Daimler (Germany)

The effect of monotonic and cyclic tensile loadings on the magnetic properties of a non-oriented electrical steel (NOES) and grain-oriented electrical steel (GOES) was studied using a single strip tester to measure the magnetic properties of both materials after mechanical loading. In tensile tests, specimens were stressed up to different load levels below the yield stress and then unloaded. For NOES, only the highest investigated load level, with a maximum stress (σ max ) of 350 MPa, produced a measurable shift in the knee point of the magnetization curve. This was attributed to microyielding and the respective formation of heterogeneous dislocation structures, that generated long-range internal stresses. Cyclic loading was performed under tension-tension conditions with R σ = 0.1 at σ max = 200, 320, and 350 MPa, followed by magnetic single strip tester measurements at fatigue states of 0.1%, 1%, 10%, 50%, and 100% of the fatigue life. At σ max = 350 MPa, ratcheting was detected after 1% of the expected lifetime, whereas no ratcheting occurred at lower stresses. Nevertheless, magnetic degradation was observed not only after cyclic loading at 350 MPa, but also at 320 MPa, as indicated by increased field demand of the magnetic field strength and higher specific losses. This is attributed to dislocation accumulation and the associated internal stresses. Additionally, domain imaging from MOKE microscopy, before and after cyclic loading, revealed refined structures after 10% of the fatigue life at σ max = 350 MPa, while for the lower load levels, no clear trend in domain evolution was observed. GOES tested at a single load level of 40% of the yield stress showed no systematic influence of monotonic or cyclic loading on magnetic properties could be identified, mainly due to scatter arising from large grain size and small specimen geometry. • Magnetic properties were measured after monotonic and cyclic mechanical loading of non-oriented and grain-oriented electrical steels. • Non-oriented electrical steel exhibits magnetic degradation after monotonic and cyclic loading at 85% of the yield strength. • EBSD and MOKE measurements were combined to analyze microstructure and magnetic domain configurations before and after cyclic loading. • Cyclic loading leads to domain refinement and partial reorientation relative to the initial magnetic domain structure.

Abstract. Tropical dry forests play an important role in the global carbon cycle, but their responses to climate variability are still not well understood. Using a three-year period (April 2022 to March 2025) of eddy covariance measurements, we studied seasonal and annual controls on carbon balances in a Tumbesian dry forest in Southern Ecuador. During the study period, the forest functioned as a net carbon sink, with a net ecosystem exchange (NEE) of -285 gCm−2 year−1. The strongest carbon uptake occurred during the wet period (Feb–May) with 173.86 ± 66 gCm−2 month−1, while it was reduced to 39.80 ± 8.12 gCm−2 month−1 in the dry season (August–November). Light use efficiency (LUE) and water use efficiency (WUE) were used to characterize the functional controls on carbon fluxes at both seasonal and annual scales. WUE showed relatively stable water–carbon exchange, whereas LUE displayed clear seasonal variation, reflecting the strong influence of seasonal vegetation growth and greenness. Principle component analysis (PCA) was conducted to further analyze controlling mechanisms in carbon fluxes. Seasonal results showed that gross primary productivity (GPP) was mainly controlled by energy-related factors, while ecosystem respiration (Reco) was primarily driven by a moisture–temperature gradient. Annually, GPP was predominantly influenced by variations in vapor pressure deficit (VPD), soil temperature (Ts), and incoming radiation (Rg), reflecting a strong coupling between surface energy balance and atmospheric moisture demand. These drivers were further modulated by ENSO related climate variability, as reflected by shifts in their PCA loadings across years. Overall, the results reveal a decoupling between photosynthesis and respiration and show that tropical dry forests are highly vulnerable to increasing climate extremes, highlighting the need for improved representation of these processes in Earth system models.

<div class="section abstract"><div class="htmlview paragraph">In automotive engineering, understanding driving behavior is crucial for decision on specifications of future system designs. This study introduces an innovative approach to modeling driving behavior using Graph Attention Networks (GATs). By leveraging spatial relationships encoded in H3 indices, a graph-based model constructed, which captures dependencies between various vehicle operational parameters and their operational regions using H3 indices. The model utilizes CAN signal features such as speed, fuel efficiency, engine temperature, and categorical identifiers of vehicle type and sub-type. Additionally, regional indices are incorporated to enrich the contextual information. The GAT model processes these heterogeneous features, learning to identify patterns indicative of driving behavior. This approach offers several significant advantages. Firstly, it enhances the accuracy of driving behavior modeling by effectively capturing the complex spatial and operational dependencies inherent in vehicle data. The use of GATs allows for the dynamic weighting of different features, ensuring that the most relevant information is prioritized in the analysis. Secondly, the integration of regional indices provides a deeper contextual understanding, enabling the model to discern region-specific driving patterns that might otherwise be overlooked. Furthermore, this method facilitates the identification of abnormal behavioral trends, offering valuable insights for design engineers. By understanding region-based driving behavior, engineers can modify vehicle systems to better meet the needs of specific areas, leading to improved performance and user satisfaction. The combination of graph-based methods with attention mechanisms represents a significant advancement in vehicle performance monitoring, paving the way for a more comprehensive understanding of driving behavior across different regions.</div></div>

<div class="section abstract"><div class="htmlview paragraph">As electric trucks become more central to modern logistics, the need for smarter, more adaptive route planning is growing rapidly. This paper presents a key navigation feature for analyzing and recalibrating such optimized routes in real time. Integrating map features into the navigation mode improves user experience by offering real-time navigation and dynamic route adjustments based on traffic updates, road closures, vehicle coordinates and deviation in expected energy consumption. This study compares the performance of Server sent events (SSE), web sockets, and Application programming interface (API) polling methodologies, focusing on metrics such as data transmission efficiency, latency, resource utilization, scalability, and reliability. Our results demonstrate the advantages and limitations of each method, providing insights into their suitability for real-time route optimization in electric truck logistics. The results highlight the potential of SSE in achieving efficient and timely data updates, contributing to more effective route planning and resource management. Additionally, we discuss how API Polling, Web sockets, and SSE each make sense in different scenarios when creating a navigation system (drive mode), considering factors such as the frequency of updates, network conditions, and system architecture. This research underscores the importance of choosing the right communication protocol and integrating advanced map features to enhance the performance and reliability of logistics systems.</div></div>

<div class="section abstract"><div class="htmlview paragraph">With the fast development of computational analysis tools and capacities during the past ten years, complex and substantial computer-aided engineering (CAE) simulations are now economically possible. While the cost of crash tests has risen steadily, the fidelity and complexity, which numerical simulations could address, has multiplied keeping the cost of computational analysis more stable. The fundamental goal of CAE is to achieve significant reduction in the number of physical tests conducted during the product development process. However, validating the CAE model with physical tests is essential to ensure accuracy and reliability. Simulations performed using a validated CAE model could be used to make decisions like airbag deployment or high voltage shutdown without an actual physical test being conducted. This paper discusses validating an electric commercial vehicle CAE model during a side impact thus emphasizing the safety of a high voltage battery system. The critical parameters considered for fine-tuning the CAE model are discussed. The qualitative comparison looks at global displacement of the vehicle, local deformations and failure of the structural components. The acceleration time histories measured from different locations of the test vehicle are compared with the same obtained from the CAE simulation for quantitative correlation. While most of the quantitative comparison metrics are limited to time domain, this paper discusses a time, frequency and time-frequency domain comparison of the acceleration curves measured from test and CAE. The qualitative and quantitative comparison of the results shows the correlation level of the CAE simulation with the physical test outcome.</div></div>

Words like robotaxis and shuttles are likely to come up when talking about autonomous vehicles. If you look at the subject in more detail, it becomes clear that freightFreight transportation transportation, the engine and driver of the economy, also has enormous potential. This section describes how freight transportation works, where the potential lies and it explains how this can be exploited using the two very popular use cases ‘middle mile’ and ‘last mile delivery’.

A general risk assessment in traffic requires that three major factors are taken into account: Exposure to a dangerous situation, Controllability in this situation, and Severity of the damage caused if the situation is not controlled. Significant safety improvements have historically been achieved by focusing on Severity and Controllability through “passive safety” and “active safety” strategies, respectively. On top of these ever-improving strategies, Autonomous Driving offers the opportunity to further improve safety by reducing Exposure to dangerous situations through anticipatory, precautious, and cooperative behavior. The prerequisites for this behavior, known as “tactical safety”, are the main focus of this chapter. In particular, the need to take this risk component into account when driving fast on motorways is covered. Further, scenarios for the targeted introduction and verification of tactical safety behavior are also proposed.

Root anatomical traits regulate water transport and resource acquisition in forest ecosystems, yet their variation and coordination with aboveground traits remain poorly understood in tropical forests. We investigated patterns of interspecific variation in four root anatomical traits (vessel diameter, vessel density, vessel lumen fraction, and theoretical hydraulic conductivity) across 20 tree species representing contrasting growth strategies in a premontane tropical forest of southern Ecuador. Using 160 root samples from transport roots (4-8 mm diameter), we quantified anatomical traits through microscopy and calculated theoretical hydraulic conductivity. We analyzed correlations with wood density and leaf functional traits and performed principal component analyses to assess trait coordination. Species exhibited substantial variation in root anatomical traits, ranging from acquisitive strategies with large vessel diameters (67.6 μm in Ocotea sp.) and high hydraulic conductivity (73.9 kg m-1 MPa-1 s-1 in Alchornea glandulosa) to conservative strategies with high vessel density (>185 vessels/mm2 in Leonia crassa and Aspidosperma rigidum). However, 60% of species displayed intermediate trait values, suggesting compensatory strategies rather than extreme specialization. We documented strong negative correlations between vessel diameter and both vessel density (r = -0.74) and wood density (r = -0.51), pointing at hydraulic efficiency-safety trade-offs. Principal component analysis revealed that leaf traits operated orthogonally to root anatomical traits, indicating independent axes of functional variation rather than coordinated whole-plant strategies. These decoupling challenges traditional plant economics spectrum assumptions and evidence that plants optimize above- and belowground functions through independent evolutionary pathways. Our findings highlight the prevalence of intermediate hydraulic strategies in tropical tree communities and provide new insights into the functional organization of diverse forest ecosystems.

<div class="section abstract"><div class="htmlview paragraph">Tippers transporting loose bulk cargo during prolonged descents are subject to two critical operational challenges: cargo displacement and rear axle lifting. Uncontrolled cargo movement, often involving loose aggregates or soil, arises due to gravitational forces and insufficient restraint systems. This phenomenon can lead to cabin damage, loss of control, and hazardous discharge of materials onto roadways. Simultaneously, load imbalances during descent can cause rear axle lift, increasing stress on the front steering axle, resulting in tire slippage and compromised maneuverability.</div><div class="htmlview paragraph">This study proposes a dynamic control strategy that adjusts the tipper lift angle in real time to align with the descent angle of the road. By synchronizing the trailer bed angle with the slope of the terrain, the system minimizes cargo instability, maintains rear axle contact, and enhances braking performance, including engine and exhaust braking systems. Computational modelling is employed to assess the performance of this approach across varying road gradients, vehicle speeds, and terrain characteristics. The paper further outlines the development of an automated control system for real-time angle adjustment and its integration into the vehicle’s existing electrical architecture.</div></div>