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Floodplain forests drive fruit-eating fish diversity at the Amazon Basin-scale
Get PDFUnlike most rivers globally, nearly all lowland Amazonian rivers have unregulated flow, supporting seasonally flooded floodplain forests. Floodplain forests harbor a unique tree species assemblage adapted to flooding and specialized fauna, including fruit-eating fish that migrate seasonally into floodplains, favoring expansive floodplain areas. Frugivorous fish are forest-dependent fauna critical to forest regeneration via seed dispersal and support commercial and artisanal fisheries. We implemented linear mixed effects models to investigate drivers of species richness among specialized frugivorous fishes across the ~6,000,000 km Amazon Basin, analyzing 29 species from 9 families (10,058 occurrences). Floodplain predictors per subbasin included floodplain forest extent, tree species richness (309,540 occurrences for 2,506 species), water biogeochemistry, flood duration, and elevation, with river order controlling for longitudinal positioning along the river network. We observed heterogeneous patterns of frugivorous fish species richness, which were positively correlated with floodplain forest extent, tree species richness, and flood duration. The natural hydrological regime facilitates fish access to flooded forests and controls fruit production. Thus, the ability of Amazonian floodplain ecosystems to support frugivorous fish assemblages hinges on extensive and diverse seasonally flooded forests. Given the low functional redundancy in fish seed dispersal networks, diverse frugivorous fish assemblages disperse and maintain diverse forests; vice versa, diverse forests maintain more fish species, underscoring the critically important taxonomic interdependencies that embody Amazonian ecosystems. Effective management strategies must acknowledge that access to diverse and hydrologically functional floodplain forests is essential to ensure the long-term survival of frugivorous fish and, in turn, the long-term sustainability of floodplain forests
Development and test of a dual-pathway model of personal and community factors driving new energy technology adoption - The case of V2G in three European countries
Get PDFMethods for Enhancing Industrial Applications with Explainable Artificial Intelligence
Get PDFWith the growing adoption of Artificial Intelligence (AI) and the successful application of deep learning methods in various domains, AI, particularly deep learning, is increasingly influencing people’s lives. Despite the continuous performance advancements and success of AI models, many manufacturers still hesitate to deploy AI on a larger scale, as failures due to a black-box AI model in production can cause significant financial damage and potentially harm workers. The emerging topic of Explainable Artificial Intelligence (XAI) offers approaches and algorithms that introduce transparency into black-box models by producing explanations for an AI System’s inner workings and decisions. Specifically, in industrial settings, where complex problems and decision-making processes are widespread, enabling transparent automation and decision support is crucial. However, while research in XAI is trending, applying XAI in industrial settings is challenging. For many data types (e.g., images or tabular data), XAI methods are well-studied. Nevertheless, support for time series, ubiquitous in industrial settings, is missing. Furthermore, to use any XAI method in
deployment, understanding the explainers’ quality, strengths, and weaknesses is vital to prevent ambiguous and incorrect explanations. Well-performing XAI methods can help users understand the reasons behind a deep learner’s prediction and enable the recognition of spurious correlations learned by a deep learner or missing information in the collected data. However, reverting incorrect predictions and learned patterns is not in the scope of XAI. Nevertheless, reverting such is especially important in industrial settings, where only a limited amount of (often) noisy data is available. Explanations and explanation correction can potentially provide the opportunity to include domain knowledge of users and enable a deep learner to infer the missing context and close this gap. The main contributions of this thesis are to facilitate and enable a more widespread use of XAI in industrial settings by providing methods, frameworks, and in-depth evaluations addressing the application obstacles. Individual contributions of this thesis are summarized as follows:
1. An extensible framework enabling unified access to time series explainers, including a new counterfactual explainer considering various time series transformation mechanisms and thereby enabling plausible explanations on uni- and multivariate time series.
2. A framework and in-depth evaluation of time series explainers, a metric measuring causal coherence of counterfactual approaches and corresponding benchmark datasets.
3. A methodology combining continuous, interactive, and explanatory interactive machine learning to enable human supervised transfer learning in industrial contexts.
The contributions are evaluated in accordance with predefined requirements and to state-of-the-art setups. The outcome (i) allows facilitated access to time series explainers and the generation of more plausible counterfactual explanations, helping to bridge the gap between complex models and contrastive explanations; (ii) indicates the need for additional research specifically for multivariate time series explainers and the generation of causal coherent counterfactual explanation; and (iii) shows that explanation based AI-expert alignment enables continuous learning under human supervision
Modeling resistive-inductive evolution of currents in Wendelstein 7-X
Get PDFThis research investigates the temporal evolution of the toroidal plasma current in the Wendelstein 7-X (W7-X) stellarator under different heating, fueling, and current drive
scenarios. The THRIFT code has been modernized and its predictions of the evolution of the toroidal current have been compared against experimentally measured currents in W7-X. Good agreement is found with respect to the characteristic timescale between experimentally measured and simulated toroidal currents. The total bootstrap current is under-predicted owing to the applicability of the BOOTSJ model for the plasma collisionalities in question. Edge plasma resistivity is found to play an important role in the asymptotic behavior of the evolution of the current, indicating a possible limitation of the minimum plasma temperature when applying this model. Simulations of ECCD and heating power steps show THRIFT is capable of capturing the dynamical evolution of the current in response to changes in current sources. Future uses of THRIFT include validating and benchmarking other non-inductive current models
Thermalization of a flexible microwave stripline measured by a superconducting qubit
Get PDFWith the demand for scalable cryogenic microwave circuitry continuously rising, recently developed flexible microwave striplines offer the tantalizing perspective of increasing the cabling density by an order of magnitude without thermally overloading the cryostat. We use a superconducting quantum circuit to test the thermalization of input flex cables with integrated 60 dB of attenuation distributed at various temperature stages. From the measured decoherence rate of a superconducting fluxonium qubit, we estimate a residual population of the readout resonator of 2.2 0.9 x 10 photons and we measure a 0.28 ms thermalization time for the flexible stripline attenuators. Furthermore, we confirm that the qubit reaches an effective temperature of 26:4 mK, close to the base temperature of the cryostat, practically the same as when using a conventional semi-rigid coaxial cable setup
Impact of social media on triggering nonsuicidal self‑injury in adolescents: a comparative ambulatory assessment study
Get PDFBackground
Nonsuicidal self-injury (NSSI) is a prevalent and concerning behavior among adolescents, often triggered by negative interpersonal events. As social media is essential in the daily life of adolescents, gaining a better understanding of the impact of negative online events on NSSI urges and behaviors, distinct from that of real-life events, is warranted.
Methods
We recruited 25 adolescents with a history of NSSI and 25 healthy controls. Participants reported on their stress, affect, and NSSI urges four times daily over seven days using ambulatory assessment. We examined the immediate effects of negative events in real-life and on social media on these psychological outcomes.
Results
In adolescents who engage in NSSI, negative events on social media were positively associated with perceived stress, negative affect, and NSSI urges to a greater extent than real-life negative events. However, NSSI events during the sampling period were mostly triggered by real-life events. While the frequency of social media use was generally similar between groups, those with NSSI reported experiencing more negative events on social media.
Conclusions
Our findings highlight the significant impact of social media on the mental health of adolescents who engage in NSSI, possibly exacerbating stress and negative affect more than real life events. These results underscore the need for targeted interventions addressing online interactions to mitigate NSSI behaviors and improve adolescent mental health
Search for charged-lepton flavor violation in the production and decay of top quarks using trilepton final states in proton-proton collisions at √s = 13 TeV
Get PDFA search is performed for charged-lepton flavor violating processes in top quark () production and decay. The data were collected by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 138 fb−1. The selected events are required to contain one opposite-sign electron-muon pair, a third charged lepton (electron or muon), and at least one jet of which no more than one is associated with a bottom quark. Boosted decision trees are used to distinguish signal from background, exploiting differences in the kinematics of the final states particles. The data are consistent with the standard model expectation. Upper limits at 95% confidence level are placed in the context of effective field theory on the Wilson coefficients, which range between 0.024–0.424 TeV−2 depending on the flavor of the associated light quark and the Lorentz structure of the interaction. These limits are converted to upper limits on branching fractions involving up (charm) quarks, → ( →), of 0.032(0.498) ×10−6, 0.022(0.369) ×10−6, and 0.012(0.216) ×10−6 for tensorlike, vectorlike, and scalarlike interactions, respectively
Potential for damage to fruits during transport through cross-section constrictions
Get PDFFruit preparations are used in various forms in the food industry. For example, they are used as an ingredient
in dairy products such as yogurt with added fruit. The dispersed fruit pieces can be described as soft
particles with viscoelastic material behavior. The continuous phase is represented by fluids with complex flow
behavior depending on the formulation. Characterization has shown that the fluids exhibit a yield stress and
pseudoplastic behavior, which can be described by the Herschel–Bulkley model. Since damage to fruit pieces
is undesirable in industrial transport processes, the potential for damage to fruit pieces during transport of
pipes in cross-sectional constrictions is analyzed. The analysis is performed numerically using the homogenized
lattice Boltzmann method and validated by an experiment on industrial fruit preparations at pilot plant scale.
The results show a strong dependence of the damage potential on the (local) Metzner–Reed Reynolds number
Monodisperse macromolecules via the Passerini reaction
Get PDFIn nature, DNA, peptides and proteins are high-precision macromolecules with perfectly defined primary structures. DNA, for instance, carries the genetic information of living organisms and provides the information necessary for the generation of essential proteins for biochemical processes, such as self-replication, biocatalysis, selfassembly and molecular recognition, which play an important role in living organisms. Inspired by the highly defined structures of these biomolecules, scientists have explored a new field of research: the synthesis and characterization of sequence-defined macromolecules. In 2013, sequence-controlled polymers were first defined in a review by Lutz, Ouchi and Sawamoto as follows: “…macromolecules in which monomer units of different chemical nature are arranged in an ordered fashion.” Initially, the synthesis of these defined and structurally complex macromolecules was the focus of attention, and scientists have researched and developed different synthetic routes and improved the degree of precision and control. Three different strategies are mainly used: the iterative stepwise method, the two-way (i.e. bidirectional) growth method and the iterative exponential growth method. The iterative stepwise method allows each monomer unit to be controlled as much as possible. The exponential growth strategy (IEG) and the two-way growth strategy can more quickly construct uniform macromolecules, but are limited in terms of the degree of definition. Different synthetic strategies can be carried out in solution, in the solid phase or in the fluorine phase. Multicomponent reactions are ideal for synthesizing sequence-defined oligomers due to their high yields and selectivity. Furthermore, due to their highly modular nature, it is straightforward to introduce different functionalities in the side chains or main chains to increase the structural diversity of the oligomers. A common multicomponent reaction for synthesizing sequence-defined oligomers is the Passerini three-component reaction. It can be used alone or in combination with other reactions to provide an efficient route for synthesizing different structures and sequences. After reporting many different sequence-defined oligomer synthesis methods, some research groups have used tandem mass spectrometry, single mass spectrometry and other analytical methods to read out the sequence, proving its application in the field of data storage