Estimating reaction constants by ab initio molecular modeling: a study on the oxidation of phenol to catechol and hydroquinone in advanced oxidation processes

Molecular modeling is growing as a research tool in Chemical Engineering studies, as can be seen by a simple research on the latest publications in the field. Molecular investigations retrieve information on properties often accessible only by expensive and time-consuming experimental techniques, such as those involved in the study of radical-based chain reactions. In this work, different quantum chemical techniques were used to study phenol oxidation by hydroxyl radicals in Advanced Oxidation Processes used for wastewater treatment. The results obtained by applying a DFT-based model showed good agreement with experimental values available, as well as qualitative insights into the mechanism of the overall reaction chain. Solvation models were also tried, but were found to be limited for this reaction system within the considered theoretical level without further parameterization.

Optical flow estimation with consistent spatio-temporal coherence models

[EN] In this work we propose a new variational model for the consistent estimation of motion fields. The aim of this work is to develop appropriate spatio-temporal coherence models. In this sense, we propose two main contributions: a nonlinear flow constancy assumption, similar in spirit to the nonlinear brightness constancy assumption, which conveniently relates flow fields at different time instants; and a nonlinear temporal regularization scheme, which complements the spatial regularization and can cope with piecewise continuous motion fields. These contributions pose a congruent variational model since all the energy terms, except the spatial regularization, are based on nonlinear warpings of the flow field. This model is more general than its spatial counterpart, provides more accurate solutions and preserves the continuity of optical flows in time. In the experimental results, we show that the method attains better results and, in particular, it considerably improves the accuracy in the presence of large displacements.

A bayesian model for mixed responses and skew laten variable to measure HRQoL in children

The aim of the thesi is to formulate a suitable Item Response Theory (IRT) based model to measure HRQoL (as latent variable) using a mixed responses questionnaire and relaxing the hypothesis of normal distributed latent variable. The new model is a combination of two models already presented in literature, that is, a latent trait model for mixed responses and an IRT model for Skew Normal latent variable. It is developed in a Bayesian framework, a Markov chain Monte Carlo procedure is used to generate samples of the posterior distribution of the parameters of interest. The proposed model is test on a questionnaire composed by 5 discrete items and one continuous to measure HRQoL in children, the EQ-5D-Y questionnaire. A large sample of children collected in the schools was used. In comparison with a model for only discrete responses and a model for mixed responses and normal latent variable, the new model has better performances, in term of deviance information criterion (DIC), chain convergences times and precision of the estimates.

Fiber beam-columns models with flexure-shear interaction for nonlinear analysis of reinforced concrete structures

The aim of this study was to develop a model capable to capture the different contributions which characterize the nonlinear behaviour of reinforced concrete structures. In particular, especially for non slender structures, the contribution to the nonlinear deformation due to bending may be not sufficient to determine the structural response. Two different models characterized by a fibre beam-column element are here proposed. These models can reproduce the flexure-shear interaction in the nonlinear range, with the purpose to improve the analysis in shear-critical structures. The first element discussed is based on flexibility formulation which is associated with the Modified Compression Field Theory as material constitutive law. The other model described in this thesis is based on a three-field variational formulation which is associated with a 3D generalized plastic-damage model as constitutive relationship. The first model proposed in this thesis was developed trying to combine a fibre beamcolumn element based on the flexibility formulation with the MCFT theory as constitutive relationship. The flexibility formulation, in fact, seems to be particularly effective for analysis in the nonlinear field. Just the coupling between the fibre element to model the structure and the shear panel to model the individual fibres allows to describe the nonlinear response associated to flexure and shear, and especially their interaction in the nonlinear field. The model was implemented in an original matlab® computer code, for describing the response of generic structures. The simulations carried out allowed to verify the field of working of the model. Comparisons with available experimental results related to reinforced concrete shears wall were performed in order to validate the model. These results are characterized by the peculiarity of distinguishing the different contributions due to flexure and shear separately. The presented simulations were carried out, in particular, for monotonic loading. The model was tested also through numerical comparisons with other computer programs. Finally it was applied for performing a numerical study on the influence of the nonlinear shear response for non slender reinforced concrete (RC) members. Another approach to the problem has been studied during a period of research at the University of California Berkeley. The beam formulation follows the assumptions of the Timoshenko shear beam theory for the displacement field, and uses a three-field variational formulation in the derivation of the element response. A generalized plasticity model is implemented for structural steel and a 3D plastic-damage model is used for the simulation of concrete. The transverse normal stress is used to satisfy the transverse equilibrium equations of at each control section, this criterion is also used for the condensation of degrees of freedom from the 3D constitutive material to a beam element. In this thesis is presented the beam formulation and the constitutive relationships, different analysis and comparisons are still carrying out between the two model presented.

New biosensor applications of surface plasmon and hydrogel optical waveguide spectroscopy

Rapid and sensitive detection of chemical and biological analytes becomes increasingly important in areas such as medical diagnostics, food control and environmental monitoring. Optical biosensors based on surface plasmon resonance (SPR) and optical waveguide spectroscopy have been extensively pushed forward in these fields. In this study, we combine SPR, surface plasmon-enhanced fluorescence spectroscopy (SPFS) and optical waveguide spectroscopy with hydrogel thin film for highly sensitive detection of molecular analytes.rnrnA novel biosensor based on SPFS which was advanced through the excitation of long range surface plasmons (LRSPs) is reported in this study. LRSPs are special surface plasmon waves propagating along thin metal films with orders of magnitude higher electromagnetic field intensity and lower damping than conventional SPs. Therefore, their excitation on the sensor surface provides further increased fluorescence signal. An inhibition immunoassay based on LRSP-enhanced fluorescence spectroscopy (LRSP-FS) was developed for the detection of aflatoxin M1 (AFM1) in milk. The biosensor allowed for the detection of AFM1 in milk at concentrations as low as 0.6 pg mL-1, which is about two orders of magnitude lower than the maximum AFM1 residue level in milk stipulated by the European Commission legislation.rnrnIn addition, LRSPs probe the medium adjacent to the metallic surface with more extended evanescent field than regular SPs. Therefore, three-dimensional binding matrices with up to micrometer thickness have been proposed for the immobilization of biomolecular recognition elements with large surface density that allows to exploit the whole evanescent field of LRSP. A photocrosslinkable carboxymethyl dextran (PCDM) hydrogel thin film is used as a binding matrix, and it is applied for the detection of free prostate specific antigen (f-PSA) based on the LRSP-FS and sandwich immunoassay. We show that this approach allows for the detection of f-PSA at low femto-molar range, which is approximately four orders of magnitude lower than that for direct detection of f-PSA based on the monitoring of binding-induced refractive index changes.rnrnHowever, a three dimensional hydrogel binding matrix with micrometer thickness can also serve as an optical waveguide. Based on the measurement of binding-induced refractive index changes, a hydrogel optical waveguide spectroscopy (HOWS) is reported for a label-free biosensor. This biosensor is implemented by using a SPR optical setup in which a carboxylated poly(N-isoproprylacrylamide) (PNIPAAm) hydrogel film is attached on a metallic surface and modified by protein catcher molecules. Compared to regular SPR biosensor with thiol self-assembled monolayer (SAM), HOWS provides an order of magnitude improved resolution in the refractive index measurements and enlarged binding capacity owing to its low damping and large swelling ratio, respectively. A model immunoassay experiment revealed that HOWS allowed detection of IgG molecules with a 10 pM limit of detection (LOD) that was five-fold lower than that achieved for SPR with thiol SAM. For the high capacity hydrogel matrix, the affinity binding was mass transport limited.rnrnThe mass transport of target molecules to the sensor surface can play as critical a role as the chemical reaction itself. In order to overcome the diffusion-limited mass transfer, magnetic iron oxide nanoparticles were employed. The magnetic nanoparticles (MNPs) can serve both as labels providing enhancement of the refractive index changes, and “vehicles” for rapidly delivering the analytes from sample solution to an SPR sensor surface with a gradient magnetic field. A model sandwich assay for the detection of β human chorionic gonadotropin (βhCG) has been utilized on a gold sensor surface with metallic diffraction grating structure supporting the excitation of SPs. Various detection formats including a) direct detection, b) sandwich assay, c) MNPs immunoassay without and d) with applied magnetic field were compared. The results show that the highly-sensitive MNPs immunoassay improves the LOD on the detection of βhCG by a factor of 5 orders of magnitude with respect to the direct detection.rn

Agent-based simulation for renewable energy incentive design

In this thesis, we propose a novel approach to model the diffusion of residential PV systems. For this purpose, we use an agent-based model where agents are the families living in the area of interest. The case study is the Emilia-Romagna Regional Energy plan, which aims to increase the produc- tion of electricity from renewable energy. So, we study the microdata from the Survey on Household Income and Wealth (SHIW) provided by Bank of Italy in order to obtain the characteristics of families living in Emilia-Romagna. These data have allowed us to artificial generate families and reproduce the socio-economic aspects of the region. The families generated by means of a software are placed on the virtual world by associating them with the buildings. These buildings are acquired by analysing the vector data of regional buildings made available by the region. Each year, the model determines the level of diffusion by simulating the installed capacity. The adoption behaviour is influenced by social interactions, household’s economic situation, the environmental benefits arising from the adoption and the payback period of the investment.

Modelling and simulating in systems biology: an approach based on multi-agent systems

Systems Biology is an innovative way of doing biology recently raised in bio-informatics contexts, characterised by the study of biological systems as complex systems with a strong focus on the system level and on the interaction dimension. In other words, the objective is to understand biological systems as a whole, putting on the foreground not only the study of the individual parts as standalone parts, but also of their interaction and of the global properties that emerge at the system level by means of the interaction among the parts. This thesis focuses on the adoption of multi-agent systems (MAS) as a suitable paradigm for Systems Biology, for developing models and simulation of complex biological systems. Multi-agent system have been recently introduced in informatics context as a suitabe paradigm for modelling and engineering complex systems. Roughly speaking, a MAS can be conceived as a set of autonomous and interacting entities, called agents, situated in some kind of nvironment, where they fruitfully interact and coordinate so as to obtain a coherent global system behaviour. The claim of this work is that the general properties of MAS make them an effective approach for modelling and building simulations of complex biological systems, following the methodological principles identified by Systems Biology. In particular, the thesis focuses on cell populations as biological systems. In order to support the claim, the thesis introduces and describes (i) a MAS-based model conceived for modelling the dynamics of systems of cells interacting inside cell environment called niches. (ii) a computational tool, developed for implementing the models and executing the simulations. The tool is meant to work as a kind of virtual laboratory, on top of which kinds of virtual experiments can be performed, characterised by the definition and execution of specific models implemented as MASs, so as to support the validation, falsification and improvement of the models through the observation and analysis of the simulations. A hematopoietic stem cell system is taken as reference case study for formulating a specific model and executing virtual experiments.

Analysis and numerical solution of the Peterlin viscoelastic model

Liquids and gasses form a vital part of nature. Many of these are complex fluids with non-Newtonian behaviour. We introduce a mathematical model describing the unsteady motion of an incompressible polymeric fluid. Each polymer molecule is treated as two beads connected by a spring. For the nonlinear spring force it is not possible to obtain a closed system of equations, unless we approximate the force law. The Peterlin approximation replaces the length of the spring by the length of the average spring. Consequently, the macroscopic dumbbell-based model for dilute polymer solutions is obtained. The model consists of the conservation of mass and momentum and time evolution of the symmetric positive definite conformation tensor, where the diffusive effects are taken into account. In two space dimensions we prove global in time existence of weak solutions. Assuming more regular data we show higher regularity and consequently uniqueness of the weak solution. For the Oseen-type Peterlin model we propose a linear pressure-stabilized characteristics finite element scheme. We derive the corresponding error estimates and we prove, for linear finite elements, the optimal first order accuracy. Theoretical error of the pressure-stabilized characteristic finite element scheme is confirmed by a series of numerical experiments.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

Active processes, morphology, and dynamics of icy debris fans: Landform evolution along rapidly degrading escarpments in alpine regions undergoing recent deglaciation

In the past few decades the impacts of climate warming have been significant in alpine glaciated regions. Many valley glaciers formerly linked as distributary glaciers to high-level icecaps have decoupled at their icefalls, exposing major escarpments and generating a suite of dynamic landforrns dominated by mass wasting. Ice-dominated landforms, here termed icy debris fans, develop rapidly by ice avalanching, rockfall, and icy debris flow. Field-based reconnaissance studies at two alpine settings, the Wrangell Mountains of Alaska and the Southern Alps of New Zealand, provide a preliminary morphogenetic model of spatial and temporal evolution of icy debris fans in a range of alpine settings. The influence of these processes on landform evolution is largely unrecognized in the literature dealing with post-glacial landform adjustment known as the paraglacial. A better understanding of these dynamic processes will be increasingly important because of the extreme geohazards characterizing these areas. Our field studies show that after glacier decoupling, icy debris fans begin to form along the base of bedrock escarpments at the mouths of catchments and prograde over valley glaciers. The presence of a distinct catchment, apex, and fan morphology distinguishes these landforms from other landforms common in periglacial hillslope settings receiving abundant clastic debris and ice. Ice avalanching is the most abundant process involved in icy debris fan formation. Fans developed below weakly incised catchments are dominated by ice avalanching and are composed primarily of ice with minor lithic detritus. Typically, avalanches fall into the fan catchments where sediments transform into grainflows that flow onto the fans. Once on the fans, avalanche deposits ablate rapidly, flattening and concentrating lithic fragments at the surface. Icy debris fans may become thick enough to become glaciers with splay crevasse systems. Fans developed below larger, more complex catchments are composed of higher proportions of lithic detritus resulting from temporary storage of ice and lithic detritus deposits within the catchment. Episodic outbursts of meltwater from the icecap may mix with the stored sediments and mobilize icy debris flows (mixture of ice and lithic clasts) onto the fans. Our observations indicate that the entire evolutionary cycle of icy debris fans probably occurs during an early paraglacial interval (i.e., decades to 100 years). Observations comparing avalanche frequency, volume, and fan morphologic evolution at the Alaska site between 2006 and 2010 illustrate complex response between icy debris fans even within the same cirque - where one fan may be growing while others are downwasting because of differences in ice supply controlled by their respective catchments and icecap contributions. As ice supply from the icecap diminishes through time, icy debris fans rapidly downwaste and eventually evolve into talus cones that receive occasional but ephemeral ice avalanches.

A high-resolution soil erosion risk map of Switzerland as strategic policy support system

Soil erosion models and soil erosion risk maps are often used as indicators to assess potential soil erosion in order to assist policy decisions. This paper shows the scientific basis of the soil erosion risk map of Switzerland and its application in policy and practice. Linking a USLE/RUSLE-based model approach (AVErosion) founded on multiple flow algorithms and the unit contributing area concept with an extremely precise and high-resolution digital terrain model (2 m × 2 m grid) using GIS allows for a realistic assessment of the potential soil erosion risk, on single plots, i.e. uniform and comprehensive for the agricultural area of Switzerland (862,579 ha in the valley area and the lower mountain regions). The national or small-scale soil erosion prognosis has thus reached a level heretofore possible only in smaller catchment areas or single plots. Validation was carried out using soil loss data from soil erosion damage mappings in the field from long-term monitoring in different test areas. 45% of the evaluated agricultural area of Switzerland was classified as low potential erosion risk, 12% as moderate potential erosion risk, and 43% as high potential erosion risk. However, many of the areas classified as high potential erosion risk are located at the transition from valley to mountain zone, where many areas are used as permanent grassland, which drastically lowers their current erosion risk. The present soil erosion risk map serves on the one hand to identify and prioritise the high-erosion risk areas, and on the other hand to promote awareness amongst farmers and authorities. It was published on the internet and will be made available to the authorities in digital form. It is intended as a tool for simplifying and standardising enforcement of the legal framework for soil erosion prevention in Switzerland. The work therefore provides a successful example of cooperation between science, policy and practice.

Parameter estimation for transformer modeling

Large Power transformers, an aging and vulnerable part of our energy infrastructure, are at choke points in the grid and are key to reliability and security. Damage or destruction due to vandalism, misoperation, or other unexpected events is of great concern, given replacement costs upward of $2M and lead time of 12 months. Transient overvoltages can cause great damage and there is much interest in improving computer simulation models to correctly predict and avoid the consequences. EMTP (the Electromagnetic Transients Program) has been developed for computer simulation of power system transients. Component models for most equipment have been developed and benchmarked. Power transformers would appear to be simple. However, due to their nonlinear and frequency-dependent behaviors, they can be one of the most complex system components to model. It is imperative that the applied models be appropriate for the range of frequencies and excitation levels that the system experiences. Thus, transformer modeling is not a mature field and newer improved models must be made available. In this work, improved topologically-correct duality-based models are developed for three-phase autotransformers having five-legged, three-legged, and shell-form cores. The main problem in the implementation of detailed models is the lack of complete and reliable data, as no international standard suggests how to measure and calculate parameters. Therefore, parameter estimation methods are developed here to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, λ-i saturation characteristic, capacitive effects, and frequency dependency of winding resistance and core loss. Steady-state excitation, and de-energization and re-energization transients are simulated and compared with an earlier-developed BCTRAN-based model. Black start energization cases are also simulated as a means of model evaluation and compared with actual event records. The simulated results using the model developed here are reasonable and more correct than those of the BCTRAN-based model. Simulation accuracy is dependent on the accuracy of the equipment model and its parameters. This work is significant in that it advances existing parameter estimation methods in cases where the available data and measurements are incomplete. The accuracy of EMTP simulation for power systems including three-phase autotransformers is thus enhanced. Theoretical results obtained from this work provide a sound foundation for development of transformer parameter estimation methods using engineering optimization. In addition, it should be possible to refine which information and measurement data are necessary for complete duality-based transformer models. To further refine and develop the models and transformer parameter estimation methods developed here, iterative full-scale laboratory tests using high-voltage and high-power three-phase transformer would be helpful.

Unsticking the centre-piece – the liberation of European copyright law?

Following European legislative initiatives in the field of copyright limitations and exceptions, policy flexibilities formerly available to mem- ber states has been greatly diminished. The law in this area is increasingly incapable of accommodating any expansion in the scope of freely permitted acts, even where such expansion may be an appropriate response to changes in social and technological conditions. In this article, the causes of this problem are briefly canvassed and a number of potential solutions are noted. It is suggested that one such solution – the adoption of an open, factor-based model similar to s 107 of the United States’ Copyright Act – has not received the serious attention it deserves. The fair use paradigm has generally been dismissed as excessively unpredictable, contrary to international law and/or culturally alien. Drawing on recent fair use scholarship, it is argued here that these disadvantages are over-stated and that the potential for the development of a European fair use model merits investigation.

A model of the roles of essential kinases in the induction and expression of late long-term potentiation.

The induction of late long-term potentiation (L-LTP) involves complex interactions among second-messenger cascades. To gain insights into these interactions, a mathematical model was developed for L-LTP induction in the CA1 region of the hippocampus. The differential equation-based model represents actions of protein kinase A (PKA), MAP kinase (MAPK), and CaM kinase II (CAMKII) in the vicinity of the synapse, and activation of transcription by CaM kinase IV (CAMKIV) and MAPK. L-LTP is represented by increases in a synaptic weight. Simulations suggest that steep, supralinear stimulus-response relationships between stimuli (e.g., elevations in [Ca(2+)]) and kinase activation are essential for translating brief stimuli into long-lasting gene activation and synaptic weight increases. Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of brief (tetanic) electrical stimuli. The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression. For example, the cAMP antagonist RpcAMPs, which inhibits L-LTP induction, is predicted to inhibit ERK activation. The model also appears useful to clarify similarities and differences between hippocampal L-LTP and long-term synaptic strengthening in other systems.

Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes

There is a need for accurate predictions of ecosystem carbon (C) and water fluxes in field conditions. Previous research has shown that ecosystem properties can be predicted from community abundance-weighted means (CWM) of plant functional traits and measures of trait variability within a community (FDvar). The capacity for traits to predict carbon (C) and water fluxes, and the seasonal dependency of these trait-function relationships has not been fully explored. Here we measured daytime C and water fluxes over four seasons in grasslands of a range of successional ages in southern England. In a model selection procedure, we related these fluxes to environmental covariates and plant biomass measures before adding CWM and FDvar plant trait measures that were scaled up from measures of individual plants grown in greenhouse conditions. Models describing fluxes in periods of low biological activity contained few predictors, which were usually abiotic factors. In more biologically active periods, models contained more predictors, including plant trait measures. Field-based plant biomass measures were generally better predictors of fluxes than CWM and FDvar traits. However, when these measures were used in combination traits accounted for additional variation. Where traits were significant predictors their identity often reflected seasonal vegetation dynamics. These results suggest that database derived trait measures can improve the prediction of ecosystem C and water fluxes. Controlled studies and those involving more detailed flux measurements are required to validate and explore these findings, a worthwhile effort given the potential for using simple vegetation measures to help predict landscape-scale fluxes.