Robert Bosch (Netherlands)

Background Despite growing scientific evidence and health guidelines, the global health paradox persists, with rising lifestyle-related diseases and escalating healthcare costs exposing the inadequacy of current efforts. Objective Three multidisciplinary congresses were held to generate evidence-based conclusions aimed at addressing the global health paradox. Methods A total of 58 experts from 62 entities participated in the international research and knowledge-exchange panels. Experts reviewed the latest findings to develop practical strategies and identify key research and policy priorities, focusing on the Healthy Eating & Active Living (HEAL) approach. Results, conclusion, and relevance The expert consortium endorsed a 33 evidence-based consensual-statement policy roadmap for addressing global health challenges, emphasizing that the HEAL approach can significantly contribute to the “Prevention First” appeal and broad ethical, social, ecological, and economic advantages, and ultimately supporting policy change.

Polyamide 6,6 (PA66) is a key engineering polymer, whose unique mechanical properties arise from strong interchain hydrogen bonding. However, its hygroscopic nature makes it highly sensitive to water uptake, which markedly alters its thermomechanical behavior. Contrary to traditional experimental approaches, this study uses atomistic molecular dynamics (MD) simulations to investigate the role of water in modifying the glass transition temperature (Tg) and the viscoelastic response of amorphous PA66. Simulations capture a nonmonotonic dependence of Tg on water content. At low water concentrations, isolated water molecules bind to amide groups and restrict chain mobility, while beyond ∼2.5 wt %, water clustering disrupts the hydrogen-bond network and causes a pronounced Tg depression. Analysis of amide-group fluctuations reveals a master correlation between local segmental dynamics and bulk density, verifying the known temperature-humidity equivalence in terms of density variation. The computed Young's modulus exhibits systematic softening with increasing temperature and water content, consistent with experimental trends, albeit a more pronounced impact of water at low temperatures. Time-temperature superposition behavior is observed for both dry and hydrated systems. This work provides molecular-scale information on the hygromechanical coupling in PA66 and demonstrates the ability of MD simulations to predict water-induced transitions that govern the macroscopic behavior of polyamides.

The development in automotive industries towards electrification and automation raises special attention to Safety-Related Availability (SaRA) in onboard power supply systems (powernets), where reliable battery diagnosis becomes the crucial enabler. The Safety of the Intended Functionality (SOTIF, ISO 21448) complements traditional Functional Safety (FuSa, ISO 26262) by addressing functional insufficiencies of Advanced Driver Assistance Systems (ADAS). However, applying SOTIF to other use cases, such as the battery diagnosis, remains underexplored.This work presents a SOTIF-oriented probabilistic validation framework that systematically integrates uncertainty quantification (UQ) and reliability analysis (RA) techniques. This framework adopts a Bayesian neural network (BNN) with heteroscedastic noise modeling as the core modeling tools. We jointly quantify epistemic and aleatory uncertainties to guide risk estimation, active test selection, and hazardous scenario discovery. To improve explainability, we applied SHAP-based sensitivity analysis with additional care given to input dependencies.The framework is validated using battery test data generated from a high-fidelity electrochemical battery model, which serves as a representative proxy for real-world batteries. This work aims to provide a practical extension to SOTIF for real-world deployment of complex safety functions and encourage further research in this direction.

Abstract We present a highly accurate measurement setup and related methods for the efficient characterization of inductive rotary position sensors. The test bench is designed to measure analog sine/cosine output signals of the device under test (DUT) under nominal conditions as well as deliberate misalignment, such as offset and tilt tolerances- enabling analysis of DUT performance w.r.t. the angle deviation, the harmonic decomposition, and tolerance sensitivities. The uncertainty of measurements with the proposed test bench setup is quantified with a repeatability study and an effort is made to determine the systematic deviation. By means of laser-guided positioning, the printed circuit board (PCB) of the DUT is automatically positioned relative to the rotary axis (runout ±5 μm) in airgap and radial direction (repeatability 5 μm, total deviation 30 μm). The setup is capable of high-speed measurements at a rotation speed of at least 2000 min−1. The proposed measurement setup is flexible to accommodate a wide range of rotary position sensors and enables fast (less than 3 s per tolerance scenario) and automated characterization of the DUT angle deviation with excellent accuracy better than 10′′ (0.003∘).

Gestures enable non-verbal human-robot communication, especially in noisy environments like agile production. Traditional deep learning-based gesture recognition relies on task-specific architectures using images, videos, or skeletal pose estimates as input. Meanwhile, Vision Foundation Models (VFMs) and Vision Language Models (VLMs) with their strong generalization abilities offer potential to reduce system complexity by replacing dedicated task-specific modules. This study investigates adapting such models for dynamic, full-body gesture recognition, comparing V-JEPA (a state-of-the-art VFM), Gemini Flash 2.0 (a multimodal VLM), and HD-GCN (a top-performing skeleton-based approach). We introduce NUGGET, a dataset tailored for human-robot communication in intralogistics environments, to evaluate the different gesture recognition approaches. In our experiments, HD-GCN achieves best performance, but V-JEPA comes close with a simple, task-specific classification head—thus paving a possible way towards reducing system complexity, by using it as a shared multi-task model. In contrast, Gemini struggles to differentiate gestures based solely on textual descriptions in the zero-shot setting, highlighting the need of further research on suitable input representations for gestures.

Thermal Interface Materials are used to transfer heat from a semiconductor to a heatsink. They are applied along a dispense path onto the semiconductor and spread over its entire surface once the heatsink is joined. To plan this application path, design engineers typically perform an iterative trial-and-error procedure of elaborate simulations and manual experiments. State-of-the-art path planning approaches are mainly focused on applications with a constant path width. They cannot be transferred to Thermal Interface Materials since the resulting area coverage depends on their complex flow behavior. We propose the fully automated optimization approach TIMtrace , which clearly outperforms the current manual path planning and respects all relevant manufacturing constraints. Generally, the design of an optimization problem highly influences its convergence properties. Our chosen path parameterization keeps the dimensionality of the independent variables reasonably low. We analyze different loss function components and respective weighting strategies with regard to both optimization convergence and result quality. TIMtrace enables an automated planning of dispense paths that do not contain voids, do not violate taboo zones and can be transferred easily to automated dispensing machines. An optimum dispense path helps to increase the reliability of the thermal interface and to make manufacturing more sustainable by reducing material waste. We show results on multiple real products from automotive series production, including experimental validation on actual series manufacturing equipment. • Novel approach for Coverage Path Planning problem. • Consistently outperform state-of-the-art approach for Thermal Interface Material. • Proposed method reduces: engineering efforts (by 80 %), material waste, cycle-time. • Results are validated on series manufacturing equipment using two actual products.

Prior research yielded many techniques to mitigate software compromise for low-end Internet of Things (IoT) devices. Some of them detect software modifications via remote attestation and similar services, while others preventatively ensure software (static) integrity. However, achieving runtime (dynamic) security, e.g., control-flow integrity (CFI), remains a challenge. Control-flow attestation (CFA) is one approach that minimizes the burden on devices. However, CFA is not a real-time counter-measure against runtime attacks since it requires communication with a verifying entity. This poses significant risks if safety- or time-critical tasks have memory vulnerabilities. To address this issue, we construct EILID - a hybrid architecture that ensures software execution integrity by actively monitoring control-flow violations on low-end devices. EILID is built atop CASU [1], a prevention-based (i.e., active) hybrid Root-of-Trust (RoT) that guarantees software immutability. EI LID achieves fine-grained backward-edge and function-level forward-edge CF I via semi-automatic code instrumentation and a secure shadow stack.