Multivariate spectroscopic methods for the analysis of solutions

In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. These spectroscopic methods consisting of spectroscopic analysis, a high level of automation and chemometric data evaluation can lead to analytical methods with a high analytical capacity, and for these methods, the term high-capacity analysis (HCA) is suggested. It is further shown how chemometric evaluation of the multivariate data in chromatographic analyses decreases the need for baseline separation. The thesis is based on six papers and the chemometric tools used are experimental design, principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC). The analytical techniques utilised are scanning ultraviolet-visible (UV-Vis) spectroscopy, diode array detection (DAD) used in non-column chromatographic diode array UV spectroscopy, high-performance liquid chromatography with diode array detection (HPLC-DAD) and fluorescence spectroscopy. The methods proposed are exemplified in the analysis of pharmaceutical solutions and serum proteins. In Paper I a method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of UV-Vis spectroscopy, orthogonal signal correction and multivariate calibration with PLS and SIMCA classification. Paper II proposes a new method for the rapid determination of pharmaceutical solutions by the use of non-column chromatographic diode array UV spectroscopy, i.e. a conventional HPLC-DAD system without any chromatographic column connected. In Paper III an investigation is made of the ability of a control sample, of known content and identity to diagnose and correct errors in multivariate predictions something that together with use of multivariate residuals can make it possible to use the same calibration model over time. In Paper IV a method is proposed for simultaneous determination of serum proteins with fluorescence spectroscopy and multivariate calibration. Paper V proposes a method for the determination of chromatographic peak purity by means of PCA of HPLC-DAD data. In Paper VI PARAFAC is applied for the decomposition of DAD data of some partially separated peaks into the pure chromatographic, spectral and concentration profiles.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Models and methods for power monolithic microwave integrated circuits

Computer aided design of Monolithic Microwave Integrated Circuits (MMICs) depends critically on active device models that are accurate, computationally efficient, and easily extracted from measurements or device simulators. Empirical models of active electron devices, which are based on actual device measurements, do not provide a detailed description of the electron device physics. However they are numerically efficient and quite accurate. These characteristics make them very suitable for MMIC design in the framework of commercially available CAD tools. In the empirical model formulation it is very important to separate linear memory effects (parasitic effects) from the nonlinear effects (intrinsic effects). Thus an empirical active device model is generally described by an extrinsic linear part which accounts for the parasitic passive structures connecting the nonlinear intrinsic electron device to the external world. An important task circuit designers deal with is evaluating the ultimate potential of a device for specific applications. In fact once the technology has been selected, the designer would choose the best device for the particular application and the best device for the different blocks composing the overall MMIC. Thus in order to accurately reproducing the behaviour of different-in-size devices, good scalability properties of the model are necessarily required. Another important aspect of empirical modelling of electron devices is the mathematical (or equivalent circuit) description of the nonlinearities inherently associated with the intrinsic device. Once the model has been defined, the proper measurements for the characterization of the device are performed in order to identify the model. Hence, the correct measurement of the device nonlinear characteristics (in the device characterization phase) and their reconstruction (in the identification or even simulation phase) are two of the more important aspects of empirical modelling. This thesis presents an original contribution to nonlinear electron device empirical modelling treating the issues of model scalability and reconstruction of the device nonlinear characteristics. The scalability of an empirical model strictly depends on the scalability of the linear extrinsic parasitic network, which should possibly maintain the link between technological process parameters and the corresponding device electrical response. Since lumped parasitic networks, together with simple linear scaling rules, cannot provide accurate scalable models, either complicate technology-dependent scaling rules or computationally inefficient distributed models are available in literature. This thesis shows how the above mentioned problems can be avoided through the use of commercially available electromagnetic (EM) simulators. They enable the actual device geometry and material stratification, as well as losses in the dielectrics and electrodes, to be taken into account for any given device structure and size, providing an accurate description of the parasitic effects which occur in the device passive structure. It is shown how the electron device behaviour can be described as an equivalent two-port intrinsic nonlinear block connected to a linear distributed four-port passive parasitic network, which is identified by means of the EM simulation of the device layout, allowing for better frequency extrapolation and scalability properties than conventional empirical models. Concerning the issue of the reconstruction of the nonlinear electron device characteristics, a data approximation algorithm has been developed for the exploitation in the framework of empirical table look-up nonlinear models. Such an approach is based on the strong analogy between timedomain signal reconstruction from a set of samples and the continuous approximation of device nonlinear characteristics on the basis of a finite grid of measurements. According to this criterion, nonlinear empirical device modelling can be carried out by using, in the sampled voltage domain, typical methods of the time-domain sampling theory.

Safety factors in sheet pile wall design: considerations by using traditional methods and FEM analysis

The purpose of the work is: define and calculate a factor of collapse related to traditional method to design sheet pile walls. Furthermore, we tried to find the parameters that most influence a finite element model representative of this problem. The text is structured in this way: from chapter 1 to 5, we analyzed a series of arguments which are usefull to understanding the problem, while the considerations mainly related to the purpose of the text are reported in the chapters from 6 to 10. In the first part of the document the following arguments are shown: what is a sheet pile wall, what are the codes to be followed for the design of these structures and what they say, how can be formulated a mathematical model of the soil, some fundamentals of finite element analysis, and finally, what are the traditional methods that support the design of sheet pile walls. In the chapter 6 we performed a parametric analysis, giving an answer to the second part of the purpose of the work. Comparing the results from a laboratory test for a cantilever sheet pile wall in a sandy soil, with those provided by a finite element model of the same problem, we concluded that:in modelling a sandy soil we should pay attention to the value of cohesion that we insert in the model (some programs, like Abaqus, don’t accept a null value for this parameter), friction angle and elastic modulus of the soil, they influence significantly the behavior of the system (structure-soil), others parameters, like the dilatancy angle or the Poisson’s ratio, they don’t seem influence it. The logical path that we followed in the second part of the text is reported here. We analyzed two different structures, the first is able to support an excavation of 4 m, while the second an excavation of 7 m. Both structures are first designed by using the traditional method, then these structures are implemented in a finite element program (Abaqus), and they are pushed to collapse by decreasing the friction angle of the soil. The factor of collapse is the ratio between tangents of the initial friction angle and of the friction angle at collapse. At the end, we performed a more detailed analysis of the first structure, observing that, the value of the factor of collapse is influenced by a wide range of parameters including: the value of the coefficients assumed in the traditional method and by the relative stiffness of the structure-soil system. In the majority of cases, we found that the value of the factor of collapse is between and 1.25 and 2. With some considerations, reported in the text, we can compare the values so far found, with the value of the safety factor proposed by the code (linked to the friction angle of the soil).

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

Numerical and analytical vibro-acoustic modelling of porous materials: the Cell Method for poroelasticity and the case of inclusions

Porous materials are widely used in many fields of industrial applications, to achieve the requirements of noise reduction, that nowadays derive from strict regulations. The modeling of porous materials is still a problematic issue. Numerical simulations are often problematic in case of real complex geometries, especially in terms of computational times and convergence. At the same time, analytical models, even if partly limited by restrictive simplificative hypotheses, represent a powerful instrument to capture quickly the physics of the problem and general trends. In this context, a recently developed numerical method, called the Cell Method, is described, is presented in the case of the Biot's theory and applied for representative cases. The peculiarity of the Cell Method is that it allows for a direct algebraic and geometrical discretization of the field equations, without any reduction to a weak integral form. Then, the second part of the thesis presents the case of interaction between two poroelastic materials under the context of double porosity. The idea of using periodically repeated inclusions of a second porous material into a layer composed by an original material is described. In particular, the problem is addressed considering the efficiency of the analytical method. A analytical procedure for the simulation of heterogeneous layers based is described and validated considering both conditions of absorption and transmission; a comparison with the available numerical methods is performed. ---------------- I materiali porosi sono ampiamente utilizzati per diverse applicazioni industriali, al fine di raggiungere gli obiettivi di riduzione del rumore, che sono resi impegnativi da norme al giorno d'oggi sempre più stringenti. La modellazione dei materiali porori per applicazioni vibro-acustiche rapprensenta un aspetto di una certa complessità. Le simulazioni numeriche sono spesso problematiche quando siano coinvolte geometrie di pezzi reali, in particolare riguardo i tempi computazionali e la convergenza. Allo stesso tempo, i modelli analitici, anche se parzialmente limitati a causa di ipotesi semplificative che ne restringono l'ambito di utilizzo, rappresentano uno strumento molto utile per comprendere rapidamente la fisica del problema e individuare tendenze generali. In questo contesto, un metodo numerico recentemente sviluppato, il Metodo delle Celle, viene descritto, implementato nel caso della teoria di Biot per la poroelasticità e applicato a casi rappresentativi. La peculiarità del Metodo delle Celle consiste nella discretizzazione diretta algebrica e geometrica delle equazioni di campo, senza alcuna riduzione a forme integrali deboli. Successivamente, nella seconda parte della tesi viene presentato il caso delle interazioni tra due materiali poroelastici a contatto, nel contesto dei materiali a doppia porosità. Viene descritta l'idea di utilizzare inclusioni periodicamente ripetute di un secondo materiale poroso all'interno di un layer a sua volta poroso. In particolare, il problema è studiando il metodo analitico e la sua efficienza. Una procedura analitica per il calcolo di strati eterogenei di materiale viene descritta e validata considerando sia condizioni di assorbimento, sia di trasmissione; viene effettuata una comparazione con i metodi numerici a disposizione.

Slope stability analysis by multi-temporal DEMs and 3D modelling: The 2002 and 2007 Stromboli landslide events

Natural hazard related to the volcanic activity represents a potential risk factor, particularly in the vicinity of human settlements. Besides to the risk related to the explosive and effusive activity, the instability of volcanic edifices may develop into large landslides often catastrophically destructive, as shown by the collapse of the northern flank of Mount St. Helens in 1980. A combined approach was applied to analyse slope failures that occurred at Stromboli volcano. SdF slope stability was evaluated by using high-resolution multi-temporal DTMMs and performing limit equilibrium stability analyses. High-resolution topographical data collected with remote sensing techniques and three-dimensional slope stability analysis play a key role in understanding instability mechanism and the related risks. Analyses carried out on the 2002–2003 and 2007 Stromboli eruptions, starting from high-resolution data acquired through airborne remote sensing surveys, permitted the estimation of the lava volumes emplaced on the SdF slope and contributed to the investigation of the link between magma emission and slope instabilities. Limit Equilibrium analyses were performed on the 2001 and 2007 3D models, in order to simulate the slope behavior before 2002-2003 landslide event and after the 2007 eruption. Stability analyses were conducted to understand the mechanisms that controlled the slope deformations which occurred shortly after the 2007 eruption onset, involving the upper part of slope. Limit equilibrium analyses applied to both cases yielded results which are congruent with observations and monitoring data. The results presented in this work undoubtedly indicate that hazard assessment for the island of Stromboli should take into account the fact that a new magma intrusion could lead to further destabilisation of the slope, which may be more significant than the one recently observed because it will affect an already disarranged deposit and fractured and loosened crater area. The two-pronged approach based on the analysis of 3D multi-temporal mapping datasets and on the application of LE methods contributed to better understanding volcano flank behaviour and to be prepared to undertake actions aimed at risk mitigation.

Fluid dynamic analysis and modelling of industrial chemical equipment

The research is aimed at contributing to the identification of reliable fully predictive Computational Fluid Dynamics (CFD) methods for the numerical simulation of equipment typically adopted in the chemical and process industries. The apparatuses selected for the investigation, specifically membrane modules, stirred vessels and fluidized beds, were characterized by a different and often complex fluid dynamic behaviour and in some cases the momentum transfer phenomena were coupled with mass transfer or multiphase interactions. Firs of all, a novel modelling approach based on CFD for the prediction of the gas separation process in membrane modules for hydrogen purification is developed. The reliability of the gas velocity field calculated numerically is assessed by comparison of the predictions with experimental velocity data collected by Particle Image Velocimetry, while the applicability of the model to properly predict the separation process under a wide range of operating conditions is assessed through a strict comparison with permeation experimental data. Then, the effect of numerical issues on the RANS-based predictions of single phase stirred tanks is analysed. The homogenisation process of a scalar tracer is also investigated and simulation results are compared to original passive tracer homogenisation curves determined with Planar Laser Induced Fluorescence. The capability of a CFD approach based on the solution of RANS equations is also investigated for describing the fluid dynamic characteristics of the dispersion of organics in water. Finally, an Eulerian-Eulerian fluid-dynamic model is used to simulate mono-disperse suspensions of Geldart A Group particles fluidized by a Newtonian incompressible fluid as well as binary segregating fluidized beds of particles differing in size and density. The results obtained under a number of different operating conditions are compared with literature experimental data and the effect of numerical uncertainties on axial segregation is also discussed.

Mixed finite-element methods for elliptic convection-dominated problems arising in semiconductor physics

In this work we develop and analyze an adaptive numerical scheme for simulating a class of macroscopic semiconductor models. At first the numerical modelling of semiconductors is reviewed in order to classify the Energy-Transport models for semiconductors that are later simulated in 2D. In this class of models the flow of charged particles, that are negatively charged electrons and so-called holes, which are quasi-particles of positive charge, as well as their energy distributions are described by a coupled system of nonlinear partial differential equations. A considerable difficulty in simulating these convection-dominated equations is posed by the nonlinear coupling as well as due to the fact that the local phenomena such as "hot electron effects" are only partially assessable through the given data. The primary variables that are used in the simulations are the particle density and the particle energy density. The user of these simulations is mostly interested in the current flow through parts of the domain boundary - the contacts. The numerical method considered here utilizes mixed finite-elements as trial functions for the discrete solution. The continuous discretization of the normal fluxes is the most important property of this discretization from the users perspective. It will be proven that under certain assumptions on the triangulation the particle density remains positive in the iterative solution algorithm. Connected to this result an a priori error estimate for the discrete solution of linear convection-diffusion equations is derived. The local charge transport phenomena will be resolved by an adaptive algorithm, which is based on a posteriori error estimators. At that stage a comparison of different estimations is performed. Additionally a method to effectively estimate the error in local quantities derived from the solution, so-called "functional outputs", is developed by transferring the dual weighted residual method to mixed finite elements. For a model problem we present how this method can deliver promising results even when standard error estimator fail completely to reduce the error in an iterative mesh refinement process.

Methods for Clearance Influence Analysis in Planar and Spatial Mechanisms

This doctoral dissertation presents a new method to asses the influence of clearancein the kinematic pairs on the configuration of planar and spatial mechanisms. The subject has been widely investigated in both past and present scientific literature, and is approached in different ways: a static/kinetostatic way, which looks for the clearance take-up due to the external loads on the mechanism; a probabilistic way, which expresses clearance-due displacements using probability density functions; a dynamic way, which evaluates dynamic effects like the actual forces in the pairs caused by impacts, or the consequent vibrations. This dissertation presents a new method to approach the problem of clearance. The problem is studied from a purely kinematic perspective. With reference to a given mechanism configuration, the pose (position and orientation) error of the mechanism link of interest is expressed as a vector function of the degrees of freedom introduced in each pair by clearance: the presence of clearance in a kinematic pair, in facts, causes the actual pair to have more degrees of freedom than the theoretical clearance-free one. The clearance-due degrees of freedom are bounded by the pair geometry. A proper modelling of clearance-affected pairs allows expressing such bounding through analytical functions. It is then possible to study the problem as a maximization problem, where a continuous function (the pose error of the link of interest) subject to some constraints (the analytical functions bounding clearance- due degrees of freedom) has to be maximize. Revolute, prismatic, cylindrical, and spherical clearance-affected pairs have been analytically modelled; with reference to mechanisms involving such pairs, the solution to the maximization problem has been obtained in a closed form.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Numerical methods for the dispersion analysis of Guided Waves

The use of guided ultrasonic waves (GUW) has increased considerably in the fields of non-destructive (NDE) testing and structural health monitoring (SHM) due to their ability to perform long range inspections, to probe hidden areas as well as to provide a complete monitoring of the entire waveguide. Guided waves can be fully exploited only once their dispersive properties are known for the given waveguide. In this context, well stated analytical and numerical methods are represented by the Matrix family methods and the Semi Analytical Finite Element (SAFE) methods. However, while the former are limited to simple geometries of finite or infinite extent, the latter can model arbitrary cross-section waveguides of finite domain only. This thesis is aimed at developing three different numerical methods for modelling wave propagation in complex translational invariant systems. First, a classical SAFE formulation for viscoelastic waveguides is extended to account for a three dimensional translational invariant static prestress state. The effect of prestress, residual stress and applied loads on the dispersion properties of the guided waves is shown. Next, a two-and-a-half Boundary Element Method (2.5D BEM) for the dispersion analysis of damped guided waves in waveguides and cavities of arbitrary cross-section is proposed. The attenuation dispersive spectrum due to material damping and geometrical spreading of cavities with arbitrary shape is shown for the first time. Finally, a coupled SAFE-2.5D BEM framework is developed to study the dispersion characteristics of waves in viscoelastic waveguides of arbitrary geometry embedded in infinite solid or liquid media. Dispersion of leaky and non-leaky guided waves in terms of speed and attenuation, as well as the radiated wavefields, can be computed. The results obtained in this thesis can be helpful for the design of both actuation and sensing systems in practical application, as well as to tune experimental setup.

Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

Forward modelling of petrological crust-forming processes on the early Earth

Tonalite-trondhjemite-granodiorite (TTG) gneisses form up to two-thirds of the preserved Archean continental crust and there is considerable debate regarding the primary magmatic processes of the generation of these rocks. The popular theories indicate that these rocks were formed by partial melting of basaltic oceanic crust which was previously metamorphosed to garnet-amphibolite and/or eclogite facies conditions either at the base of thick oceanic crust or by subduction processes.rnThis study investigates a new aspect regarding the source rock for Archean continental crust which is inferred to have had a bulk compostion richer in magnesium (picrite) than present-day basaltic oceanic crust. This difference is supposed to originate from a higher geothermal gradient in the early Archean which may have induced higher degrees of partial melting in the mantle, which resulted in a thicker and more magnesian oceanic crust. rnThe methods used to investigate the role of a more MgO-rich source rock in the formation of TTG-like melts in the context of this new approach are mineral equilibria calculations with the software THERMOCALC and high-pressure experiments conducted from 10–20 kbar and 900–1100 °C, both combined in a forward modelling approach. Initially, P–T pseudosections for natural rock compositions with increasing MgO contents were calculated in the system NCFMASHTO (Na2O–CaO–FeO–MgO–Al2O3–SiO2–H2O–TiO2) to ascertain the metamorphic products from rocks with increasing MgO contents from a MORB up to a komatiite. A small number of previous experiments on komatiites showed the development of pyroxenite instead of eclogite and garnet-amphibolite during metamorphism and established that melts of these pyroxenites are of basaltic composition, thus again building oceanic crust instead of continental crust.rnThe P–T pseudosections calculated represent a continuous development of their metamorphic products from amphibolites and eclogites towards pyroxenites. On the basis of these calculations and the changes within the range of compositions, three picritic Models of Archean Oceanic Crust (MAOC) were established with different MgO contents (11, 13 and 15 wt%) ranging between basalt and komatiite. The thermodynamic modelling for MAOC 11, 13 and 15 at supersolidus conditions is imprecise since no appropriate melt model for metabasic rocks is currently available and the melt model for metapelitic rocks resulted in unsatisfactory calculations. The partially molten region is therfore covered by high-pressure experiments. The results of the experiments show a transition from predominantly tonalitic melts in MAOC 11 to basaltic melts in MAOC 15 and a solidus moving towards higher temperatures with increasing magnesium in the bulk composition. Tonalitic melts were generated in MAOC 11 and 13 at pressures up to 12.5 kbar in the presence of garnet, clinopyroxene, plagioclase plus/minus quartz (plus/minus orthopyroxene in the presence of quartz and at lower pressures) in the absence of amphibole but it could not be explicitly indicated whether the tonalitic melts coexisting with an eclogitic residue and rutile at 20 kbar do belong to the Archean TTG suite. Basaltic melts were generated predominantly in the presence of granulite facies residues such as amphibole plus/minus garnet, plagioclase, orthopyroxene that lack quartz in all MAOC compositions at pressures up to 15 kbar. rnThe tonalitic melts generated in MAOC 11 and 13 indicate that thicker oceanic crust with more magnesium than that of a modern basalt is also a viable source for the generation of TTG-like melts and therefore continental crust in the Archean. The experimental results are related to different geologic settings as a function of pressure. The favoured setting for the generation of early TTG-like melts at 15 kbar is the base of an oceanic crust thicker than existing today or by melting of slabs in shallow subduction zones, both without interaction of tonalic melts with the mantle. Tonalitic melts at 20 kbar may have been generated below the plagioclase stability by slab melting in deeper subduction zones that have developed with time during the progressive cooling of the Earth, but it is unlikely that those melts reached lower pressure levels without further mantle interaction.rn