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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.
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.
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.
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.
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.
The politics of fear
(2022)
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.
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.