Soil indigenous microbes interact with maize plants in high-arsenic soils to limit the translocation of inorganic arsenic species to maize upper tissues

Arsenic (As) is a toxic metalloid that can enter the food chain through uptake by plants from soils followed by production of plant-based food. While soil–plant transfer of As in crops, especially rice, is relatively well studied, the role of soil microbes in As translocation in maize is not well understood. We performed a greenhouse pot experiment with maize plants grown at different soil As levels to study the role of soil microbes on uptake of different As species by maize. Three soil treatments with varying disturbance of the soil microbes (native soil, sterilized soil, and sterilized soil reconditioned with soil indigenous microbes) were intersected with three levels of As in soils (0, 100 and 200 mg kg⁻¹ spiked As, aged for 8 weeks) in a greenhouse experiment, where maize was grown for 5 months. Compared to uncontaminated soils, maize in high-As soils tended to accumulate more As in stems and less in leaves and grains, proportionally. Arsenic levels in stems were increased in sterilized soils due to the disturbance of the microbiome. The sterilization effects caused a phosphorus and manganese deficiency, leading to a higher As uptake in plants, that increased with rising As levels and resulted in a lower total dry biomass of the plants. In summary, this study highlights the role of soil indigenous microbes in limiting the uptake and translocation of inorganic As into maize. Compared to rice, cultivating maize plants in high-As soils is recommended

Characterization of a mock up nuclear waste package using energy resolved MeV neutron analysis

Reliable radiographic methods for characterizing nuclear waste packages non-destructively (without the need to open containers) have the potential to significantly contribute to safe handling and future disposal options, particularly for legacy waste of unknown content. Due to required shielding of waste containers and the need to characterize materials consisting of light elements, X-ray methods are not suitable. Here, energy-resolved MeV neutron radiography is demonstrated as a first-of-its-kind application for non-destructive and remote examination of mock up nuclear waste packages from a safe position using time-of-flight techniques enabled by a novel event-mode imaging detector system. Energy-resolved neutron transmission spectra were measured spatially, permitting the detection of analogue materials to actual nuclear waste such as water, melamine, and ion exchange resin within a 2.54 cm wall thickness steel pipe. The results demonstrate the capability to locate the materials through this wall thickness by radiography and tomographic reconstruction, revealing detailed 3D distributions and structural anomalies. The method effectively detects residual water in ion exchange resin, highlighting its sensitivity to moisture content, a crucial parameter for nuclear waste characterization. Monte Carlo simulations are in agreement with the experimental findings, providing a pathway to simulate waste forms more difficult to tackle experimentally. This work paves the way to apply sub-nanosecond intense MeV neutron sources, such as laser-driven neutron sources under development, to nuclear waste characterization

Jointly exploring client drift and catastrophic forgetting in dynamic learning

Federated and Continual Learning have emerged as promising paradigms for the privacy-aware use of Deep Learning in dynamic environments by addressing spatial and temporal constraints on data availability. However, Client Drift and Catastrophic Forgetting are fundamental obstacles to ensuring robust performance. Existing work only addresses these problems separately, neglecting the fact that the root cause behind them, namely an unexpected shift in the data distribution, is connected. We propose a unified analysis framework for building a controlled test environment where we can jointly model spatial and temporal shifts, more closely emulating real dynamic settings. By generating a 3D landscape of the combined performance impact, we show that a moderate combination of both shifts can even improve the performance of the resulting model ("Generalization Bump"). We apply a simple and commonly used method from continual learning in the federated setting and observe this reoccurring phenomenon

Mitigating Security Risks by Understanding Security-related API-misuses and Advancing Detection of Misuses

Context: Software is ubiquitous today, and the benefits of widespread software usage come at a risk. Attacks on critical infrastructure and the number of reported vulnerabilities increase. To ensure secure software, the mitigation of potential vulnerabilities is essential. Static analyses, application programming interfaces (APIs) that focus on usability, and memory-safe languages are solutions to achieve this aim. However, these approaches are not yet effective enough, as the number of vulnerabilities constantly increases. To examine the effectiveness of approaches and enhance the detection of critical API usages, we focus on cryptographic and Unsafe APIs. So far, the precision and recall of analyses applied on public projects are insufficiently discussed, as well as other limitations that can impact the reports. Further, the effectiveness of “usable” APIs is only evaluated in user studies, and approaches to identify Unsafe usages provide no further information, such as the underlying motivation for the usage. Method: We introduced two novel benchmarks for cryptographic API misuses and conducted two empirical studies to investigate the capabilities and limitations of existing cryptographic API misuse detectors. Further, we conducted an empirical study on the effectiveness of the API design and another to understand to which extent Go applications use the Unsafe API. In addition, we built a theoretical model of vulnerabilities and introduced several novel tools and a classifier. Results: The evaluation upon our benchmarks provided insights into the capabilities of the detectors and presented the importance of test cases beyond synthetic instances. Our first two empirical studies revealed that not all reported API misuses should be fixed, i. e. due to the usage context, and that every second project has API misuses that depend on each other. Furthermore, our third empirical study indicated that the API design positively impacts the number of observed misuses. Regarding the Unsafe API, our analysis revealed that the Unsafe API is used frequently and can cause vulnerabilities. We reported vulnerable usages and over 70 % of these are fixed by the maintainers. In addition, our classifier can effectively predict for what and why Unsafe is used. Conclusion: Each result contributes novel insights and shows the importance of understanding usages of security-critical APIs in public projects. Overall, this thesis examined the effectiveness of approaches that prevent (mis)uses of security-critical APIs and enhance their detection to mitigate vulnerabilities. To conclude, this thesis provides the foundations for assessing detectors and advances the detection and results in actual fixes of insecure API usages

Verse within Prose : Annotating and Classifying Narrative Functions of Embedded Poems in Chinese Qing (1644-1912) Vernacular Fiction

What narrative functions do poems serve when interwoven with vernacular prose? This article takes what has often been labeled as "embedded poems" or "parasitic poems" in late imperial Chinese fiction as the primary subject of study. We examine the narrative roles of these poems within a selected corpus of Qing dynasty fiction, specifically investigating if an approach that combines human annotation with large language models can aptly capture and automatically classify their narrative functions. Through two rounds of iterative annotation and large language model testing, we demonstrate both the potential and limitations of this approach. As one of the few studies that applies large language models to Chinese literary research, our work lays the groundwork for future large-scale investigations into the dynamics between verse and prose in classical Chinese literature, incorporating both canonical works and beyond

Opening Worlds: Narrative Beginnings and the Role of Setting

Beginnings are central to narrative structure, shaping the reader's engagement with the storyworld. This study examines the role of setting in narrative openings, using a large-scale dataset of German-language fiction and non-fiction. Drawing on Herman's concept of "worldmaking" and Hoffmann's phenomenological model of space, we classify settings into four types: Aktionsraum (action space), gestimmter Raum (space reflecting mood and atmosphere), Anschauungsraum (field of vision), and "descriptive space". Using a multiclass text classification model, we analyze their distribution across narrative time, historical time, and genre focusing specifically on their prominence in story openings. Our findings show that openings tend to prioritize establishing what the depicted world feels and looks like, emphasizing affect and visual description before shifting toward movement and the mobilization of setting through dynamic character interaction. Comparative and historical analyses reveal that these trends are unique to fiction and have increased over time. By leveraging computational models, we provide an empirical foundation for understanding how narrative beginnings structure fictional worlds

Accuracy Evaluation of 3D Pose Reconstruction Algorithms Through Stereo Camera Information Fusion for Physical Exercises with MediaPipe Pose

In recent years, significant research has been conducted on video-based human pose estimation (HPE). While monocular two-dimensional (2D) HPE has been shown to achieve high performance, monocular three-dimensional (3D) HPE poses a more challenging problem. However, since human motion happens in a 3D space, 3D HPE offers a more accurate representation of the human, granting increased usability for complex tasks like analysis of physical exercise. We propose a method based on MediaPipe Pose, 2D HPE on stereo cameras and a fusion algorithm without prior stereo calibration to reconstruct 3D poses, combining the advantages of high accuracy in 2D HPE with the increased usability of 3D coordinates. We evaluate this method on a self-recorded database focused on physical exercise to research what accuracy can be achieved and whether this accuracy is sufficient to recognize errors in exercise performance. We find that our method achieves significantly improved performance compared to monocular 3D HPE (median RMSE of 30.1 compared to 56.3, p-value below 10−6) and can show that the performance is sufficient for error recognition

Life Cycle Assessment of an emerging overhead line hybrid truck in short-haul pilot operation

In order to investigate the contribution of electromobility to climate change, greenhouse gas (GHG) emissions of electric vehicles need to be studied in a life cycle (LC) perspective using the method of Life Cycle Assessment (LCA). Most LCA studies focus on passenger cars, but a high share of GHG emissions in the mobility sector comes from freight transport. Here we present a LCA of an emerging overhead line hybrid truck (OH truck) based on a unique database of real data from an eHighway field trial in Germany. We develop a LC transport model that includes vehicle, infrastructure and use phase of regional freight transport. The assessment of the present pilot operation is used as a reference for the evaluation of further developments using a scenario approach. As a first example, we apply the LC transport model to a scenario with realistic short-term improvements from the field trial. The comparison to freight transport with conventional trucks shows GHG savings of about 22%. From the detailed LC transport model outcomes, we derive the contributions of the vehicle and infrastructure components to GHG emissions as well as further environmental impact categories and we evaluate the operational phase, e.g. by identifying the break-even point for GHG savings in dependence of the utilization frequency. The findings can be used to identify further improvements of the OH truck technology. At the same time, our model enables a thorough investigation of future scenarios, allowing a robust comparison of possible alternatives for decarbonizing freight transport

The effect of propagation saw test geometries on critical cut length

For a slab avalanche to release, a crack in a weak snow layer beneath a cohesive snow slab has to initiate and propagate. Information on crack propagation is essential for assessing avalanche triggering potential. In the field, this information can be gathered with the propagation saw test (PST), a field test that provides valuable data on crack propagation propensity. The first PSTs were performed about 20 years ago and standards have since been established. However, there are still differences in how the PST is performed. Standards in North America require the column ends to be cut vertically, whereas in Europe they are typically cut normal to the slope. In this study, we investigate the effect of these different column geometries on the critical cut length. To this end, we conducted 27 pairs of PST experiments, each pair consisting of one PST with slope-normal cut ends and one PST with vertical-cut ends. Our experiments showed that PSTs with normal cut ends have up to 50 % shorter critical cut lengths, and the difference predominantly depends on the slope angle and slab thickness. We developed two load-based models to convert critical cut lengths between the test geometries: (i) a uniform slab model that treats the slab as one uniform layer and (ii) a layered model that accounts for stratification. For validation, we compare these models with a modern fracture mechanical model. For the rather uniform slabs of our experiments, both load-based models were in excellent agreement with measured data. For slabs with an artificial layering, the uniform load–model predictions reveal deviations from the fracture mechanical model, whereas the layered model was still in excellent agreement. This study reveals the influence that the geometry of field tests and the slope angle of the field site have on test results. It also shows that only accurately prepared field tests can be reliable and therefore meaningful. However, we provide models to correct for imprecise field test geometry effects on the critical cut length

Multicaloric effect in FeRh, exploiting the thermal hysteresis in a multi-stimuli cycle combining pulsed magnetic field and uniaxial load

Large magnetocaloric effects can be observed in materials with first-order magneto-structural phase transition. However, materials with large thermal hysteresis show a reduced effect in moderate fields (⁠~2 T) because the external field is insufficient to induce a fully reversible transformation. The hysteresis can be overcome or even exploited by applying a second external stimulus. A multi-stimuli test bench has been built to demonstrate the multicaloric effect in FeRh alloy using a pulsed magnetic field up to 9 T and a uniaxial stress of up to 700 MPa. A cyclic multicaloric effect of ±2.5 K could be observed for a sequential application of a pulsed field of 3 T and a uniaxial stress of 700 MPa. The interplay among external field strength, thermal hysteresis, and the transition width enables the use of pulsed magnetic fields and allows a decoupling of the applied magnetic field and the heat transfer process in the multi-stimuli cycle