Environmental weathering of natural and artificial stones used in historical architecture: influence of microstructure and new restoration methods

For some study cases (the Cathedral of Modena, Italy, XII-XIV century; the Ducal Palace in Mantua, Italy, XVI century; the church of San Francesco in Fano, Italy, XIV-XIX century), considered as representative of the use of natural and artificial stones in historical architecture, the complex interaction between environ-mental aggressiveness, materials’ microstructural characteristics and degradation was investigated. From the results of such analyses, it was found that materials microstructure plays a fundamental role in the actual extent to which weathering mechanisms affect natural and artificial stones. Consequently, the need of taking into account the important role of material microstructure, when evaluating the environmental aggressiveness to natural and artificial stones, was highlighted. Therefore, a possible quantification of the role of microstructure on the resistance to environmental attack was investigated. By exposing stone samples, with significantly different microstructural features, to slightly acidic aqueous solutions, simulating clean and acid rain, a good correlation between weight losses and the product of carbonate content and specific surface area (defined as the “vulnerable specific surface area”) was found. Alongside the evaluation of stone vulnerability, the development of a new consolidant for weathered carbonate stones was undertaken. The use of hydroxya-patite, formed by reacting the calcite of the stone with an aqueous solution of di-ammonium hydrogen phosphate, was found to be a promising consolidating tech-nique for carbonates stones. Indeed, significant increases in the mechanical prop-erties can be achieved after the treatment, which has the advantage of simply con-sisting in a non-hazardous aqueous solution, able to penetrate deeply into the stone (> 2 cm) and bring significant strengthening after just 2 days of reaction. Furthermore, the stone sorptivity is not eliminated after treatment, so that water and water vapor exchanges between the stone and the environment are not com-pletely blocked.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Cell-free cryopreserved arterial allografts from multiorgan donors: a new strategy to fabricate artificial blood vessels suited for peripheral vascular surgery

Critical lower limb ischemia is a severe disease. A common approach is infrainguinal bypass. Synthetic vascular prosthesis, are good conduits in high-flow low-resistance conditions but have difficulty in their performance as small diameter vessel grafts. A new approach is the use of native decellularized vascular tissues. Cell-free vessels are expected to have improved biocompatibility when compared to synthetic and are optimal natural 3D matrix templates for driving stem cell growth and tissue assembly in vivo. Decellularization of tissues represent a promising field for regenerative medicine, with the aim to develop a methodology to obtain small-diameter allografts to be used as a natural scaffold suited for in vivo cell growth and pseudo-tissue assembly, eliminating failure caused from immune response activation. Material and methods. Umbilical cord-derived mesenchymal cells isolated from human umbilical cord tissue were expanded in advanced DMEM. Immunofluorescence and molecular characterization revealed a stem cell profile. A non-enzymatic protocol, that associate hypotonic shock and low-concentration ionic detergent, was used to decellularize vessel segments. Cells were seeded cell-free scaffolds using a compound of fibrin and thrombin and incubated in DMEM, after 4 days of static culture they were placed for 2 weeks in a flow-bioreactor, mimicking the cardiovascular pulsatile flow. After dynamic culture, samples were processed for histological, biochemical and ultrastructural analysis. Discussion. Histology showed that the dynamic culture cells initiate to penetrate the extracellular matrix scaffold and to produce components of the ECM, as collagen fibres. Sirius Red staining showed layers of immature collagen type III and ultrastructural analysis revealed 30 nm thick collagen fibres, presumably corresponding to the immature collagen. These data confirm the ability of cord-derived cells to adhere and penetrate a natural decellularized tissue and to start to assembly into new tissue. This achievement makes natural 3D matrix templates prospectively valuable candidates for clinical bypass procedures

Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

Inverse Static Analysis of Massive Parallel Arrays of Three-State Actuators via Artificial Intelligence

Massive parallel robots (MPRs) driven by discrete actuators are force regulated robots that undergo continuous motions despite being commanded through a finite number of states only. Designing a real-time control of such systems requires fast and efficient methods for solving their inverse static analysis (ISA), which is a challenging problem and the subject of this thesis. In particular, five Artificial intelligence methods are proposed to investigate the on-line computation and the generalization error of ISA problem of a class of MPRs featuring three-state force actuators and one degree of revolute motion.

Complex chemical dynamics through engineering-like methods

Most of the problems in modern structural design can be described with a set of equation; solutions of these mathematical models can lead the engineer and designer to get info during the design stage. The same holds true for physical-chemistry; this branch of chemistry uses mathematics and physics in order to explain real chemical phenomena. In this work two extremely different chemical processes will be studied; the dynamic of an artificial molecular motor and the generation and propagation of the nervous signals between excitable cells and tissues like neurons and axons. These two processes, in spite of their chemical and physical differences, can be both described successfully by partial differential equations, that are, respectively the Fokker-Planck equation and the Hodgkin and Huxley model. With the aid of an advanced engineering software these two processes have been modeled and simulated in order to extract a lot of physical informations about them and to predict a lot of properties that can be, in future, extremely useful during the design stage of both molecular motors and devices which rely their actions on the nervous communications between active fibres.