Open Access

his entry provides the complete set of workshop materials for the Prototyping Sustainability – Designing Sustainable IT (ProS) (in German: "Prototyping Sustainability - nachhaltige IT gestalten") workshop, designed to engage young people in critically exploring the environmental, economic, and social dimensions of digitalization. The materials support a hands-on, participatory learning format that combines design thinking, experiential education, and Education for Sustainable Development (ESD) principles. The package includes all necessary resources for facilitators and participants in English, such as: surveys, building guides and supplementary materials for the sustainability-themed building block ice breaker, card-based reflection activity materials, resources for prototyping (sustainability catalogs, personas, cut-out kits), presentation template and checklist, peer voting template as well as overall workshop presentation.

This dataset contains high-resolution spatiotemporal measurements of diel vertical migration (DVM) in zooplankton under varying natural light conditions. The data were collected from Lake Stechlin, Germany in 2022 and include depth-stratified concentrations of zooplankton species (Cladocera and Copepoda) categorized by size class. The dataset also includes concurrent environmental variables such as temperature, chlorophyll-a concentration, moon phase, and modelled underwater illumination. These data provide insight into size-specific responses of zooplankton DVM under different light conditions, contributing to our understanding of natural light-driven behaviours in aquatic ecosystems. This research was supported by a Humboldt Research Fellowship from the Alexander von Humboldt Foundation, by the European Union under the Horizon Europe Programme (Grant Agreement No. 101135471, AquaPLAN), by the German Federal Agency for Nature Conservation (BfN; FKZ 3521 84 1000, BELLVUE), by the German Federal Ministry of Education and Research (BMBF; Grant No. 033L041B), and by the German Research Foundation (DFG; Grant No. GE 1775/2-1).

This paper aims to provide some guidance for planning and conducting a student excursion for the digitalization of cultural heritage. The paper reviews the benefits and challenges of student excursions in the context of the digitalization of cultural heritage and proposes some criteria and factors to consider when selecting a destination, forming a student group, and designing the activities. The paper also suggests some best practices and tips for organizing a successful and enjoyable student excursion that fosters learning, collaboration, and intercultural exchange.

Digital technologies play a dual role in sustainability transitions: they can enable more sustainable practices while simultaneously reinforcing unsustainable patterns of use. Although design is increasingly recognized as a critical lever for steering sustainability-oriented behavior, little is known about how users perceive and prioritize sustainability design cues when these compete with functional, performance-oriented, and hedonic cues. Thus, this study examines how digital design cues shape sustainability-oriented decision-making by analyzing participatory design data from 18 sustainability-focused workshops with secondary-school students. Drawing on an affordance-based analytical lens, we conducted an affordance-oriented qualitative content analysis of 109 digital product concepts and 374 peer evaluation statements using an iterative coding procedure to identify sustainability affordances and competing affordances and examine how they are prioritized during evaluative judgments of digital product concepts. The findings reveal distinct context-dependent shifts in affordance salience. While sustainability affordances were most prominent in prototype concepts, where they accounted for 58% of all sustainability-related codes, they declined to 32% in positive evaluations and 10% in negative evaluations. Conversely, functional and performance affordances gained prominence during evaluative judgments. These patterns indicate that affordance salience varies across contexts and help explain why sustainability considerations may become less salient in evaluative decision settings. Interpreting these patterns through the concept of affordance competition, the study shows how multiple perceived action possibilities simultaneously compete for attention. By applying an affordance-based perspective to digital sustainability, the study contributes to research on sustainability-oriented design by highlighting how competing affordances shape the visibility and evaluation of sustainability considerations across different contexts. The findings offer practical implications for designing digital artifacts that make sustainability affordances more visible, meaningful, and competitive in everyday decision contexts, particularly for future generations of users.

This record contains online supplementary materials for the demo paper “Learner Control in Microlearning: Guided Progression and Scripted Branching with H5P” by Katharina Frosch and Friederike Lindauer, forthcoming in the Springer LNCS proceedings of Mindful TEL: Learning Technologies Shaped with Intention (EC-TEL 2026), Valencia, Spain, 14–18 September 2026. The materials complement a pilot evaluation of three H5P-based microlearning prototypes on passkeys and provide additional context for the prototype implementations, learning analytics indicators, survey items, and descriptive results reported in the paper. The supplementary materials are organized as follows: Supplement1_Learner_Control_By_H5P_Prototype.pdf: Summarizes the learner-control configurations of the three H5P prototypes. Supplement1a_Screencast_Game_Map.mp4, Supplement1b_Screencast_Course_Presentation.mp4, and Supplement1c_Screencast_Interactive_Video.mp4: Show how learners can navigate through the three H5P prototypes. Supplement2_Indicators_And_Additional_Results.pdf: Provides details on data sources, learning analytics indicators, survey items, and additional descriptive results. This work was co-funded by the European Regional Development Fund and the State of Brandenburg under the StaF programme for the SCALE-C project, SeCure Model for semiAutomated LEarning Content in Cybersecurity (project reference: 86003742).

In this dataset, we survey data obtained in study on the effectiveness of microlearning to enhance competencies related to climate change. The study was conducted as an online field experiment with 140 participants who engaged in a microlearning activity on the sustainable use of smartphones between April 8, 2024, and May 31, 2024. Data was collected based on online surveys administered to participants before and after the microlearning intervention. The dataset includes information on: Socio-demographic characteristics of participants (age, gender, employment status) Results from knowledge quizzes on sustainable smartphone use Assessments of sustainability action competencies (knowledge of action possibilities, confidence in one's influence, willingness to act) Data on learner satisfaction with the microlearning activity The codebook as well as the description of scales and measurement provides detailed descriptions of all variables included in the dataset. Furthermore, to illustrate the microlearning activity, we have added an animated GIF.

This archive contains the simulation code, plotting code, generated intermediate JSON data, generated figure outputs, and original yield data used for the Nelson–Siegel–Svensson orthogonal reparametrization experiments.

The digitalization of healthcare systems increases their exposure to security incidents. Security analysts use standard CVE (Common Vulnerabilities and Exposures) records to identify and mitigate vulnerabilities. However, CVEs are often incomplete or overly generic, requiring the addition of structured, actionable information to support effective decision-making. Manually performing this augmentation is unfeasible due to the rapidly growing number of published CVEs. In this paper we evaluate the capabilities of LLMs (Large Language Models) to classify and analyze CVEs within the medical IT systems domain. We propose a framework where LLMs parse structured JSON context and answer a set of specific natural language questions, enabling the categorization of vulnerabilities by their position in the medical chain, affected component types, and mapping to the MITRE ATT&CK framework. While recent studies show that general LLMs can achieve high accuracy in objective CVSS elements and learn CNA-oriented patterns, they often struggle with subjective impact metrics. Our results demonstrate that domain-specific classification through natural language prompting provides the necessary granularity for medical risk prioritization. We conclude that this augmentation effectively bridges the gap in standard CVE records, allowing for a better understanding of how vulnerabilities impact critical healthcare infrastructure and patient safety.

The effective alignment of soft skills with established competency frameworks is essential for the automated alignment of competences within workforce analytics frameworks. This paper presents a semantic mapping approach using Natural Language Processing (NLP) to align soft skills from the SkillsMatch framework with the contents of the e-Competence Framework (European standard EN 16234-1) as it does not explicitly illustrate those skills in the text. Cosine similarity index provides an indication of the semantic closeness of each soft skill to the e-CF dimensions to map them according to score thresholds, with additional confirmation by experts. This approach provides an automated, scalable solution to enable the semantic linkage between soft and technical competences, otherwise unfeasible in terms of effort and time due to the huge amount of information to be analysed. The result is additional explicit information on soft skills that enriches and extends the standard EN 16234-1 offering pragmatic guidance to practitioners for the application in real contexts. Moreover, the proposed method underscores the importance of integrating embedding-based semantic similarity modelling to work with competence frameworks and standards to extract and analyse information for additional practical value, overcoming the limitations of traditional manual methods.

We present a multi-user map application based on a novel multiview concept that enables simultaneous and independent interaction with shared geospatial content. Each user operates a personal View Finder and Focus View, color-coded for clarity, while a shared, immutable Context View provides a common reference frame. The system supports both touch-based and tangible interaction techniques, including gesture control, virtual joysticks, and physical objects. Users can flexibly arrange their workspaces on a multi-touch table, supporting both individual exploration and collaborative tasks.

This study compares Tangible User Interfaces (TUIs) with conventional touch interfaces (CTIs) on multi-touch tables for exploratory multi-user map applications. We developed a prototype that enables multiple users to interact with map content simultaneously and independently. Four interaction methods were implemented and evaluated: gesture-based touch control, widget-based touch joystick control, and two tangible interaction variants (a joystick and car-steering metaphor). A user study with 15 participants was conducted in which users navigated predefined routes with varying difficulty. We collected both objective performance measurements and subjective user assessments. While touch-based methods yielded higher accuracy in objective metrics, TUI-based interactions were rated significantly better by participants in terms of user experience. Notably, the car-steering tangible control was particularly well-received, highlighting how physical, playful interaction can enhance usability despite lower accuracy. These findings contribute to our understanding of how different interaction paradigms support collaborative exploration on multi-touch surfaces.

We discuss the farthest objects on Earth observable for the unaided, healthy naked eye during the daytime, i.e., the maximum visual range for observers on Earth. Visual range depends first on the properties of the material between observer and object and its interaction processes with radiation, but second also on our visual perception system. After a rough comparison of ranges in water, glass, and the atmosphere, we focus on the physical basis of visual range for the latter. As a contrast phenomenon, visual range refers to allowed light paths within the atmosphere. It results from the interplay of geometry, refraction, and light scattering. We present a concise overview of this field by qualitative descriptions and quantitative estimates as well as classroom demonstration experiments. The starting point is the common geometrical visual ranges, followed by extensions due to refraction and limitations due to contrast, which depend on scattering and absorption processes within the atmosphere. The quantitative discussion of scattering is very helpful to easily understand the huge ranges in nature from meters in dense fog to hundreds of kilometers in clear atmospheres. Extreme visual ranges from about 300 km to above 500 km require optimal atmospheric conditions, cleverly chosen locations and times, and a sophisticated topography analysis. Even longer visual ranges are possible when looking through the vertical atmosphere. From the ISS, daytime ranges well above 1000 km are possible.

Naked eye studies of the clear night sky reveal that a certain percentage of all observable stars can be perceived as having color. Subjective estimates differ widely, ranging from just a few to a maximum of above two hundred. Explanations are based on the emission spectra of the stars, which are modified by interstellar dust clouds, the Earth atmosphere, and mostly the inverse square law. Color changes occur not only for variation of the star’s angular elevation above the horizon, but as well for decreasing nighttime sky brightness due to the transition from photopic via mesopic to scotopic vision. The maximum number of stars showing color to the naked eye depends on star illuminances on Earth and the background sky luminance. The limit of observing color is found to correspond to apparent visual magnitudes around , defining the number of colored stars. This also means that naked eye perception of stars with color is only possible for a certain star distance range, which is well below the maximum naked eye visual range of stars.

Granular gases are not only of interest in fundamental physics, but they can also serve as a test ensembles for the validity of collision models employed in (loose) granular matter. The theoretical literature mainly addresses spheres under ideal conditions and simulations allow full access to all particle parameters, but experiments cannot fulfill these idealizations. We investigate granular gases of soft, rough spheres by combining microgravity experiments and adjusted simulations. We introduce Smart Particles with embedded autarkic micro-sensors for in-situ measurements of rotation rates and accelerations. Additionally, we extract 3D positions, translations and orientations of the particles from stereoscopic video data using Machine-Learning based algorithms. We address the partition of kinetic energy between the degrees of freedom, the angular and translational velocity as well as collision statistics. A simulation is adjusted to experiment parameters, showing good agreement of translational motion, but qualitative differences in the decay of rotational kinetic energy.

When granular gases in microgravity are continuously excited mechanically, spatial inhomogeneities of the particle distribution can emerge. At a sufficiently large overall packing fraction, a significant share of particles tend to concentrate in strongly overpopulated regions, so-called clusters, far from the excitation sources. This dynamical clustering is caused by a complex balance between energy influx and dissipation. The mean number density of particles, the geometry of the container, and the excitation strength influence cluster formation. A quantification of clustering thresholds is not trivial. We generate ‘synthetic’ data sets by Discrete Element Method simulations of frictional spheres in a cuboid container and apply established criteria to classify the local packing fraction profiles. Machine learning approaches that predict dynamic clustering from known system parameters on the basis of classical test criteria areoposed and tested. It avoids the necessity of complex numerical simulations.

Microgravity (µg)-generated three-dimensional (3D) multicellular aggregates can serve as models of tissue and disease development. They are relevant in the fields of cancer and in vitro metastasis or regenerative medicine (tissue engineering). Driven by the 3R concept—replacement, reduction, and refinement of animal testing—µg-exposure of human cells represents a new alternative method that avoids animal experiments entirely. New Approach Methodologies (NAMs) are used in biomedical research, pharmacology, toxicology, cancer research, radiotherapy, and translational regenerative medicine. Various types of human cells grow as 3D spheroids or organoids when exposed to µg-conditions provided by µg simulating instruments on Earth. Examples for such µg-simulators are the Rotating Wall Vessel, the Random Positioning Machine, and the 2D or 3D clinostat. This review summarizes the most recent literature focusing on µg-engineered tissues. We are discussing all reports examining different tumor cell types from breast, lung, thyroid, prostate, and gastrointestinal cancers. Moreover, we are focusing on µg-generated spheroids and organoids derived from healthy cells like chondrocytes, stem cells, bone cells, endothelial cells, and cardiovascular cells. The obtained data from NAMs and µg-experiments clearly imply that they can support translational medicine on Earth.

In dieser Arbeit wird ein hybrides Intrusion Detection System (IDS) entwickelt und evaluiert. Das System kombiniert das anomaliebasierte IDS Zeek mit dem signaturbasierten IDS Suricata und einem dazwischen geschalteten Python-Regelgenerator. Zeek analysiert aufgezeichneten Netzwerkverkehr aus dem Datensatz CIC-IDS2017 und schreibt Anomalien in Logdateien, aus denen der Regelgenerator automatisch Suricata-Regeln ableitet. Suricata nutzt diese Regeln zusätzlich zu ET Open, um wiederkehrende Angriffe schneller zu erkennen. Die Evaluation erfolgt offline auf PCAP-Dateien und vergleicht drei Konfigurationen: Zeek-Baseline, Suricata-Baseline (ET Open) und die hybride Variante. Bewertet wird anhand von Confusion-Matrixbasierten Metriken (u. a. Recall, Precision, F1-Score, False-PositiveRate). Die Ergebnisse zeigen, dass der hybride Ansatz die Erkennungsrate im Vergleich zu den Baselines deutlich erhöht und in mehreren Angriffsszenarien einen Recall nahe 1,0 erreicht. Gleichzeitig steigt jedoch die False-Positive-Rate erheblich an, insbesondere bei komplexem HTTP-Verkehr. Damit beantwortet die Arbeit die Forschungsfrage ambivalent: Die Kopplung von Zeek-Anomalien und automatischer SuricataRegelgenerierung ist technisch machbar und effektiv im Hinblick auf die Angriffserkennung, aber in der vorliegenden Form noch nicht für den produktiven Einsatz geeignet. Der entwickelte Prototyp dient als reproduzierbarer Referenzaufbau und zeigt klar, an welchen Stellen feinere Regelmodelle, Feedback-Mechanismen und realistischere Betriebsumgebungen notwendig sind.

Objective Genetic generalized epilepsies (GGEs) comprise the most common genetically determined epilepsy syndromes, following a complex mode of inheritance. Although many important common and rare genetic factors causing or contributing to these epilepsies have been identified in the past decades, many features of the genetic architecture are still insufficiently understood. This study integrates genome-wide association study (GWAS) data from the International League Against Epilepsy Consortium on Complex Epilepsies with transcriptome-wide association studies to identify genes whose genetically regulated expression levels are associated with epilepsy. Methods To achieve this, we used multiple computational approaches, including MAGMA, a tool for gene analysis of GWAS data, and its derivatives E-MAGMA and H-MAGMA, to improve gene mapping accuracy by utilizing tissue-specific expression and chromatin interaction data. Furthermore, we developed ME-MAGMA to incorporate methylation quantitative trait loci data, providing insights into epigenetic factors. Results We identified a total of 897 false discovery rate-corrected (<.05) candidates. These include voltage-gated calcium channels, voltage-gated potassium channels, and other genes such as NPRL2, CACNB2, and KCNT1 associated with epilepsy pathogenesis that act as key players in neuronal communication and signaling in the brain. Significance In this study, we propose new candidate genes to expand the dataset of potential epilepsy-causing genes. Further research on these genes may enhance our understanding of the complex regulatory mechanisms underlying GGE and other types of epilepsy, potentially revealing targets for therapeutic intervention.

This paper presents an approach to teaching and consolidating skills in the context of sustainability “Prototyping Sustainability–Designing Sustainable IT” (ProS), using the workshop format for participatory and creative learning. The workshop integrates principles from Education for Sustainable Development (ESD), transformative and experiential learning, participatory design, and critical reflection on the digital age to engage participants in critically examining the environmental, economic, and social impacts of digital technologies in the context of Sustainable Development Goals (SDGs). Structured in five modular phases, from self-reflection and knowledge activation to collaborative prototyping and peer evaluation, the workshop offers a hands-on, gamified learning experience centred on real-world sustainability challenges. Learners create user-centred paper-based prototypes for digital products using tactile materials, persona-driven scenarios and knowledge of sustainable product characteristics gained in the workshop. Outcome measurement is supported through pre- and post-workshop surveys, peer voting templates, and paper-based prototype artefacts, enabling rich insight into behavioural intentions and learning gains. The paper discusses the educational value and sustainability relevance of the workshop engaging young people in critically reflecting on the environmental, economic, and social consequences of digitalization. Finally, it highlights challenges and limitations and proposes directions for future research.

Die Bachelorarbeit erkundet die Verwendung eines Forward-Looking-Sonars zur Hinderniserkennung unter Wasser auf dem „Sonobot-5“ der Firma EvoLogics. Es werden zwei klassische, ein probabilistischer und ein Deep Learning-basierter Ansatz verglichen. Ziel ist es, eine echtzeitfähige Hinderniserkennung zu ermöglichen, die zur Kollisionsvermeidung verwendet werden kann.

Die vorliegende Arbeit untersucht die Integration von Cyber Threat Intelligence in IT- und Gesundheitsumgebungen. Ziel war es, zu evaluieren, ob CTI-Daten in bestehende Sicherheitslösungen eingebunden werden können. Außerdem sollte festgestellt werden, welchen Mehrwert die Daten im Hinblick auf die Erkennung und Abwehr von Bedrohungen bieten. Hierzu wurde eine Laborumgebung aufgebaut, bestehend aus OpenCTI zur Sammlung und Verwaltung von Bedrohungsinformationen, Logstash zur Datenweiterleitung sowie Wazuh zur Analyse und Alarmierung. Über einen Zeitraum von acht Wochen wurden Daten aus verschiedenen Open-Source-Feeds (u. a. CVE, MITRE, AlienVault, ThreatFox) gesammelt, verarbeitet und auf ihre Relevanz für den Gesundheitssektor geprüft. Zur Bewertung der Qualität der CTI-Daten wurde ein Beispielszenario für eine Krankenhausinfrastruktur entwickelt. Dieses Szenario umfasste typische Systeme und Hersteller im Gesundheitssektor, darunter Krankenhausinformationssysteme, Medizingeräte sowie Netzwerkinfrastrukturen. Die gesammelten Daten wurden auf diese Infrastruktur gemappt, um zu prüfen, in welchem Umfang spezifische Bedrohungen abgedeckt werden. Die Ergebnisse zeigen, dass eine technische Integration grundsätzlich möglich ist und für generische IT-Bedrohungen belastbare Indikatoren vorliegen. Spezifische Informationen für den Gesundheitssektor konnten jedoch nur eingeschränkt identifiziert werden. Daraus ergibt sich, dass die Nutzung spezialisierter Feeds, etwa aus dem Health-ISAC, notwendig ist, um sektorspezifische Bedrohungen zuverlässig abdecken zu können.

This study explores the impact of Large Language Models (LLMs) in higher education, focusing on an automated oral examination simulation using a prototype. The design considerations of the prototype are described, and the system is evaluated with a select group of educators and students. Technical and pedagogical observations are discussed. The prototype proved to be effective in simulating oral exams, providing personalized feedback, and streamlining educators' workloads. The promising results of the prototype show the potential for LLMs in democratizing education, inclusion of diverse student populations, and improvement of teaching quality and efficiency.

The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain’s unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected.

This paper investigates cybersecurity threats in medical IT (Information Technology) systems exposed to the Internet. To that end, we develop a methodology and build a data processing pipeline that allows to gather data from different OSINT (Open Source Intelligence) sources, and processes it to obtain relevant cybersecurity metrics. To validate its operation and usefulness, we apply it to two countries, Germany and Spain, allowing to study the main threats that affect medical IT systems in these countries. Our initial findings reveal that 20% of German hosts and 15% of Spanish hosts tagged as medical devices have at least one CVE (Common Vulnerabilities and Exposures) with a CVSS (Common Vulnerability Scoring System) graded as critical (i.e., value 8 or greater). Moreover, we found that 74% of CVEs found in German hosts are dated from earlier than 2020, whereas for Spanish hosts the percentage is 60%. This indicates that medical IT systems exposed to the Internet are seldom updated, which further increases their exposure to cyberthreats. Based on these initial findings, we finish the paper providing some insights on how to improve cybersecurity of these systems.

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO2), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO2 measurement is activated when the specified T and RH change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and RH signals, the number of CO2 measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO2 measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO2 concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations.

In recent years, many studies have shown that light pollution adversely affects wildlife, ecosystems, and human well-being. To assess and mitigate these impacts, it is crucial that measurements of night sky quality are reliable and comparable across sites and instruments. However, the lack of standardised night sky brightness metrology and the use of a wide variety of measurement instruments with varying spectral responsivity and field-specific measurement units hinder meaningful comparison. We collected night sky spectra from 44 nights at dark locations (existing and proposed dark sky parks). Based on this observational dataset, we created a larger random set of spectra. These data served to fit conversion parameters for a wide variety of units. We demonstrate that RGB cameras, when used as multichannel measuring devices, enable the retrieval of measurements that facilitate conversions between different units. Furthermore, even airglow can be quantified from a given measurement, enabling the determination of oxygen and sodium emission line contributions. Since this contribution is not negligible, quantitative measurements of its magnitude are crucial for accurately assessing light pollution at dark-sky sites. Using our spectral measurement database, we constructed the most probable transformation from the cameras’ R, G, and B channel 𝑑𝑠𝑢 values to other units, such as the astronomical Bessel V band magnitudes. The unit conversion formulas provided in this paper are valid for mildly polluted sites (existing and proposed dark sky places), in the 21-22 magV∕arcsec2 range.

Diese Arbeit entwickelt und realisiert automatisierte Reaktionsme-chanismen zur Netzwerkisolierung kompromittierter Endgeräte mit-tels der Open-Source-Plattform Wazuh. Es werden zwei Ansätze als Proof of Concept implementiert und verglichen: eine host-basierte Isolierung durch Deaktivierung der lokalen Netzwerk-schnittstelle (Windows/Linux) und eine netzwerk-basierte Methode, die den Switch-Port des Geräts zentral abschaltet. Die Validierung in einer Testumgebung zeigt, dass die host-basierte Methode eine schnelle Reaktion ermöglicht, während der netzwerk-basierte An-satz unempfindlicher gegenüber Manipulationen auf dem Host ist.

Recently, an increasing number of IT security incidents involving malware, which makes use of hidden and steganographic channels for malicious communication (a.k.a. as "stegomalware"), can be observed in the wild. Especially the use of images to hide malicious code is rising. In consideration of this shift, a new model is proposed in this paper, which aims to help security professionals to identify and analyze incidents revolving around steganographic malware in the future. The model focuses on practical aspects of steganalysis of communication data to elaborate linking properties to previous code analysis knowledge. The model features two distinct roles that interact with a knowledge base which stores malware features and helps building a context for the incident. For evaluation, two image steganography malware types are chosen from popular databases (malpedia and MITRE ATT&CK®), which are analyzed in multiple steps including steganalysis and code analysis. It is conceptually shown, how the extracted features can be stored in a knowledge base for later use to identify stegomalware from communication data without the need of a thorough code analysis. This allows to uncover previously hidden meta-information about the examined malicious programs, enrich the incident’s forensic context traces and thus allows for thorough forensic insights, including attribution and improved preventive security measures in the future.

Light pollution is an emerging ecological threat. To mitigate its negative consequences, creative inter- and transdisciplinary solutions and societal interactions are needed. To this end, we introduce nocturnal umbrella species representative of light-sensitive biodiversity whose protection will safeguard vital ecosystem services and a wide range of co-occurring species.

Light pollution poses significant ecological challenges for nocturnal animals reliant on natural light for migration, orientation, and circadian rhythms. The physiological effects of abrupt exposure to artificial light at night (ALAN) on migratory fish, such as the light experienced passing near illuminated infrastructures, remain poorly understood. This study investigates the physiological responses of brown trout (Salmo trutta) smolts to low-intensity (0.02 lx) and short-term (30 s) ALAN, simulating nocturnal migration light conditions near illuminated bridges. To evaluate the influence of social dynamics, trout were tested individually (solitary) or in groups of six. Using continuous cardiac monitoring with data storage tags, alongside analyses of oxidative stress markers and adenylate kinase (AK) activity in the heart, we identified distinct patterns of physiological responses. Solitary fish exhibited significant heart rate variability (HRV) increases following repeated ALAN exposure, suggesting impaired physiological regulation under repeated ALAN exposure. In contrast, trout in groups displayed consistently lower HRV over the entire 90-min experiment, implying that social dynamics likely influenced a sustained oxidative stress response, corroborated by increased AK activity. Oxidative stress markers further reflected social effects, with significant upregulation of key antioxidant enzymes (sod1, sod2, gpx1, gpx4) and elevated lipid peroxidation, identifying lipids as primary oxidative targets. The observed divergence between superoxide dismutase (SOD) activity and sod gene expression suggests adaptive post-transcriptional regulation to maintain redox balance under combined environmental and social stress. These findings reveal that social dynamics under ALAN can amplify physiological stress, potentially affecting migratory outcomes.

We introduce CGAPoseNet+ GCAN, which enhances CGAPoseNet, an architecture for camera pose regression, with a Geometric Clifford Algebra Network (GCAN). With the addition of the GCAN we obtain a geometry-aware pipeline for camera pose regression from RGB images only. CGAPoseNet employs Clifford Geometric Algebra to unify quaternions and translation vectors into a single mathematical object, the motor, which can be used to uniquely describe camera poses. CGAPoseNet solves the issue of balancing rotation and translation components in the loss function, and can obtain comparable results to other approaches without the need of expensive tuning of the loss function or additional information about the scene, such as 3D point clouds, which might not always be available. CGAPoseNet, however, like several approaches in the literature, only learns to predict motor coefficients, and it is unaware of the mathematical space in which predictions sit in and of their geometrical meaning. By leveraging recent advances in Geometric Deep Learning, we modify CGAPoseNet with a GCAN: proposals of possible motor coefficients associated with a camera frame are obtained from the InceptionV3 backbone, and the GCAN downsamples them to a single motor through a sequence of layers that work in G_ 4, 0. The network is hence geometry-aware, has multivector-valued inputs, weights and biases and preserves the grade of the objects that it receives in input. CGAPoseNet+ GCAN has almost 4 million fewer trainable parameters, it reduces the average rotation error by 41% and the average translation error by 8.8% compared to CGAPoseNet. Similarly, it reduces rotation and translation errors by 32.6% and 19.9%, respectively, compared to the best performing PoseNet strategy. CGAPoseNet+ GCAN reaches the state-of-the-art results on 13 commonly employed datasets. To the best of our knowledge, it is the first experiment in GCANs applied to the problem of camera pose regression.

We employ Clifford Group Equivariant Neural Network (CGENN) layers to predict protein coordinates in a Protein Structure Prediction (PSP) pipeline. PSP is the estimation of the 3D structure of a protein, generally through deep learning architectures. Information about the geometry of the protein chain has been proven to be crucial for accurate predictions of 3D structures. However, this information is usually flattened as machine learning features that are not representative of the geometric nature of the problem. Leveraging recent advances in geometric deep learning, we redesign the 3D projector part of a PSP architecture with the addition of CGENN layers . CGENNs can achieve better generalization and robustness when dealing with data that show rotational or translational invariance such as protein coordinates, which are independent of the chosen reference frame. CGENNs inputs, outputs, weights and biases are objects in the Geometric Algebra of 3D Euclidean space, i.e. G3,0,0, and hence are interpretable from a geometrical perspective. We test 6 approaches to PSP and show that CGENN layers increase the accuracy in term of GDT scores by up to 2.1\%, with fewer trainable parameters compared to linear layers and give a clear geometric interpretation of their outputs.

Climate change, but also geopolitical circumstances, are moving topics such as energy efficiency and renewable energies more and more into the focus of the population, economy , and politics. As a result, the will to optimize new and existing energy systems extends from private individuals to companies and even entire communities. This work describes the development and usage of a new software called FINEconcepts which creates a digital twin of an energy system. This virtual model can then be used to optimize the energy system based on annual costs, CO2 emissions or other relevant criteria such as self-sufficiency. Because all system components, which include renewable technologies as well, can be added as a building block with chosen but changeable parameters, the software allows the user to explore and awaken interest and understanding of technologies that were previously considered too costly, irrelevant, or unrealistic. Implemented projects in small and large companies as well as in residential areas did prove, that the usage of FINEconcepts leads not only to more efficient energy systems by increasing the use of renewable energy, but also increased knowledge and understanding in terms of energy. Besides economics, ecology and security, understanding is an equally important factor in achieving a sustainable energy supply.

Das Projekt semPart – Semantik der Partitur versucht, durch Übertragung von Methoden der Informatik auf die musikalische Notation exakte Aussagen über deren Semantik zu erzielen. Dies geschieht durch Remodellierung, also durch das Erstellen kleiner mathematischer Modelle, die jeweils das historisch-kulturell bestimmte Dekodieren von isolierten Parameter-Schichten eines Notates nachbilden. Erstes wichtiges Ergebnis ist, dass es in keinem Bereich jemals eine einzige ›richtige‹ Definition von Syntax und Semantik geben kann, sondern einen Katalog von vielfältigen theoretisch möglichen und in der Praxis auch angetroffenen Varianten. Diese sind durch ihre Remodellierung exakt identifizier- und benennbar; ihre Gesamtheit bildet ein Raster, durch das Notationsstile und -werkzeuge beschreib- und vergleichbar werden. Dieses Raster und die Remodelle sind besonders politisch wichtig, zur Förderung einer öffentlichen Diskussion von Notationssystemen und ihren Eigenschaften, da zunehmend die Arbeitsweise von digitalen Werkzeugen die notationelle Praxis zu überformen und zu filtrieren droht. The project semPart – Semantics of the Musical Score tries to clarify the semantics of musical notation by applying methods from informatics. This is done by remodelling, i. e., constructing small mathematical models that mimic the mentally and culturally determined processes taking place when decoding isolated parameters in musical notation. The first important result is that there can never be one single ‘correct’ definition of semantics in any area but rather a catalogue of many theoretically possible variants encountered in practice. Through their remodelling, these can be precisely identified and named; their totality constitutes a classification grid applicable to styles, corpora, single scores and digital tools such as editors or encoding standards. This grid and the remodels are of particular political importance in promoting a public discussion on notation systems and their characteristics, given that the increasing use of digital processing systems threatens to over-shape and narrow down notational practice.

In industrialised countries, one in ten patients suffers harm during hospitalization. Critical Incident Reporting Systems (CIRS) aim to minimize this by learning from errors and identifying potential risks. However, a lack of interoperability among the 16 CIRS in Germany hampers their effectiveness.

This paper presents a coaching assistant for network operator processes based on a Retrieval-Augmented Gen- eration (RAG) system leveraging open-source Large Language Models (LLMs) as well as Embedding Models. The system addresses challenges in employee onboarding and training, particularly in the context of increased customer contact due to more complex and extensive processes. Our approach incorporates domain-specific knowledge bases to generate precise, context-aware recommendations while mitigating LLM hallucination. We introduce our systems architecture to run all components on-premise in an our own datacenter, ensuring data security and process knowledge control. We also describe requirements for underlying knowledge doc- uments and their impact on assistant answer quality. Our system aims to improve onboarding accuracy and speed while reducing senior employee workload. The results of our study show that realizing a coaching assistant for German network operators is reasonable, when addressing performance, correctness, integration and locality. However current results regarding accu- racy do not yet meet the requirements for productive use.

The study examines the effectiveness of microlearning in developing the capacity to address climate change and adapt to environmental challenges. Conducted as an online field experiment with 140 participants, the study focused on the impacts of smartphone use as an illustrative learning domain for education in climate change-related areas. Using a pre–post research design and simultaneous equation models, the study found improvements in knowledge retention, with a median increase of 38%. The results indicated that the brief microlearning units also promoted sustainability action competencies, such as confidence in one's influence and willingness to act sustainably. This suggests that higher-order learning processes were also triggered, although to a much lesser extent. Furthermore, learner satisfaction was identified as a mediating variable for these positive outcomes. The study concludes that short bursts of knowledge delivered through microlearning activities can be used alongside traditional training methods to build the critical skills needed for initiatives such as the EU Green Deal.

This paper presents a low-cost hardware platform for outdoor robots, being suitable for education, industrial prototyping and private use. The choice of components is discussed, including platform, sensors and controller as well as GPS and image processing hardware. Furthermore, a software approach is proposed, allowing students and researchers to easily implement own algorithms for localization, navigation and the tasks to fulfill. Several robots can be integrated in a framework which connects various different hardware platforms, called the BOSPORUS network. Together with other components, they form an intelligent network for gathering sensor and image data, sensor data fusion, navigation and control of mobile platforms. The architecture of a reference platform on the campus of the Brandenburg University of Applied Sciences is presented and evaluated.

Die Beratung von Forschenden spielt eine zentrale Rolle bei der Umsetzung und Etablierung des Forschungsdatenmanagements (FDM) im Forschungsalltag. Nach wie vor benötigen viele Forschende Unterstützung im Umgang mit ihren Forschungsdaten. Dies kann einerseits durch einen eher generischen Kenntnisstand zum Thema FDM bedingt sein, andererseits stellt der FDM-Kosmos einen hochfluiden Bereich dar – mit fortlaufend aktualisierten oder gänzlich neuen Tools und Infrastrukturen sowie veränderten Anforderungen an Forschende auch von Seiten der Forschungsförderer. Eine zentrale FDM-Anlaufstelle für die Ratsuchenden ist an den Hochschulen unerlässlich und folglich auch unter den Maßnahmen des Verbundprojekts „IN-FDM-BB – Institutionalisiertes und nachhaltiges Forschungsdatenmanagement in Brandenburg“ zu finden. Seit Oktober 2022 (Projektbeginn) werden an den beteiligten brandenburgischen Hochschulen entsprechende FDM-Kontaktpunkte auf- bzw. ausgebaut um die (Erst-)Beratung von Forschenden durchführen zu können. Darüber hinaus werden unterstützende Angebote etwa für rechtliche/ethische Fragen konzipiert und etabliert, um eine umfassende Beratung auch für spezifische Aspekte des FDMs zu ermöglichen. Bedarfsgerechte Materialien und Handreichungen, ebenso Bestandteil des Projektplans, ergänzen das individuelle Beratungsangebot. Der vorliegende Werkstattbericht stellt Erkenntnisse und Herausforderungen der Erprobungsphase dar und skizziert Ansätze, um diesen Herausforderungen lösungsorientiert begegnen zu können. Wertvolle Einblicke in die Beratungspraxis aller am Projekt beteiligter Hochschulen bilden dabei die Grundlage.

Digital platform (DP) enterprises have risen to the top of the global economy by inverting traditional business models. They earn money through matchmaking, transaction facilitation, and efficient orchestration of other stakeholders' resources. Small- and medium-sized enterprises (SMEs) play a decisive role in this success story: they offer products and services on many leading DPs and thereby feed platform networks as sellers and suppliers. On the other hand, while DPs enable SMEs to reach new customers and outsource costly managerial tasks, many SMEs struggle with the negative consequences of asymmetric power dynamics and dependency risks in platform business. This paper analyzes 14 cases of SMEs that face power asymmetry as DP value providers, unraveling challenges encountered and coping practices employed in this situation. The findings reveal that power asymmetry between dominant DPs and SMEs creates significant challenges, including exploitative relationships, loss of autonomy, surveillance pressures, and devaluation of SME contributions, while SMEs cope through disintermediation, multi-homing, individual resistance tactics, and striving for more equitable DP models. Moreover, three relationality types are identified: DPs being SMEs' “partners,” “tools,” and “necessary evil.” This article offers a new perspective on boundaries of SME strategies in the DP economy and contributes with empirical insights to the advancing field of entrepreneurial platform research.

Artificial light propagating towards the night sky can be scattered back to Earth and reach ecosystems tens of kilometres away from the original light source. This phenomenon is known as artificial skyglow. Its consequences on freshwaters are largely unknown. In a large-scale lake enclosure experiment, we found that skyglow at levels of 0.06 and 6 lux increased the abundance of anoxygenic aerobic phototrophs and cyanobacteria by 32 (+/- 22) times. An ecosystem metabolome analysis revealed that skyglow increased the production of algal-derived metabolites, which appeared to stimulate heterotrophic activities as well. Furthermore, we found evidence that skyglow decreased the number of bacteria-bacteria interactions. Effects of skyglow were more pronounced at night, suggesting that responses to skyglow can occur on short time scales. Overall, our results call for considering skyglow as a reality of increasing importance for microbial communities and carbon cycling in lake ecosystems.

Es wird das vielleicht einfachst mögliche Modell vorgestellt, mit dem zeitabhängige CO2 Konzent-rationen c(t) in der Atmosphäre ausgehend von verschiedenen globalen Emissionsszenarien für CO2berechnet werden. Dazu wird eine einzelne inhomogene lineare Differenzialgleichung 1. Ordnung hergeleitet, deren Parameter sich aus den quantitativen Daten des global carbon project sowie Mauna Loa Daten für CO2 Konzentrationen errechnen. Das Modell wird erstens getestet am Zeitraum 1960 bis 2020 mit vergleichsweise guter quantitativer Übereinstimmung zu Messdaten. Zweitens wird für zwei typische IPCC Emissions-Szenarien ein Vergleich der Modellvorhersagen mit denen der kom-plexen IPCC Earth-System-Klimamodelle diskutiert mit qualitativer Übereinstimmung des zeitli-chen Verlaufs. Drittens werden Ergebnisse einiger ausgewählter neuer Emissionsszenarien präsen-tiert. Ungeachtet einiger Abweichungen zu komplexeren Klimamodellen zeichnet sich unser Mo-dell durch zwei wichtige Vorteile für die Lehre aus. Zum einen ist es sehr einfach für Studierende und begabte Schüler nutzbar, da die erforderliche Lösung der Differentialgleichung bereits mit han-delsüblicher Tabellenkalkulationssoftware wie z.B. Excel programmiert werden kann. Dadurch ge-stattet es zum anderen auch sehr einfach, den zeitlichen Verlauf von Emissionsszenarien zu verän-dern und innerhalb weniger Sekunden Veränderungen aufgrund geänderter Eingaben zu berechnen. Insofern eignet sich das Modell sehr gut als Einstieg in das Thema Klimamodellierung in einführen-den Hochschulvorlesungen zum Themenbereich Kohlenstoffkreislauf und Klimawandel. In der Schule kann es gegen Ende der Sekundarstufe 2 beispielsweise im Projektunterricht zum Themen-komplex Nachhaltigkeit in Physik und/oder Mathematik eingesetzt werden

Granular gases are fascinating non-equilibrium systems with interesting features such as spontaneous clustering and non-Gaussian velocity distributions. Mixtures of different components represent a much more natural composition than monodisperse ensembles but attracted comparably little attention so far. We present the observation and characterization of a mixture of rod-like particles with different sizes and masses in a drop tower experiment. Kinetic energy decay rates during granular cooling and collision rates were determined and Haff’s law for homogeneous granular cooling was confirmed. Thereby, energy equipartition between the mixture components and between individual degrees of freedom is violated. Heavier particles keep a slightly higher average kinetic energy than lighter ones. Experimental results are supported by numerical simulations.

Im Rahmen des Projekts "SzA4Hosp - Systeme zur Angriffserkennung in der Medizinischen Versorgung" wurde eine Erhebung für deutsche Krankenhäuser durchgeführt, bei der genutzte Quellen für Cyber Threat Intelligence identifiziert wurden. Diese Arbeit stellt die 41 dabei erfassten Quellen hinsichtlich verschiedener Attribute wie Format, Inhalt und Bereitstellungsmodell vor. Abschließend erfolgt eine übergreifende Auswertung der Quellen in Bezug auf die Formate, in denen sie Informationen bereitstellen. Es wird gezeigt, dass die Verwendung von natürlicher Sprache in den untersuchten Quellen deutlich verbreiteter ist, als der Einsatz strukturierter Formate.

When it comes to human resource management (HRM), small and medium-sized enterprises (SMEs) primarily rely on informal approaches. Thereby, HR practitioners often listen to their guts feeling instead of using more systematic HR practices that are, as research evidence shows, more effective for attracting, motivating and retaining talented staff. This paper elaborates on the question how SMEs may profit from the design and implementation of evidence-based, systematic HRM instruments. The analysis focuses on the antecedents of the so called science-practitioner gap in HRM and why it is even more pronounced in SMEs as compared to larger companies. As a conclusion, we find that SMEs need to design HRM instruments that are systematic and evidence-based in their approach, but can be used by HR practitioners on an intuitive basis without ex ante training or cumbersome formal procedures. However, designing HRM instruments based on a so-called "walk-up-and-use" approach means beating new paths. Given the scarce resources for people management in SMEs, such innovative HRM instruments cannot be developed by a single SME alone. Therefore, the paradigm of open cooperation as it has been successfully applied in open source software (OSS) development is evaluated , in particular whether it can be transferred on the design of HRM instruments that take into account the specific needs of SMEs. First of all, we conceptualize a general framework how HR practitioners from SMEs and HR experts from business and academia can jointly develop Open HRM instruments. Then we discuss the social side of Open HRM, in particular why someone should contribute to Open HRM without receiving immediate profits, how to ensure the quality of openly developed HRM instruments and other gov-ernance issues. As a conclusion, we find that-despite a number of technical and social challenges that have to be overcome-Open HRM has a great potential in order to open up evidence-based HRM for SMEs.

In modern societies, technology has a high value, as nearly all systems are controlled by information technology. This thesis aims to develop a technical representation of the MITRE ATT&CK matrix to enable faster and more effective responses to cyber attacks. Based on a analysis of common cyber attack models, such as the Diamnond Model of Intrusion Analysis and the Lockheed Martin Cyber Kill Chain, the MITRE ATT&CK matrix is used to develop an extended Kill Chain model. In this new model, MITRE tactics are grouped into five sequential attack phases, allowing for a clearer differentiation between actions taken during an attack. Aditionally, one ongoing group of MITRE tactics was identified. Building on this model, two graph-based representations are developed: a simple graph structure and a hierarchical, tree-shaped graph of MITRE tactics and techniques. The following discussion addresses the practical application of the new model, as well as potential further developments, such as integrating AI to analyse and predict existing and potential attack patterns.