The 10 most recently published documents
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.
The rapid development of Artificial Intelligence (AI) has profoundly transformed translation practices and poses new challenges for higher education. This article presents a multi-stage teaching project designed for MA students that critically explores the potentials and limitations of AI-based translation tools. Through the comparative analysis of literary and contemporary texts—most notably Franz Kafka’s short prose piece “Gib’s auf ”—students examine outputs from tools such as DeepL, Google Translate, ChatGPT, and Matecat. The project combines text analysis, comparison of machine translations, post-editing, and collaborative translation workshops, including direct interaction with a contemporary author. Results show that while AI tools provide efficient and often accurate support, they remain limited in conveying stylistic nuance, pragmatics, and cultural meaning. The study demonstrates that translation quality depends on human interpretation, creativity, and responsibility, highlighting AI as a didactic catalyst rather than a substitute for professional translational competence.
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.
