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The object of the current Thematic Issue is not to focus on the individuals (the cross-border commuters) but on the organization of the cross-border labor markets. We move from a micro perspective to a macro perspective in order to underline the diversity of the cross-border labor markets (at the French borders, for example) and shed light on the many aspects that impact cross-border supply or demand. Trying to understand the whole system that goes beyond the cross-border flows, the question we address in this thematic issue is about the organization of the labor markets: is the system organized in a cross-border way? Or do the borders still prevent a genuinely integrated cross-border labor market?
B/ordering the Anthropocene: Inter- and Transdisciplinary Perspectives on Nature-Culture Relations
(2020)
In and with this thematic issue we would like to invite you to engage in productive boundary work and to critically examine the relationship between nature and culture in the Anthropocene. A few years ago, the term Anthropocene was proposed by Paul Crutzen as a term for the current geological epoch, in which humankind (the ‘anthropos’) is seen as the central driving force for global changes in ecological systems. This epoch is characterized by the blurring of boundaries between society and nature, science and politics, as well as by the increased drawing of boundaries between social groups, lifestyles, and the Global North and Global South. With this issue, we would like to give an impetus to explore boundary phenomena in the relationship between nature and society, which up to now have not been the focus of Border Studies. The challenges and problems of the Anthropocene require cross-border thinking and research that stimulates a new reflexivity and commitment, to which the multidisciplinary field of Border Studies can contribute.
Structured Eurobonds - Optimal Construction, Impact on the Euro and the Influence of Interest Rates
(2020)
Structured Eurobonds are a prominent topic in the discussions how to complete the monetary and fiscal union. This work sheds light on several issues going hand in hand with the introduction of common bonds. At first a crucial question is on the optimal construction, e.g. what is the optimal common liability. Other questions that arise belong to the time after the introduction. The impact on several exchnage rates is examined in this work. Finally an approximation bias in forward-looking DSGE models is quantified which would lead to an adjustment of central bank interest rates and therefore has an impact on the other two topics.
Internet interventions have gained popularity and the idea is to use them to increase the availability of psychological treatment. Research suggests that internet interventions are effective for a number of psychological disorders with effect sizes comparable to those found in face-to-face treatment. However, when provided as an add-on to treatment as usual, internet interventions do not seem to provide additional benefit. Furthermore, adherence and dropout rates vary greatly between studies, limiting the generalizability of the findings. This underlines the need to further investigate differences between internet interventions, participating patients, and their usage of interventions. A stronger focus on the processes of change seems necessary to better understand the varying findings regarding outcome, adherence and dropout in internet interventions. Thus, the aim of this dissertation was to investigate change processes in internet interventions and the factors that impact treatment response. This could help to identify important variables that should be considered in research on internet interventions as well as in clinical settings that make use of internet interventions.
Study I (Chapter 5) investigated early change patterns in participants of an internet intervention targeting depression. Data from 409 participants were analyzed using Growth Mixture Modeling. Specifically a piecewise model was applied to model change from screening to registration (pretreatment) and early change (registration to week four of treatment). Three early change patterns were identified; two were characterized by improvement and one by deterioration. The patterns were predictive of treatment outcome. The results therefore indicated that early change should be closely monitored in internet interventions, as early change may be an important indicator of treatment outcome.
Study II (Chapter 6) picked up on the idea of analyzing change patterns in internet interventions and extended it by using the Muthen-Roy model to identify change-dropout patterns. A sligthly bigger sample of the dataset from Study I was analyzed (N = 483). Four change-dropout patterns emerged; high risk of dropout was associated with rapid improvement and deterioration. These findings indicate that clinicians should consider how dropout may depend on patient characteristics as well as symptom change, as dropout is associated with both deterioration and a good enough dosage of treatment.
Study III (Chapter 7) compared adherence and outcome in different participant groups and investigated the impact of adherence to treatment components on treatment outcome in an internet intervention targeting anxiety symptoms. 50 outpatient participants waiting for face- to-face treatment and 37 self-referred participants were compared regarding adherence to treatment components and outcome. In addition, outpatient participants were compared to a matched sample of outpatients, who had no access to the internet intervention during the waiting period. Adherence to treatment components was investigated as a predictor of treatment outcome. Results suggested that especially adherence may vary depending on participant group. Also using specific measures of adherence such as adherence to treatment components may be crucial to detect change mechanisms in internet interventions. Fostering adherence to treatment components in participants may increase the effectiveness of internet interventions.
Results of the three studies are discussed and general conclusions are drawn.
Implications for future research as well as their utility for clinical practice and decision- making are presented.
Evidence points to autonomy as having a place next to affiliation, achievement, and power as one of the basic implicit motives; however, there is still some research that needs to be conducted to support this notion.
The research in this dissertation aimed to address this issue. I have specifically focused on two issues that help solidify the foundation of work that has already been conducted on the implicit autonomy motive, and will also be a foundation for future studies. The first issue is measurement. Implicit motives should be measured using causally valid instruments (McClelland, 1980). The second issue addresses the function of motives. Implicit motives orient, select, and energize behavior (McClelland, 1980). If autonomy is an implicit motive, then we need a valid instrument to measure it and we also need to show that it orients, selects, and energizes behavior.
In the following dissertation, I address these two issues in a series of ten studies. Firstly, I present studies that examine the causal validity of the Operant Motive Test (OMT; Kuhl, 2013) for the implicit affiliation and power motives using established methods. Secondly, I developed and empirically tested pictures to specifically assess the implicit autonomy motive and examined their causal validity. Thereafter, I present two studies that investigated the orienting and energizing effects of the implicit autonomy motive. The results of the studies solidified the foundation of the OMT and how it measures nAutonomy. Furthermore, this dissertation demonstrates that nAutonomy fulfills the criteria for two of the main functions of implicit motives. Taken together, the findings of this dissertation provide further support for autonomy as an implicit motive and a foundation for intriguing future studies.
The present thesis is devoted to a construction which defies generalisations about the prototypical English noun phrase (NP) to such an extent that it has been termed the Big Mess Construction (Berman 1974). As illustrated by the examples in (1) and (2), the NPs under study involve premodifying adjective phrases (APs) which precede the determiner (always realised in the form of the indefinite article a(n)) rather than following it.
(1) NoS had not been hijacked – that was too strong a word. (BNC: CHU 1766)
(2) He was prepared for a battle if the porter turned out to be as difficult a customer as his wife. (BNC: CJX 1755)
Previous research on the construction is largely limited to contributions from the realms of theoretical syntax and a number of cursory accounts in reference grammars. No comprehensive investigation of its realisations and uses has as yet been conducted. My thesis fills this gap by means of an exhaustive analysis of the construction on the basis of authentic language data retrieved from the British National Corpus (BNC). The corpus-based approach allows me to examine not only the possible but also the most typical uses of the construction. Moreover, while previous work has almost exclusively focused on the formal realisations of the construction, I investigate both its forms and functions.
It is demonstrated that, while the construction is remarkably flexible as concerns its possible realisations, its use is governed by probabilistic constraints. For example, some items occur much more frequently inside the degree item slot than others (as, too and so stand out for their particularly high frequency). Contrary to what is assumed in most previous descriptions, the slot is not restricted in its realisation to a fixed number of items. Rather than representing a specialised structure, the construction is furthermore shown to be distributed over a wide range of possible text types and syntactic functions. On the other hand, it is found to be much less typical of spontaneous conversation than of written language; Big Mess NPs further display a strong preference for the function of subject complement. Investigations of the internal structural complexity of the construction indicate that its obligatory components can optionally be enriched by a remarkably wide range of optional (if infrequent) elements. In an additional analysis of the realisations of the obligatory but lexically variable slots (head noun and head of AP), the construction is highlighted to represent a productive pattern. With the help of the methods of Collexeme Analysis (Stefanowitsch and Gries 2003) and Co-varying Collexeme Analysis (Gries and Stefanowitsch 2004b, Stefanowitsch and Gries 2005), the two slots are, however, revealed to be strongly associated with general nouns and ‘evaluative’ and ‘dimension’ adjectives, respectively. On the basis of an inspection of the most typical adjective-noun combinations, I identify the prototypical semantics of the Big Mess Construction.
The analyses of the constructional functions centre on two distinct functional areas. First, I investigate Bolinger’s (1972) hypothesis that the construction fulfils functions in line with the Principle of Rhythmic Alternation (e.g. Selkirk 1984: 11, Schlüter 2005). It is established that rhythmic preferences co-determine the use of the construction to some extent, but that they clearly do not suffice to explain the phenomenon under study. In a next step, the discourse-pragmatic functions of the construction are scrutinised. Big Mess NPs are demonstrated to perform distinct information-structural functions in that the non-canonical position of the AP serves to highlight focal information (compare De Mönnink 2000: 134-35). Additionally, the construction is shown to place emphasis on acts of evaluation. I conclude the construction to represent a contrastive focus construction.
My investigations of the formal and functional characteristics of Big Mess NPs each include analyses which compare individual versions of the construction to one another (e.g. the As Big a Mess, Too Big a Mess and So Big a Mess Constructions). It is revealed that the versions are united by a shared core of properties while differing from one another at more abstract levels of description. The question of the status of the constructional versions as separate constructions further receives special emphasis as part of a discussion in which I integrate my results into the framework of usage-based Construction Grammar (e.g. Goldberg 1995, 2006).
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., leaf_type=broadleaved). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type mixed, amounting to a total area of 87% of landuse=forests from the OSM database. These areas comprise an important information source to derive and update forest type maps. In order to leverage this information content, a methodology for stratification of leaf types inside these areas has been developed using image segmentation on aerial imagery and subsequent classification of leaf types. The presented methodology achieves an overall classification accuracy of 85% for the leaf types needleleaved and broadleaved in the selected forest areas. The resulting stratification demonstrates that through approaches, such as that presented, the derivation of forest type maps from OSM would be feasible with an extended and improved methodology. It also suggests an improved methodology might be able to provide updates of leaf type to the OSM database with contributor participation.
The dissertation includes three published articles on which the development of a theoretical model of motivational and self-regulatory determinants of the intention to comprehensively search for health information is based. The first article focuses on building a solid theoretical foundation as to the nature of a comprehensive search for health information and enabling its integration into a broader conceptual framework. Based on subjective source perceptions, a taxonomy of health information sources was developed. The aim of this taxonomy was to identify most fundamental source characteristics to provide a point of reference when it comes to relating to the target objects of a comprehensive search. Three basic source characteristics were identified: expertise, interaction and accessibility. The second article reports on the development and evaluation of an instrument measuring the goals individuals have when seeking health information: the ‘Goals Associated with Health Information Seeking’ (GAINS) questionnaire. Two goal categories (coping focus and regulatory focus) were theoretically derived, based on which four goals (understanding, action planning, hope and reassurance) were classified. The final version of the questionnaire comprised four scales representing the goals, with four items per scale (sixteen items in total). The psychometric properties of the GAINS were analyzed in three independent samples, and the questionnaire was found to be reliable and sufficiently valid as well as suitable for a patient sample. It was concluded that the GAINS makes it possible to evaluate goals of health information seeking (HIS) which are likely to inform the intention building on how to organize the search for health information. The third article describes the final development and a first empirical evaluation of a model of motivational and self-regulatory determinants of an intentionally comprehensive search for health information. Based on the insights and implications of the previous two articles and an additional rigorous theoretical investigation, the model included approach and avoidance motivation, emotion regulation, HIS self-efficacy, problem and emotion focused coping goals and the intention to seek comprehensively (as outcome variable). The model was analyzed via structural equation modeling in a sample of university students. Model fit was good and hypotheses with regard to specific direct and indirect effects were confirmed. Last, the findings of all three articles are synthesized, the final model is presented and discussed with regard to its strengths and weaknesses, and implications for further research are determined.
Laboratory landslide experiments enable the observation of specific properties of these natural hazards. However, these observations are limited by traditional techniques: frequently used high-speed video analysis and wired sensors (e.g. displacement). These techniques lead to the drawback that either only the surface and 2D profiles can be observed or wires confine the motion behaviour. In contrast, an unconfined observation of the total spatiotemporal dynamics of landslides is needed for an adequate understanding of these natural hazards.
The present study introduces an autonomous and wireless probe to characterize motion features of single clasts within laboratory-scale landslides. The Smartstone probe is based on an inertial measurement unit (IMU) and records acceleration and rotation at a sampling rate of 100 Hz. The recording ranges are ±16 g (accelerometer) and ±2000∘ s−1 (gyroscope). The plastic tube housing is 55 mm long with a diameter of 10 mm. The probe is controlled, and data are read out via active radio frequency identification (active RFID) technology. Due to this technique, the probe works under low-power conditions, enabling the use of small button cell batteries and minimizing its size.
Using the Smartstone probe, the motion of single clasts (gravel size, median particle diameter d50 of 42 mm) within approx. 520 kg of a uniformly graded pebble material was observed in a laboratory experiment. Single pebbles were equipped with probes and placed embedded and superficially in or on the material. In a first analysis step, the data of one pebble are interpreted qualitatively, allowing for the determination of different transport modes, such as translation, rotation and saltation. In a second step, the motion is quantified by means of derived movement characteristics: the analysed pebble moves mainly in the vertical direction during the first motion phase with a maximal vertical velocity of approx. 1.7 m s−1. A strong acceleration peak of approx. 36 m s−2 is interpreted as a pronounced hit and leads to a complex rotational-motion pattern. In a third step, displacement is derived and amounts to approx. 1.0 m in the vertical direction. The deviation compared to laser distance measurements was approx. −10 %. Furthermore, a full 3D spatiotemporal trajectory of the pebble is reconstructed and visualized supporting the interpretations. Finally, it is demonstrated that multiple pebbles can be analysed simultaneously within one experiment. Compared to other observation methods Smartstone probes allow for the quantification of internal movement characteristics and, consequently, a motion sampling in landslide experiments.
Estimation and therefore prediction -- both in traditional statistics and machine learning -- encounters often problems when done on survey data, i.e. on data gathered from a random subset of a finite population. Additional to the stochastic generation of the data in the finite population (based on a superpopulation model), the subsetting represents a second randomization process, and adds further noise to the estimation. The character and impact of the additional noise on the estimation procedure depends on the specific probability law for subsetting, i.e. the survey design. Especially when the design is complex or the population data is not generated by a Gaussian distribution, established methods must be re-thought. Both phenomena can be found in business surveys, and their combined occurrence poses challenges to the estimation.
This work introduces selected topics linked to relevant use cases of business surveys and discusses the role of survey design therein: First, consider micro-econometrics using business surveys. Regression analysis under the peculiarities of non-normal data and complex survey design is discussed. The focus lies on mixed models, which are able to capture unobserved heterogeneity e.g. between economic sectors, when the dependent variable is not conditionally normally distributed. An algorithm for survey-weighted model estimation in this setting is provided and applied to business data.
Second, in official statistics, the classical sampling randomization and estimators for finite population totals are relevant. The variance estimation of estimators for (finite) population totals plays a major role in this framework in order to decide on the reliability of survey data. When the survey design is complex, and the number of variables is large for which an estimated total is required, generalized variance functions are popular for variance estimation. They allow to circumvent cumbersome theoretical design-based variance formulae or computer-intensive resampling. A synthesis of the superpopulation-based motivation and the survey framework is elaborated. To the author's knowledge, such a synthesis is studied for the first time both theoretically and empirically.
Third, the self-organizing map -- an unsupervised machine learning algorithm for data visualization, clustering and even probability estimation -- is introduced. A link to Markov random fields is outlined, which to the author's knowledge has not yet been established, and a density estimator is derived. The latter is evaluated in terms of a Monte-Carlo simulation and then applied to real world business data.
The study analyzes the long-term trends (1998–2019) of concentrations of the air pollutants ozone (O3) and nitrogen oxides (NOx) as well as meteorological conditions at forest sites in German midrange mountains to evaluate changes in O3 uptake conditions for trees over time at a plot scale. O3 concentrations did not show significant trends over the course of 22 years, unlike NO2 and NO, whose concentrations decreased significantly since the end of the 1990s. Temporal analyses of meteorological parameters found increasing global radiation at all sites and decreasing precipitation, vapor pressure deficit (VPD), and wind speed at most sites (temperature did not show any trend). A principal component analysis revealed strong correlations between O3 concentrations and global radiation, VPD, and temperature. Examination of the atmospheric water balance, a key parameter for O3 uptake, identified some unusually hot and dry years (2003, 2011, 2018, and 2019). With the help of a soil water model, periods of plant water stress were detected. These periods were often in synchrony with periods of elevated daytime O3 concentrations and usually occurred in mid and late summer, but occasionally also in spring and early summer. This suggests that drought protects forests against O3 uptake and that, in humid years with moderate O3 concentrations, the O3 flux was higher than in dry years with higher O3 concentrations.
Climate change is expected to cause mountain species to shift their ranges to higher elevations. Due to the decreasing amounts of habitats with increasing elevation, such shifts are likely to increase their extinction risk. Heterogeneous mountain topography, however, may reduce this risk by providing microclimatic conditions that can buffer macroclimatic warming or provide nearby refugia. As aspect strongly influences the local microclimate, we here assess whether shifts from warm south-exposed aspects to cool north-exposed aspects in response to climate change can compensate for an upward shift into cooler elevations.
A lack of ability to inhibit prepotent responses, or more generally a lack of impulse control, is associated with several disorders such as attention-deficit/hyperactivity disorder and schizophrenia as well as general damage to the prefrontal cortex. A stop-signal task (SST) is a reliable and established measure of response inhibition. However, using the SST as an objective assessment in diagnostic or research-focused settings places significant stress on participants as the task itself requires concentration and cognitive effort and is not particularly engaging. This can lead to decreased motivation to follow task instructions and poor data quality, which can affect assessment efficacy and might increase drop-out rates. Gamification—the application of game-based elements in nongame settings—has shown to improve engaged attention to a cognitive task, thus increasing participant motivation and data quality.
Data used for the purpose of machine learning are often erroneous. In this thesis, p-quasinorms (p<1) are employed as loss functions in order to increase the robustness of training algorithms for artificial neural networks. Numerical issues arising from these loss functions are addressed via enhanced optimization algorithms (proximal point methods; Frank-Wolfe methods) based on the (non-monotonic) Armijo-rule. Numerical experiments comprising 1100 test problems confirm the effectiveness of the approach. Depending on the parametrization, an average reduction of the absolute residuals of up to 64.6% is achieved (aggregated over 100 test problems).
Currently, new business models created in the sharing economy differ considerably and they differ in the formation of trust as well. If and how trust can be created is shown by a comparison of two examples which diverge in their founding philosophy. The chosen example of community-based economy, Community Supported Agriculture (CSA), no longer trusts the capitalist system and therefore distances itself and creates its own environment including a new business model. It is implemented within rather small groups where trust is created by personal relations and face-to-face communication. On the contrary, the example of a platform economy, the accommodation-provider company Airbnb, shows trust in the system and pushes technological innovations through the use of platform applications. It promotes trust and confidence in the progress of technology. For the conceptual analysis, the distinction between personal trust and system trust defined by Niklas Luhmann is adopted. The analysis describes two different modes of trust formation and how they push distrust or improve trust. Grounded on these analyses, assumptions on the process of trust formation within varying models of the sharing economy are formulated as well as a hypothesis about possible developments is introduced for further research.
The parameterization of the boundary layer is a challenge for regional climate models of the Arctic. In particular, the stable boundary layer (SBL) over Greenland, being the main driver for substantial katabatic winds over the slopes, is simulated differently by different regional climate models or using different parameterizations of the same model. However, verification data sets with high-resolution profiles of the katabatic wind are rare. In the present paper, detailed aircraft measurements of profiles in the katabatic wind and automatic weather station data during the experiment KABEG (Katabatic wind and boundary-layer front experiment around Greenland) in April and May 1997 are used for the verification of the regional climate model COSMO-CLM (CCLM) nested in ERA-Interim reanalyses. CCLM is used in a forecast mode for the whole Arctic with 15 km resolution and is run in the standard configuration of SBL parameterization and with modified SBL parameterization. In the modified version, turbulent kinetic energy (TKE) production and the transfer coefficients for turbulent fluxes in the SBL are reduced, leading to higher stability of the SBL. This leads to a more realistic representation of the daily temperature cycle and of the SBL structure in terms of temperature and wind profiles for the lowest 200 m.
This thesis addresses three different topics from the fields of mathematical finance, applied probability and stochastic optimal control. Correspondingly, it is subdivided into three independent main chapters each of which approaches a mathematical problem with a suitable notion of a stochastic particle system.
In Chapter 1, we extend the branching diffusion Monte Carlo method of Henry-Labordère et. al. (2019) to the case of parabolic PDEs with mixed local-nonlocal analytic nonlinearities. We investigate branching diffusion representations of classical solutions, and we provide sufficient conditions under which the branching diffusion representation solves the PDE in the viscosity sense. Our theoretical setup directly leads to a Monte Carlo algorithm, whose applicability is showcased in two stylized high-dimensional examples. As our main application, we demonstrate how our methodology can be used to value financial positions with defaultable, systemically important counterparties.
In Chapter 2, we formulate and analyze a mathematical framework for continuous-time mean field games with finitely many states and common noise, including a rigorous probabilistic construction of the state process. The key insight is that we can circumvent the master equation and reduce the mean field equilibrium to a system of forward-backward systems of (random) ordinary differential equations by conditioning on common noise events. We state and prove a corresponding existence theorem, and we illustrate our results in three stylized application examples. In the absence of common noise, our setup reduces to that of Gomes, Mohr and Souza (2013) and Cecchin and Fischer (2020).
In Chapter 3, we present a heuristic approach to tackle stochastic impulse control problems in discrete time. Based on the work of Bensoussan (2008) we reformulate the classical Bellman equation of stochastic optimal control in terms of a discrete-time QVI, and we prove a corresponding verification theorem. Taking the resulting optimal impulse control as a starting point, we devise a self-learning algorithm that estimates the continuation and intervention region of such a problem. Its key features are that it explores the state space of the underlying problem by itself and successively learns the behavior of the optimally controlled state process. For illustration, we apply our algorithm to a classical example problem, and we give an outlook on open questions to be addressed in future research.
Although gravitropism forces trees to grow vertically, stems have shown to prefer specific orientations. Apart from wind deforming the tree shape, lateral light can result in prevailing inclination directions. In recent years a species dependent interaction between gravitropism and phototropism, resulting in trunks leaning down-slope, has been confirmed, but a terrestrial investigation of such factors is limited to small scale surveys. ALS offers the opportunity to investigate trees remotely. This study shall clarify whether ALS detected tree trunks can be used to identify prevailing trunk inclinations. In particular, the effect of topography, wind, soil properties and scan direction are investigated empirically using linear regression models. 299.000 significantly inclined stems were investigated. Species-specific prevailing trunk orientations could be observed. About 58% of the inclination and 19% of the orientation could be explained by the linear models, while the tree species, tree height, aspect and slope could be identified as significant factors. The models indicate that deciduous trees tend to lean down-slope, while conifers tend to lean leeward. This study has shown that ALS is suitable to investigate the trunk orientation on larger scales. It provides empirical evidence for the effect of phototropism and wind on the trunk orientation.
In this thesis we study structure-preserving model reduction methods for the efficient and reliable approximation of dynamical systems. A major focus is the approximation of a nonlinear flow problem on networks, which can, e.g., be used to describe gas network systems. Our proposed approximation framework guarantees so-called port-Hamiltonian structure and is general enough to be realizable by projection-based model order reduction combined with complexity reduction. We divide the discussion of the flow problem into two parts, one concerned with the linear damped wave equation and the other one with the general nonlinear flow problem on networks.
The study around the linear damped wave equation relies on a Galerkin framework, which allows for convenient network generalizations. Notable contributions of this part are the profound analysis of the algebraic setting after space-discretization in relation to the infinite dimensional setting and its implications for model reduction. In particular, this includes the discussion of differential-algebraic structures associated to the network-character of our problem and the derivation of compatibility conditions related to fundamental physical properties. Amongst the different model reduction techniques, we consider the moment matching method to be a particularly well-suited choice in our framework.
The Galerkin framework is then appropriately extended to our general nonlinear flow problem. Crucial supplementary concepts are required for the analysis, such as the partial Legendre transform and a more careful discussion of the underlying energy-based modeling. The preservation of the port-Hamiltonian structure after the model-order- and complexity-reduction-step represents a major focus of this work. Similar as in the analysis of the model order reduction, compatibility conditions play a crucial role in the analysis of our complexity reduction, which relies on a quadrature-type ansatz. Furthermore, energy-stable time-discretization schemes are derived for our port-Hamiltonian approximations, as structure-preserving methods from literature are not applicable due to our rather unconventional parametrization of the solution.
Apart from the port-Hamiltonian approximation of the flow problem, another topic of this thesis is the derivation of a new extension of moment matching methods from linear systems to quadratic-bilinear systems. Most system-theoretic reduction methods for nonlinear systems rely on multivariate frequency representations. Our approach instead uses univariate frequency representations tailored towards user-defined families of inputs. Then moment matching corresponds to a one-dimensional interpolation problem rather than to a multi-dimensional interpolation as for the multivariate approaches, i.e., it involves fewer interpolation frequencies to be chosen. The notion of signal-generator-driven systems, variational expansions of the resulting autonomous systems as well as the derivation of convenient tensor-structured approximation conditions are the main ingredients of this part. Notably, our approach allows for the incorporation of general input relations in the state equations, not only affine-linear ones as in existing system-theoretic methods.
The formerly communist countries in Central and Eastern Europe (transitional economies in Europe and the Soviet Union – for example, East Germany, Czech Republic, Hungary, Lithuania, Poland, Russia) and transitional economies in Asia – for example, China, Vietnam had centrally planned economies, which did not allow entrepreneurship activities. Despite the political-socioeconomic transformations in transitional economies around 1989, they still had an institutional heritage that affects individuals’ values and attitudes, which, in turn, influence intentions, behaviors, and actions, including entrepreneurship.
While prior studies on the long-lasting effects of socialist legacy on entrepreneurship have focused on limited geographical regions (e.g., East-West Germany, and East-West Europe), this dissertation focuses on the Vietnamese context, which offers a unique quasi-experimental setting. In 1954, Vietnam was divided into the socialist North and the non-socialist South, and it was then reunified under socialist rule in 1975. Thus, the intensity of differences in socialist treatment in North-South Vietnam (about 21 years) is much shorter than that in East-West Germany (about 40 years) and East-West Europe (about 70 years when considering former Soviet Union countries).
To assess the relationship between socialist history and entrepreneurship in this unique setting, we survey more than 3,000 Vietnamese individuals. This thesis finds that individuals from North Vietnam have lower entrepreneurship intentions, are less likely to enroll in entrepreneurship education programs, and display lower likelihood to take over an existing business, compared to those from the South of Vietnam. The long-lasting effect of formerly socialist institutions on entrepreneurship is apparently deeper than previously discovered in the prominent case of East-West Germany and East-West Europe as well.
In the second empirical investigation, this dissertation focuses on how succession intentions differ from others (e.g., founding, and employee intentions) regarding career choice motivation, and the effect of three main elements of the theory of planned behavior (e.g., entrepreneurial attitude, subjective norms, and perceived behavioral control) in transition economy – Vietnam context. The findings of this thesis suggest that an intentional founder is labeled with innovation, an intentional successor is labeled with roles motivation, and an intentional employee is labeled with social mission. Additionally, this thesis reveals that entrepreneurial attitude and perceived behavioral control are positively associated with the founding intention, whereas there is no difference in this effect between succession and employee intentions.