https://libdoc.whz.de/opus4

An Overview of Supply Chain Ontologies

Yen Mai; Ralph Riedel; Michael Martin; Sanju Tiwari — Fri, 02 Oct 2026 10:33:38 +0200

Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization

Marika Kaden; Ronny Schubert; Tina Geweniger; Wieland Hermann; Thomas Villmann — Tue, 01 Sep 2026 08:55:54 +0200

Leipzig heart center ECG-database: arrhythmias in children and patients with congenital heart disease

Tue, 01 Sep 2026 07:39:22 +0200

Objective: Electrocardiogramms (ECG) interpretation is increasingly supported by algorithms trained on large-scale datasets. Here we present a first manually annotated open-source ECG-database of children and adults with congenital heart defect (CHD) with arrhythmia annotations. Approach: Signal data (ECG and intracardiac electrograms) were extracted from children and patients with CHD undergoing electrophysiological studies (EPS). The dataset is provided in the wave form database format. Annotations were manually annotated using the LightWAVE® software. Main results: The dataset comprises 39 ECGs from 39 patients (duration ranging from 00:01:17 h to 02:30:31 h). A total of 113 924 beats were annotated. A large part of beats (33%) are normal sinus rhythm. However, the database also includes sinus rhythm beats with preexcitation (12%) or complete right bundle branch block (11%). Supraventricular tachycardias (23%) are further categorized into atrioventricular reentrant tachycardias (AVRT; 15.5%) and atrioventricular nodal reentrant tachycardias (AVNRT; 7.5%). Also, ventricular tachycardias (1.7%) and very rare conditions as aberrant conducted AVRT or AVNRT or AVNRT with 2nd degree heart block are represented. Additionally paced beats are included (12%), thereof 7% atrial paced beats and 5% ventricular paced beats. Premature beats (premature ventricular or atrial beats) make up 7% of the dataset. Significance: This open-source database (https://physionet.org/content/leipzig-heart-center-ecg) containing many pathologies in children and patients with CHD may provide a foundation for ECG algorithms aimed at detecting ECG pathologies in this special patient population.

Editorial: Beyond the hype: a global perspective on the real-world utility of AI in healthcare research and service delivery

Tonio Schoenfelder; Tom Schaal; Sven Hellbach — Mon, 31 Aug 2026 11:13:13 +0200

A Model-Driven Methodology for Embedding AI Bias Mitigation Requirements into SYSML: Application to Smart Home Early Chronic Kidney Disease (CKD) Systems

Tue, 25 Aug 2026 11:35:44 +0200

Barrierefrei und transparent im Web

Lara Sophie König; Jörg Klewer — Mon, 27 Jul 2026 09:06:15 +0200

Schriftliche Prüfung Akustik – ChatGPT auf dem Weg zur 1,0?

Martin Dannemann; Tom Ehrig; Tina Geweniger; Ennes Sarradj — Wed, 08 Jul 2026 14:39:23 +0200

Breadth-First Search Trees with Many or Few Leaves

Jesse Beisegel; Ekkehard Köhler; Robert Scheffler; Martin Strehler — Mon, 22 Jun 2026 13:24:19 +0200

Patterns for Guiding Creativity

Ralf Laue — Thu, 04 Jun 2026 14:55:42 +0200

Shaping Tomorrow's Factories: A Panel on Simulation-Driven Manufacturing

Mon, 01 Jun 2026 16:30:48 +0200

From Scenario Farming to Learning: A Modular Low-Code Framework for Decision Support in Scheduling

Madlene Leißau; Christoph Laroque — Mon, 01 Jun 2026 16:15:22 +0200

Tailoring TSXOR for Compression of Regular Interval Time Series in Timescale

Alexander Stiwi; Manoel Rüffer; Hanna Köpcke; Tobias Teich — Mon, 01 Jun 2026 15:44:26 +0200

This paper presents a relational integration of the TSXor compression algorithm for regularly sampled time-series data. We design and implement a schema and compression pipeline within TimescaleDB, allowing lossless compression and in-database decompression using SQL and PL/pgSQL. Our approach replaces full timestamps with delta-based offsets and encodes floating-point values using reference-based XOR encoding. We evaluate the system on multiple real-world datasets, showing moderate compression ratios (up to 23%) and fast decompression performance. While the SQL-based implementation introduces storage and execution overhead, the integration enables transparent querying and lays the foundation for further database-native optimizations of time-series compression.

A unified hyperspectral imaging data format for cloud-based analysis and visualization

Tue, 28 Apr 2026 13:06:22 +0200

Rule-based clinical decision support system for automated assessment of left ventricular diastolic function during stress echocardiography

Gulnora Rozikhodjaeva; Omonulla Juraev; H.-Christian Brauweiler; Tom Schaal — Mon, 20 Apr 2026 09:32:16 +0200

Heart failure with preserved ejection fraction (HFpEF) remains challenging to diagnose due to the complexity of diastolic function assessment during stress echocardiography, where multiple hemodynamic parameters must be evaluated under time pressure. Explainable artificial intelligence, specifically rule-based Clinical Decision Support Systems (CDSS), offers promising improvements in reproducibility and interpretability. Methods: A rule-based CDSS was developed and clinically validated to automate left ventricular diastolic function assessment during semi-supine bicycle stress echocardiography. A prospective cohort of 134 patients (mean age 61.3 ± 8.7 years) with exertional dyspnea and preserved left ventricular ejection fraction (LVEF >50%) was enrolled, excluding individuals with significant valvular pathologies, arrhythmias, or unstable ischemia. Echocardiographic and Doppler data were collected using Toshiba Aplio500 and Esaote MyLabSIGMA systems. The algorithm incorporated manual input of measurements, computed derived indices (e.g., diastolic reserve index, myocardial stiffness, vascular resistance), and applied rule-based logic in accordance with ASE/EACVI (2016/2022) guidelines and the ESC HFpEF consensus. Results: The CDSS generated diagnostic conclusions within 3 min per case, matching expert assessments in 93% of cases and correctly identifying stress-induced diastolic dysfunction in 85%. It demonstrated high diagnostic agreement (ICC > 0.94) and discrimination (AUC = 0.92). Rule-based outputs, such as “Impaired diastolic reserve” or “Right ventricular dysfunction under load,” were based on combinations of parameters (e.g., E/e′ > 15, Δe′ ≤ 0, TAPSE < 17 mm, PCWR > 12 mmHg). Conclusion: The explainable, guideline-compliant CDSS enables real-time, transparent analysis of diastolic function, supporting improved diagnostic consistency and augmented physician decision-making in cardiovascular care.

From Dialogue to Digital Memory: An Approach to Structuring Informal Organizational Knowledge with AI in Ambient Intelligence Contexts

Mon, 20 Apr 2026 07:49:37 +0200

An AI-Supported Approach Model for Personalizing Learning Processes

Mon, 20 Apr 2026 07:37:02 +0200

AI-supported qualitative analysis of free-text responses on home care burden and support needs in Saxony

Elisabeth Rau; Silke Geithner; Tom Schaal — Mon, 13 Apr 2026 09:19:14 +0200

The number of people in Germany requiring care has risen steadily, increasing the importance of informal care. This form of care is often associated with considerable psychological and physical strain. The aim of this study is to systematically categorize and qualitatively analyze the free-text responses from a survey on home care using an artificial intelligence-based approach in order to identify key challenges and support needs in home care from the perspective of informal caregivers and non-caregiving relatives. The study used data from a 2019 survey on home care in Saxony. Free-text responses were categorized and analyzed using GPT-4 Turbo within a hybrid human-AI workflow. All AI outputs were subsequently validated and corrected by researchers. Respondents reported substantial financial burdens for both care recipients and informal caregivers. They also highlighted structural barriers to accessing services and insufficient support from the care system. Improving home care requires structural measures, including the expansion of low-threshold counseling services, more flexible leave regulations, stronger financial security for informal caregivers, and the sustainable strengthening of care infrastructures. Given an AI error rate of 36.45%, the study emphasizes the need for human post-processing to ensure analytical accuracy.

Moderner Übersetzungsunterricht unter Nutzung digitaler Technologien

Anja Lange; Iryna Gaman — Tue, 07 Apr 2026 13:22:03 +0200

Fachdeutsch vermitteln mit KI: Didaktisierung von Lehrmaterialien und neue didaktische Konzepte durch den Einsatz von Künstlicher Intelligenz

Anja Lange; Guldastan Ismailova — Tue, 07 Apr 2026 13:12:44 +0200

Auf dem Weg zu einem kritisch-engagierten Ansatz und einem KI-integrierenden Curriculum im BA-Studiengang „Wirtschaftskommunikation Deutsch“

Janina M. Vernal Schmidt — Mon, 09 Mar 2026 12:54:31 +0100

KI-Berichterstattung zwischen Euphorie und Realität – KI-Newsletter als Informationsmedium

Ines-Andrea Busch-Lauer — Mon, 09 Mar 2026 12:53:53 +0100

Präsentationen im Zeitalter Künstlicher Intelligenz. Einsatz, Reflexion und Beurteilung im fremd- und fachsprachlichen Unterricht an Hochschulen

Sandra Braun — Mon, 09 Mar 2026 12:53:46 +0100

Wenn KI auf Fach und Sprache trifft

Mon, 09 Mar 2026 12:53:41 +0100

Was passiert, wenn Künstliche Intelligenz (KI) auf die Herausforderungen der Fachkommunikation trifft? Wie tragfähig sind die aktuellen Modelle und Konzepte für die Arbeit mit KI im fachlichen und fachsprachlichen Kontext? Expertinnen und Experten für Fachkommunikationsforschung aus neun Ländern stellen in diesem Band ihre aktuellen KI-Projekte in Forschung und Lehre vor. Im Mittelpunkt stehen dabei Modellbildung, KI-Kompetenz, Terminologie, Fachübersetzen und Dolmetschen sowie die Vermittlung von Fachsprache im Hochschulkontext. Mit Blick auf die Qualität fachkommunikativer Forschung und Lehre der Zukunft thematisieren sie Potenziale und Risiken der Nutzung von KI.

Tracking und Ausgabeverfahren

Mon, 23 Feb 2026 07:02:31 +0100

VR/AR-Geräte für die Ein- und Ausgabe

Paul Grimm; Wolfgang Broll; Rigo Herold; Dirk Reiners; Carolina Cruz-Neira — Mon, 09 Feb 2026 14:42:07 +0100