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High harmonic generation (HHG) is an extreme non-linear phenomenon where strong laser fields interact with a medium to produce coherent and high-frequency harmonics of the incident light. It has emerged as a rapidly growing research area in bulk materials since its first observation in ZnO crystals in 2011. Over the past decade, pioneering studies have already been made in understanding the details of the microscopic mechanism behind this phenomenon, like the role of intra- and inter-band transitions, the contribution of the modulus and the phase of the dipole moment to even and odd harmonic peaks, the role of the oscillating dipoles, effects of broken symmetry, etc. However, the role of electron-electron correlations in the HHG from strongly correlated materials is much less understood. In these materials the interactions between electrons play a significant role, leading to complex and intriguing physical behaviors. In this dissertation, on the example of ZnO, perovskites BaTiO3 and BiFeO3, and transition-metal oxide VO2 I will study the role of electron-electron interaction effects in the HH spectra by using the time-dependent density-functional theory (TDDFT) approach with the exchange-correlation kernel obtained with dynamical mean- field theory (DMFT). In DMFT, one takes into account time-resolved on-site electron-electron interactions (neglected in most of other approaches) that are crucial for a larger part of strongly correlated materials. As I demonstrate, correlation effects significantly modify the HH spectrum, e.g., through the ultrafast modification of the spectrum of the system, as it was found for ZnO. As the next step, I explored the effects of electron-electron correlations in the HH spectrum of BaTiO3 perturbed by intense, few-cycle mid-infrared laser excitations. The correlation effects in this system lead to the emergence of "super-harmonics" - periodic enhancements and suppressions of specific harmonic orders that depend on the correlation strength. I extended my analysis to the case of BiFeO3, where in addition to correlation effects the effects of memory in HHG were analyzed. I have found that both correlation effects and memory lead to an extension of the harmonic cutoff. In my final part, I explored the effect of electron correlations on the HH spectrum of VO2 and compared my findings with the experiment. The obtained results may shed light on the often important role of electron correlations in the HH spectra of solids, providing valuable insights into ultrafast dynamics in complex materials, and contributing to advancements in nonlinear optics and strong-field physics, with the potential for novel photonic devices and imaging techniques in the attosecond and femtosecond regimes.

This thesis deals with data-driven stochastic models of daily temperature time series recorded at weather stations. These univariate time series are long-range correlated, i.e. their autocorrelation functions possess a power-law decay. In addition, their marginal distributions violate Gaussianity and their response functions are nonlinear, calling for nonlinear models. We present two methods for inferring nonlinear long-range correlated stochastic models of single-trajectory data and use them to reconstruct models of daily mean temperature data recorded at Potsdam Telegrafenberg, Germany. The first method employs fractional filtering using the estimated Hurst exponent of the time series. We render the time series short-range correlated with the first-order difference approximation of the Grünwald-Letnikov fractional derivative, the inverse of the fractional integration operation used in ARFIMA processes. Subsequently, we reconstruct a Markovian model of the fractionally differenced time series. The second inference method is ‘fractional Onsager-Machlup optimization’ (fOMo), a maximum likelihood framework apt to infer nonlinear force and diffusion terms of overdamped stochastic differential equations driven by arbitrarily correlated Gaussian noise, in particular fractional Gaussian noise. The optimization corresponds to the minimization of a stochastic action as studied in statistical field theory. The optimal drift and diffusion terms then render a given time series the most probable path of the model. Both inference methods show excellent results for temperature time series. They are applicable to other stationary, monofractal time series and thus may prove beneficial in biophysics, e.g. active matter dynamics and anomalous diffusion, neurophysics and finance. Finally, we employ stochastic temperature models reconstructed via the fractional filtering method for predictions. A forecast of the first frost date at Potsdam Telegrafenberg using the mean first-passage time of model trajectories and the zero degree temperature line shows small predictive power. The second application extends the stochastic temperature model to include an external forcing by a meteorological index time series that is associated to long-lived circulation patterns in the atmosphere. A causal analysis of Arctic Oscillation (AO) and North-Atlantic Oscillation indices and European extreme temperatures reveals the largest influence of the AO index on daily extreme winter temperatures in southern Scandinavia. We therefore reconstruct a nonlinear long-range correlated stochastic model of daily maximum and minimum winter temperatures recorded at Visby Flygplats, Sweden, with external driving by the AO index. Binary temperature forecasts show predictive power for up to 35 (30) days lead time for daily maximum (minimum) temperatures. An AR(1) model possesses predictive power for only 10 (5) days lead time for daily maximum (minimum) temperature, proving the potential of nonlinear long-range correlated models for predictions.:1 Introduction 1.1 Long-Range Correlations in Geophysical Time Series 1.2 Stochastic Modeling of Geophysical Time Series 1.3 Structure of the Thesis 2 Preliminaries 2.1 Time Series and Stochastic Processes 2.1.1 Stochastic Processes 2.1.2 Basic Concepts of Time Series Analysis 2.1.3 Classification of Stochastic Processes 2.1.4 Inference of Stochastic Processes 2.2 Markov Processes 2.2.1 Fokker-Planck Equation 2.2.2 Langevin Equation 2.2.3 Stochastic Integration 2.2.4 Correspondence of Langevin Equation and Fokker-Planck Equation 2.2.5 Numerical Solution of Langevin Equation 2.2.6 Path Integral Formulation 2.2.7 Discrete-Time Processes 2.3 Long-Range Correlated Processes 2.3.1 Self-Similarity and Long-Range Correlations 2.3.2 Fractional Calculus 2.3.3 Fractional Brownian Motion and Fractional Gaussian Noise 2.3.4 Stochastic Differential Equations driven by fGn 2.3.5 Numerical Solution of SDE driven by fGn 2.3.6 ARFIMA Processes 2.4 Estimation of the Hurst parameter 2.4.1 Estimation Methods 2.4.2 Detrended Fluctuation Analysis 2.5 Discussion of Previous Approaches to Modeling LRC Data 2.5.1 Generalized Langevin Equation 2.5.2 Modified Discrete Langevin Equation 2.5.3 Atmospheric Response Functions 3 Inference via Fractional Differencing 3.1 Surface Temperature Time Series 3.2 Fractional Differencing of Time Series 3.2.1 Removing Long-Range Correlations 3.2.2 Memory Selection 3.2.3 Testing for Markovianity 3.3 Finite-Time Kramers-Moyal Analysis 3.3.1 Kernel-Based Regression of Kramers-Moyal Moments 3.3.2 The Adjoint Fokker-Planck Equation 3.3.3 Numerical Procedure 3.3.4 Inferred Drift and Diffusion Terms 3.3.5 Model Data Generation 3.3.6 Results for Temperature Anomalies 3.4 Discrete-Time Langevin Equation 3.4.1 Estimation of Force and Diffusion Terms 3.4.2 Model Data Generation 3.4.3 Nonlinear Toy Model 3.4.4 Application to Temperature Data 3.4.5 Results for Temperature Anomalies 3.5 Discussion 4 Inference via Fractional Onsager-Machlup Optimization 4.1 Derivation of the Maximum Likelihood Estimator 4.2 Analytical Approaches 4.2.1 Force Estimation for Fixed Diffusion 4.2.2 Diffusion Estimation for Fixed Drift 4.2.3 Fractional Ornstein-Uhlenbeck Process 4.2.4 Superposition of Noise Processes 4.3 Numerical Procedure 4.4 Toy Model with Double-Well Potential 4.4.1 Comparison with Markovian Estimate 4.4.2 Finite-Size Error Scaling 4.5 Application to Temperature Data 4.5.1 Consistency of Inferred Drift and Diffusion 4.5.2 Comparison of Synthetic Data and Temperature 4.5.3 Residual Noise 4.6 Discussion 5 Predictions with Long-Range Correlated Models 5.1 First Frost Date 5.1.1 Forecast Ensemble and Forecast Error 5.1.2 Numerical Details 5.1.3 Results 5.2 Causal Analysis of Meteorological Indices and European Extreme Temperatures 5.2.1 Measures for Causal Influence 5.2.2 Causal Analysis Results 5.2.3 Causal Analysis for Visby Flygplats, Sweden 5.3 Forecasting Winter Temperature Extremes at Visby Flygplats, Sweden 5.3.1 Model Inference and Forecast 5.3.2 Root-Mean-Square Error Analysis 5.3.3 Binary Forecasts of Temperature Extremes 5.4 Discussion 6 Conclusion and Outlook 6.1 Inference of Nonlinear LRC Models 6.2 Predictions with LRC models 6.3 Further Research Directions 6.3.1 Method Extensions 6.3.2 Meteorological Applications 6.3.3 Data Interpolation 6.3.4 Anomalous Diffusion and Active Matter Dynamics Bibliography / Diese Arbeit befasst sich mit datengetriebenen stochastischen Modellen von Tagestemperatur-Zeitreihen, die von Wetterstationen aufgezeichnet wurden. Diese univariaten Zeitreihen sind langreichweitig korreliert, d.h. ihre Autokorrelationsfunktionen fallen gemäß eines Potenzgesetzes ab. Darüber hinaus sind ihre Randverteilungen nicht-Gaußsch und ihre Antwortfunktionen nichtlinear, was nichtlineare Modelle erforderlich macht. Wir stellen zwei Methoden zur Rekonstruktion nichtlinearer, langreichweitig korrelierter stochastischer Modelle von Einzeltrajektorien vor und verwenden sie zur Rekonstruktion von Modellen aus Tagesmitteltemperaturdaten, die an der Wetterstation Potsdam Telegrafenberg, Deutschland, aufgezeichnet wurden. Die erste Methode verwendet eine fraktionale Filterung unter Verwendung des geschätzten Hurst-Exponenten der Zeitreihe. Dabei werden die langreichweitigen Korrelationen der Zeitreihe mit der Differenzenapproximation erster Ordnung der fraktionalen Grünwald-Letnikov-Ableitung, der inversen Operation der in ARFIMA-Prozessen verwendeten fraktionalen Integration, entfert. Anschließend rekonstruieren wir ein Markov-Modell der fraktional differenzierten, nun kurzreichweitig korrelierten Zeitreihe. Die zweite Inferenzmethode ist die ‘fractional Onsager-Machlup optimization’ (fOMo), ein Maximum-Likelihood-Schätzer, der nichtlineare Kraft- und Diffusionsterme von überdämpften stochastischen Differentialgleichungen rekonstruiert, die von beliebig korreliertem Gaußschen Rauschen, insbesondere fraktionalem Gaußschen Rauschen, angetrieben werden. Die Optimierung entspricht der Minimierung einer stochastischen Wirkung, wie sie in der statistischen Feldtheorie untersucht wird. Die optimalen Drift- und Diffusionsterme machen die gegebene Zeitreihe dann zum wahrscheinlichsten Pfad des Modells. Beide Inferenzmethoden zeigen exzellente Ergebnisse für Temperaturzeitreihen. Sie sind auf weitere stationäre, monofraktale Zeitreihen anwendbar und können daher in der Biophysik, z. B. der Dynamik aktiver Materie und anomaler Diffusion, in der Neurophysik und im Finanzwesen nützlich sein. Schließlich verwenden wir stochastische Temperatur-Modelle, die mit Hilfe der Methode der fraktionalen Filterung rekonstruiert wurden, für Vorhersagen. Eine Vorhersage des ersten Frosttages im Herbst mit Temperaturdaten der Wetterstation Potsdam Telegrafenberg unter Verwendung der mittleren Erstauftreffszeit von Modelltrajektorien und der Null-Grad-Temperaturlinie zeigt nur geringe Vorhersagekraft. Die zweite Anwendung erweitert das stochastische Temperaturmodell um einen zusätzlichen Antrieb durch eine meteorologische Indexzeitreihe, welche langlebige Zirkulationsmuster in der Atmosphäre charakterisiert. Eine Kausalsanalyse des Einflusses der Indizes der Arktischen Oszillation und der Nordatlantischen Oszillation auf Extremtemperaturen in Europa zeigt den größten Einfluss des Arktischen-Oszillations-Index auf die täglichen Maximal- und Minimaltemperaturen im Winter in Südskandinavien. Darauf aufbauend rekonstruieren wir ein nichtlineares, langreichweitig korreliertes stochastisches Modell der Tagesmaximal- und -minimaltemperaturen im Winter der Wetterstation Visby Flygplats in Schweden mit zusätzlichem Antrieb durch den Arktischen Oszillationsindex. Binäre Vorhersagen des Modells besitzen einen Vorhersagehorizont von bis zu 35 (30) Tagen für Tages-Maximal-(Minimal-)Temperaturen. Binäre Vorhersagen mithilfe eines AR(1)-Modells besitzen einen Vorhersagehorizont von nur 10 (5) Tagen für tägliche Maximal-(Minimal-)Temperaturen. Dies beweist das Potenzial nichtlinearer, langreichweitig korrelierter Modelle für Vorhersagen.:1 Introduction 1.1 Long-Range Correlations in Geophysical Time Series 1.2 Stochastic Modeling of Geophysical Time Series 1.3 Structure of the Thesis 2 Preliminaries 2.1 Time Series and Stochastic Processes 2.1.1 Stochastic Processes 2.1.2 Basic Concepts of Time Series Analysis 2.1.3 Classification of Stochastic Processes 2.1.4 Inference of Stochastic Processes 2.2 Markov Processes 2.2.1 Fokker-Planck Equation 2.2.2 Langevin Equation 2.2.3 Stochastic Integration 2.2.4 Correspondence of Langevin Equation and Fokker-Planck Equation 2.2.5 Numerical Solution of Langevin Equation 2.2.6 Path Integral Formulation 2.2.7 Discrete-Time Processes 2.3 Long-Range Correlated Processes 2.3.1 Self-Similarity and Long-Range Correlations 2.3.2 Fractional Calculus 2.3.3 Fractional Brownian Motion and Fractional Gaussian Noise 2.3.4 Stochastic Differential Equations driven by fGn 2.3.5 Numerical Solution of SDE driven by fGn 2.3.6 ARFIMA Processes 2.4 Estimation of the Hurst parameter 2.4.1 Estimation Methods 2.4.2 Detrended Fluctuation Analysis 2.5 Discussion of Previous Approaches to Modeling LRC Data 2.5.1 Generalized Langevin Equation 2.5.2 Modified Discrete Langevin Equation 2.5.3 Atmospheric Response Functions 3 Inference via Fractional Differencing 3.1 Surface Temperature Time Series 3.2 Fractional Differencing of Time Series 3.2.1 Removing Long-Range Correlations 3.2.2 Memory Selection 3.2.3 Testing for Markovianity 3.3 Finite-Time Kramers-Moyal Analysis 3.3.1 Kernel-Based Regression of Kramers-Moyal Moments 3.3.2 The Adjoint Fokker-Planck Equation 3.3.3 Numerical Procedure 3.3.4 Inferred Drift and Diffusion Terms 3.3.5 Model Data Generation 3.3.6 Results for Temperature Anomalies 3.4 Discrete-Time Langevin Equation 3.4.1 Estimation of Force and Diffusion Terms 3.4.2 Model Data Generation 3.4.3 Nonlinear Toy Model 3.4.4 Application to Temperature Data 3.4.5 Results for Temperature Anomalies 3.5 Discussion 4 Inference via Fractional Onsager-Machlup Optimization 4.1 Derivation of the Maximum Likelihood Estimator 4.2 Analytical Approaches 4.2.1 Force Estimation for Fixed Diffusion 4.2.2 Diffusion Estimation for Fixed Drift 4.2.3 Fractional Ornstein-Uhlenbeck Process 4.2.4 Superposition of Noise Processes 4.3 Numerical Procedure 4.4 Toy Model with Double-Well Potential 4.4.1 Comparison with Markovian Estimate 4.4.2 Finite-Size Error Scaling 4.5 Application to Temperature Data 4.5.1 Consistency of Inferred Drift and Diffusion 4.5.2 Comparison of Synthetic Data and Temperature 4.5.3 Residual Noise 4.6 Discussion 5 Predictions with Long-Range Correlated Models 5.1 First Frost Date 5.1.1 Forecast Ensemble and Forecast Error 5.1.2 Numerical Details 5.1.3 Results 5.2 Causal Analysis of Meteorological Indices and European Extreme Temperatures 5.2.1 Measures for Causal Influence 5.2.2 Causal Analysis Results 5.2.3 Causal Analysis for Visby Flygplats, Sweden 5.3 Forecasting Winter Temperature Extremes at Visby Flygplats, Sweden 5.3.1 Model Inference and Forecast 5.3.2 Root-Mean-Square Error Analysis 5.3.3 Binary Forecasts of Temperature Extremes 5.4 Discussion 6 Conclusion and Outlook 6.1 Inference of Nonlinear LRC Models 6.2 Predictions with LRC models 6.3 Further Research Directions 6.3.1 Method Extensions 6.3.2 Meteorological Applications 6.3.3 Data Interpolation 6.3.4 Anomalous Diffusion and Active Matter Dynamics Bibliography

This study compares three groups of undergraduate college males in heterosexual dating relationships: those who are physically and psychologically abusive (n=39), those who are solely psychologically abusive (n=44), and those who are non-abusive (n=34). These three groups are compared along the following variables: self-reported history of experiencing family of origin violence; self-reported history of witnessing family of origin violence; level of self-reported impulsivity; level of self-reported satisfaction with life; level of self-reported alcohol use; level of self-reported relationship satisfaction; and amount of self-reported anger management skill. An analysis of variance (ANOVA) revealed significant main effects among the three groups of males along several of the variables examined (Wilks' Lambda F = 4.80, df = 10, 220, p <.001). Post hoc tests revealed significant differences among the three groups of males. This study revealed that these three groups differ significantly along their levels of alcohol use (F = 10.16, p <.001), their reported levels of relationship satisfaction (F = 4.23, p <.05), and their levels of anger management skills (F = 14.56, p<.001). This information can be helpful to clinicians and educators who are working with college populations. It would seem that psychoeducation might be useful for some of these men so that they might develop alternatives to violence, and may hopefully decrease the risk factors associated with the perpetration of relationship violence. Intervening early and effectively with these dating relationships can be a substantive step towards preventing the escalation and maintenance of violence in relationships. / Master of Science

Turbulent wakes generated from an aircraft or submarine vehicles has been of main interest to researchers due to the broad band noise associated with such wakes. One such case is the noise generated by spiral vortices shed of from one blade interacting with another oncoming blade of helicopter rotor. Consequently, researchers have been trying to understand the basic physics and evolution of such wakes. Although there has been numerous studies done on plane wakes, there has been little research being done on laterally curved wakes. Single and two-point velocity measurements were taken on a plane and laterally curved turbulent wakes to understand the evolution and effect of lateral curvature into the far wake region. The analyses provide useful information in modeling curved or spiral wakes such as turbulence field surrounding tip vortices shed from a wing. In order to achieve our objectives, the Virginia Tech 3’ x 2’ subsonic wind tunnel was used to take velocity measurements of toroidal ring model and a straight cylinder as a control case. Velocity measurements were done using four sensor hot-wire anemometers, to obtain all mean velocity, Reynolds stress, triple product components of the turbulence field. Single point, spectra and two-point measurements of the wakes were performed throughout the development into the far wake region. The single point results reveal the universality of the mean axial velocity, however the Reynolds stresses and triple products were not universal illustrating that the turbulence field has its own length and velocity scales different from that of the mean flow. The effect of lateral curvature is mainly evidenced in the early development of the curved ring wake. The turbulent energy budget reveals similar trend for both wakes and plane wake achieves approximate equilibrium. The spectra result reveals for the plane wake that self-preservation is achieved for all scales of motion, while the ring wake does not achieve such a state. While the longitudinal correlations of both wakes are similar in form, in general difference in form and orientation prevailed over all indicating the difference in the turbulent structure of both wakes. Linear stochastic estimation reveals the presence of spanwise and double-roller eddy structures in the plane wake and only spanwise eddies were detected for the ring wake. / Master of Science

Doctorat en sciences, Spécialisation physique / info:eu-repo/semantics/nonPublished

Depuis la mise en place en 2010, d’une nouvelle tarification française sur le tarif de l’énergie géothermique, l’Alsace est la région de France la plus dynamique quant à la réalisation de forages géothermiques profonds à haute température (>150°C). Ainsi, l’approche géologique, qui a été primordiale pour les forages de Rittershoffen, le sera encore davantage pour les projets à venir compte tenu de la méconnaissance géologique de certaines zones profondes du bassin rhénan. Cette étude propose d’appréhender la compréhension de l’architecture stratigraphique et séquentielle des formations de la couverture sédimentaire rhénane. 15 puits profonds ont été réinterprétés et corrélés à travers l’ensemble du bassin, permettant l’élaboration d’une colonne stratigraphique complète incluant le sommet et la base de chaque formation. Ces réinterprétations ont également mis en lumière le signal caractéristique de la diagraphie gamma-ray (GR) de chacune des formations de la colonne stratigraphique rhénane. De plus, la caractérisation lithostratigraphique du passage entre le socle et la couverture sédimentaire a fait l’objet d’un axe important de recherche. / Since the introduction of a new French pricing system for geothermal energy in 2010, Alsace has been the most dynamic region in France for deep geothermal drilling at high temperatures (>150°C). Thus, the geological approach, which has been essential for the Rittershoffen boreholes, will be even more for future projects given the lack of geological knowledge of some deep parts of the URG. This study aims at understanding the stratigraphic and sequential architecture of the formations of the URG sedimentary cover. 15 deep wells were reinterpreted and correlated throughout the basin, allowing the development of a complete stratigraphic column including the top and base of each formation. These reinterpretations also highlighted the characteristic gamma-ray signal (GR) of each of the formations in the URG stratigraphic column. In addition, the lithostratigraphic characterization of the transition between the basement and the sedimentary cover was the subject of an important research focus.

This research was geared towards the investigation of management strategies (factors) that affect the performance ofMSMEs in Kenya. Many developed countries record a time in history when entrepreneurial activities led to revival of economical growth after decline. This implies MSMEs is a very vital sector especially for a developing country like Kenya. MSMEs stagnate and their performance is uncertain according to writers such as Namusonge, Management inadequacies have been suggested in several studies. The objectives of this research was to, 1. To identifY the critical management factors affecting the performance of MSMEs in Kenya; ii. To establish the process through which managerial factors affect the performance of a MSMEs in Kenya ; m. To determine the integrative effect of various management factors in the MSMES in Kenya; IV. To establish the effect of demographics and management factors on performance, v. To establish effects of external environment on internal management factors A conceptual model was formulated from the literature review showing relationships of the management strategies and the environment they operate in. These relationships became the basis for the hypotheses which were later tested. In chapter 4, a mini research (pilot study) was conducted in May 2007,whose main aim was to test the reliability and validity of the research instruments. The 36 questionnaires returned were analysed through descriptive method. Results obtained indicated the instruments were reliable and the results valid. A few corrections suggested were made. The major correction was addition of question 35 to collect financial information. The data collection was done between mid August and mid October 2007.In chapter 5, the researcher analysesd the results of the survey after receiving 180 questionnaires. Time was a constraint. In chapter 6, the hypotheses and conceptual model were analysed and the results obtained suggested that, most strategies did not affect the profitability separately but severally. The integrated effect of the management strategies and the associated factors had a higher impact on performance of the MSMES than any individual strategies. In chapter 7, the conclusions, summaries and Recommendations are given. / Business Management / D. Com. (Business Management and Policy)

An exploratory study was undertaken to investigate the relationship between personality preference groupings, as described by Jung’s (1959) type theory, and emotional intelligence, as measured by Bar-On’s emotional intelligence quotient (Bar-On, 1997). The sample group consisted of 1 121 recruitment candidates for a South African investment bank. The sixteen personality types, as measured by the Myers-Briggs Type Indicator, were represented in the sample. The statistical analysis conducted for this study included comparison of means, correlation analysis and analysis of variance. The results indicated statistically significant relationships between the preferences of Extroversion, Judgement, their combined preference grouping and emotional intelligence. No statistically significant relationships were found between the preference groupings of Intuition and Thinking, Sensing and Thinking, Intuition and Feeling, and Sensing and Feeling. The preferred Feeling preference type consistently scored the lowest in terms of emotional intelligence scores. / Industrial and Organisational Psychology / M.A. (Industrial and Organisational Psychology

The rapid neutron-capture process, known as the r-process, is responsible for the origin of about half the stable nuclei heavier than iron observed in nature. Though the r-process is believed to take place in explosive stellar environments and to involve a large number (few thousands) of exotic nuclei, this nucleosynthesis process remains poorly understood from the astrophysics as well as nuclear physics points of view. On the nuclear physics side, the nuclei are too exotic to be studied in the laboratory, even though great efforts are constantly made to extend the experimental limits away from the eta-$stability region. Therefore, theoretical models are indispensable to estimate the nuclear properties of interest in the r-process nucleosynthesis modelling. So far, models used to predict the properties of the exotic nuclei were based on parametrized macroscopic-type approaches the reliability of which is questionable when extrapolating far away from the experimentally known region. This work is devoted to the improvement of nuclear predictions, such as the nuclear ground- and excited-state properties, needed as input data to model the r-process. In order to give the predictions a reliable character, we rely on the microscopic mean-field Hartree-Fock theory based on the Skyrme-type interaction. Pairing correlations play an important role in the description of nuclei, and become essential for nuclei located near the drip lines, since the scattering of pairs of quasi-particles into the continuum increases significantly. In this work, we brought to the Hartree-Fock model the self-consistent treatment of the pairing correlations within the Hartree-Fock-Bogoliubov (HFB) theory. Further improvements are made in the restoration of symmetries broken by correlations added in the form of additional degrees of freedom in the wave function. These include the translational invariance restored by calculating the recoil energy, the particle-number symmetry by an exact projection after variation, the rotational symmetry by an approximate cranking correction and the parity symmetry for reflection asymmetric shapes. In addition, the renormalization of the HFB equations has been studied as well and allows to eliminate the dependence of the total energy with respect to the cutoff energy. The effective nucleon-nucleon interaction is determined by adjusting its parameters on all available experimental masses, with some constraints derived from fundamental nuclear matter properties. A systematic study of the influence on mass predictions for each of the above cited improvements as well as of some uncertainties affecting the particle-hole and particle-particle interactions has been conducted. In spite of quite important differences in the input physics, we find a great stability in the mass predictions for exotic neutron-rich nuclei, though local mass differences can be significant. Each of the Skyrme force derived in the present work has been tested on the predictions of basic ground-state properties (including charge radii, quadrupole moments, single-particle levels), fission barriers and electric dipole $gamma-$ray strengths. The HFB predictions globally reproduce experimental data with a level of accuracy comparable with the widely-used droplet-like models. The microscopic character of the approach followed in the present work makes however the predictions for exotic neutron-rich nuclei involved in the r-process more reliable. The influence of such improved nuclear mass predictions on the r-process abundance distribution is studied in the specific scenario of the prompt supernova explosion mechanism.