Für die Integration von Social Virtual Reality (SVR) in den Englischunterricht zur Förderung inter- und transkultureller Kompetenzen fehlen ein umfassender Rahmen, empirische Belege, Bewertungsmethoden und Überlegungen zur Skalierbarkeit. Das limitiert den effektiven und evidenzbasierten Einsatz von SVR in der Bildung und schränkt potenziell seine Fähigkeit ein, den Aufbau von inter- und transkultureller Kompetenz zu unterstützen und Diskriminierung, Stereotypenbildung und rassistisches Denken beim Sprachenlernen zu bekämpfen. Das Teilprojekt von CoTeach leistet hier einen wichtigen Beitrag, indem ein Seminar für angehende Lehrpersonen entwickelt und evaluiert wurde, das die Teilnehmenden über Potenziale und Herausforderungen von SVR im inter- und transkulturellen Kompetenzaufbau informierte. Darüber hinaus ermöglicht das Seminarkonzept das Einüben wichtiger Handlungskompetenzen im Umgang mit SVR, sodass ein Übertrag für spätere Unterrichtsplanung gelingen kann. Der interdisziplinäre Ansatz hebt die Synergie zwischen inter- und transkultureller Bildung und Human--Computer Interaction (HCI) hervor und zeigt die Wichtigkeit innovativer Lehrkonzepte mithilfe von digitalen Tools.

Angesichts der Entwicklungen im Medienbereich und der Globalisierung ergeben sich neue Aufgaben für Schule und Unterricht und damit auch für die Förderung medienpädagogischer und interkultureller Kompetenz von (angehenden) Lehrpersonen. Vor diesem Hintergrund wird in diesem Beitrag die interdisziplinäre Entwicklung eines Weiterbildungskonzepts für Dozierende in der Lehrpersonenbildung unter Nutzung von Social Virtual Reality (SVR) vorgestellt. Bedeutsame pädagogische Vorgehensweisen des Konzepts sind im Sinne einer Handlungsorientierung die Auseinandersetzung mit komplexen Aufgabenstellungen, Rollenspiele, Dialoge und Portfolioarbeit. Die technische Weiterentwicklung der SVR-Lernumgebung beinhaltet die Erstellung neuartiger Aufgaben sowie die Integration stilisierter Avatare und virtueller Objekte. Die Zielerreichung des Weiterbildungskonzepts und die Wirkung der Designelemente wurden empirisch untersucht. Aus hochschuldidaktischer bzw. medienpädagogischer Sicht zeigen die Ergebnisse u. a. positive Entwicklungen der interkulturellen Kompetenz von Dozierenden. Konsequenzen für zukünftige Forschung und Weiterbildungspraxis werden abschließend diskutiert.

Algorithmisches Denken ist eine wichtige Voraussetzung für das Erlernen des Programmierens. Die abstrakte Natur und das tatsächliche Verständnis von Algorithmen spielen aber oft eine untergeordnete Rolle, wenn das Programmieren mithilfe spezifischer Umgebungen und Programmiersprachen erlernt wird. Anstatt zugrunde liegende Muster zu erkennen, erwerben die Lernenden oft hauptsächlich die Fähigkeit, konkrete Probleme in einer konkreten Programmiersprache zu lösen. Es stellt sich daher die Frage, wie abstrakte Algorithmen losgelöst von einer konkreten Programmiersprache erfahrbar und verständlich gemacht werden können. Robot Karol ist eine in Schulen oft verwendete Lernumgebung, in der ein Roboter durch einen kleinen Befehlssatz programmier- und steuerbar ist. In Anlehnung an bereits existierende 2D-Versionen von Robot Karol sollen bei dem in diesem Beitrag vorgestellten Ansatz die Nutzer:innen in einer mit Virtual Reality (VR) umgesetzten Umgebung verschiedene Aufgaben im Stil des Robot Karol erfüllen. Der von uns gewählte VR-Ansatz beinhaltet im Gegensatz zu existierenden Desktop-Versionen keine syntaktische Komponente. Die Aktionen der Nutzer:innen werden direkt umgesetzt und visualisiert, wodurch die wesentlichen algorithmischen Abläufe greifbarer gemacht werden sollen.

Mobile agent (MA) technology exhibits remarkable efficiency when integrated into Wireless Sensor Networks (WSNs) for information processing tasks. MAs reduce network overhead by executing processing code locally on nodes and selectively transmitting significant data to designated remote sensor nodes, thereby enhancing data fusion and acquisition while minimizing energy depletion. However, in large-scale networks, relying on a single MA leads to significant delays, necessitating the use of multiple MAs to operate asynchronously and minimize latency. The challenge lies in effectively grouping nodes to ensure MAs reach their intended destinations. To address this challenge, this paper introduces a novel approach, the Adaptive FCM Clustering Algorithm (AFCM), a fuzzy-based clustering algorithm designed for addressing network partitioning challenges in Multiple Mobile Agent Itinerary Planning (MIP). A systematic analysis of the existing literature examines various MIP algorithms, emphasizing their strengths and uncovering potential research gaps. AFCM is specifically developed to create disjoint and load-balanced partitions tailored for multi-mobile agent itinerary planning. A Methodical analysis with three traditional clustering algorithms is conducted. The correctness of the Adaptive Fuzzy C-Means (AFCM) algorithm is demonstrated through a detailed manual application on a wireless network comprising 15 nodes.

BACKGROUND: Metabolic age is defined as an estimation of a person's age based on their basal metabolic rate (BMR) and other physiological health indicators. Unlike chronological age, which simply measures the number of years lived since birth, metabolic age is based on various health and fitness markers that estimate the body's "true" biological age and can be assessed using various methodologies, including bioimpedance. The aim of this study was to evaluate how age, sex, social class, smoking habits, physical activity, and adherence to the Mediterranean diet influence metabolic age. METHODS: A cross-sectional, descriptive study was conducted on 8590 Spanish workers in the Balearic Islands. A series of sociodemographic variables and health-related habits were assessed, while metabolic age was measured using bioimpedance. A metabolic age exceeding chronological age by 12 years or more was considered high. A descriptive analysis of categorical variables was performed by calculating their frequency and distribution. By applying multivariate models, specifically multinomial logistic regression, we observe that all independent variables (sex, age, social class, physical activity, mediterranean diet, and smoking) show varying levels of association with the occurrence of high metabolic age values. Among these independent variables, those showing the highest degree of association, represented by odds ratios, are physical activity, adherence to the Mediterranean diet, and social class. In all cases, the observed differences demonstrate a high level of statistical significance (p < 0.001). RESULTS: The factors with the greatest influence were physical inactivity, with an OR of 5.07; and low adherence to the Mediterranean diet, with an OR of 2.8; followed by social class, with an OR of 2.51. Metabolic age increased with chronological age and was higher in males, with an OR of 1.38. Smoking also had a negative impact on metabolic age, with an OR of 1.19. CONCLUSIONS: Mediterranean diet is associated with a higher metabolic age. The most influential factors on metabolic age are physical activity and adherence to the Mediterranean diet, followed by the individual's socioeconomic class. Smoking also contributes to increased metabolic age, albeit to a lesser extent.

Over the next ten years, electric power utilities will be required to invest billions of dollars to meet public policy goals for a greener, smarter electricity grid. Renewable generation portfolio standards, electric vehicle infrastructure, advanced metering infrastructure, and the replacement of aging grid assets are some of the factors driving these new investments. The Grid Intelligent Planning Framework using GridLAB-D is an advanced forecasting solution that allows utility business and engineering experts to collaborate on forecasting models, and thereby to reduce the time needed for a capital investment planning cycle. This solution facilitates wise and timely investment decisions as the pace of change accelerates in the electric power industry. © 2011 Springer-Verlag.

Vor 75 Jahren wurde der Deutsche Gewerkschaftsbund ins Leben gerufen. Seine Gründungsgeschichte erinnert uns heute daran, wie umkämpft die Ausrichtung der neuen Bundesrepublik nach dem Krieg gewesen ist.

Our research introduces a novel framework for analyzing programmer psychological traits by combining automated code analysis and sentiment assessment. Our contribution is in the form of a framework that can identify key behavioral traits such as attention to detail, collaboration orientation, and problem-solving tendencies. Unlike traditional survey-based methods, this approach offers objectivity and applicability across diverse programming contexts. Experimental results demonstrate its potential to contribute to software engineering, developer productivity research, and cognitive profiling of programmers.

Legal question answering (Legal QA) aims to provide accurate and timely answers to legal questions, significantly reducing the workload of legal professionals. This approach improves the efficiency of the judiciary and ensures prompt, professional legal assistance to the public. Currently, a major challenge is the absence of a large-scale dataset tailored for Chinese generative legal question answering. To address this, our study developed a comprehensive automatic question answering dataset for Chinese civil law, named cLegal-QA, which comprises 14,000 high-frequency questions from Chinese legal communities. This dataset spans various legal disputes and includes questions, disputes, scenarios, multiple lawyer responses, and gold-standard answers from human annotators. Additionally, we employed a generative QA model specifically designed for the cLegal-QA dataset. The results indicate that fully-supervised models, notably UniLM, T5, and BART, substantially outperform zero-shot models on this dataset, with ChatYuan being the most effective among the zero-shot models. Our analysis also reveals that answers labeled with 60--80% accuracy yield the highest efficiency. Furthermore, we evaluated the real-world performance of these models with expert validation and applied transfer learning to new civil disputes. While the QA models demonstrate commendable performance on the dataset, there is still potential for further improvement.

INTRODUCTION: Major electrocardiogram abnormalities (MECG) are common in middle-aged and older individuals and could be an important factor in predicting cardiovascular events. OBJECTIVE: To analyse the association between MECG (Minnesota classification) and CVE independently of classic cardiovascular risk factors (CVRF), and to assess whether they improve the prediction according to the Spanish Coronary Event Risk Function (FRESCO). METHOD: 1.752 participants included in three nodes of the PREDIMED study aged between 55 and 80 years with medium or high CVRF. Mean follow-up time was 5.1 years. Cumulative CVE incidence was estimated by sex with and without MECG, and hazard ratios by sex were estimated using multivariate Cox regressions adjusted for randomization group and CCRF (FRESCO). Harrel's C Indices, Nam d'Agostino, Net Reclassification Improvement, and Integrated Discrimination Improvement were calculated. RESULTS: At baseline, 25% of the participants shows major electrocardiogram abnormalities (AMECG). During follow-up, there were 112 cardiovascular events (16 cardiovascular deaths, 15 acute myocardial infarctions, 38 anginas, 43 strokes). MECG were significantly associated with the onset of CVE. In men, left ventricular hypertrophy (LVH) criteria were associated with T-wave inversion (HR: 17.88, 95% CI: 5.51-58.03, pvalor<.001) and QT interval prolongation (HR: 2.41, 95% CI: 1.38-4.21, pvalor=.002); in women, atrial fibrillation (HR: 5.7, 95% CI: 1.76-18.72, pvalor=.006) and ST-segment depression (HR: 3.24, 95% CI: 1.36-7.71, pvalor<.001) were associated. No significant improvement in MECG prediction compared to FRESCO was observed. CONCLUSIONS: MECG are independently associated with the occurrence of CVE, but do not improve risk prediction beyond traditional risk factors.

In Compiler Design courses, students learn how a program written in high level programming language and designed for humans understanding is systematically converted into low level assembly language understood by machines, through different representations. This article presents the design, educative characteristics and possibilities of a modular and didactic compiler for a Pascal-like programming minilanguage that is super-set of Niklaus Wirth's PL/0. The main feature is that it implements the compiling phases in such a way that the information delivered to each next one may be reflected as an XML document, which can be studied separately. It is also shown that its design is suitable for being included as learning tool into compiler design courses. It is possible to implement a compiler in a high-level language like Python

Ein Gespenst geht um in Europa, ja in der ganzen Welt – das Gespenst der Cancel Culture. Glaubt man diversen Zeitungen, dürfen insbesondere weiße Männer jenseits der vierzig praktisch nichts mehr sagen, wenn sie nicht ihren guten Ruf oder gar ihren Job riskieren wollen. Ist da etwas dran? Oder handelt es sich häufig um Panikmache, bei der Aktivist:innen zu einer Gefahr für die moralische Ordnung stilisiert werden, um ihre berechtigten Anliegen zu diskreditieren? Der Ursprung der Cancel Culture wird üblicherweise an US-Universitäten verortet. Adrian Daub lehrt im kalifornischen Stanford Literaturwissenschaft. Er zeigt, wie während der Reagan-Jahre entwickelte Deutungsmuster über Campus-Romane verbreitet und auf die Gesellschaft insgesamt übertragen wurden. Man pickt einige wenige Anekdoten heraus und reicht sie herum, was auch hierzulande zu einer verzerrten Wahrnehmung führt. Anhand quantitativer Analysen zeichnet Daub nach, wie diese Diagnosen immer weitere Kreise zogen, bis sie auch die Twitter-Kanäle deutscher Politiker erfassten.

Given the exponential increase in data volumes, the variety of data types, and the complexity of processing, researchers have begun to look at another type of database model that is moving away from the classic rules of the relational model. Among the NoSQL models proposed in a set of research works, there is the document-oriented model. This model relies on the key-document pair. The documents are JSON or XML type. A single key can thus retrieve all information hierarchically and nested, which would require multiple joins in the SQL model. In this work we propose a method of storing data in a nested document-oriented database. The latter will be obtained from a standardized conceptual model by applying the transformation rules from the data conceptual model (DCM) to the nested document-oriented model (NDOM). A performance study will also be carried out in this work to show NDOM model ability to process massive data through aggregation queries.

The Social Web is a set of social relations that link people through World Wide Web. This Social Web encompasses how the websites and software are designed and developed to support social relations. The new paradigms, tools and web services introduced by Social Web are widely accepted by internet users. The main drawbacks of these tools are it acts as independent data silos; hence interoperability among applications is a complex issue. This paper focuses on this issue and how best we can use semantic web technologies to achieve interoperability among applications.