Moneerh Aleedy, Fatma Alshihri, Souham Meshoul
et al.
Translation education (TE) demands significant effort from educators due to its labor-intensive nature. Developing computational tools powered by artificial intelligence (AI) can alleviate this burden by automating repetitive tasks, allowing instructors to focus on higher-level pedagogical aspects of translation. This integration of AI has the potential to significantly enhance the efficiency and effectiveness of translation education. The development of effective AI-based tools for TE is hampered by a lack of high-quality, comprehensive datasets tailored to this specific need, especially for Arabic. While the Saudi Learner Translation Corpus (SauLTC), a unidirectional English-to-Arabic parallel corpus, constitutes a valuable resource, its current format is inadequate for generating the parallel sentences required for a didactic translation corpus. This article proposes leveraging large language models like the Generative Pre-trained Transformer (GPT) to transform SauLTC into a parallel sentence corpus. Using cosine similarity and human evaluation, we assessed the quality of the generated parallel sentences, achieving promising results with an 85.2% similarity score using Language-agnostic BERT Sentence Embedding (LaBSE) in conjunction with GPT, outperforming other investigated embedding models. The results demonstrate the potential of AI to address critical dataset challenges in quest of effective data driven solutions to support translation education.
Background The rapid development of information technology has significantly propelled the integration and evolution of product design technologies and their related algorithms. This review systematically investigates the pivotal role of AI-driven product form generation technologies in promoting industrial design innovation and sustainable development. Methodology By employing bibliometric tools (Citespace) combined with visualization analysis, we propose a seven-stage technical framework encompassing “identification-extraction-analysis-generation-data mapping-decision-making-optimization.” Results The study traces the historical evolution, current research trends, and future development of product form generation design technologies. It highlights that artificial intelligence, as the core driving force, has substantially enhanced automated modeling and multi-objective optimization capabilities. However, challenges remain in areas such as data standardization deficits, limited dynamic adaptability, and insufficient cross-disciplinary collaboration. Future priorities should include: (1) strengthening algorithmic robustness to manage complex design scenarios; (2) integrating multimodal user feedback mechanisms to elevate interactive experiences; (3) constructing interpretable generative models to ensure design credibility; and (4) exploring green design-oriented intelligent algorithm deployment strategies with embedded ethical considerations.
Planare, isolierte Anionen SiP, SiAs, GeP und GeAs liegen in den Kristallen der dunkel metallisch glänzenden Titelverbindungen (Zintl‐Phasen) vor. Die 24‐Valenzelektronen‐Einheiten sind die ersten Beispiele für CO‐isostere Anionen, in denen Zentralatome und Liganden höheren Perioden angehören. Aus den gemessenen Bindungslängen lassen sich Bindungsordnungen (nach Pauling) von 1.22, 1.28, 1.12 bzw. 1.26 ableiten.
Un análisis crítico de algunas confusiones que rondan el proceso de paz entre el Gobierno y las FARC, y que se refieren a los objetivos (¿La terminación del conflicto armado? ¿El perdón de las víctimas a los victimarios?), en procura de clarificar qué lugar corresponde al odio y al perdón. El gran objetivo del proceso de paz es la finalización del conflicto armado entre el Estado colombiano y las FARC y la consecución a largo plazo de la paz social, y no es –ni puede ser– el perdón, un acto reservado a la esfera íntima y autónoma de las víctimas, que está más allá de lo que pueden pactar las partes en conflicto (únicas llamadas a acordar el cese de hostilidades), no es articulable políticamente y no puede ser impuesto por medio de leyes ni a los victimarios (para que pidan perdón), ni las víctimas (para que perdonen).