Throughout computer history, it has been repeatedly demonstrated that critical software vulnerabilities can significantly affect the components involved. In the Free/Libre and Open Source Software (FLOSS) ecosystem, most software is distributed through package repositories. Nowadays, monitoring critical dependencies in a software system is essential for maintaining robust security practices. This is particularly important due to new legal requirements, such as the European Cyber Resilience Act, which necessitate that software projects maintain a transparent track record with Software Bill of Materials (SBOM) and ensure a good overall state. This study provides a summary of the current state of available FLOSS package repositories and addresses the challenge of identifying problematic areas within a software ecosystem. These areas are analyzed in detail, quantifying the current state of the FLOSS ecosystem. The results indicate that while there are well-maintained projects within the FLOSS ecosystem, there are also high-impact projects that are susceptible to supply chain attacks. This study proposes a method for analyzing the current state and identifies missing elements, such as interfaces, for future research.

Surface roughness plays a critical role in ultrashort pulse laser ablation, particularly for industrial applications using burst mode operations, multi-pulse laser processing, and the generation of laser-induced periodic surface structures. Hence, we address the impact of surface roughness on the resulting laser ablation topography predicted by a simulation model and compared to experimental results. We present a comprehensive multi-scale simulation framework that first employs finite-difference-time-domain simulations for calculating the surface fluence distribution on a rough surface measured by an atomic-force-microscope followed by the two-temperature model coupled with hydrodynamic/solid mechanics simulation for the initial material heating. Lastly, a computational fluid dynamics model for material relaxation and fluid flow is developed and employed. Final state results of aluminum and AISI 304 stainless steel simulations demonstrated alignment with established ablation models and crater dimension prediction. Notably, Al exhibited significant optical scattering effects due to initial surface roughness of 15 nm - being 70 times below the laser wavelength, leading to localized, selective ablation processes and substantially altered crater topography compared to idealized conditions. Contrary, AISI 304 with RMS roughness of 2 nm showed no difference. Hence, we highlight the necessity of incorporating realistic, material-specific surface roughness values into large-scale ablation simulations. Furthermore, the induced local fluence variations demonstrated the inadequacy of neglecting lateral heat transport effects in this context.

The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe agents' health status as susceptible, exposed, infectious or recovered. Infectious agents exhale pathogens bound to persistent aerosols, whereas susceptible agents absorb pathogens when moving through an aerosol cloud left by the infectious agent. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a 'high risk' benchmark scenario to distinguish critical from non-critical situations. Simulating indoor situations show that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or even decision-makers to better assess the spread of COVID-19 and similar diseases.

Background: Pose estimation of rigid objects is a practical challenge in optical metrology and computer vision. This paper presents a novel stochastic-geometrical modeling framework for object pose estimation based on observing multiple feature points. Methods: This framework utilizes mixture models for feature point densities in object space and for interpreting real measurements. Advantages are the avoidance to resolve individual feature correspondences and to incorporate correct stochastic dependencies in multi-view applications. First, the general modeling framework is presented, second, a general algorithm for pose estimation is derived, and third, two example models (camera and lateration setup) are presented. Results: Numerical experiments show the effectiveness of this modeling and general algorithm by presenting four simulation scenarios for three observation systems, including the dependence on measurement resolution, object deformations and measurement noise. Probabilistic modeling utilizing mixture models shows the potential for accurate and robust pose estimations while avoiding the correspondence problem.

Agent-based models (ABMs) have emerged as distinguished tools for epidemic modeling due to their ability to capture detailed human contact patterns. ABMs can support decision-makers in times of outbreaks and epidemics substantially. However, as a result of missing correspondingly resolved data transmission events are often modeled based on simplified assumptions. In this article, we present a framework to assess the impact of these simplifications on epidemic prediction outcomes, considering superspreading and workplace transmission events. We couple the VADERE microsimulation model with the large-scale MEmilio-ABM and compare the outcomes of four outbreak events after 10 days of simulation in a synthetic city district generated from German census data. In a restaurant superspreading event, where up to four households share tables, we observe 17.2~\% more infections on day 10 after the outbreak. The difference increases to 46.0 % more infections when using the simplified initialization in a setting where only two households share tables. We observe similar outcomes (41.3 % vs. 9.3 % more infections) for two workplace settings with different mixing patterns between teams at work. In addition to the aggregated difference, we show differences in spatial dynamics and transmission trees obtained with complete or reduced outbreak information. We observe differences between simplified and fully detailed initializations that become more pronounced when the subnetworks in the outbreak setting are mixing less. In consequence and aside from classical calibration of models, the significant outcome differences should drive us to develop a more profound understanding of how and where simplified assumptions about transmission events are adequate.

The goal of this paper is to demonstrate the general modeling and practical simulation of random equations with mixture model parameter random variables. Random equations, understood as stationary (non-dynamical) equations with parameters as random variables, have a long history and a broad range of applications. The specific novelty of this explorative study lies on the demonstration of the combinatorial complexity of these equations and their solutions with mixture model parameters. In a Bayesian argumentation framework, we derive a likelihood function and posterior density of approximate solutions while avoiding significant restrictions about the type of nonlinearity of the equation or mixture models, and demonstrate their numerically efficient implementation for the applied researcher. In the results section, we are specifically focusing on expressive example simulations showcasing the combinatorial potential of random linear equation systems and nonlinear systems of random conic section equations. Introductory applications to portfolio optimization, stochastic control and random matrix theory are provided in order to show the wide applicability of the presented methodology.