Accurate and timely forecasts of road surface conditions are crucial for efficient winter maintenance, enhanced traffic safety, and the optimized use of de-icing agents. Road surface phenomena, in complex fields present challenges to traditional forecasting methods due to their nonlinear and localized nature. This study presents a machine learning framework predicting real-time road states (dry, wet, icy, snowy) across Bavaria, Germany. It integrates data from over 516 Road Weather Stations (RWS), thermal measurements from winter maintenance vehicles, and elevation data from the Open Elevation API. Data undergoes temporal alignment, spatial interpolation, and missing-value imputation. Decision Trees form the core model for interpretability and nonlinear pattern handling. Each RWS employs a localized model, while a generalized version covers unmonitored roads via spatial adjustments. With over 85% accuracy, the system facilitates dynamic winter maintenance and minimizes resource waste. Cyber-physical in smart mobility and transportation networks support improved real-time hazard responses. This approach shows how scalable infrastructure can be made resilient using machine learning.

This paper presents a low-complexity, open-source platform designed to empower small and medium-sized enterprises (SMEs) in the premium business to business (B2B) packaging industry with advanced digital capabilities for product personalization and rapid design visualization. Addressing the sector’s persistent barriers such as limited IT resources, manual workflows, and lack of structured supplier data access, the proposed system integrates dynamic web scraping for automated supplier data acquisition with real-time image processing for printable area detection on packaging components, particularly bottles. Leveraging open-source tools like Beautiful Soup, OpenCV, and Shapely, the platform eliminates reliance on time-intensive manual integration and supports agile, data-driven design workflows. The development process is guided by human-centered design principles to ensure usability and alignment with SME operational realities. Results demonstrate that this approach significantly streamlines catalog management and design preparation, offering a scalable pathway for SMEs to achieve digital transformation and maintain competitive differentiation in an increasingly digitalized packaging market.

We present SITUATE, a novel dataset designed for training and evaluating Vision Language Models on counting tasks with spatial constraints. The dataset bridges the gap between simple 2D datasets like VLMCountBench and often ambiguous real-life datasets like TallyQA, which lack control over occlusions and spatial composition. Experiments show that our dataset helps to improve generalization for out-of-distribution images, since a finetune of Qwen VL 2.5 7B on SITUATE improves accuracy on the Pixmo count test data, but not vice versa. We cross validate this by comparing the model performance across established other counting benchmarks and against an equally sized fine-tuning set derived from Pixmo count.

Digital transformation in health and social care extends far beyond the adoption of new technologies and requires coordinated organizational change. The projectpulsnetz –Mensch und Technik im Gemeinwesen (MuTiG)addresses this challenge with an Integrative Model for Leadership and Employee Development that links individual upskilling with strategic organizational transformation to advance digital maturity.Led by an interdisciplinary consortium, the project began with a comprehensive needs assessment and subsequently developed modular training programs, tailored organizational consulting, and a digital knowledge platform to foster long-term learning and peer exchange. Implementation is continuously evaluated using standardized online surveys and qualitative interviews. To date, more than 3,700 professionals from health and social care organizations have participated. Survey response rates have been moderate to high, and feedback is largely positive, with mean ratings typicallyabove 3.5 (0–4 Likert-scale); trainer performance and support receive the highest scores. Participants in leadership roles reported slightly lower levels of new learning, likely due to their higher prior knowledge.Preliminary findings suggest that sustainable digital transformation requires a combined focus on individual skill development, organizational learning, and structural adaptation. The MuTiG model provides a scalable, practice-oriented, and transferable framework that can guide health and social care organizations in their digital transformation journeys. While long-term impact cannot yet be fully assessed due to the project’s ongoing nature, early results underline its potential to support lasting digital transformation.

Triple Graph Grammars (TGGs) are a visual, intuitive approach for specifying model transformations, allowing the automatic derivation of model management operations including forward/backward transformations and incremental synchronisation with guaranteed, desirable properties.
The conceptual simplicity of TGGs comes at a price, however, as all TGG tools impose substantial limits on practical expressiveness (measured by ease of specification, size, and readability in this paper), rendering TGGs unsuitable for real-world transformations and representing a major barrier to their mainstream adoption.
This paper discusses excerpts of model transformations that are exceedingly difficult (and perhaps even impossible) to specify using TGGs, analyses the underlying causes, and suggests suitable extensions of existing language features.
Our goal is to inspire research that improves the practical expressiveness of TGGs and facilitates applications of the approach.

This paper revisits the Families to Persons Case with a significant extension: concurrent model synchronization. Building on the original benchmark test cases, we introduce new tests for synchronizing models and resolving conflicts, thereby enhancing the framework's capability to benchmark bidirectional transformation tools under more realistic conditions. This advancement is crucial for assessing the tools' performance in concurrent engineering scenarios requiring data consistency across multiple models.

Fragt man, wie eine Umgebung auf Menschen wirkt, könnte man einerseits Eigenschaften dieser Umgebung beschreiben, oder andererseits untersuchen, wie Wahrnehmungseindrücke im Individuum entstehen. Bedeutung wird allerdings weder passiv registriert noch mental konstruiert. Nach Francisco Varela, Evan Thompson und Eleanor Rosch ist sie an tatsächliche und mögliche lebenserhaltende Aktivitäten eines Organismus gebunden. Lebewesen und Umwelt beziehen sich demnach wechselseitig aufeinander: Die Umwelt wird durch die Interaktionsmöglichkeiten und Bedürfnisse des Lebewesens bedingt; das Lebewesen wiederum organisiert sich durch die Begegnung mit dieser für ihn spezifischen Umwelt. Die Interaktion zwischen Mensch und Umwelt steht bei verschiedenen kognitionswissenschaftlichen und phänomenologischen Ansätzen im Fokus. Skizzenhaft betrachtet der folgende Text entsprechende Dynamiken. Einleitende Beispiele aus dem weit gefassten Bereich gestalteter Umgebungen dienen nicht nur als Einstiegspunkte, sondern bieten als gestalterische Erkundungen einen reichhaltigeren Zugang zu den verhandelten Phänomenen, als es dieser Text kann. Schließlich stellen sich Fragen – sowohl zur Erforschung als auch zur Gestaltung der Beziehung zwischen Mensch und Welt.

Rowhammer bit flips in DRAM enable software attackers to fully compromise a great variety of systems. Hardware mitigations can be precise and efficient but suffer from long deployment cycles and very limited or no update capabilities. Consequently, refined attack methods have repeatedly bypassed deployed hardware protections, repeatedly leaving commodity systems vulnerable to Rowhammer attacks.

In this paper, we present Memory Band-Aid, a principled defense-in-depth against Rowhammer. Memory Band-Aid is no replacement for long-term, efficient hardware mitigations but a defense-in-depth that is activated when hardware mitigations are discovered to be insufficient on a specific system generation. For this purpose, Memory Band-Aid introduces per-thread and per-bank rate limits for DRAM accesses in the memory controller, ensuring that the minimum number of row activations for Rowhammer bit flips cannot be reached. We implement a proof-of-concept of Memory Band-Aid on Ubuntu Linux and test it on 3 Intel and 3 AMD systems. In a micro-benchmark to cause DRAM pressure, we observe a slow down up to a factor of 5.2. In a collection of realistic Phoronix macro-benchmarks, we observe a low overhead of 0 % to 9.4 %. Both overheads only apply to untrusted throttled workloads, e.g., sandboxes, for instance in browsers. Especially as Memory Band-Aid can be enabled on demand, we conclude that Memory Band-Aid is an important defense-in-depth that should be deployed in practice as a second defense layer.

Rowhammer is a disturbance error in Dynamic Random-Access Memory (DRAM) that can be deliberately triggered from software by repeatedly reading, i. e., hammering, proximate memory locations in different DRAM rows. While numerous studies evaluated the Rowhammer effect, in particular how it can be triggered and how it can be exploited, most studies only use a small sample size of Dual In-line Memory Modules (DIMMs). Only few studies provided indication for the prevalence of the effect, with clear limitations to specific hardware configurations or FPGA-based experiments with precise control of the DIMM, limiting how far the results can be generalized.

In this paper, we perform the frist large-scale study of the Rowhammer effect involving 1 006 data sets from 822 systems. We measure Rowhammer prevalence in a fully automated crossplatform framework, FLIPKIT, using the available state-of-theart software-based DRAM and Rowhammer tools. Our framework automatically gathers information about the DRAM and uses 5 tools to reverse-engineer the DRAM addressing functions, and based on the reverse-engineered functions uses 7 tools to mount Rowhammer. We distributed the framework online and via USB thumb drives to thousands of participants from December 30, 2024, to June 30, 2025. Overall, we collected 1 006 datasets from systems with various CPUs, DRAM generations, and vendors. Our study reveals that out of 1 006 datasets, 453 (371 of the 822 unique systems) succeeded in the first stage of reverseengineering the DRAM addressing functions, indicating that successfully and reliably recovering DRAM addressing functions remains a significant open problem. In the second stage, 126 (12.5 % of all datasets) exhibited bit flips in our fully automated Rowhammer attacks. Our results show that fully-automated, i. e., weaponizable, Rowhammer attacks work on a lower share of systems than FPGA-based and lab experiments indicated but with 12.5 % enough to be a practical vector for threat actors. Furthermore, our results highlight that the two most pressing research challenges around Rowhammer exploitability are more reliable reverse-engineering addressing functions, as 50 % of datasets without bit flips failed in the DRAM reverse-engineering stage, and reliable Rowhammer attacks across diverse processor microarchitectures1, as only 12.5 % of datasets contained bit flips. Addressing each of these challenges could double the number of systems susceptible to Rowhammer and make Rowhammer a more pressing threat in real-world scenarios.