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"Il nostro moderno Algardi": Giuseppe Maria Mazza, scultore bolognese tra Sei e Settecento
Get PDFOggetto della ricerca è uno studio monografico sul prolifico e longevo scultore bolognese Giuseppe Maria Mazza (Bologna 1653-1741), il cui arco di attività copre gli anni che dalla seconda metà del Seicento conducono al quarto decennio del Settecento. Il profilo dello scultore è stato ripercorso a partire dalla figura semisconosciuta del padre Camillo (Bologna, 1601-1672), attivo solo marginalmente in patria, e in modo più consistente al di fuori delle mura felsinee, in particolare a Padova e a Venezia.
Analogamente si è potuta indagare la prima formazione di Giuseppe Maria Mazza, avvenuta principalmente a Bologna con il pittore Lorenzo Pasinelli (Bologna 1629-1700), e con lo scultore Gabriele Brunelli (Bologna 1615-1682). Si è poi proceduto con lo studio delle opere che segnano l’affermazione dello scultore: tra gli ultimi decenni del Seicento e i primi tre del Settecento, Giuseppe Maria Mazza detiene a Bologna il monopolio della scultura, partecipando quale sodale dei pittori Giovan Gioseffo dal Sole (Bologna, 1654–1719) e Marcantonio Franceschini (Bologna 1648–1729) alle principali imprese decorative che interessano la città, e intessendo un rapporto privilegiato con i collezionisti privati a cui destina numerose opere di piccolo formato perlopiù in terracotta ma anche in marmo.
Seguendo la geografia della carriera artistica dello scultore, il raggio dello studio è stato ampliato alle altre numerose località in cui egli risulta attestato: dagli altri centri dell’Emilia Romagna (Parma, Modena, Reggio Emilia, Novellara, Ferrara, Imola, Rimini, Forlì, Cesena), alle Marche (Fano e Pesaro) e all’Umbria (Foligno). Particolare importanza è stata riservata allo studio dei due episodi professionali che ne attestano il prestigio fuori dalle mura cittadine e a livello europeo: ovvero le commissioni del principe Johann Adam I di Liechtenstein e l’attività svolta a Venezia
An Optimization Index to Identify the optimal Design Solution of Bridges
No full textStructural optimization has become an important tool for structural designers, since it allows a better exploitation of material, thus decreasing structure self-weight and saving material costs. Moreover, it helps the designer to find innovative design solutions and structural forms that not only better exploit material but also give the structure higher aesthetic value from an architectural point of view. When applied to real scale structures like bridges, this approach leads to the definition of voids patterns delimiting regions where fluxes of force migrate from force application point to boundary regions and suggests innovative layouts without renouncing to formal and structural aspects. Nevertheless, the criticality of this powerful tool is related to the ease of defining entire families of possible candidate solutions, by modifying input volume reduction ratio to reduce structural weight as much as possible or defining several starting trial solutions based on the judgment of designer. In this case, structural optimization still leads to the best material distribution, but finding the best compromise between material saving and structural performance is a designer choice. To face this aspect, a global optimization index (GOI) has been defined and applied to the structural optimization of a steel-concrete arch bridge built is San Donà in the province of Venice, Italy. On the basis of this work, a generalized version of the optimization index is proposed and its analytical formulation is discussed in detail in this thesis. The application of proposed optimization index is extended from topology optimization to other optimization techniques. Moreover it allows not only to identify best candidate solution originated by a unique reference model, but even comparing structural performances between candidates solution derived by several starting trial solutions. Through structural optimization procedure performed on three different type bridges, namely footbridges supported by concrete shell, Calatrava Bridge (steel arch bridge) and two cable-stayed bridges, the effectiveness of proposed optimization index is validated. The results show that the proposed optimization index provides to the designer a mathematical procedure able to highlight the best choice among several candidate solutions obtained by the optimization procedure. With the proposed optimization index, a suitable score for each design solution of specific starting layout is assigned, therefore the best overall layout solution which is the best compromise between material saving and structural performance can be highlighted among single-family multi-solutions or multi-families or multi-solutions
Mass Spectrometry Imaging: Looking Fruits at Molecular Level
No full textMass spectrometry imaging (MSI) is a MS-based technique. It provides a way of ascertaining both spatial distribution and relative abundance of a large variety of analytes from various biological sample surfaces. MSI is able to generate distribution maps of multiple analytes simultaneously without any labeling and does not require a prior knowledge of the target analytes, thus it has become an attractive molecular histology tool. MSI has been widely used in medicine and pharmaceutical fields, while its application in plants is recent although information regarding the spatial organization of metabolic processes in plants is of great value for understanding biological questions such as plant development, plant environment interactions, gene function and regulatory processes.
The application of MSI to these studies, however, is not straightforward due to the inherent complexity of the technique. In this thesis, the issues of plant sample preparation, surface properties heterogeneity, fast MSI analysis for spatially resolved population studies and data analysis are addressed. More specifically, two MSI approaches, namely matrix assisted laser desorption ionization (MALDI) imaging and desorption electrospray ionization (DESI) imaging, have been evaluated and compared by mapping the localization of a range of secondary and primary metabolites in apple and grapes, respectively. The work based on MALDI has been focused on the optimization of sample preparation for apple tissues to preserve the true quantitative localization of metabolites and on the development of specific data analysis tool to enhance the chemical identification in untargeted MSI (chapter 3). MALDI imaging allows high-spatial localization analysis of metabolites, but it is not suitable for applications where rapid and high throughput analysis is required when the absolute quantitative information is not necessary as in the case of screening a large number of lines in genomic or plant breeding programs. DESI imaging, in contrast, is suitable for high throughput applications with the potential of obtaining statistically robust results. However, DESI is still in its infancy and there are several fundamental aspects which have to be investigated before using it as a reliable technique in extensive imaging applications. With this in mind, we investigated how DESI imaging can be used to map the distribution of the major organic acids in different grapevine tissue parts, aiming at statistically comparing their distribution differences among various grapevine tissues and gaining insights into their metabolic pathways in grapevine. Our study demonstrated that this class of molecules can be successfully detected in grapevine stem sections, but the surface property differences within the structurally heterogeneous grapevine tissues can strongly affect their semi-quantitative detection in DESI, thereby masking their true distribution. Then we decided to investigate this phenomenon in details, in a series of dedicated imaging studies, and the results have been presented in chapter 4. At the same time, during DESI experiments we have observed the production of the dianions of small dicarboxylates acids. We further studied the mechanism of formation of such species in the ion source proposing the use of doubly charged anions as a possible proxy to visualize the distributions of organic acid salts directly in plant tissues (chapter 5). The structural organization of the PhD thesis is as below:
Chapter one and Chapter two describe the general MSI principle, compare the most widely used MSI ion sources, and discuss the current status in MSI data pre-processing and statistical methods. Due to the importance of sample preparation in MSI, sample handling for plant samples is independently reviewed in chapter two, with all the essential steps being fully discussed. The first two chapters describe the comprehensive picture regarding to MSI in plants.
Chapter three presents high spatial and high mass resolution MALDI imaging of flavonols and dihydrochalcones in apple. Besides its importance in plant research, our results demonstrate that how data analysis as such Intensity Correlation Analysis could benefit untargeted MSI analysis.
Chapter four discusses how sample surface property differences in a structurally/biologically heterogeneous sample affect the quantitative mapping of analytes in the DESI imaging of organic acids in grapevine tissue sections.
Chapter five discusses the mechanism of formation of dicarboxylate dianions in DESI and ESI
Chapter six summarizes the work in the thesis and discusses the future perspectives
The local Development dynamics of the third sector in Kenya: the Empowerment Dimension
No full textThis dissertation contributes to the local development discourse by presenting a third sector perspective from the Sub Saharan Africa. The study examines the third sector in Kenya using a seven point criteria constructed from various schools of thought. The criteria is made of aspects such as organization, autonomy, profit distribution, governance, degree of voluntarism, contribution to social inclusion and extent of entrepreneurial dynamism. It studies selected third sector organizations that include cooperatives, faith based organizations, non-governmental organizations, micro-finance institutions and self-help groups. The study uses these organizations to understand the contribution of the sector in solving social problems. The study uses different sets of designs, methodologies and data for each of the sections on the third sector actors. In some cases, data are drawn from Kenya Bureau of Statistics, government ministries, UN bodies, the Central Bank of Kenya, and both Kenyan and international public data domains. The section on self-help groups that has been used for empirical analysis uses two sets of data: one from the administrative offices of Riruta Location made of 523 observations and another collected by the researcher from a sample of 122 self-help groups. The former tests success self-rating determinants while the latter tests economic empowerment effects. The study also applies case studies in order to corroborate empirical and statistical findings.
A number of findings emanate from the study. First, the traditional cultural way of life and the cooperative activities amongst different ethnic communities provided important initial conditions for the build-up of the third sector in Kenya. Second, the colonial administration played a role in the formation of the modern third sector through their policy on community development and other policies that encouraged cooperation between government and third sector actors in service delivery. Third, in post-independence Kenya, the Harambee concept gave the sector an indigenous image anchored in community dimension, mutual and self-help emphasis. Fourth, the growth of some aspects of this sector suffered as a result of excessive government control between independence to the 2000s. Fifth, World Bank induced changes that swept across Africa in the 1980’s to 2000’s had both positive and negative influence on the sector. Last but not least, the coming to power of a new government in 2002 brought about an increase in the number of civil society actors. The third sector in Kenya helps to fill welfare gaps as a result of minimal, absent or shrinking public service spending. Although the term “third sector” is not commonly used in Kenya, its multiple actors contribute in the promotion of social inclusion of marginalized persons and regions. It helps to empower individuals economically, enhancing civic participation, infrastructure building and social welfare provision. Before 1980s the government controlled the sector closely, in the 1990s however it became more autonomous. The sector is also characterized by an explicit pursuance of a social mission, limited profit distribution and a resource mix approach. Though the policies of most of the sectors actors are quite enabling, the policies governing some of the actors are not conducive. This study is unique in two distinct ways. First, unlike earlier studies which were particularly actor-specific, the study offers a systematic approach to the discourse on the third sector in Kenya with respect to local development. Previous studies looked at the civil societies, non-profit organizations and other individual third sector actors separately. The holistic and systematic approach of this study demonstrates the collective contribution of the third sector in enhancing social welfare and development of the local communities. Secondly, it has explored the challenges faced by the sector which must be redressed in order to sustain its vibrancy. The study did suffer from lack of quality data. However, triangulation was used to ensure that much is learnt from the available data and to overcome any ensuing analytical limitations
Exploiting Text Corpora for Data Enrichment in Language and Vision Applications
No full textDuring the last decade, machine learning techniques have been used successfully in many applications. The performance of these systems depends largely on the quality and quantity of the training data. For many tasks, the data itself is not rich enough. For example, text documents such as user-queries, users-comments and short advertisements consist of only few words. Therefore direct word-based representations are sparse which makes it difficult to measure good similarities for clustering or classification. In many other applications, training data is too expensive to fully obtain. In the task of human action recognition from still images, the total number of possible actions is the cartesian product of objects and verbs. This combinatorial explosion of verb-object relations makes the task of learning human actions directly from their visual appearance computationally prohibitive and makes the collection of proper-sized image datasets infeasible. This thesis proposes a framework to enrich poor data with knowledge automatically extracted from large-scale text corpora. It considers various text modeling techniques to extract knowledge. The data enrichment framework is illustrated in different tasks in both language and vision applications.
For language applications, we apply data enrichment to query classification. A topic model is estimated on external text corpora as a reference set. This model is then used to analyze topics for short queries and categories, generating shared context between them. The experimental results show that the data enrichment process increases the performance of the system, helping to find better categories for a given query.
For vision applications, we employ the knowledge extracted from large scale text corpora to predict objects in context and recognize human actions in images. We investigate the problem of modeling text corpora for knowledge extraction and discuss which model is the most suitable for each particular task. In the first task, we learn the relations between objects from text corpora to predict how different objects often occur together using a probability model. This knowledge is then used to help predict new objects given other objects in the images. In the human action recognition task, we combine the knowledge extracted from external text corpora with the visual features from the images. Based on the visually recognized objects, scenes and relative positions between the human and objects in these images, the most plausible actions are suggested using the knowledge learned from the general external text. This model allows recognizing unseen actions and even outperforms a visual Bag-of-Words model in a realistic scenario where only few visual training examples are available
Grapevine acidity: SVM tool development and NGS data analyses.
No full textSingle Nucleotide Polymorphisms (SNPs) represent the most abundant type of genetic variation and they are a valuable tool for several biological applications like linkage mapping, integration of genetic and physical maps, population genetics as well as evolutionary and protein structure-function studies. SNP genotyping by mapping DNA reads produced via Next generation sequencing (NGS) technologies on a reference genome is a very common and convenient approach in our days, but still prone to a significant error rate. The need of defining in silico true genetic variants in genomic and transcriptomic sequences is prompted by the high costs of the experimental validation through re-sequencing or SNP arrays, not only in terms of money but also time and sample availability. Several open-source tools have been recently developed to identify small variants in whole-genome data, but still the candidate variants, provided in the VCF output format, present a high false positive calling rate. Goal of this thesis work is the development of a bioinformatic method that classifies variant calling outputs in order to reduce the number of false positive calls. With the aim to dissect the molecular bases of grape acidity (Vitis vinifera L.), this tool has been then used to select SNPs in two grapevine varieties, which show very different content of organic acids in the berry. The VCF parameters have been used to train a Support Vector Machine (SVM) that classifies the VCF records in true and false positive variants, cleaning the output from the most likely false positive results. The SVM approach has been implemented in a new software, called VerySNP, and applied to model and non-model organisms. In both cases, the machine learning method efficiently recognized true positive from false positive variants in both genomic and transcriptomic sequences. In the second part of the thesis, VerySNP was applied to identify true SNPs in RNA-seq data of the grapevine variety Gora Chirine, characterized by low acidity, and Sultanine, a normal acidity variety closely related to Gora. The comparative transcriptomic analysis crossed with the SNP information lead to discover non-synonymous polymorphisms inside coding regions and, thus, provided a list of candidate genes potentially affecting acidity in grapevine
Experimental Essays on Social and Agency Dilemmas
No full textEconomic research frequently uses experimental methods to study, in the laboratory or in the field, behaviour of economic agents.
The advantage of the laboratory experimental method is the collection of data which is, in some cases, otherwise impossible to obtain. In addition, experiments permit to test, ceteris paribus, the impact of a certain treatment on the behaviour of the economic agents.
These are the reasons for the application of laboratory experimental methods in the three essays of this thesis; which are focused on possible measures for rising compliant behaviour in social and agency dilemmas. Tax compliance has been selected for two essays on social dilemma, while asset management has been chosen for one essay on agency dilemma.
In the tax compliance context, we refer to a compliant behaviour when subjects do not engage tax evasion: the support of compliance has been studied through non monetary (dis)incentives (Chapter 2) and through direct participation of taxpayers in the tax system (Chapter 3).
In the asset management context (Chapter 4), we refer to a compliant behaviour when a fund manager, managing her clients's money, follows the client's disposition even if this implies a payoff reduction for the manager herself. Accountability and monetary punishment are the measures studied in order to reduce opportunistic behaviour of managers and rise their compliance
Bayesian Inference for Brain Decoding
No full textBrain decoding, a paradigm that consists of predicting stimuli or mental states from concurrent functional brain data, is steadily gaining popularity in neuroimaging based research. While the machine learning and pattern recognition techniques involved in classification-based analysis are getting more and more sophisticated, the statistical methods used for the evaluation of the obtained results are not developing at the same speed. This represents a problem because inadequately evaluated results can be misleading and further claims based on them might be unfunded. This PhD thesis presents a sound procedure for the evaluation of classification results from brain decoding experiments overcoming some limitations of current common practice. An evaluation method within the Bayesian hypothesis testing framework, namely Bayesian test of independence, is presented, which recasts the question whether there is evidence that the classifier has learned from data to discriminate the classes or not as a test of independence between predicted and true class labels. Within the multi-class setting a classifier can learn the complete discrimination of all classes or only subsets of classes. The Bayesian test for partial independence is derived for the latter case. This approach closes a gap as common practice methods do not allow for analysis of potential subsets except for additional binary analysis.
Data from simulated and real experiments are considered when comparing the novel methods to common practice. The proposed procedure is robust with imbalanced datasets, it reduces potential issues that arise when having a single null-hypothesis, there is the possibility to incorporate prior knowledge for the learning case, it is compatible with small sample size test sets, and it prevents the misleading interpretation of the estimated prediction accuracy. Experimental evidence shows, that the proposed Bayesian test of independence not only addresses the question whether learning from data has taken place or not but also that it can be successfully used in settings where common practice tests are stressed to their limits
A collaborative Platform for multilingual Ontology Development
No full textThe world is extremely diverse and its diversity is obvious in the cultural differences and the large number of spoken languages being used all over the world. In this sense, we need to collect and organize a huge amount of knowledge obtained from multiple resources differing from one another in many aspects. A possible approach for doing that is to think of designing effective tools for construction and maintenance of linguistic resources and localized domain ontologies based on well-defined knowledge representation methodologies capable of dealing with diversity and the continuous evolvement of human knowledge. In this thesis, we present a collaborative platform which allows for knowledge organization in a language-independent manner and provides the appropriate mapping from a language independent concept to one specific lexicalization per language. This representation ensures a smooth multilingual enrichment process for linguistic resources and a robust construction of ontologies using language-independent concepts. The collaborative platform is designed following a workflow-based development methodology that models linguistic resources as a set of collaborative objects and assigns a customizable workflow to build and maintain each collaborative object in a community driven manner, with extensive support of modern web 2.0 social and collaborative features
Eshelby-like forces in elastic structures: theory, experiments and applications
No full textThe Eshelbian force is the main concept of a celebrated theoretical framework associated with the motion of dislocations and, more in general, defects in solid. Similarly, it is proven that a force driving the configuration of an elastic structure is generated through the motion and release mechanism of flexural and torsional energy. This configurational force, analytically derived through different approaches and experimentally validated, provides counterintuitive but crucial effects in elasticity. In particular, it affects equilibrium paths in systems with variable length and instabilities, bifurcation and restabilization occurring in a structure penetrating in a movable constraint. Furthermore, this configurational force (called 'Eshelby-like' in analogy to continuum mechanics) opens a totally new perspective in the mechanics of deformable mechanisms, with possible broad applications in new weighing devices (the 'elastica arm scale'), torsional locomotion along perfectly smooth channel and configurational actuators, capable of transforming torque into propulsive force