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Codebook of the Global Dataset of Child Benefits
No full textThis codebook is an updated and extended version of the previously published Codebook of the Historical Dataset of Child Benefit (HDCB) from 2021 (Tonelli et al. 2021). The current version extends coverage of countries and includes more detailed information. Users are strongly encouraged to refer to this updated codebook when working with the data to ensure they are using the most accurate and comprehensive information available.
The Global Dataset of Child Benefits (GDCB) constitutes the most comprehensive and systematic longitudinal compilation of child benefit policies worldwide, spanning from 1926 to 2021. Developed within the framework of the Global Welfare State Information System (WeSIS), this dataset includes institutional and legal coverage data, as well as data on the scope of benefits across 120 countries, including both statutory entitlements and nationally administered cash transfer programs which have not been enacted into legal frameworks. Through an extensive data collection process, the GDCB captures the global diffusion, evolution, and institutional diversity of child benefit schemes. It distinguishes between employment-based and citizenship- or residency-based benefits and systematically documents targeting mechanisms and conditionalities. Particular attention is devoted to capturing the extension of child-related transfers in the Global South, integrating measures of effective coverage, poverty-targeted interventions and behavioral conditionalities. The dataset harmonizes national administrative data, legal texts, and international sources, offering standardized, cross-nationally comparable indicators. It thus provides a critical empirical foundation for scholarly inquiry into the historical development of welfare state institutions, the stratification of social protection regimes, and the pathways to universalism in family policy.1
Using decision trees to identify intersectional subgroups at risk for cancer screening non-attendance: three European case studies
No full textAs in many relevant public health areas, attendance in cancer screening programs is stratified by social dimensions, yet current additive approaches fail to capture the complexity of discrimination leading to health inequalities. In fact, social dimensions interact, shaping experiences of discrimination in accessing cancer screening. This dissertation advances the study of complex social inequalities by developing different analytical strategies that explore the use of decision trees under the framework of intersectionality to identify subgroups at risk of non-attendance. Three European case studies were analysed: breast cancer screening (BCS) in Germany, BCS in Spain, and colorectal cancer (CRC) screening in Sweden.
Three analytical strategies were explored: (i) comparing decision tree-based and evidence-informed approaches (BCS Germany), (ii) using decision trees to reduce intersectional complexity (CRC Sweden), and (iii) employing decision trees as predictive tools (BCS Spain). Findings reveal key Individual-regional interactions. In Spain, regions significantly influenced BCS attendance, reflecting economic disparities and screening program timelines. In Germany, partnership cohabitation was a protective factor, while certain regions had higher non-attendance risks. In Sweden, organized screening programs mitigated inequalities, while opportunistic screening revealed disparities based on gender, migration background, and income.
This dissertation contributes to identifying intersectional subgroups at risk of non-attendance by inductively selecting social dimensions and modelling non-linear and nuanced interactions between categories across subgroups. It also advances the methodological field of quantitative intersectionality by proposing decision trees to simplify intersectional complexity, improving results' interpretability. Overall, it enhances understanding of cancer screening inequalities and proposes methodological tools for public health research
Bürgerschaftliche Potenziale: Akteursidentifikation und -aktivierung in der Wärmewende – Aktualisierung der Sinus-Milieus® für Bremen
No full textProjekt Wärmewende Nordwest – Digitalisierung zur Umsetzung von Wärmewende- und Mehrwertanwendungen für Gebäude, Campus, Quartiere und Kommunen im Nordwesten - Arbeitspakete: Potentiale für eine stadtweite und quartiersbezogene Transformation der Wärmeversorgung sowie Bildungsformate für nachhaltige Entwicklung.Die vorliegende Studie aktualisiert die Sinus-Milieustruktur für Bremen für deren Einsatz im Kontext der Energiewende. Ausgangspunkt bildet die theoretische Fundierung, die darin besteht, die Sinus-Milieus als modellhafte Repräsentation sozialer Gruppen anhand von Werten, Einstellungen und sozioökonomischen Merkmalen zu verstehen. Die Arbeit beleuchtet die signifikanten Verschiebungen und Um-strukturierungen innerhalb der Milieulandschaft.
Für die empirische Analyse wurde ein aktueller Datensatz verwendet, der Sinus-Milieus auf Gebäudeebene in Bremen mithilfe georeferenzierter Adress- und Koordinatendaten abbildet. Mittels GIS-gestützter Analysen sowie Python-basierter Verfahren erfolgte eine Aggregation der dominanten Milieus auf Baublock- sowie 100 m x 100 m Gitterebene. Die methodische Herangehensweise fokussiert auf die präzise räumliche Verortung der Sinus-Milieus, wobei durch eine Kombination von Adressabgleich und Geodatenfilterung eine verlässliche Darstellung der sozioökonomischen Strukturen gewährleistet wird. Diese differenzierte Darstellung der sozialen Zusammensetzung Bremens ist für weitere geostatistische Untersuchungen und die Planung kommunaler Maßnahmen im Kontext der Wärmewende von zentraler Bedeutung.
Die Ergebnisse der aktualisierten Milieustruktur dienen als Grundlage, um transformationsrelevante Potenziale in der Bevölkerung zu identifizieren und zielgruppenspezifische Planungen sowie Kommunikations- und Beteiligungsstrategien zu entwickeln. Damit leistet die Studie einen wichtigen Beitrag zur sozial gerechten Gestaltung der Energiewende.717
Fatigue investigations of laser powder bed fusion generated austenitic and tool steel samples with consideration of heat treatments
No full textAs a promising and progressing additive manufacturing (AM) technique for fabricating serial metallic parts, laser powder bed fusion (LPBF) or laser beam melting (LBM) is most widely used in functional and structural metal material areas. For the two most commonly applied steels, austenitic stainless AISI 316L and carbon-bearing tool steel AISI H13, it is of great value for researchers to comprehensively study the dynamic properties of these steel parts produced via LBM. In this work, in-depth investigations on the influences of the LBM process parameters, on the resulting microstructure and mechanical properties, with a focus on the fatigue performance are performed. The individual effects of input energy density, scan pattern, and polar angle on the various occurring defect groups and microstructure load capacities are analyzed. Meanwhile, the influences of post-surface treatment as well as heat treatments are studied and evaluated. Results present generally inferior fatigue properties, which are impacted differently by the various defects depending on the shapes, sizes, and orientations. At the same time, inspiring results of comparable or even outperforming microstructure load capacities and fatigue strengths to the conventional values are observed. With further research and advancement in technology, the replacement of conventional 316L and H13 materials by additively generated parts can be realized
The Health Care System in India
No full textThis country report provides a description of the emergence of a health care system under public responsibility in India. The inception of the health care system refers to the first legislation stipulating entitlements to medical care. The report also includes a brief description of major health care reforms, and the current organization of the health care system in India. This report is part of the CRC 1342 Social Policy Country Briefs Series.Deutsche Forschungsgemeinschaft (DFG)4
Human factors in access control: analysis and design
No full textThe ubiquity of communication technologies has led to the increased need of users to regulate access to their data. Traditional access control systems were mostly developed for military, governmental, and organizational settings. Even more, many of them assumed the presence of a responsible central authority that authors and enforces security policies. This is not true anymore. The development of Internet technologies and high availability of personal devices create new demands from users. In collaborative environments, where a central authority is missing or not completely responsible for user data, the user needs convenient methods to regulate access to their data.
Contemporary research approaches the problem of usable access control systems in different ways. One of the directions is dedicated to behavioral and perceptual aspects: how users perceive the permission management and what they do to achieve their access control goals. Although this approach provides useful observations, the existing research exhibits a number of inconsistencies. Another direction leverages prototype and model development, revealing a different problem: it is hardly possible to make a useful generalization based on individual use cases.
In this thesis we investigate the principles that underlie user perception of access control systems. We start from exploratory study of privacy protection behaviors. Subsequently, moving to a more specific level of access control, we rely on cognitive science and state that the utilization of visual metaphors and categorization might be beneficial for end users. The thesis presents two case studies to test these assumptions. Additionally, we further investigate the mechanism of categorization and design two models for collaborative platforms. The models have been tested for feasibility in simulated environments.
Our results suggest that both metaphors and categorization can be leveraged to improve different aspects of the access control system usability. First, we found out that visual metaphors are capable of implicitly transferring information about an access control system. Second, we established some prominent parallels between categorization in human cognition and the corresponding mechanisms of access control. Lastly, we demonstrated the applicability of categories as the main primitives of user-centric access control models
Power dynamics in AI discourse. A case Study of the discourse in German policy, media, and social media from 2012 to 2021.
No full textArtificial Intelligence (AI) technologies were accompanied by growing attention by the public discourses in the last decade. Media and political discourses have drawn academic interest due to their potential to shape AI’s trajectory through their influence on research, funding, and technology adoption. However, the mutual influence between these two spheres in shaping AI development has received limited attention. This dissertation addresses this gap by analysing the interplay between media and political actors in the German discourse on AI during its formative phase from 2012 to 2021.
To achieve this, the study introduces a theoretical framework combining the concepts of agenda-setting and discursive power to analyse the power dynamics within AI discourse. A mixed-methods approach was used, comprising content analysis and topic modelling to map the discourse across traditional media, policy documents, and Twitter accounts from German media and political actors. Additionally, frame and speaker analyses were conducted on two subissues, namely AI regulation and Sustainable AI.
The findings reveal a shift in media and social media discourse as political actors began formulating AI policies, leading to a hype cycle between 2018 and 2020 and a thematic shift toward economic and political dimensions of the discourse. In discussions of AI regulation, influential entities such as the EU and Big Tech corporations played important roles, framing the discourse to suit their strategic interests. The Sustainable AI discourse emphasised AI as a solution to societal challenges, a framing dominated by corporate and political actors.
This dissertation not only provides a nuanced understanding of how AI technologies are framed in public discourses but also demonstrates the interconnected roles of media and politics in shaping emerging technologies. By integrating agenda-setting with the concept of discursive power, the study advances a framework for examining technological developments and contributes to theoretical and practical understandings of AI discourse
Regional Capabilities for Green Hydrogen: Insights from Northern and Western Germany
No full textGreen hydrogen can play a major role in future net-zero energy systems. This paper investigates how existing technological and production capabilities can support the emergence and growth of green hydrogen value chains in Northern and Western Germany. Drawing on evolutionary economic geography, we argue that the development of the hydrogen value chain depends on the relatedness between existing knowledge bases and hydrogen technologies, and further recombinant capabilities, as well as the processes involved in acquiring capabilities. Our analysis focuses on seven NUTS 2 regions with favorable conditions for the development of hydrogen hubs, which are low cost of renewable energy production, access to hydrogen infrastructure, and political support for the hydrogen economy. We comparatively examine the regional capabilities using patent data to map technological innovation, firm-level data to identify key corporate actors, and regionalized export statistics to assess production capabilities. Based on our findings, we argue that the development of green hydrogen hubs might be facilitated by alignment between a region’s existing innovation capabilities, production capabilities, and hub specialization, with place-based policy approaches tailored towards each region’s unique profile.250
SWEET1 sugar transporter paralogs in hybrid poplar: Functional analysis and phenotypic characterization of CRISPR/Cas9 knockout lines
No full textSugar transporters of the SWEET family play a crucial role in carbon allocation in plants and enable the transport of sugars between different tissues in all plant organs. In trees, this process is particularly important for the supply of root systems and symbiotic partners such as ectomycorrhizae. The SWEET1 gene family in Populus tremula x alba comprises seven highly similar paralogs that have arisen due to the complex ge-nome structure of this hybrid species. However, the functional significance and evolu-tionary specialization of these paralogs was largely unknown.
The aim of this work was to characterize the functional role of SWEET1 paralogs by CRISPR/Cas9-mediated knockout and to investigate their importance for ectomycor-rhizal symbiosis. For this purpose, a multiplex CRISPR/Cas9 system was developed that targets all seven paralogs simultaneously. Evolutionary analyses using a haplotype-resolved genome enabled a systematic nomenclature and identification of gene clusters on chromosome 2 and 5. Transgenic lines were generated by agrobacteria-mediated transformation and characterized at the molecular, physiological and morphological level.
The results showed that SWEET1 paralogs are functionally specialized: While complete knockout of all paralogs resulted in severe growth defects, chimeric lines that had lost only chromosome 2 paralogs exhibited normal development. Complete knockout lines accumulated only about half the biomass of wild-type controls and showed altered carbon allocation with reduced leaf mass density and shifted biomass distribution in favor of leaves. Grafting experiments provided additional evidence for a disturbance of source-sink dynamics. Contrary to expectation, both knockout lines formed structurally normal ectomycorrhizas with Pisolithus microcarpus, suggesting that SWEET1 transporters are not required for the basic establishment of the symbiosis.
This study demonstrates for the functional specialization of gene duplicates in a complex hybrid genome and establishes methodological approaches for the analysis of gene families in tree hybrids. The results expand our understanding of carbon allocation in trees and have implications for forest management and breeding
Speech separation for monolingual and multilingual cocktail party scenarios
No full textIn everyday speech communication, humans face noisy multi-speaker soundscapes with overlapping sound sources, typically described as the cocktail party problem. Without much effort, humans can focus their attention on a specific voice or sound source while fading out the remaining voices or sound sources, referred to as selective auditory attention. For the development of future hearing aids and sophisticated algorithms for speech-based human-computer interaction, it is of great interest to equip machines with the same ability.
The research field dedicated to the development of machine learning algorithms that embed this human ability goes by the name of speech separation. The underlying scientific problem is to develop algorithms that work in the wide range of everyday cocktail party scenarios just as the selective auditory attention ability of humans.
This cumulative dissertation concerns deep neural network based single-channel speech separation and addresses five scientific problems (P): Target speaker absence and monologues (P1) deals with attended speakers in cocktail party scenarios who stop speaking for a while or hold monologues. Reliable target speaker reference (P2) formulates methods to enhance the reliability and stability of speech separation algorithms controlled by brain signals. Speech mode generalization (P3) has the goal to develop algorithms that work for multiple speech modes, such as normal and whispered speech. Cross-language generalization (P4) aims to develop algorithms that work for several seen and unseen languages. The multilingual cocktail party problem (P5) considers a cocktail party problem in which multiple languages are spoken and develops language-based speech separation algorithms. The contributions of this dissertation to solving P1-P5 mark a step towards the overall goal of equipping machines with a human-like ability of selective auditory attention that works reliably in real-world cocktail party scenarios