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O volume editado por Yvonne Hendrich (Johannes Gutenberg Universität Mainz) e Benjamin Meisnitzer (Universität Leipzig) contém dezassete trabalhos redigidos em português e em alemão, que propõem reflexões em torno de um tema abrangente e atual, a relação entre a língua e identidade no espaço lusófono. De acordo com os editores, “[o]s autores lançam luz sobre esta relação por vezes conflituosa, a questão da construção da própria identidade, mas também a questão da unidade que a língua dá aos países da lusofonia” (22-23).
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 &gt; 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.
Direct laser interference patterning (DLIP) is a well‐established technique for fabricating micro‐ and nano‐scale structures that can enhance the properties of surfaces such as reduced friction and wear. However, achieving full automation requires reliable in‐line process monitoring to ensure consistent structure quality. In this study, an infrared monitoring camera is implemented to capture spatially resolved temperature distributions during DLIP processing. Stainless‐steel samples are structured while systematically varying the laser fluence (2.5–5.6 J cm−2), and path velocity (1–20 mm s−1). The resulting surface structures are characterized using confocal microscopy to extract key topographical parameters. A convolutional neural network is trained using 180 000 process images from the IR system and the corresponding topographical data. The model identifies clear correlations between laser fluence, thermal signatures, and surface topography. For specific parameters, prediction accuracies of up to 94% are achieved. These results demonstrate that combining infrared monitoring with machine learning enables indirect yet accurate prediction of surface features, paving the way for enhanced process control and quality assurance in DLIP and related manufacturing processes.
[Mathematisches kann hier nicht korrekt dargestellt werden. Die beiden Angaben "-2" und "-1" sind im Original hochgestellt.]
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.
This article deals with the use of Artificial Intelligence (AI) in teaching German for specific purposes. It examines the potential of AI for the didactic design of exercises and teaching materials, for providing personalized feedback, and for supporting differentiated instruction. As a case study, a project conducted during the summer semester 2025 in Kyrgyzstan is presented, in which ChatGPT-supported exercises were developed and evaluated. Additionally, the perspectives of teachers regarding the opportunities and risks of using AI in specialized language teaching are discussed.
The article examines the strategic partnership in the development of the Eurasian transcontinental transport corridor system, as well as in the Republic of Kazakhstan and the People’s Republic of China. Special focus has been placed on the Kazakhstani section of the Middle Corridor (Trans-Caspian International Transport Route) – a multilateral, multimodal route connecting Chinese and European marketplaces through Kazakhstan and the Caspian Sea. The aim of the work is to determine how the mechanisms of synergy of infrastructure (infrastructure synergies) and factors defining corridor sustainability are determined using World Bank data (WITS, Logistics Performance Index), analytical material of international organizations, and scientific papers registered in Scopus. The article states that “physical” investments in railway, port, and terminal infrastructure can only yield long-term economic benefits if they are accompanied by trade facilitation, the electronic integration of all procedures, and the formation of institutional corridor governance structures. It has also been demonstrated that the primary restriction in the Middle Corridor is the extreme variability in transit times and costs; this restriction occurs in the majority of cases at intermodal nodes and border crossing points. Therefore, the authors propose a framework for developing corridors based on services, in which priorities are established end-to-end using indicators of logistics service reliability, and transit nodes are converted into logistics and industrial clusters. The practical importance of the research lies in substantiating the direction for Kazakhstan’s investment policies and forms of cooperation with China to reduce delays, increase predictability, and increase domestic value-added.
Background: Following the current gold standard, autologous cancellous bone is used as part of alveolar cleft osteoplasty. To fill the bone defects, autologous bone material is harvested from the iliac crest using manual tools such as shepard chisels or trephine drills. The bone augmentations obtained in this way have a simple geometry, usually cylinders, and must then be manually adapted to the defect to be filled by the surgeon using surgical forceps and scissors. There are no established routines for the manufacturing of patient-specific, cost-effective surgical cutting guides. However, the accuracy of fit of the augmentations plays an important role in the healing process.
Methods: This paper focuses on a concept for the creation of necessary sequenced incision geometries based on 3D X-ray data of alveolar cleft defects.
Results: As a result, a procedure is to be developed for the preoperative design of individualised surgical cutting guides based on image data. We described a workflow to segment the cleft defect using reverse engineering from Cone-beam computed tomography (CBCT) data. The data was further processed and a keyhole contour was created. A stamping guide and a cutting guide were then derived. The stamping guides were scaled 5, 10 and 15% larger than the defect. In addition, two half-shells were produced, which will be used to investigate the clamping forces and the biological consequences in a follow-up study.
Conclusions: This article presents a developed routine for creating patient-specific templates and demonstrates its feasibility.
In this article, I present changes to the content of the Bachelor’s degree programme German Business Communication that I consider necessary in the light of AI-driven developments both in science and economic fields. I focus on developing critical thinking when students use Generative Artificial Intelligence (GenAI) in their scientific work. This fits in well with the programme’s critical and engaged academic approach. The article sets out with an empirical consideration of AI in academic settings and GenAI application within the framework of the degree programme. Based upon this, a teaching/learning unit on GenAI use in a module of the programme is presented, which was designed and implemented in the winter semester 2025/26. The preliminary results reveal that further considerations are required to expand teaching units on AI tools for specific areas of business communication.
Since its inception in early 2022, Artificial Intelligence (AI) has been debated controversially in the media. The emergence of chatbots, Large Language Models (LLMs), generative AI and AI agents has spurred the discussion about AI strengths, weaknesses and the risks of using AI in business, education, and media. There is rising social concern about the effects of man-machine interaction as well as deep fakes. Therefore, AI media coverage spans from broad acceptance to the critical evaluation of ethical risks. This article examines the perception of AI based upon a qualitative corpus analysis of digital newsletters. The study considers German and English newsletter communications on AI and considers the role of this genre in adapting this disruptive new technology by describing macrostructure, visual elements, style and interactivity with the readers.
The genre “presentation” represents a central communicative act in academic settings, particularly in foreign-language and subject-specific language courses accompanying academic studies, where technical content must be appropriately adapted to the target audience in oral format. However, it is precisely in this context that a special focus is needed on the influence and potential of AI in the creation and implementation of presentations. The objective of this article is to discuss how AI-tools — such as those for text generation, visualization, or language analysis — can support the creation, delivery, and reflection of presentations, but also what negative effects they may have on the development of students’ presentation skills in specialized and foreign language university courses. An overview of relevant AI tools that can also be applied in later professional presentation practice and is intended to provide teachers and learners with a practical repertoire of digital aids. The findings provide impetus for the responsible and skill-oriented integration of AI into foreign language teaching.
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.
Die vorliegende Arbeit beschäftigt sich mit der Elektromobilitätswende und den daraus resultierenden Herausforderungen für die deutsche Automobilindustrie. Der erstarkende Nachhaltigkeitsgedanke der modernen Gesellschaft verändert das Mobilitäts- und Nachfrageverhalten an weltweiten Märkten. Internationale Automobilhersteller stehen im aktuellen Wandel zwischen politischem Willen und multidimensionalen Grenzen der Umsetzbarkeit. Die wirtschaftlich aufstrebende Volksrepublik China konnte sich dabei in jüngster Vergangenheit als Leitmarkt etablieren. Der chinesische Automobilbau gewinnt dabei ebenso an Bedeutung und neue Marken, wie „BYD“ verstärken den Wettbewerbsdruck auf deutsche Hersteller zunehmend. Die Abhandlung ordnete die Bedeutung der deutschen Automobilindustrie für den Wirtschaftsstandort Deutschland anhand der Analyse quantitativer Indikatoren ein. Zusätzlich wurden traditionelle Stärken, Herausforderungen und Strategien in Bezug auf die Mobilitätswende für die drei erfolgreichsten deutschen Automobilunternehmen „Volkswagen“, „Mercedes Benz“ und „BMW“ untersucht. Die Relevanz Chinas, sowie die Besonderheiten des Marktumfeldes werden daran anknüpfend betrachtet, bevor die etablierten deutschen Hersteller mit dem chinesischen Disruptor „BYD“ gegenübergestellt wurden. Durchgeführt wurde hierzu eine Wettbewerbsanalyse mit Ausrichtung auf Produktdifferenziertheit nach dem „Fünf-Kräfte-Modell“ (Five-Forces) von Michael E. Porter. Die Gesamtstudie wurde abschließend durch eine Kombination von SWOT- und PESTEL-Analyse ausgewertet. Es wurden wesentliche Potentiale und Herausforderungen für deutsche Automobilhersteller abgeleitet. Diese sind nach globalem und chinesischem Kontext aufgeschlüsselt und in politische, ökonomische, soziale und technologische Dimensionen eingeordnet. Die Ergebnisse können als Grundlage für weiterführende Forschung konkreter Aspekte dienen.
Die Integration Künstlicher Intelligenz (KI) in die globale Wirtschaft löst eine tiefgreifende Transformation von Beschäftigungsverhältnissen aus. Die vorliegende explorative Studie untersucht diese Dynamik, ausgehend von einer technologischen Einordnung kognitiver Systeme, und analysiert sektorale Unterschiede in der KI-Adaption – mit Fokus auf das Gesundheitswesen, das Finanzwesen und das verarbeitende Gewerbe. Ein Kernstück der Untersuchung bildet die Bewertung künftiger Kompetenzprofile: Während hochspezialisierte technische Fähigkeiten und ausgeprägte Sozialkompetenzen an Bedeutung gewinnen, unterliegen repetitive Tätigkeiten einem hohen Substitutionsrisiko. Zur Visualisierung dieser disruptiven Kräfte wird das „Hufeisenmodell der KI-bedingten Arbeitsmarktdisruption“ entwickelt. Es zeigt auf, dass insbesondere die breite Mittelschicht der Gefahr einer Verdrängung ausgesetzt ist, während Tätigkeiten im Niedriglohnsektor und im Bereich der Hochspezialisierung bestehen bleiben. Die Arbeit schließt mit einer Debatte über die drohende soziale Einkommenskluft und liefert damit eine fundierte Basis für strategische Entscheidungen und weiterführende Forschungsfragen.
Na sua Grammaire du sens et de l‘expression, Charaudeau (1992: 4) coloca a descrição das intenções do sujeito falante no centro de uma gramática de orientação comunicativa. Contudo, nas gramáticas tradicionais, o princípio de organização não parte das intenções do sujeito falante, mas de categorias formais da língua.
O exemplo da enunciação de objetivos acionais possui um interesse especial para uma aproximação comunicativa à gramática, pois abre a possibilidade de tratar, num mesmo paradigma de gramática comunicativa, as formas verbais gramaticalizadas que na gramática tradicional são categorizadas como tempos e modos verbais e, ao mesmo tempo, verbos gramaticalizados em diferentes graus. Além disso, faz uma junção entre a verbalização de um processo mental específico (a formação de um objetivo acional; cf. Rehbein 1977; Lock 1996: 105) e a ação que tiver como objetivo realizar. Há de considerar também que, por sua vez, cada meio linguístico localiza o objetivo acional, de maneira específica, no processo mental da sua formação (Lobato 1971: 289; Wunderlich 1981; Johnen 2003: 250-256).
O objetivo desta contribuição é em primeiro lugar indagar o lugar da enunciação de objetivos acionais numa gramática comunicativa do português e fazer um levantamento de subcategorias gramaticais relevantes que as gramáticas tradicionais não consideram. Em segundo lugar, apresentar-se-á um esboço para um tratado da enunciação de objetivos acionais numa gramática comunicativa do português.
AI-driven risk estimation: a GPT-based approach to news monitoring for manufacturing resilience
(2026)
In today’s rapidly evolving commercial landscape, manufacturing enterprises face significant challenges in maintaining resilience amid disruptions such as pandemics, natural disasters, and geopolitical conflicts. To address these challenges, we introduce a novel GPT-based early detection tool designed for real-time supply chain risk assessment. This system integrates proprietary company data, including supply chain portfolios, with publicly available information, such as news articles, to estimate risk scores for respective supply chains, thereby enhancing decision-making processes. Leveraging advanced machine learning techniques–Generative Pretrained Transformers (GPT), zero-shot learning, and structured outputs–the tool operates locally to ensure data privacy and minimize information leakage. Utilizing the "news-please" crawler and the "Llama 3.1" GPT model, the system continuously monitors selected media sources, providing timely risk assessments. Our research demonstrates the tool’s potential to enhance proactive risk management in supply chains, validated through testing on both real and augmented datasets. By evaluating four exemplary supply chains, we characterize the tool’s capability to support decision-making in unpredictable global environments. The results indicate that, while the system occasionally exhibits oversensitivity, it consistently aids in identifying critical events that may impact supply chain operations. Future developments will focus on refining the tool’s accuracy and expanding its applications, particularly in monitoring regulatory changes.
Background: The use of generative AI, as represented by ChatGPT, holds promising potential for nursing education. This manifests itself in various areas, including personalized learning, simulation training and teaching process support. However, its integration requires careful consideration of ethical implications, adaptation of curricula and a high level of digital competence on the part of teachers. Only in this way can potential risks, such as the distortion of knowledge, bias and educational inequalities, be avoided.
Methods: Relevant publications were identified between 2019 and 2025 as part of a comprehensive literature search in the specialist databases PubMed, Embase, CINAHL and Scopus. The search was conducted using combined search terms that included the terms “generative AI”, “ChatGPT” and “nursing”. After removing duplicates and screening (PRISMA-guided), 140 full texts were analysed and divided into two publications. This rapid overview focuses on the topic of generative AI in nursing education.
Results: As part of the analysis of the included studies, five thematic areas were identified, which were divided into the categories of nursing education, competence development and nursing skills, implementation possibilities, examination quality and ethical considerations, and evaluated. A key theme is the dual potential of this technology: it can enrich learning through features such as virtual tutors and improved exam preparation, but it also requires critical consideration of ethical issues such as plagiarism, data bias and the need for human oversight.
Outlook: In this context, the conclusion emphasises the urgent need to adapt curricula and provide targeted further training for teachers so that GenAI can be used responsibly and effectively—rather than, as is often the case at present, by banning it altogether.
Hochschulbericht 2019 | 2020
(2021)
Hochschulbericht 2021 | 2022
(2023)
Hochschulbericht 2023 | 2024
(2025)
