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The Kohat fold and thrust belt in Pakistan shows a significantly different structural style due to the structural evolution on the double décollement compared to the rest of the Subhimalaya. In order to better understand the spatio-temporal structural evolution of the Kohat fold and thrust belt, we combine balanced cross sections with apatite (U?Th-Sm)/He (AHe) and apatite fission track (AFT) dating. The AHe and AFT ages appear to be totally reset, allowing us to date exhumation above structural ramps. The results suggest that deformation began on the frontal Surghar thrust at-15 Ma, predating or coeval with the development of the Main Boundary thrust at-12 Ma. Deformation propagated southward from the Main Boundary thrust on double de?collements between 10 Ma and 2 Ma, resulting in a disharmonic structural style inside the Kohat fold and thrust belt. Thermal modeling of the thermochronologic data suggest that samples inside Kohat fold and thrust belt experienced cooling due to formation of the duplexes; this deformation facilitated tectonic thickening of the wedge and erosion of the Miocene to Pliocene foreland strata. The spatial distribution of AHe and AFT ages in combination with the structural forward model suggest that, in the Kohat fold and thrust belt, the wedge deformed in-sequence as a supercritical wedge (-15-12 Ma), then readjusted by out-sequence deformation (-12-0 Ma) within the Kohat fold and thrust belt into a sub-critical wedge.
Antisemitismus
(2023)
Ist Antisemitismus ein Rassismus, der sich gegen Jüdinnen und Juden richtet? Nein, er ist ein eigenständiges Phänomen, zu dessen Besonderheiten gehört, dass er häufig mit einem System der Weltverschwörung verknüpft wird. Doch es gibt rassistischen Antisemitismus. Auch die Shoah basierte auf einer rassistischen Einteilung von Menschen.
CrashNet
(2021)
Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder-decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.
Online hate speech has become a widespread problem in the daily life of adolescents. Despite growing societal and academic interest in this online risk, not much is known about the relationship between online hate speech victimization (OHSV) and adolescents' mental well-being.
In addition, potential factors influencing the magnitude of this relationship remain unclear. To address these gaps in the literature, this study investigated the relationship between OHSV and depressive symptoms and the buffering effects of resilience in this relationship. The sample consists of 1,632 adolescents (49.1% girls) between 12 and 18 years old (M-age = 13.83, SDage = 1.23), recruited from nine schools across Spain.
Self-report questionnaires were administered to assess OHSV, depressive symptoms, and resilience. Regression analyses revealed that OHSV was positively linked to depressive symptoms.
In addition, victims of online hate speech were less likely to report depressive symptoms when they reported average or high levels of resilience (i.e., social competence, personal competence, structured style, social resources, and family cohesion) compared with those with low levels of resilience.
Our findings highlight the need for the development of intervention programs and the relevance of focusing on internal and external developmental assets to mitigate negative outcomes for victims of online hate speech.
Deciphering chemical mediators regulating specialized metabolism in a symbiotic cyanobacterium
(2022)
Genomes of cyanobacteria feature a variety of cryptic biosynthetic pathways for complex natural products, but the peculiarities limiting the discovery and exploitation of the metabolic dark matter are not well understood. Here we describe the discovery of two cell density-dependent chemical mediators, nostoclide and nostovalerolactone, in the symbiotic model strain Nostoc punctiforme, and demonstrate their pronounced impact on the regulation of specialized metabolism. Through transcriptional, bioinformatic and labeling studies we assigned two adjacent biosynthetic gene clusters to the biosynthesis of the two polyketide mediators. Our findings provide insight into the orchestration of specialized metabolite production and give lessons for the genomic mining and high-titer production of cyanobacterial bioactive compounds.
Surface melting of the Greenland Ice Sheet contributes a large amount to current and future sea level rise. Increased surface melt may lower the reflectivity of the ice sheet surface and thereby increase melt rates: the so-called melt-albedo feedback describes this self-sustaining increase in surface melting. In order to test the effect of the melt-albedo feedback in a prognostic ice sheet model, we implement dEBM-simple, a simplified version of the diurnal Energy Balance Model dEBM, in the Parallel Ice Sheet Model (PISM). The implementation includes a simple representation of the melt-albedo feedback and can thereby replace the positive-degree-day melt scheme. Using PISM-dEBM-simple, we find that this feedback increases ice loss through surface warming by 60 % until 2300 for the high-emission scenario RCP8.5 when compared to a scenario in which the albedo remains constant at its present-day values. With an increase of 90 % compared to a fixed-albedo scenario, the effect is more pronounced for lower surface warming under RCP2.6. Furthermore, assuming an immediate darkening of the ice surface over all summer months, we estimate an upper bound for this effect to be 70 % in the RCP8.5 scenario and a more than 4-fold increase under RCP2.6. With dEBM-simple implemented in PISM, we find that the melt-albedo feedback is an essential contributor to mass loss in dynamic simulations of the Greenland Ice Sheet under future warming.
Knowing the source and runout of debris flows can help in planning strategies aimed at mitigating these hazards. Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo River basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random-walk and Perla et al.'s (PCM) two-parameter friction model components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial crossvalidation, including exploring the model's sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance, larger samples sizes (i.e. >= 80) had higher model performance and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using the open-source R software and the System for Automated Geoscientific Analyses geographic information system (SAGA-GIS) will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.