Open Access

To improve the properties of thermochemical heat storage materials, salt mixtures were evaluated for their heat storage capacity and cycle stability as part of the innovation incubator project “Thermochemical battery” of the Leuphana university Lüneburg. Based on naturally occurring compound minerals, 16 sulfates, 18 chlorides and 5 chloride multi-mixtures, 18 bromides and 5 intermixtures between sulfates, chlorides and bromides were synthesized either from liquid solution or by dry mixing for TGA/DSC screening before continuing the heat storage evaluation with five different measurement setups at a laboratory scale. The TGA/DSC analysis served as a screening process to reduce the number of testing materials for the upscaled experiments. The evaluation process consisted of a three-cycle dehydration/hydration measurement at Tmax = 100°C and Tmax = 200°C. In case of the bromide samples a measurement of hydration conditions with Tmax = 110°C and a water flow at e = 18.68mbar, were added to the procedure to detect the maximum water uptake temperature. Also, a single dehydration to a temperature of Tmax = 500°C was implemented to observe melting behavior and to easier calculate the samples’ stages of hydration from the remaining anhydrous mass. Materials which showed high energy storage density and improved cycle stability during this first evaluation were cleared for multi-cycle measurements of 10 to 25 dehydration and hydration cycles at Tmax = 100 to 120°C and the evaluations at m = 20 to 100g scale. An estimate for the specific heat capacities at different temperatures of the materials which passed the initial stage was calculated from the TGA/DSC results as well. The laboratory scale measurement setup went through five stages of refining, which led to reducing the intended maximum sample mass from m = 100g to m = 20g. A switch from supplied liquid water to water vapor as the used reactant was also implemented in exchange for improved dehydration conditions. Introducing a vacuum pump for evaporating the water limited the influence of outside heat sources during hydration and in-situ dehydration was enabled as to not disturb the state the samples were settling in between measurements. Baseline calculation from blanc measurements with glass powder and attempts to calculate the specific heat capacity cp of the tested materials by 6 applying the Joule-Lenz-law to the measurement apparatus was another step of method development. The evaluation process of the laboratory scale tests at the final setting consisted of 1 to 5 cycle measurements of in-situ dehydration and hydrations with applied vacuum for t = 30 minutes at p ~ 30mbar. Upscaling the sample mass to m = 20g allowed for a close observation of different material behaviors. Agglomeration, melting and dissolving of the m = 10mg samples during the TGA/DSC analysis can be deducted from the recorded measurement curves and the state of the sample after measurement. However, at laboratory scale the visible volume changes, observed sample consistency after agglomeration and an automatic removal of molten and dissolved sample mass during the measurement allowed for a better characterization and understanding of the magnitude of the actual changes. This was done for the first time, particularly for mixed salts. Of the original number of 62 samples, 4 mixtures which passed the initial TGA/DSC screening namely {2MgCl2+ KCl}, {2MgCl2+CaCl2}, {5SrBr2+8CaCl2} and {2ZnCl2 + CaCl2} were chosen for further evaluation. The multi-cycle TGA/DSC measurements of {2MgCl2+ KCl}, {2MgCl2+CaCl2} and {5SrBr2+8CaCl2} showed an improved cycle stability for all three materials over the untreated educts. Of the four materials {2ZnCl2 + CaCl2} displayed the strongest deliquescence during hydration in the upscaled experimental setup. {2MgCl2+CaCl2} proved to be the most stable material regarding the heat storage density. The {MgCl2} content of the mixture is likely to partially or completely react to {Mg(OH)Cl} at temperatures of T > 110°C, which however does not impede the heat storage density. {5SrBr2+8CaCl2} displayed a low melting point in hydrated state, causing a fast material loss. This makes it an undesirable storage material. A lower heating rate may still help to avoid an early melting. The {2MgCl2+KCl} mixture was the most temperature stable of the mixtures showing no melting or dissolving behavior. A reaction of the {MgCl2} component of the mixture to {Mg(OH)Cl} was not observed within the applied temperature range of T = 25 to 200°C.

Panic disorder is a common anxiety disorder, which is associated with high subjective burden as well as a high cost for the health economy. According to the National Treatment Guideline S3, cognitive behavior therapy is recommended as the most effective psychological treatment. However, many people in need do not have access to cognitive behavior therapy. Internet-based interventions have proven to be an effective way to provide access to evidence-based treatment to those affected, thereby reducing gaps in care. For anxiety disorders, such as panic disorder and agoraphobia, a good effectiveness of internet-based interventions has been proven in numerous international studies. However, the internet has changed over the last few years: mobile technologies have considerable potential to further improve the adherence and effectiveness of internet-based interventions. Against this background, we developed the hybrid online training

My dissertation embraces four empirical papers addressing socio-economic issues relevant to policy-makers and society as a whole. These papers cover important aspects of human life including health at birth, life satisfaction, unemployment periods and retirement decisions, and are intended to provide a contribution to the respective research areas. The analyses are carried out applying advanced econometric methods and are based on data sets consisting of survey data as well as administrative records. The joint paper with Alessandro Palma and Daniela Vuri "Prenatal Air Pollution Exposure and Neonatal Health" in Chapter 2 investigates the causal impact of prenatal exposure to air pollution on neonatal health in Italy in the 2000s combining detailed information on mother’s residential location from birth certificates with PM10 concentrations from air pollution monitors. Variation in local weekly rainfall is exploited as an instrumental variable for non-random air pollution exposure. Using quasi-experimental variation in rainfall shocks allows to identify the effect of PM10, ruling out potential bias due to confounder pollutants. The paper estimates the effect of exposure for both the entire pregnancy period and separately for each trimester to test whether the neonatal health effects are driven by pollution exposure during a particular gestation period. This information enhances our understanding of the mechanisms at work and help prevent pregnant mothers from most dangerous exposure periods. Additionally, the effects of prenatal exposure to PM10 are estimated by maternal labor market status and maternal education level to understand how the pollution burden is shared across different population groups. This decomposition allows to identify possible mechanisms through which environmental inequality reinforces the negative impact of early-life exposure to air pollution. This study finds that average PM10 and days with PM10 level above the hazard limit reduce birth weight, gestational age, and measures of overall newborn health. Effects are largest for third trimester exposure and for low-income and less educated mothers. These findings imply that further policy efforts are needed to fully protect fetuses from the adverse effects of air pollution and to mitigate the environmental inequality of health at birth. The joint paper with Christian Pfeifer "Life Satisfaction in Germany After Reunification: Additional Insights on the Pattern of Convergence" in Chapter 3 updates previous findings on the total East-West gap in overall life satisfaction and its trend by using data from the German Socio-Economic Panel for the years 1992 to 2013. Additionally, the effects are separately analyzed for men and women as well as for four birth cohorts. The results indicate that reported life satisfaction is, on average, significantly lower in East than in West German federal states and that part of the raw East-West gap is due to differences in household income and unemployment status. The conditional East-West gap decreased in the first years after the German reunification and remained quite stable and sizable since the mid-nineties. The results further indicate that gender differences are small. Finally, the East-West gap is significantly smaller and shows a trend towards convergence for younger birth cohorts. The joint paper with Christian Pfeifer "Unemployment Benefits Duration and Labor Market Outcomes: Evidence from a Natural Experiment in Germany" in Chapter 4 explores the effects 2 of a major reform of unemployment benefits in Germany on the labor market outcomes of individuals with some health impairment. The reform induced a substantial reduction in the potential duration of regular unemployment benefits for older workers. This work analyzes the reform in a wider framework of institutional interactions, which allows to distinguish between its intended and unintended effects. The results based on routine data collected by the German Statutory Pension Insurance and a Difference-in-Differences design provide causal evidence for a significant decrease in the number of days in unemployment benefits and increase in the number of days in employment. However, they also suggest a significant increase in the number of days in unemployment assistance, granted upon exhaustion of unemployment benefits. Transitions to unemployment assistance represent an unintended effect, limiting the success of a policy change that aims to increase labor supply via reductions in the generosity of the unemployment insurance system. The single-authored paper "How Older Workers Respond to Raised Early Retirement Age: Evidence from a Kink Design in Germany" in Chapter 5 explores how an increase in the early retirement age affects labor force participation of older workers. The analysis is based on a social security reform in Germany, which raised the early retirement age over several birth cohorts to boost employment of older people and ultimately alleviate the burden on the public pension system. Detailed administrative data from the Federal Employment Agency allow to distinguish between employment and unemployment as well as disability pensions and retirement benefits claims. Using a Regression Kink design in a quasi-experimental framework, I show that the raised early retirement age had positive employment effects and negative effects on retirement benefits claims. The reform did not affect unemployment benefits or disability pensions claims. My results also show that some population groups are more sensitive to a reduction in retirement options and more likely to seek benefits from other government programs. In this respect, I find that workers in manufacturing sector respond to the raised early retirement age by claiming benefits from the disability insurance program designed to compensate for reduced earnings capacity due to severe health problems. The treatment heterogeneity analysis further suggests that high-wage workers are more likely to delay exits from employment, which is in line with incentives but might also indicate an increased inequality within the affected birth cohorts induced by the reform. Finally, women seem to rely on alternative sources of income such as retirement benefits for women, or spouse's or partner's income not observed in the data. All things considered, workers did not adjust to the increased early retirement age by substituting early retirement with other government programs but rather responded to the reform in line with the policy intent. At the same time, the findings point to heterogeneous behavioral responses across different population groups. This implies that raising the early retirement age is an effective policy tool to increase employment only among older people who have the real choice to delay employment exits. Therefore, reforms that raise statutory ages should ensure social support for workers only marginally attached to the labor market or not able to work longer due to potential health problems or other circumstances.

Network analysis methods have long been used in the social sciences. About 25 years ago, these methods gained popularity in various other domains and many real-world phenomena have been modeled using networks. Well-known examples include (online) social networks, economic networks, web graphs, metabolic networks, infrastructure networks, and many more. Technological development made it possible to store and process data on a scale not imaginable decades ago — a development that also includes network data. A particular characteristic of network data is that, unlike standard data, the objects of interest, called nodes, have relationships to (possibly all) other objects in the network. Collecting empirical data is often complicated and cumbersome, hence, the observed data are typically incomplete and might also contain other types of errors. Because of the interdependent structure of network data, these errors have a severe impact on network analysis methods. This cumulative dissertation is about the impact of erroneous network data on centrality measures, which are methods to assess the position of an object, for example a person, with respect to all other objects in a network. Existing studies have shown that even small errors can substantially alter these positions. The impact of errors on centrality measures is typically quantified using a concept called robustness. The articles included in this dissertation contribute to a better understanding of the robustness of centrality measures in several aspects. It is argued why the robustness needs to be estimated and a new method is proposed. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can be used as a basis for decision making. The relationship between network properties and the robustness of centrality measures is analyzed. Experimental and analytical approaches show that centrality measures are often more robust in networks with a larger average degree. The study of the impact of non-random errors on the robustness suggests that centrality measures are often more robust if missing nodes are more likely to belong to the same community compared to missingness completely at random. For the development of imputation procedures based on machine learning techniques, a process for the evaluation of node embedding methods is proposed.

Online behaviors analysis consists of extracting patterns from server-logs. The works presented here were carried out within the “mBook” project which aimed to develop indicators of the quantity and quality of the learning process of pupils from their usage of an eponymous electronic textbook for History. In this thesis, we investigate several models that adopt different points of view on the data. The studied methods are either well established in the field of pattern mining or transferred from other fields of machine learning and data-mining. We improve the performance of archetypal analysis in large dimensions and apply it to unveil correlations between visibility time of particular objects in the e-textbook and pupils’ motivation. We present next two models based on mixtures of Markov chains. The first extracts users’weekly browsing patterns. The second is designed to process essions at a fine resolution, which is sine qua non to reveal the significance of scrolling behaviors. We also propose a new paradigm for online behaviors analysis that interprets sessions as trajectories within the page-graph. In this respect, we establish a general framework for the study of similarity measures between spatio-temporal trajectories, for which the study of sessions is a particular case. Finally, we construct two centroid-based clustering methods using neural networks and thus lay the foundations for unsupervised behaviors analysis using neural networks. Keywords: online behaviors analysis, educational data mining, Markov models, archetypal analysis, spatio-temporal trajectories, neural network