Simulation of photoinduced processes in organic chromophores: mapping photochemically relevant states for accurate dynamics

The simulation of ultrafast photoinduced processes is a fundamental step towards the understanding of the underlying molecular mechanism and interpretation/prediction of experimental data. Performing a computer simulation of a complex photoinduced process is only possible introducing some approximations but, in order to obtain reliable results, the need to reduce the complexity must balance with the accuracy of the model, which should include all the relevant degrees of freedom and a quantitatively correct description of the electronic states involved in the process. This work presents new computational protocols and strategies for the parameterisation of accurate models for photochemical/photophysical processes based on state-of-the-art multiconfigurational wavefunction-based methods. The required ingredients for a dynamics simulation include potential energy surfaces (PESs) as well as electronic state couplings, which must be mapped across the wide range of geometries visited during the wavepacket/trajectory propagation. The developed procedures allow to obtain solid and extended databases reducing as much as possible the computational cost, thanks to, e.g., specific tuning of the level of theory for different PES regions and/or direct calculation of only the needed components of vectorial quantities (like gradients or nonadiabatic couplings). The presented approaches were applied to three case studies (azobenzene, pyrene, visual rhodopsin), all requiring an accurate parameterisation but for different reasons. The resulting models and simulations allowed to elucidate the mechanism and time scale of the internal conversion, reproducing or even predicting new transient experiments. The general applicability of the developed protocols to systems with different peculiarities and the possibility to parameterise different types of dynamics on an equal footing (classical vs purely quantum) prove that the developed procedures are flexible enough to be tailored for each specific system, and pave the way for exact quantum dynamics with multiple degrees of freedom.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Contaminants of emerging concern in drinking water: assessment of the effectiveness of removal by drinking water treatments and possible risks to human health

Contaminants of emerging concern are increasingly detected in the water cycle, with endocrine-disrupting chemicals (EDCs) receiving attention due to their potential to cause adverse health effects even at low concentrations. Although the EU has recently introduced some EDCs into drinking water legislation, most drinking water treatment plants (DWTPs) are not designed to remove EDCs, making their detection and removal in DWTPs an important challenge. The aim of this doctoral project was to investigate hormones and phenolic compounds as suspected EDCs in drinking waters across the Romagna area (Italy). The main objectives were to assess the occurrence of considered contaminants in source and drinking water from three DWTPs, characterize the effectiveness of removal by different water treatment processes, and evaluate the potential biological impact on drinking water and human health. Specifically, a complementary approach of target chemical analysis and effect-based methods was adopted to explore drinking water quality, treatment efficacy, and biological potential. This study found that nonylphenol (NP) was prevalent in all samples, followed by BPA. Sporadic contamination of hormones was found only in source waters. Although the measured EDC concentrations in drinking water did not exceed threshold guideline values, the potential role of DWTPs as an additional source of EDC contamination should be considered. Significant increases in BPA and NP levels were observed during water treatment steps, which were also reflected in estrogenic and mutagenic responses in water samples after the ultrafiltration. This highlights the need to monitor water quality during various treatment processes to improve the efficiency of DWTPs. Biological assessments on finished water did not reveal any bioactivity, except for few treated water samples that exhibited estrogenic responses. Overall, the data emphasize the high quality of produced drinking water and the value of applying integrated chemical analysis and in vitro bioassays for water quality assessment.

Numerical and physical modelling of axially loaded piles for offshore wind turbines

The scope of the thesis is to broaden the knowledge about axially loaded pipe piles, that can play as foundations for offshore wind turbines based on jacket structures. The goal of the work was pursued by interpreting experimental data on large-scale model piles and by developing numerical tools for the prediction of their monotonic response to tensile and compressive loads to failure. The availability of experimental results on large scale model piles produced in two different campaigns at Fraunhofer IWES (Hannover, Germany) represented the reference for the whole work. Data from CPTs, blow counts during installation and load-displacement curves allowed to develop considerations on the experimental results and comparison with empirical methods from literature, such as CPT-based methods and Load Transfer methods. The understanding of soil-structure interaction mechanisms has been involved in the study in order to better assess the mechanical response of the sand with the scope to help in developing predictive tools of the experiments. A lack of information on the response of Rohsand 3152 when in contact with steel was highlighted, so the necessity of better assessing its response was fulfilled with a comprehensive campaign of interface shear test. It was found how the response of the sand to ultimate conditions evolve with the roughness of the steel, which is a precious information to take account of when attempting the prediction of a pile capacity. Parallel to this topic, the work has developed a numerical modelling procedure that was validated on the available large-scale model piles at IWES. The modelling strategy is intended to build a FE model whose mechanical properties of the sand come from an interpretation of commonly available geotechnical tests. The results of the FE model were compared with other predictive tools currently used in the engineering practice.

Applied genomics to monitor the genetic diversity and integrity of honey bee (Apis mellifera) populations and define sustainable strategies to benefit the beekeeping sector

Honey bees are considered keystone species in ecosystem, the effect of harmful pesticides for the honey bees, the action of extreme climatic waves and their consequence on honey bees health can cause the loss of many colonies which could contribute to the reduction of the effective population size and incentive the use of non-autochthonous queens to replace dead colonies. Over the last decades, the use of non-ligustica bee subspecies in Italy has increased and together with the mentioned phenomena exposed native honey bees to hybridization, laeding to a dramatic loss of genetic erosion and admixture. Healthy genetic diversity within honey bee populations is critical to provide tolerance and resistance to current and future threatening. Nowadays it is urgent to design strategies for the conservation of local subspecies and their valorisation on a productive scale. In this Thesis we applied genomics tool for the analysis of the genetic diversity and the genomic integrity of honey bee populations in Italy are described. In this work mtDNA based methods are presented using honey bee DNA or honey eDNA as source of information of the genetic diversity of A. mellifera at different level. Taken together, the results derived from these studies should enlarge the knowledge of the genetic diversity and integrity of the honey bee populations in Italy, filling the gap of information necessary to design efficient conservation programmes. Furthermore, the methods presented in these works will provide a tool for the honey authentication to sustain and valorise beekeeping products and sector against frauds.

A study of brain functional connectivity and cognitive processes in long COVID-19 patients

In this work, a prospective study conducted at the IRCCS Istituto delle Scienze Neurologiche di Bologna is presented. The aim was to investigate the brain functional connectivity of a cohort of patients (N=23) suffering from persistent olfactory dysfunction after SARS-CoV-2 infection (Post-COVID-19 syndrome), as compared to a matching group of healthy controls (N=26). In particular, starting from individual resting state functional-MRI data, different analytical approaches were adopted in order to find potential alterations in the connectivity patterns of patients’ brains. Analyses were conducted both at a whole-brain level and with a special focus on brain regions involved in the processing of olfactory stimuli (Olfactory Network). Statistical correlations between functional connectivity alterations and the results of olfactory and neuropsychological tests were investigated, to explore the associations with cognitive processes. The three approaches implemented for the analysis were the seed-based correlation analysis, the group-level Independent Component analysis and a graph-theoretical analysis of brain connectivity. Due to the relative novelty of such approaches, many implementation details and methodologies are not standardized yet and represent active research fields. Seed-based and group-ICA analyses’ results showed no statistically significant differences between groups, while relevant alterations emerged from those of the graph-based analysis. In particular, patients’ olfactory sub-graph appeared to have a less pronounced modular structure compared to the control group; locally, a hyper-connectivity of the right thalamus was observed in patients, with significant involvement of the right insula and hippocampus. Results of an exploratory correlation analysis showed a positive correlation between the graphs global modularity and the scores obtained in olfactory tests and negative correlations between the thalamus hyper-connectivity and memory tests scores.

PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods

Background: With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it. Results: PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available. Conclusions: PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Fogalmak, módszerek. Páros összehasonlításokon alapuló rangsorolási módszerek (Ranking methods based on paired comparisons)

A páronként összehasonlított alternatívák rangsorolásának problémája egyaránt felmerül a szavazáselmélet, a statisztika, a tudománymetria, a pszichológia és a sport területén. A nemzetközi szakirodalom alapján részletesen áttekintjük a megoldási lehetőségeket, bemutatjuk a gyakorlati alkalmazások során fellépő kérdések kezelésének, a valós adatoknak megfelelő matematikai környezet felépítésének módjait. Kiemelten tárgyaljuk a páros összehasonlítási mátrix megadását, az egyes pontozási eljárásokat és azok kapcsolatát. A tanulmány elméleti szempontból vizsgálja a Perron-Frobenius tételen alapuló invariáns, fair bets, PageRank, valamint az irányított gráfok csúcsainak rangsorolásra javasolt internal slackening és pozíciós erő módszereket. A közülük történő választáshoz az axiomatikus megközelítést ajánljuk, ennek keretében bemutatjuk az invariáns és a fair bets eljárások karakterizációját, és kitérünk a módszerek vitatható tulajdonságaira. _____ The ranking of the alternatives or selecting the best one are fundamental issues of social choice theory, statistics, psychology and sport. Different solution concepts, and various mathematical models of applications are reviewed based on the international literature. We are focusing on the de¯nition of paired comparison matrix, on main scoring procedures and their relation. The paper gives a theoretical analysis of the invariant, fair bets and PageRank methods, which are founded on Perron-Frobenius theorem, as well as the internal slackening and positional power procedures used for ranking the nodes of a directed graph. An axiomatic approach is proposed for the choice of an appropriate method. Besides some known characterizations for the invariant and fair bets methods, we also discuss the violation of some properties, meaning their main weakness.

New prediction methods for CO2 evaporation inside tubes: Part I - A two-phase flow pattern map and a flow pattern based phenomenological model for two-phase flow frictional pressure drops

An updated flow pattern map was developed for CO2 on the basis of the previous Cheng-Ribatski-Wojtan-Thome CO2 flow pattern map [1,2] to extend the flow pattern map to a wider range of conditions. A new annular flow to dryout transition (A-D) and a new dryout to mist flow transition (D-M) were proposed here. In addition, a bubbly flow region which generally occurs at high mass velocities and low vapor qualities was added to the updated flow pattern map. The updated flow pattern map is applicable to a much wider range of conditions: tube diameters from 0.6 to 10 mm, mass velocities from 50 to 1500 kg/m(2) s, heat fluxes from 1.8 to 46 kW/m(2) and saturation temperatures from -28 to +25 degrees C (reduced pressures from 0.21 to 0.87). The updated flow pattern map was compared to independent experimental data of flow patterns for CO2 in the literature and it predicts the flow patterns well. Then, a database of CO2 two-phase flow pressure drop results from the literature was set up and the database was compared to the leading empirical pressure drop models: the correlations by Chisholm [3], Friedel [4], Gronnerud [5] and Muller-Steinhagen and Heck [6], a modified Chisholm correlation by Yoon et al. [7] and the flow pattern based model of Moreno Quiben and Thome [8-10]. None of these models was able to predict the CO2 pressure drop data well. Therefore, a new flow pattern based phenomenological model of two-phase flow frictional pressure drop for CO2 was developed by modifying the model of Moreno Quiben and Thome using the updated flow pattern map in this study and it predicts the CO2 pressure drop database quite well overall. (C) 2007 Elsevier Ltd. All rights reserved.

New prediction methods for CO2 evaporation inside tubes: Part II - An updated general flow boiling heat transfer model based on flow patterns

Corresponding to the updated flow pattern map presented in Part I of this study, an updated general flow pattern based flow boiling heat transfer model was developed for CO2 using the Cheng-Ribatski-Wojtan-Thome [L. Cheng, G. Ribatski, L. Wojtan, J.R. Thome, New flow boiling heat transfer model and flow pattern map for carbon dioxide evaporating inside horizontal tubes, Int. J. Heat Mass Transfer 49 (2006) 4082-4094; L. Cheng, G. Ribatski, L. Wojtan, J.R. Thome, Erratum to: ""New flow boiling heat transfer model and flow pattern map for carbon dioxide evaporating inside tubes"" [Heat Mass Transfer 49 (21-22) (2006) 4082-4094], Int. J. Heat Mass Transfer 50 (2007) 391] flow boiling heat transfer model as the starting basis. The flow boiling heat transfer correlation in the dryout region was updated. In addition, a new mist flow heat transfer correlation for CO2 was developed based on the CO2 data and a heat transfer method for bubbly flow was proposed for completeness sake. The updated general flow boiling heat transfer model for CO2 covers all flow regimes and is applicable to a wider range of conditions for horizontal tubes: tube diameters from 0.6 to 10 mm, mass velocities from 50 to 1500 kg/m(2) s, heat fluxes from 1.8 to 46 kW/m(2) and saturation temperatures from -28 to 25 degrees C (reduced pressures from 0.21 to 0.87). The updated general flow boiling heat transfer model was compared to a new experimental database which contains 1124 data points (790 more than that in the previous model [Cheng et al., 2006, 2007]) in this study. Good agreement between the predicted and experimental data was found in general with 71.4% of the entire database and 83.2% of the database without the dryout and mist flow data predicted within +/-30%. However, the predictions for the dryout and mist flow regions were less satisfactory due to the limited number of data points, the higher inaccuracy in such data, scatter in some data sets ranging up to 40%, significant discrepancies from one experimental study to another and the difficulties associated with predicting the inception and completion of dryout around the perimeter of the horizontal tubes. (C) 2007 Elsevier Ltd. All rights reserved.

Development and validation of sensitive methods for determination of sibutramine hydrochloride monohydrate and direct enantiomeric separation on a protein-based chiral stationary phase

Sibutramine hydrochloride monohydrate, chemically 1-(4-chlorophenyl)-N,N-dimethyl-alpha-(2-methylpropyl) hydrochloride monohydrate (SB center dot HCl center dot H2O), was approved by the U.S. Food and Drug Administration for the treatment of obesity. The objective of this study was to develop, validate, and compare methods using UV-derivative spectrophotometry (UVDS) and reversed-phase high-performance liquid chromatography (HPLC) for the determination of SB center dot HCl center dot H2O in pharmaceutical drug products. The UVDS and HPLC methods were found to be rapid, precise, and accurate. Statistically, there was no significant difference between the proposed UVDS and HPLC methods. The enantiomeric separation of SB was obtained on an alpha-1 acid glycoprotein column. The R- and S-sibutramine were eluted in < 5 min with baseline separation of the chromatographic peaks (alpha = 1.9 and resolution = 1.9).

Predicting energy requirements in the clinical setting: Are current methods evidence based?

Estimating energy requirements is necessary in clinical practice when indirect calorimetry is impractical. This paper systematically reviews current methods for estimating energy requirements. Conclusions include: there is discrepancy between the characteristics of populations upon which predictive equations are based and current populations; tools are not well understood, and patient care can be compromised by inappropriate application of the tools. Data comparing tools and methods are presented and issues for practitioners are discussed. (C) 2003 International Life Sciences Institute.