Early phases of space missions rely on diverse engineering tools that often share similar data structures and domains but lack interoperability. As a result, data exchange between tools typically requires manual effort or custom-built converters, limiting efficient collaboration and reuse of design data. This paper proposes the concept of a Semantic Adapter to enable flexible and automated data exchange between heterogeneous engineering tools without tool-specific adaptions. The approach combines OpenAPI, as a standardized way to access and exchange tool data, with a mapping and model transformation algorithm that aligns and converts data between different tool-specific models. The concept was demonstrated and evaluated in a case study involving two concurrent engineering centers collaborating on a joint space mission study. The evaluation shows that the Semantic Adapter enables automated data retrieval and transformation, demonstrating the general feasibility of combining API specifications and model transformations for engineering data exchange. However, results also indicate that automatically writing transformed data to target tools via OpenAPI remains a challenge, especially when dealing with complex, dynamically typed models. The paper concludes by discussing limitations and directions for future work.
The resumption of Tesla's wireless power transfer research by the scientific community has opened up a new paradigm for wireless power networks. Building on the foundation established in previous works, this paper analyzes system energy costs and user service times to determine supply zones per station. Specifically, previous publications introduced two categories of energy providers: one primary source and multiple secondary sources. A cooperation protocol between primary and secondary sources was described to maximize energy savings in the user supply process. In this paper, we analyze system energy costs and user service times across the WEN coverage area to consider future versions of this protocol.
Narrative computer generation is understood as the task of constructing computational models of the way in which humans build stories. In order to imitate human cognition in intelligent systems, it is necessary to have a good knowledge of the principles that explain how human language constructs concepts. In this paper, a new tool in the form of the Interactive Narrative Graph Grammar (INGG) in which a natural language is translated into the internal language of the semantically enriched graph system is proposed. The system oriented for computer games is particularly relevant in the context of collaborative design. It enables cooperation between narrative designers, who may not have a technical background, and level or world creators in game development. By providing an intuitive yet structured way to represent story elements, relationships, and logic, this system serves as a bridge between storytelling and computational implementation.
Cross-disciplinary teams often face the challenges of semantic misalignment and low emotional engagement during the early stages of design. This study proposes an interaction system that integrates visual clustering and physiological sensing to observe semantic alignment and emotional engagement during visual interpretation in team interaction. Although AI image generation tools are increasingly being adopted to facilitate language-based communication, their inherent ambiguity and semantic distortion can lead to misunderstandings and hinder consensus building. At the same time, emotional cues that influence collaboration quality are often embedded in non-verbal interactions; if not detected in time, these can result in withdrawal or breakdowns in communication. To address these issues, this study proposes an interactive system that integrates semantic input, AI image generation, and wearable emotion sensing devices. Using VGG16 for image feature clustering and the Emotion Wings device to record real-time heart rate variability (HRV), the study analyzes the interaction between image style, semantic orientation, and emotional engagement. The results indicate that semantically balanced images help enhance participant engagement and mutual understanding, while semantic bias or character exclusion tend to lead to emotional withdrawal and low consensus.
This paper presents the development and evaluation of a Vietnamese Question Answer Generation (QAG) model specifically designed to generate review questions and exam questions in the Vietnamese subject Marxist-Lenininist Philosophy, helping learners to better manage their knowledge. Through the creation of a new QA dataset dedicated entirely to the Vietnamese subject Marxist-Lenininist Philosophy and the training and fine tuning processes, we have use a small-scale pre-trained language model (PLM) but the performance of generating Vietnamese questions is equivalent to that of LLM models. Our findings show that smaller specialized models can indeed match the capabilities of larger models, providing a more resource-efficient alternative without compromising on quality or efficiency. This study contributes to the broader discourse on the extensibility and adaptability of PLM, providing valuable insights into how to develop specialized models for specific educational purposes.
