TY - JOUR A1 - Silva, Bibiana A1 - Oliveira Costa, Ana Carolina A1 - Tchewonpi, Sorel Sagu A1 - Bönick, Josephine A1 - Huschek, Gerd A1 - Gonzaga, Luciano Valdemiro A1 - Fett, Roseane A1 - Baldermann, Susanne A1 - Rawel, Harshadrai Manilal T1 - Comparative quantification and differentiation of bracatinga (Mimosa scabrella Bentham) honeydew honey proteins using targeted peptide markers identified by high-resolution mass spectrometry JF - Food research international N2 - Honey traceability is an important topic, especially for honeydew honeys, due to the increased incidence of adulteration. This study aimed to establish specific markers to quantify proteins in honey. A proteomics strategy to identify marker peptides from bracatinga honeydew honey was therefore developed. The proteomics approach was based on initial untargeted identification of honey proteins and peptides by LC-ESI-Triple-TOF-MS/MS, which identified the major royal jelly proteins (MRJP) presence. Afterwards, the peptides were selected by the in silico digestion. The marker peptides were quantified by the developed targeted LC-QqQ-MS/MS method, which provided good linearity and specificity, besides recoveries between 92 and 100% to quantify peptides from bracatinga honeydew honey. The uniqueness and high response in mass spectrometry were backed by further complementary protein analysis (SDS-PAGE). The selected marker peptides EALPHVPIFDR (MRJP 1), ILGANVK (MRJP 2), TFVTIER (MRJP 3), QNIDVVAR (MRJP 4), FINNDYNFNEVNFR (MRJP 5) and LLQPYPDWSWTK (MRJP 7), quantified by LC-QqQ-MS/MS, highlighted that the content of QNIDVVAR from MRJP 4 could be used to differentiate bracatinga honeydew honey from floral honeys (p < 0.05) as a potential marker for its authentication. Finally, principal components analysis highlighted the QNIDVVAR content as a good descriptor of the analyzed bracatinga honeydew honey samples. KW - Honeydew honey KW - Major royal jelly proteins KW - Marker peptides KW - High-resolution mass spectrometry KW - Principal component analysis Y1 - 2020 U6 - https://doi.org/10.1016/j.foodres.2020.109991 SN - 0963-9969 SN - 1873-7145 VL - 141 PB - Elsevier CY - New York, NY [u.a.] ER - TY - JOUR A1 - Stevanato, Luca A1 - Baroni, Gabriele A1 - Oswald, Sascha A1 - Lunardon, Marcello A1 - Mareš, Vratislav A1 - Marinello, Francesco A1 - Moretto, Sandra A1 - Polo, Matteo A1 - Sartori, Paolo A1 - Schattan, Paul A1 - Rühm, Werner T1 - An alternative incoming correction for cosmic-ray neutron sensing observations using local muon measurement JF - Geophysical research letters N2 - Measuring the variability of incoming neutrons locally would be usefull for the cosmic-ray neutron sensing (CRNS) method. As the measurement of high energy neutrons is not so easy, alternative particles can be considered for such purpose. Among them, muons are particles created from the same cascade of primary cosmic-ray fluxes that generate neutrons at the ground. In addition, they can be easily detected by small and relatively inexpensive detectors. For these reasons they could provide a suitable local alternative to incoming corrections based on remote neutron monitor data. The reported measurements demonstrated that muon detection system can detect incoming cosmic-ray variations locally. Furthermore the precision of this measurement technique is considered adequate for many CRNS applications. KW - CRNS KW - soil-moisture KW - neutrons KW - muons KW - cosmic-rays Y1 - 2022 U6 - https://doi.org/10.1029/2021GL095383 SN - 0094-8276 SN - 1944-8007 VL - 49 IS - 6 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Jasser, Greta A1 - Kelly, Megan A1 - Rothermel, Ann-Kathrin T1 - Male supremacism and the Hanau terrorist attack BT - between online misogyny and far-right violence Y1 - 2020 UR - https://www.icct.nl/publication/male-supremacism-and-hanau-terrorist-attack-between-online-misogyny-and-far-right PB - International Centre for Counter-Terrorism (ICCT) CY - Den Haag ER - TY - THES A1 - Bunselmeyer, Lena T1 - Die Agenda 2030 in kommunalen Nachhaltigkeitsstrategien T1 - The Agenda 2030 in Sustainability Strategies of Local Governments N2 - Die 2016 verabschiedeten Sustainable Development Goals (SDGs) der Vereinten Nationen sind Referenzrahmen von Nachhaltigkeitsstrategien auf Bundes- Landes- und kommunaler Ebene geworden. Städte rückten im Zuge der Agenda 2030 in den Mittelpunkt. Ihre Verwaltungen befinden sich dabei in einem herausfordernden Spannungsfeld: Einerseits haben die SDGs den holistischen Anspruch, vollständig in das Handeln der Kommunen integriert zu werden. Andererseits ist für eine effektive Umsetzung eine starke Anpassung der SDGs an den lokalen Kontext notwendig. Die vorliegende Arbeit betrachtet anhand einer Fallstudie die Frage, wie Kommunen die Nachhaltigkeitsziele der Vereinten Nationen in ihre Handlungsprogramme und Nachhaltigkeitsstrategien übersetzen, und welche Faktoren Einfluss auf diesen Prozess haben. Dabei wird ein translationstheoretischer Ansatz verwendet, der die Übertragung einer Idee in einen lokalen Kontext als aktiven Transfer versteht, bei dem das Handeln der beteiligten Akteure und deren Konstruktion der aufzunehmenden Idee im Fokus steht. Die Translation wird mit Hilfe von qualitativen Interviews nachvollzogen und analysiert. Die Ergebnisse zeigen, dass die SDGs zwar anhand ihrer Relevanz für die Kommune gefiltert werden, der normative Anspruch der SDGs aber erhalten bleibt und angesichts des als gering beurteilten Fortschritts der Kommune besonderes Gewicht erhält. Zentrale Einflussfaktoren für die Translation sind die verfügbaren personellen und finanziellen Ressourcen, die Akzeptanz für die SDGs in Verwaltung, Politik und Gesellschaft und nicht zuletzt das persönliche Engagement einzelner Verwaltungsmitarbeiter*innen. N2 - The United Nations’ Sustainable Development Goals (SDGs) have become the leading set of guidelines for sustainability strategies on every government level. Cities are the Agenda 2030’s focal point. Their local governments however find themselves in a challenging dilemma: On the one hand, the SDG’s holistic approach warrants a wholesale integration into local policy. On the other hand, a substantial adaptation is necessary to integrate the Goals into the local context. This paper uses a case study to examine how municipalities translate the Sustainable Development Goals into their sustainability action plans and strategies. Moreover, it examines which factors are influential to this process. This study uses a translation theory perspective, which characterizes the transfer of an idea into the local context as an active process. It focusses on the actors and how they perceive the transferred idea. For this, qualitative interviews are conducted and analyzed. Thereby, this study shows that while SDGs are being filtered according to their relevancy for the municipality, their normative dimension remains intact. The municipal actors consider this dimension crucial vis-à-vis the lack of progress that they perceive in their municipality. This study finds that core influencing factors are the financial and personnel resources available, the acceptance of SDGs within the administration, politics and society as well as the activism of singular municipal actors. KW - SDGs KW - Kommunen KW - öffentliche Verwaltung KW - Translation KW - Nachhaltigkeitsstrategien KW - SDGs KW - local government KW - public administration KW - translation KW - sustainability strategies Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-634873 ER - TY - JOUR A1 - Svennevig, Kristian A1 - Hermanns, Reginald L. A1 - Keiding, Marie A1 - Binder, Daniel A1 - Citterio, Michele A1 - Dahl-Jensen, Trine A1 - Mertl, Stefan A1 - Sørensen, Erik Vest A1 - Voss, Peter Henrik T1 - A large frozen debris avalanche entraining warming permafrost ground-the June 2021 Assapaat landslibe, West Greenland JF - Landslides N2 - A large landslide (frozen debris avalanche) occurred at Assapaat on the south coast of the Nuussuaq Peninsula in Central West Greenland on June 13, 2021, at 04:04 local time. We present a compilation of available data from field observations, photos, remote sensing, and seismic monitoring to describe the event. Analysis of these data in combination with an analysis of pre- and post-failure digital elevation models results in the first description of this type of landslide. The frozen debris avalanche initiated as a 6.9 * 10(6) m(3) failure of permafrozen talus slope and underlying colluvium and till at 600-880 m elevation. It entrained a large volume of permafrozen colluvium along its 2.4 km path in two subsequent entrainment phases accumulating a total volume between 18.3 * 10(6) and 25.9 * 10(6) m(3). About 3.9 * 10(6) m(3) is estimated to have entered the Vaigat strait; however, no tsunami was reported, or is evident in the field. This is probably because the second stage of entrainment along with a flattening of slope angle reduced the mobility of the frozen debris avalanche. We hypothesise that the initial talus slope failure is dynamically conditioned by warming of the ice matrix that binds the permafrozen talus slope. When the slope ice temperature rises to a critical level, its shear resistance is reduced, resulting in an unstable talus slope prone to failure. Likewise, we attribute the large-scale entrainment to increasing slope temperature and take the frozen debris avalanche as a strong sign that the permafrost in this region is increasingly at a critical state. Global warming is enhanced in the Arctic and frequent landslide events in the past decade in Western Greenland let us hypothesise that continued warming will lead to an increase in the frequency and magnitude of these types of landslides. Essential data for critical arctic slopes such as precipitation, snowmelt, and ground and surface temperature are still missing to further test this hypothesis. It is thus strongly required that research funds are made available to better predict the change of landslide threat in the Arctic. KW - Assapaat landslide KW - Slope temperature KW - Global warming Y1 - 2022 U6 - https://doi.org/10.1007/s10346-022-01922-7 SN - 1612-510X SN - 1612-5118 VL - 19 SP - 2549 EP - 2567 PB - Springer CY - Heidelberg ER - TY - GEN A1 - Rothermel, Ann-Kathrin T1 - What anti-gender and anti-vaccines politics have in common BT - the construction of gender and the Covid-19 pandemic in right-wing discourses KW - anti-gender KW - featured KW - gender research KW - politics KW - science & technology Y1 - 2022 UR - https://blogs.lse.ac.uk/gender/2022/04/11/what-anti-gender-and-anti-vaccines-politics-have-in-common-the-construction-of-gender-and-the-covid-19-pandemic-in-right-wing-discourses/ PB - London School of Economics and Political Science CY - London ER - TY - JOUR A1 - Krämer, Hauke Kai A1 - Gelbrecht, Maximilian A1 - Pavithran, Induja A1 - Sujith, Ravindran A1 - Marwan, Norbert T1 - Optimal state space reconstruction via Monte Carlo decision tree search JF - Nonlinear Dynamics N2 - A novel idea for an optimal time delay state space reconstruction from uni- and multivariate time series is presented. The entire embedding process is considered as a game, in which each move corresponds to an embedding cycle and is subject to an evaluation through an objective function. This way the embedding procedure can be modeled as a tree, in which each leaf holds a specific value of the objective function. By using a Monte Carlo ansatz, the proposed algorithm populates the tree with many leafs by computing different possible embedding paths and the final embedding is chosen as that particular path, which ends at the leaf with the lowest achieved value of the objective function. The method aims to prevent getting stuck in a local minimum of the objective function and can be used in a modular way, enabling practitioners to choose a statistic for possible delays in each embedding cycle as well as a suitable objective function themselves. The proposed method guarantees the optimization of the chosen objective function over the parameter space of the delay embedding as long as the tree is sampled sufficiently. As a proof of concept, we demonstrate the superiority of the proposed method over the classical time delay embedding methods using a variety of application examples. We compare recurrence plot-based statistics inferred from reconstructions of a Lorenz-96 system and highlight an improved forecast accuracy for map-like model data as well as for palaeoclimate isotope time series. Finally, we utilize state space reconstruction for the detection of causality and its strength between observables of a gas turbine type thermoacoustic combustor. KW - State space reconstruction KW - Embedding KW - Optimization KW - Time series analysis KW - Causality KW - Prediction KW - Recurrence analysis Y1 - 2022 U6 - https://doi.org/10.1007/s11071-022-07280-2 SN - 0924-090X SN - 1573-269X VL - 108 IS - 2 SP - 1525 EP - 1545 PB - Springer CY - Dordrecht ER - TY - GEN A1 - Rothermel, Ann-Kathrin T1 - The politics of fear BT - right wing anti-gender and anti-vaccination narratives T2 - WIIS Blog Y1 - 2022 UR - https://wiisglobal.org/the-politics-of-fear-right-wing-anti-gender-and-anti-vaccination-narratives-2/#_edn1 CY - Women in International Security ER - TY - JOUR A1 - Kong, Xiangzhen A1 - Ghaffar, Salman A1 - Determann, Maria A1 - Friese, Kurt A1 - Jomaa, Seifeddine A1 - Mi, Chenxi A1 - Shatwell, Tom A1 - Rinke, Karsten A1 - Rode, Michael T1 - Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change JF - Water research : a journal of the International Association on Water Quality (IAWQ) N2 - Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso- and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems. KW - deforestation KW - climate change KW - temperate regions KW - reservoir KW - eutrophication KW - process-based modeling Y1 - 2022 U6 - https://doi.org/10.1016/j.watres.2022.118721 SN - 0043-1354 SN - 1879-2448 VL - 221 PB - Elsevier Science CY - Amsterdam [u.a.] ER - TY - THES A1 - Huegle, Johannes T1 - Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery T1 - Kausale Entdeckung in der Praxis: Nichtparametrische bedingte Unabhängigkeitstests und Werkzeuge für die Kausalentdeckung N2 - Knowledge about causal structures is crucial for decision support in various domains. For example, in discrete manufacturing, identifying the root causes of failures and quality deviations that interrupt the highly automated production process requires causal structural knowledge. However, in practice, root cause analysis is usually built upon individual expert knowledge about associative relationships. But, "correlation does not imply causation", and misinterpreting associations often leads to incorrect conclusions. Recent developments in methods for causal discovery from observational data have opened the opportunity for a data-driven examination. Despite its potential for data-driven decision support, omnipresent challenges impede causal discovery in real-world scenarios. In this thesis, we make a threefold contribution to improving causal discovery in practice. (1) The growing interest in causal discovery has led to a broad spectrum of methods with specific assumptions on the data and various implementations. Hence, application in practice requires careful consideration of existing methods, which becomes laborious when dealing with various parameters, assumptions, and implementations in different programming languages. Additionally, evaluation is challenging due to the lack of ground truth in practice and limited benchmark data that reflect real-world data characteristics. To address these issues, we present a platform-independent modular pipeline for causal discovery and a ground truth framework for synthetic data generation that provides comprehensive evaluation opportunities, e.g., to examine the accuracy of causal discovery methods in case of inappropriate assumptions. (2) Applying constraint-based methods for causal discovery requires selecting a conditional independence (CI) test, which is particularly challenging in mixed discrete-continuous data omnipresent in many real-world scenarios. In this context, inappropriate assumptions on the data or the commonly applied discretization of continuous variables reduce the accuracy of CI decisions, leading to incorrect causal structures. Therefore, we contribute a non-parametric CI test leveraging k-nearest neighbors methods and prove its statistical validity and power in mixed discrete-continuous data, as well as the asymptotic consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes. (3) To show the applicability and opportunities of causal discovery in practice, we examine our contributions in real-world discrete manufacturing use cases. For example, we showcase how causal structural knowledge helps to understand unforeseen production downtimes or adds decision support in case of failures and quality deviations in automotive body shop assembly lines. N2 - Kenntnisse über die Strukturen zugrundeliegender kausaler Mechanismen sind eine Voraussetzung für die Entscheidungsunterstützung in verschiedenen Bereichen. In der Fertigungsindustrie beispielsweise erfordert die Fehler-Ursachen-Analyse von Störungen und Qualitätsabweichungen, die den hochautomatisierten Produktionsprozess unterbrechen, kausales Strukturwissen. In Praxis stützt sich die Fehler-Ursachen-Analyse in der Regel jedoch auf individuellem Expertenwissen über assoziative Zusammenhänge. Aber "Korrelation impliziert nicht Kausalität", und die Fehlinterpretation assoziativer Zusammenhänge führt häufig zu falschen Schlussfolgerungen. Neueste Entwicklungen von Methoden des kausalen Strukturlernens haben die Möglichkeit einer datenbasierten Betrachtung eröffnet. Trotz seines Potenzials zur datenbasierten Entscheidungsunterstützung wird das kausale Strukturlernen in der Praxis jedoch durch allgegenwärtige Herausforderungen erschwert. In dieser Dissertation leisten wir einen dreifachen Beitrag zur Verbesserung des kausalen Strukturlernens in der Praxis. (1) Das wachsende Interesse an kausalem Strukturlernen hat zu einer Vielzahl von Methoden mit spezifischen statistischen Annahmen über die Daten und verschiedenen Implementierungen geführt. Daher erfordert die Anwendung in der Praxis eine sorgfältige Prüfung der vorhandenen Methoden, was eine Herausforderung darstellt, wenn verschiedene Parameter, Annahmen und Implementierungen in unterschiedlichen Programmiersprachen betrachtet werden. Hierbei wird die Evaluierung von Methoden des kausalen Strukturlernens zusätzlich durch das Fehlen von "Ground Truth" in der Praxis und begrenzten Benchmark-Daten, welche die Eigenschaften realer Datencharakteristiken widerspiegeln, erschwert. Um diese Probleme zu adressieren, stellen wir eine plattformunabhängige modulare Pipeline für kausales Strukturlernen und ein Tool zur Generierung synthetischer Daten vor, die umfassende Evaluierungsmöglichkeiten bieten, z.B. um Ungenauigkeiten von Methoden des Lernens kausaler Strukturen bei falschen Annahmen an die Daten aufzuzeigen. (2) Die Anwendung von constraint-basierten Methoden des kausalen Strukturlernens erfordert die Wahl eines bedingten Unabhängigkeitstests (CI-Test), was insbesondere bei gemischten diskreten und kontinuierlichen Daten, die in vielen realen Szenarien allgegenwärtig sind, die Anwendung erschwert. Beispielsweise führen falsche Annahmen der CI-Tests oder die Diskretisierung kontinuierlicher Variablen zu einer Verschlechterung der Korrektheit der Testentscheidungen, was in fehlerhaften kausalen Strukturen resultiert. Um diese Probleme zu adressieren, stellen wir einen nicht-parametrischen CI-Test vor, der auf Nächste-Nachbar-Methoden basiert, und beweisen dessen statistische Validität und Trennschärfe bei gemischten diskreten und kontinuierlichen Daten, sowie dessen asymptotische Konsistenz in constraint-basiertem kausalem Strukturlernen. Eine umfangreiche Evaluation auf synthetischen und realen Daten zeigt, dass der vorgeschlagene CI-Test bestehende Verfahren hinsichtlich der Korrektheit der Testentscheidung und gelernter kausaler Strukturen übertrifft, insbesondere bei geringen Stichprobengrößen. (3) Um die Anwendbarkeit und Möglichkeiten kausalen Strukturlernens in der Praxis aufzuzeigen, untersuchen wir unsere Beiträge in realen Anwendungsfällen aus der Fertigungsindustrie. Wir zeigen an mehreren Beispielen aus der automobilen Karosseriefertigungen wie kausales Strukturwissen helfen kann, unvorhergesehene Produktionsausfälle zu verstehen oder eine Entscheidungsunterstützung bei Störungen und Qualitätsabweichungen zu geben. KW - causal discovery KW - causal structure learning KW - causal AI KW - non-parametric conditional independence testing KW - manufacturing KW - causal reasoning KW - mixed data KW - kausale KI KW - kausale Entdeckung KW - kausale Schlussfolgerung KW - kausales Strukturlernen KW - Fertigung KW - gemischte Daten KW - nicht-parametrische bedingte Unabhängigkeitstests Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-635820 ER -