made. It facilitates the layout optimization and reliability improvement, allowing the correct choice of the device geometry and configuration to achieve the best possible thermal performance.

Multidimensional analysis of complex networks

Complex Networks analysis turn out to be a very promising field of research, testified by many research projects and works that span different fields. Those analysis have been usually focused on characterize a single aspect of the system and a study that considers many informative axes along with a network evolve is lacking. We propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions, space and time. To achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network as a whole. By focusing on space dimension, we characterized spatial alteration in terms of abstraction levels. We proposed a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We verified that statistical indicators depend strongly on the granularity with which a system is described and on the class of networks. We keep fixed the space axes and we isolated the dynamics behind networks evolution process. We detected new instincts that trigger social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopting special nodes (called sirens) that, thanks to their ability to attract new links, were able to construct efficient connection patterns. We simulated the dynamics of the system by considering three well-known growth models. Applying this framework to real and synthetic networks, we showed that the sirens, even when used for a limited time span, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and provided the best combinations of system's parameters, such as number of sirens, time span of utilization and attractiveness.

Analysis of Copy Number Variants identifies new candidate genes for Autism Spectrum Disorder and Intellectual Disability

Autism spectrum disorder (ASD) and Intellectual Disability (ID) are complex neuropsychiatric disorders characterized by extensive clinical and genetic heterogeneity and with overlapping risk factors. The aim of my project was to further investigate the role of Copy Numbers Variants (CNVs), identified through genome-wide studies performed by the Autism Geome Project (AGP) and the CHERISH consortium in large cohorts of ASD and ID cases, respectively. Specifically, I focused on four rare genic CNVs, selected on the basis of their impact on interesting ASD/ID candidate genes: a) a compound heterozygous deletion involving CTNNA3, predicted to cause the lack of functional protein; b) a 15q13.3 duplication containing CHRNA7; c) a 2q31.1 microdeletion encompassing KLHL23, SSB and METTL5; d) Lastly, I investigated the putative imprinting regulation of the CADPS2 gene, disrupted by a maternal deletion in two siblings with ASD and ID. This study provides further evidence for the role of CTNNA3, CHRNA7, KLHL23 and CADPS2 as ASD and/or ID susceptibility genes, and highlights that rare genetic variation contributes to disease risk in different ways: some rare mutations, such as those impacting CTNNA3, act in a recessive mode of inheritance, while other CNVs, such as those occurring in the 15q13.3 region, are implicated in multiple developmental and/or neurological disorders possibly interacting with other susceptibility variants elsewhere in the genome. On the other hand, the discovery of a tissue-specific monoallelic expression for the CADPS2 gene, implicates the involvement of epigenetic regulatory mechanisms as risk factors conferring susceptibility to ASD/ID.

Monitoring and Prediction of Thermal Emergencies in High Performance Computing Systems

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. High-Performance Computing (HPC) systems are an aggregation of computing power to deliver considerably higher performance than one typical desktop computer can provide, to solve large problems in science, engineering, or business. An HPC room in the datacenter is a complex controlled environment that hosts thousands of computing nodes that consume electrical power in the range of megawatts, which gets completely transformed into heat. Although a datacenter contains sophisticated cooling systems, our studies indicate quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial and temporal thermal and power heterogeneity. Therefore minor thermal issues/anomalies can potentially start a chain of events that leads to an unbalance between the amount of heat generated by the computing nodes and the heat removed by the cooling system originating thermal hazards. Although thermal anomalies are rare events, anomaly detection/prediction in time is vital to avoid IT and facility equipment damage and outage of the datacenter, with severe societal and business losses. For this reason, automated approaches to detect thermal anomalies in datacenters have considerable potential. This thesis analyzed and characterized the power and thermal characteristics of a Tier0 datacenter (CINECA) during production and under abnormal thermal conditions. Then, a Deep Learning (DL)-powered thermal hazard prediction framework is proposed. The proposed models are validated against real thermal hazard events reported for the studied HPC cluster while in production. This thesis is the first empirical study of thermal anomaly detection and prediction techniques of a real large-scale HPC system to the best of my knowledge. For this thesis, I used a large-scale dataset, monitoring data of tens of thousands of sensors for around 24 months with a data collection rate of around 20 seconds.

Historic and modern alloys: atmospheric corrosion and development of accelerated ageing methodologies

The atmospheric corrosion of modern and historic alloys used in cultural heritage has been investigated by applying specific accelerated ageing methods. Three main research lines were carried out, involving different materials. In the first part, the atmospheric corrosion of a modern Cu-3Si-1Mn bronze was investigated through accelerated ageing tests simulating outdoor runoff conditions. The corrosion processes were evaluated through different analyses, and the results obtained were compared to those of a traditional quaternary bronze. The second line was carried out to characterise historic aluminium alloys used in aeronautics to develop and apply innovative protection strategies for their conservation. Historic wrecks were identified and characterised through micro and macroscale observations. Moreover, accelerated ageing tests were performed on both historic and modern alloys to compare their behaviour and select the best modern substrate to be used for the development of effective coatings. The third research line aimed to develop accelerate sampling and ageing methods to investigate the role of particulate matter (PM) in the atmospheric corrosion of bronzes and metals in general. The first approach consisted in the fine-tuning of an efficient accelerated method for ambient PM sampling on bronze specimens followed by their accelerated ageing, in order to establish a correlation between the PM and the substrate’s corrosion. After the accelerated ageing of the specimens, the corrosion was evaluated by surface characterisation and correlated to the PM features. The second approach consisted in the development of a synthetic PM formulation and of an artificial deposition method, which was performed by spraying mixtures containing the main PM inorganic fractions on a G-85 bronze with an airbrush. The deposition efficiency was assessed, and the effect of synthetic PM on the bronze corrosion was evaluated. The results were compared to those obtained by ambient PM deposition.

From images to signs: Cretan Hieroglyphic and Linear A in context

This dissertation adopts a multidisciplinary approach to investigate graphical and formal features of Cretan Hieroglyphic and Linear A. Drawing on theories which understand inscribed artefacts as an interplay of materials, iconography, and texts, I combine archaeological and philological considerations with statistical and experimental observations. The work is formulated on three key-questions. The first deals with the origins of Cretan Hieroglyphic. After providing a fresh view on Prepalatial seals chronology, I identify a number of forerunners of Hieroglyphic signs in iconographic motifs attested among the Prepalatial glyptic and material culture. I further identified a specific style-group, i.e., the ‘Border and Leaf Complex’, as the decisive step towards the emergence of the Hieroglyphic graphic repertoire. The second deals with the interweaving of formal, iconographical, and epigraphic features of Hieroglyphic seals with the sequences they bear and the contexts of their usage. By means of two Correspondence Analyses, I showed that the iconography on seals in some materials and shapes is closer to Cretan Hieroglyphics, than that on the other ones. Through two Social Network Analyses, I showed that Hieroglyphic impressions, especially at Knossos, follow a precise sealing pattern due to their shapes and sequences. Furthermore, prisms with a high number of inscribed faces adhere to formal features of jasper ones. Finally, through experimental engravings, I showed differences in cutting rates among materials, as well as the efficiency of abrasives and tools unearthed within the Quartier Mu. The third question concerns overlaps in chronology, findspots and signaries between Cretan Hieroglyphic and Linear A. I discussed all possible earliest instances of both scripts and argued for some items datable to the MM I-IIA period. I further provide an insight into the Hieroglyphic-Linear A dubitanda and criteria for their interpretation. Finally, I suggest four different patterns in the creation and diversification of the two signaries.

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

TIDES unveiled: pioneering green peptides synthesis and oligonucleotides innovations in science

My PhD research focused on the development of environmentally sustainable methods for peptide synthesis. The traditional and toxic solvents and bases used in solid-phase peptide synthesis (SPPS) were replaced with eco-friendly alternatives to reduce the environmental impact. In particular, N-octylpyrrolidone was found to be an effective green solvent in combination with dimethyl carbonate, resulting in a 63-66% reduction in process mass intensity (PMI). In addition, a green base, DEAPA, was identified for Fmoc removal, which showed comparable results to piperidine, while being less regulated and toxic, and able to better control aspartimide-related side reactions. The study extended beyond SPPS to explore liquid-phase peptide synthesis (LPPS) and solution-phase peptide synthesis (SolPPS) using propylphosphonic anhydride (T3P®) as a coupling reagent. The developed green SolPPS using Cbz amino acids achieved exceptional efficiency, minimal racemisation and a PMI of 30 to introduce a single amino acid in the iterative process. This PMI value is the lowest ever reported for an oligopeptide synthesis protocol. This technique was extended to N-Boc amino acids in DCM, requiring aqueous workups and achieving 95% purity of Leu-Enkephalin. Finally, T3P® was found to be suitable for LPPS. An anchor, mimicking a resin, was used to allow precipitation or solubilisation of the growing anchored-peptide, depending on the polarity of the solvent used. Anisole and DCM resulted in a pentapeptide purity of over 95%. While at Oxford University, I synthesized a cleavable fragment that is sensitive to cathepsin B (CatB) and incorporated it into a cyclic antisense oligonucleotide (ASO) targeting the metastasis-associated lung adenocarcinoma transcript 1 (MALAT1). ASO demonstrated good stability in a simulated in vivo environment using human serum and high affinity with complementary RNA. The Cyclic-ASO was opened by CatB in optimal conditions. Experiments highlight therapeutic potential and a novel method for controlling cyclic oligonucleotide activity, potentially enhancing cellular uptake.