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Correction: Policy Recommendations for Higher Education Institutions to Begin Advancing from Digital Transformation to Bifurcation
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GND-gestützte archivische Erschließung in Arcinsys (Hessen)
No full textDie Anwendung von Normdateien wie der Gemeinsamen Normdatei (GND) in den Archiven verspricht großen Nutzen für diese, nicht zuletzt im Bereich der archivischen Erschließung: Die Verzeichnungsdaten in Archivfachinformationssystemen können mit kooperativ gepflegten und kontrollierten normierten Daten insbesondere zu Personen, Körperschaften, Geografika und Sachverhalten angereichert und das verzeichnete Archivgut so stärker kontextualisiert und leichter auffindbar gemacht werden. Hinzu kommt, dass Archive mehr als jede andere Gedächtniseinrichtung prädestiniert für die Mitarbeit an Normdateien sind: Ihre Bestände stellen wertvolle Informationsarsenale für die Korrektur fehlerhafter wie auch die Anlage neuer Normdatensätze dar.
Der vorliegende Vortrag wurde in der 7. Sitzung der Arbeitsgruppe "Archivische Erschließung mit der GND" der Interessensgruppe (IG) Archiv am 7. November 2025 gehalten. Anhand ausgewählter Erschließungsdaten aus der Praxis wird die Implementierung der GND-gestützten Erschließung im Archiv der Hochschule Darmstadt in dem Archivfachinformationssystem Arcinsys Hessen erläutert. Drei Ziele der GND-Referenzierung stehen hierbei im Zentrum: die Verknüpfung kontrollierter Normdaten für Archivnutzende, die Anreicherung mit alternativen Bezeichnern (z.B. Synonymen) sowie die Möglichkeit einer tieferen inhaltlichen Erschließung von Archivgut. Anschließend werden ein Workflow sowie datenselektive Kriterien für die aktive Mitarbeit des Hochschularchivs an der GND vorgestellt
Shortage and Need for Skilled Workers in Child and Youth Welfare—Analyses, Classifications and Impulses, Especially on the Situation in the Central Basic Service of the Youth Welfare Office
No full textDie Situation der Fachkräfte und insbesondere der Fachkräftebedarf und -mangel in der Kinder- und Jugendhilfe sind in aller Munde. In Medien und Fachkreisen häufen sich Berichte über problematische Vorkommnisse und einen immensen Druck im System aufgrund steigender Anforderungen und gleichzeitig mangelndem Personal. Freie wie öffentliche Träger thematisieren eine sich zuspitzende Situation an vielen Stellen. Der folgende Beitrag führt in den Themenschwerpunkt Fachkräftemangel und -bedarf in der Kinder- und Jugendhilfe – Analysen, Einordnungen und Impulse insbesondere zur Situation im zentralen Basisdienst (ASD) des Jugendamtes ein.Everyone is talking about the situation of professionals and, in particular, the need for and shortage of professional social workers in child and youth welfare. Media and social workers are increasingly reporting about problematic developments and incidents as well as immense pressure in the system due to increasing demands and an ongoing lack of staff. Both public and private providers are addressing the worsening situation in many ways. The following article provides an introduction to the main topic of the shortage of and need for professionals in child and youth welfare—analyses, classifications and impulses, particularly with regard to the situation in the central basic service (ASD) in youth welfare offices
A semi-automated quality assurance tool for cardiovascular magnetic resonance imaging: application to outlier detection, artificial intelligence evaluation and trainee feedback
No full textBackground
Cardiovascular magnetic resonance (CMR) offers state-of-the-art volume, function, fibrosis and oedema imaging. Quality assurance (QA) tasks, such as quantitative parameter reproducibility assessments, the evaluation of AI methods, and the assessment of trainees have become essential to CMR. However, the explainability of how qualitative differences impact quantitative differences remains underexplored. Our aim is to demonstrate a semi-automated QA tool, Lazy Luna’s (LL) applicability to typical CMR QA application cases.
Methods
A software feature error-tracing is designed that allows for quickly pinpointing qualitative reasons for quantitative differences and outliers. Three QA application cases were designed. First, LL was applied to perform outlier detection for inter- and intraobserver analyses to detect failure cases and provide qualitative explanations. Outlier detection was performed on several typical images types. Second, LL supported an Artificial intelligence (AI) evaluation , in which an AI method was compared to a CMR-expert of 144 patients. LL assessed the acceptability of AI biases for left and right ventricular (LV, RV) end-systolic, –diastolic, and stroke volumes (ESV, EDV, SV), ejection fractions (EF) and the myocardial mass (LVM). Annotations were examined to explain the qualitative differences that resulted in good and poor parameters. The AI investigation was recorded as a video. Third, LL was used to provide a Trainee Feedback to a CMR beginner. The trainee was compared to an expert on several imaging techniques to investigate outliers.
Results
For the outlier detection, LL detected segmentation differences that caused parameter differences on multiple sequences. For the AI evaluation calculated clinical parameter biases to be: LVESV:-3.1 ml, LVEDV:2.1 ml, LVSV:6.5 ml, LVEF:3.0 ml, RVESV:0.3 ml, RVEDV:-3.8 ml, RVSV:-4.2 ml, RVEF:-1.4 ml, LVM:-2 g. Inspecting the causes for outlier differences revealed that juxtaposed basal slice failures caused unacceptable LVSV deviations between AI and expert. For the trainee assessment, LL showed that trainee parameters exceeded tolerance ranges. The segmentations could be improved to better mirror expert segmentations and close the parameter gaps.
Conclusion
Lazy Luna, as a semi-automated quality assurance tool, is applicable to several quality assurance application cases in CMR
Open Data-Driven Reconstruction of Power Distribution Grid: A Land Use-Based Approach
Get PDFDisruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure networks. Comprehensive, up-to-date power grid representations are essential for both researchers developing methods for analysing and optimising power systems and first responders requiring approximate data for urgent decisions. However, traditional grid reconstruction approaches often rely on incomplete data, expert knowledge, or closed datasets, limiting their utility during emergencies. This study proposes a novel automated method for reconstructing medium-voltage (MV) power grids. The novelty of the proposed method lies in combining OpenStreetMap energy and land-use data in a unified and automated framework, thereby reducing the need for expert input. The proposed method employs a systematic aggregation of data, an estimation of energy demand, and the application of algorithmic techniques to generate synthetic MV grid models that functionally represent real networks, capturing key topological features. The resulting outputs include visual representations to support decision-makers in simulating "what-if” scenarios and ensuring rapid operational awareness. In a step toward eliminating reliance on proprietary data, our approach broadens access to critical infrastructure insights across diverse urban contexts, contributing to critical infrastructure resilience and potentially supporting both energy system research and crisis management. A case study demonstrates that a medium-sized city’s MV grid can be reconstructed in minutes without expert knowledge or geographically constrained datasets, underscoring the method’s deployment potential and practical value for emergency scenarios
Towards non-intrusive, quantitative N2O Raman measurements in ammonia flames
Get PDFUnderstanding N2O formation and consumption in ammonia combustion is crucial in realizing the impact of ammonia as an alternative fuel to mitigate the impact of climate change. This study demonstrates the feasibility of using Raman spectroscopy for in-situ N2O measurements in ammonia flames. Raman spectra were acquired along a NH3/H2/N2-air flame in a laminar opposed jet burner, using a pulsed laser combined with a three-disk rotating shutter system to suppress the luminous flame background. This setup enabled the clear detection of the N2O Raman spectrum. Raman libraries of N2, O2, H2, NO, and N2O were fitted to the spectra using a newly developed fitting routine. This yielded qualitative N2O mole fractions along the flame that align closely with numerical simulations based on recently published chemical reaction models for ammonia oxidation, paving the way for future quantitative N2O measurements in ammonia flames. Since no prior libraries for temperature-dependent N2O Raman spectra were available, a methodology for its simulation is introduced. High-resolution N2O spectra were acquired between 295 and 1091 K as validation data for the simulation. Despite minor deviations, the simulation effectively captures the spectral shape and temperature dependence of the Raman cross sections, enabling its use in the spectral fitting routine towards quantitative in-situ concentration measurements
Variational model-based reconstruction techniques for multi-patch data in Magnetic Particle Imaging
Get PDFMagnetic Particle Imaging is an emerging imaging modality through which it is possible to detect tracers containing superparamagnetic nanoparticles. The exposure of the particles to dynamic magnetic fields generates a non-linear response that is used to locate the particles and produce an image of their distribution. The bounding box that can be covered by a single scan curve depends on the strength of the gradients of the magnetic fields applied, which is limited due to the risk of causing peripheral nerve stimulation (PNS) in the patients. To address this issue, multiple scans are performed. The scan data must be merged together to produce reconstructions of larger regions of interest. In this paper we propose a mathematical framework which can deal with rather general multi-patching scenarios including rigid transformations of the field of view (FoV), the specimen and of the scanner. We show the flexibility of this framework in a variety of different scanning scenarios. Moreover, we describe an iterative reconstruction algorithm that yields a reconstruction of the target distribution by minimizing a convex functional which includes positivity constraints and sparsity enforcing priors. We show its convergence to a minimizer and perform numerical experiments on simulated data
Diffusion of AI value-driven services in the German manufacturing industries—an empirical examination of value-driven service references classified by the business Model Canvas
Get PDFThis study investigates the diffusion of AI-based service applications within the business models of German manufacturing industries, surveying 162 decision-makers. The integration of AI into business model is assessed through the Business Model Canvas (BMC) framework, evaluating its value in terms of effectiveness as well as efficiency. Rather than focusing on specific use cases, the study delves into the intended usage of value-driven AI services references to enhance effectiveness and efficiency across various elements of the business models. Through this research, eleven service values have been identified. Each service vale corresponds to a distinct element of the BMC. Decision-makers were surveyed using a Confirmation/Disconfirmation (C/D) paradigm to measure the disparities between their current and target performance levels. Consequently, this study provides valuable insights from the perspective of decision makers regarding the current and desired state of AI integration in the German manufacturing industry, taking into account AI usage or no AI usage at the time of data collection