Due to the transition towards climate neutrality, energy markets are rapidly evolving. New technologies are developed that allow electricity from renewable energy sources to be stored or to be converted into other energy commodities. As a consequence, new players enter the markets and existing players gain more importance. Market equilibrium problems are capable of capturing these changes and therefore enable us to answer contemporary research questions with regard to energy market design and climate policy. This cumulative dissertation is devoted to the study of different market equilibrium problems that address such emerging aspects in liberalized energy markets. In the first part, we review a well-studied competitive equilibrium model for energy commodity markets and extend this model by sector coupling, by temporal coupling, and by a more detailed representation of physical laws and technical requirements. Moreover, we summarize our main contributions of the last years with respect to analyzing the market equilibria of the resulting equilibrium problems. For the extension regarding sector coupling, we derive sufficient conditions for ensuring uniqueness of the short-run equilibrium a priori and for verifying uniqueness of the long-run equilibrium a posteriori. Furthermore, we present illustrative examples that each of the derived conditions is indeed necessary to guarantee uniqueness in general. For the extension regarding temporal coupling, we provide sufficient conditions for ensuring uniqueness of demand and production a priori. These conditions also imply uniqueness of the short-run equilibrium in case of a single storage operator. However, in case of multiple storage operators, examples illustrate that charging and discharging decisions are not unique in general. We conclude the equilibrium analysis with an a posteriori criterion for verifying uniqueness of a given short-run equilibrium. Since the computation of equilibria is much more challenging due to the temporal coupling, we shortly review why a tailored parallel and distributed alternating direction method of multipliers enables to efficiently compute market equilibria. For the extension regarding physical laws and technical requirements, we show that, in nonconvex settings, existence of an equilibrium is not guaranteed and that the fundamental welfare theorems therefore fail to hold. In addition, we argue that the welfare theorems can be re-established in a market design in which the system operator is committed to a welfare objective. For the case of a profit-maximizing system operator, we propose an algorithm that indicates existence of an equilibrium and that computes an equilibrium in the case of existence. Based on well-known instances from the literature on the gas and electricity sector, we demonstrate the broad applicability of our algorithm. Our computational results suggest that an equilibrium often exists for an application involving nonconvex but continuous stationary gas physics. In turn, integralities introduced due to the switchability of DC lines in DC electricity networks lead to many instances without an equilibrium. Finally, we state sufficient conditions under which the gas application has a unique equilibrium and the line switching application has finitely many. In the second part, all preprints belonging to this cumulative dissertation are provided. These preprints, as well as two journal articles to which the author of this thesis contributed, are referenced within the extended summary in the first part and contain more details.

Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer’s test conditions, validation is essential with regard to the quality of gaze data and other factors potentially threatening the validity of this signal. In this study, we evaluated the impact of accuracy and areas of interest (AOIs) size on the classification of simulated gaze (fixation) data. We defined AOIs of different sizes using the Limited-Radius Voronoi-Tessellation (LRVT) method, and simulated gaze data for facial target points with varying accuracy. As hypothesized, we found that accuracy and AOI size had strong effects on gaze classification. In addition, these effects were not independent and differed in falsely classified gaze inside AOIs (Type I errors; false alarms) and falsely classified gaze outside the predefined AOIs (Type II errors; misses). Our results indicate that smaller AOIs generally minimize false classifications as long as accuracy is good enough. For studies with lower accuracy, Type II errors can still be compensated to some extent by using larger AOIs, but at the cost of more probable Type I errors. Proper estimation of accuracy is therefore essential for making informed decisions regarding the size of AOIs in eye tracking research.

The ability to acquire knowledge helps humans to cope with the demands of the environment. Supporting knowledge acquisition processes is among the main goals of education. Empirical research in educational psychology has identified several processes mediated through that prior knowledge affects learning. However, the majority of studies investigated cognitive mechanisms mediating between prior knowledge and learning and neglected that motivational processes might also mediate the influence. In addition, the impact of successful knowledge acquisition on patients’ health has not been comprehensively studied. This dissertation aims at closing knowledge gaps on these topics with the use of three studies. The first study is a meta-analysis that examined motivation as a mediator of individual differences in knowledge before and after learning. The second study investigated in greater detail the extent to which motivation mediated the influence of prior knowledge on knowledge gains in a sample of university students. The third study is a second-order meta-analysis synthesizing the results of previous meta-analyses on the effects of patient education on several health outcomes. The findings of this dissertation show that (a) motivation mediates individual differences in knowledge before and after learning; (b) interest and academic self-concept stabilize individual differences in knowledge more than academic self-efficacy, intrinsic motivation, and extrinsic motivation; (c) test-oriented instruction closes knowledge gaps between students; (d) students’ motivation can be independent of prior knowledge in high aptitude students; (e) knowledge acquisition affects motivational and health-related outcomes; and (f) evidence on prior knowledge and motivation can help develop effective interventions in patient education. The results of the dissertation provide insights into prerequisites, processes, and outcomes of knowledge acquisition. Future research should address covariates of learning and environmental impacts for a better understanding of knowledge acquisition processes.

Despite significant advances in terms of the adoption of formal Intellectual Property Rights (IPR) protection, enforcement of and compliance with IPR regulations remains a contested issue in one of the world's major contemporary economies—China. The present review seeks to offer insights into possible reasons for this discrepancy as well as possible paths of future development by reviewing prior literature on IPR in China. Specifically, it focuses on the public's perspective, which is a crucial determinant of the effectiveness of any IPR regime. It uncovers possible differences with public perspectives in other countries and points to mechanisms (e.g., political, economic, cultural, and institutional) that may foster transitions over time in both formal IPR regulation and in the public perception of and compliance with IPR in China. On this basis, the review advances suggestions for future research in order to improve scholars' understanding of the public's perspective of IPR in China, its antecedents and implications.

The Belt and Road Initiative (BRI) has had a significant impact on China in political, economic, and cultural terms. This study focuses on the cultural domain, especially on scholarship students from the countries that signed bilateral cooperation agreements with China under the BRI. Using an integrated approach combining the difference-in-differences method and the gravity model, we explore the correlation between the BRI and the increasing number of international scholarship students funded by the Chinese government, as well as the determinants of students' decision to study in China. The panel data from 2010 to 2018 show that the launch of BRI has had a positive impact on the number of scholarship students from BRI countries. The number of scholarship recipients from non-BRI countries also increased, but at a much slower rate than those from BRI countries. The sole exception is the United States, which has trended downward for both state-funded and self-funded students.

Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.

We study planned changes in protective routines after the COVID-19 pandemic: in a survey in Germany among >650 respondents, we find that the majority plans to use face masks in certain situations even after the end of the pandemic. We observe that this willingness is strongly related to the perception that there is something to be learned from East Asians’ handling of pandemics, even when controlling for perceived protection by wearing masks. Given strong empirical evidence that face masks help prevent the spread of respiratory diseases and given the considerable estimated health and economic costs of such diseases even pre-Corona, this would be a very positive side effect of the current crisis.

Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaved, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.

Measurements of dust emissions and the modeling of dissipation dynamics and total values are related to great uncertainties. Agricultural activity, especially soil cultivation, may be an essential component to calculate and model local and regional dust dynamics and even connect to the global dust cycle. To budget total dust and to assess the impact of tillage, measurement of mobilized and transported dust is an essential but rare basis. In this study, a simple measurement concept with Modified Wilson and Cook samplers was applied for dust measurements on a small temporal and spatial scale on steep-slope vineyards in the Moselle area. Without mechanical impact, a mean horizontal flux of 0.01 g m2 min−1 was measured, while row tillage produced a mean horizontal flux of 5.92 g m2 min−1 of mobilized material and 4.18 g m2 min−1 emitted dust from site (=soil loss). Compared on this singular-event basis, emissions during tillage operations generated 99.89% of total emitted dust from the site under low mean wind velocities. The results also indicate a differing impact of specific cultivation operations, mulching, and tillage tools as well as the additional influence of environmental conditions, with highest emissions on dry soil and with additional wind impact. The dust source function is strongly associated with cultivation operations, implying highly dynamic but also regular and thus predictable and projectable emission peaks of total suspended particles. Detailed knowledge of the effects of mechanical impulses and reliable quantification of the local dust emission inventory are a basis for analysis of risk potential and choice of adequate management options.

Even though in most cases time is a good metric to measure costs of algorithms, there are cases where theoretical worst-case time and experimental running time do not match. Since modern CPUs feature an innate memory hierarchy, the location of data is another factor to consider. When most operations of an algorithm are executed on data which is already in the CPU cache, the running time is significantly faster than algorithms where most operations have to load the data from the memory. The topic of this thesis is a new metric to measure costs of algorithms called memory distance—which can be seen as an abstraction of the just mentioned aspect. We will show that there are simple algorithms which show a discrepancy between measured running time and theoretical time but not between measured time and memory distance. Moreover we will show that in some cases it is sufficient to optimize the input of an algorithm with regard to memory distance (while treating the algorithm as a black box) to improve running times. Further we show the relation between worst-case time, memory distance and space and sketch how to define "the usual" memory distance complexity classes.

Agricultural monitoring is necessary. Since the beginning of the Holocene, human agricultural practices have been shaping the face of the earth, and today around one third of the ice-free land mass consists of cropland and pastures. While agriculture is necessary for our survival, the intensity has caused many negative externalities, such as enormous freshwater consumption, the loss of forests and biodiversity, greenhouse gas emissions as well as soil erosion and degradation. Some of these externalities can potentially be ameliorated by careful allocation of crops and cropping practices, while at the same time the state of these crops has to be monitored in order to assess food security. Modern day satellite-based earth observation can be an adequate tool to quantify abundance of crop types, i.e., produce spatially explicit crop type maps. The resources to do so, in terms of input data, reference data and classification algorithms have been constantly improving over the past 60 years, and we live now in a time where fully operational satellites produce freely available imagery with often less than monthly revisit times at high spatial resolution. At the same time, classification models have been constantly evolving from distribution based statistical algorithms, over machine learning to the now ubiquitous deep learning. In this environment, we used an explorative approach to advance the state of the art of crop classification. We conducted regional case studies, focused on the study region of the Eifelkreis Bitburg-Prüm, aiming to develop validated crop classification toolchains. Because of their unique role in the regional agricultural system and because of their specific phenologic characteristics we focused solely on maize fields. In the first case study, we generated reference data for the years 2009 and 2016 in the study region by drawing polygons based on high resolution aerial imagery, and used these in conjunction with RapidEye imagery to produce high resolution maize maps with a random forest classifier and a gaussian blur filter. We were able to highlight the importance of careful residual analysis, especially in terms of autocorrelation. As an end result, we were able to prove that, in spite of the severe limitations introduced by the restricted acquisition windows due to cloud coverage, high quality maps could be produced for two years, and the regional development of maize cultivation could be quantified. In the second case study, we used these spatially explicit datasets to link the expansion of biogas producing units with the extended maize cultivation in the area. In a next step, we overlayed the maize maps with soil and slope rasters in order to assess spatially explicit risks of soil compaction and erosion. Thus, we were able to highlight the potential role of remote sensing-based crop type classification in environmental protection, by producing maps of potential soil hazards, which can be used by local stakeholders to reallocate certain crop types to locations with less associated risk. In our third case study, we used Sentinel-1 data as input imagery, and official statistical records as maize reference data, and were able to produce consistent modeling input data for four consecutive years. Using these datasets, we could train and validate different models in spatially iv and temporally independent random subsets, with the goal of assessing model transferability. We were able to show that state-of-the-art deep learning models such as UNET performed significantly superior to conventional models like random forests, if the model was validated in a different year or a different regional subset. We highlighted and discussed the implications on modeling robustness, and the potential usefulness of deep learning models in building fully operational global crop classification models. We were able to conclude that the first major barrier for global classification models is the reference data. Since most research in this area is still conducted with local field surveys, and only few countries have access to official agricultural records, more global cooperation is necessary to build harmonized and regionally stratified datasets. The second major barrier is the classification algorithm. While a lot of progress has been made in this area, the current trend of many appearing new types of deep learning models shows great promise, but has not yet consolidated. There is still a lot of research necessary, to determine which models perform the best and most robust, and are at the same time transparent and usable by non-experts such that they can be applied and used effortlessly by local and global stakeholders.

Soils in forest ecosystems bear a high potential as carbon (C) sinks in the mitigation of climate change. The amount and characteristics of soil organic matter (SOM) are driven by inputs, transformation, degradation and stabilization of organic substances. While tree species fuel the C cycle by producing aboveground and belowground litter, soil microorganisms are crucial for litter degradation as well as the formation and stabilization of SOM. Nonetheless, our knowledge about the tree species effect on the SOM status is limited, inconsistent and blurred. The investigation of tree species effects on SOM is challenging because in long-established forest ecosystems the spatial distribution of tree species is a result of the interplay of environmental factors including climate, geomorphology and soil chemistry. Moreover, tree distribution can further vary with forest successional stage and silvicultural management. Since these factors also directly affect the soil C-status, it is difficult to identify a pure “tree species effect” on the SOM status at regular forested sites. It therefore remains unclear in how far tree species-specific litter with different quality influences the microbial driven turnover and formation of SOM. Tree species effects on SOM and related soil microbial properties were investigated by examining soil profiles (comprising organic forest floor horizons and mineral soil layers) in different forest stands at the recultivated spoil heap ‘Sophienhöhe’ located at the lignite open-cast mine Hambach near Jülich, Germany. The afforested sites comprised monocultural stands of Douglas fir (Pseudotsuga menziesii), black pine (Pinus nigra), European beech (Fagus sylvatica) and red oak (Quercus rubra) as well as a mixed deciduous stand site planted mainly with hornbeam (Carpinus betulus), lime (Tilia cordata) and common oak (Quercus robur) that were grown for 35 years under identical soil and geomorphological conditions. Because the parent material used for site recultivation was free from organic matter or coal material, the SOM accumulation is entirely the result of in situ soil development due to the impact of tree species. The first study revealed that tree species had a significant effect on soil organic carbon (SOC) stocks, stoichiometric patterns of C, nitrogen (N), sulfur (S), hydrogen (H) and oxygen (O) as well as the microbial biomass carbon (MBC) content in the forest floor and the top mineral soil layers (0-5 cm, 5-10 cm, 10-30 cm). In general, forest floor SOC stocks were significantly higher at coniferous forest stands compared to deciduous tree species, whereas in mineral soil layers the differences were smaller. Thus, the impact of tree species decreased with increasing soil depth. By investigating the linkage of the natural abundance of 13C and 15N in the soil depth gradients with C:N and O:C stoichiometry, the second study showed that differences in SOC stocks and SOM quality resulted from a tree species-dependent turnover of SOM. Significantly higher turnover of organic matter in soils under deciduous tree species depended to 46 % on the quality of litterfall and root inputs (N content, C:N, O:C ratio), and on the initial isotopic signatures of litterfall. Hence, SOM composition and turnover also depends on additional – presumably microbially driven – factors. The subsequent results of the third study revealed that differences in SOM composition and related soil microbial properties were linked to different microbial communities. Phospholipid fatty acid (PLFA) patterns in the soil profiles indicated that the supply and availability of C and nutrient-rich substrates drive the distribution of fungi, Gram-positive (G+) bacteria and Gram-negative (G−) bacteria between tree species and along the soil depth gradients. The fourth study investigated the molecular composition of extractable soil microbial biomass-derived (SMB) and SOM-derived compounds by electrospray ionization Fourier transformation ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS). This was complemented by the analysis of nine monosaccharides representing microbial or plant origin. Microbially derived compounds substantially contributed to SOM and the contribution increased with soil depth. The supply of tree species-specific substrates resulted in different chemical composition of SMB with largest differences between deciduous and coniferous stands. At the same time, microorganisms contributed to SOM resulting in a strong similarity in the composition of SOM and SMB. Overall, the complex interplay of tree species-specific litter inputs and the ability, activity and efficiency of the associated soil fauna and microbial community in metabolizing the organic substrates leads to significant differences in the amount, distribution, quality and consequently, the stability of SOM. These findings are useful for a targeted cultivation of tree species to optimize soil C sequestration and other forest ecosystems services.

This intervention study explored the effects of a newly developed intergenerational encounter program on cross-generational age stereotyping (CGAS). Based on a biographical-narrative approach, participants (secondary school students and nursing home residents) were invited to share ideas about existential questions of life (e.g., about one’s core experiences, future plans, and personal values). Therefore, the dyadic Life Story Interview (LSI) had been translated into a group format (the Life Story Encounter Program, LSEP), consisting of 10 90-min sessions. Analyses verified that LSEP participants of both generations showed more favorable CGAS immediately after, but also 3 months after the program end. Such change in CGAS was absent in a control group (no LSEP participation). The LSEP-driven short- and long-term effects on CGAS could be partially explained by two program benefits, the feeling of comfort with and the experience of learning from the other generation.

Social innovation became a widely discussed topic in politics, research funding programs, and business development. Recent European and US economic and science policies have set aside significant funds to generate and foster social innovation. In view of current challenges such as digitization, Work 4.0, inclusion or migrant integration, the question of how organizations can be empowered to develop new and innovative approaches and service models to social challenges is becoming increasingly urgent. This especially applies to organizations in the fields of education and social services. In education, implementing new ideas and concepts is usually discussed as educational reform, which mostly addresses changes in policy agendas with consequences for national and international education systems. The concept of social innovation however has a different starting point: the source of new ideas and services are identified new, emergent needs in society or re-conceptualized. Such need-based perspectives might bring new impulses to the field of education. Therefore, this paper identifies important existing strands of social innovation research, which need to be considered in the emerging academic discourse on social innovation in education. Looking at social innovation through an education research lens reveals the close relation between learning, creativity, and innovation. Individuals, teams, and even organizations learn, engage in creative problem solving to create new and innovative products and services. From an organizational education perspective, the questions arise, how social innovation emerges and even more important, how the process of developing social innovation can be supported. After a brief introduction in the concept of social innovation, the paper discusses therefore the sites, where social innovation emerges, social innovators, approaches to foster social innovation as well as promoting and hindering factors for social innovation.

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.