Large language models (LLMs) are reshaping interactive digital narratives (IDNs). However, creating complex interactive narratives while preserving narrative consistency remains challenging. We present Orchid‑Creator (Orchid), an LLM-based authoring tool that represents IDNs as story graphs with a card‑based interface for scene definition and conditional transitions. We evaluated Orchid in two studies: a usability study with eight authors, and a comparative study with 20 participants (authors, developers, and players) that compared Orchid to Twine and AI Dungeon. Authors reported that Orchid’s features met their needs (card‑based interface: 4.0/5; story graph: 4.38/5; variable setup: 4.5/5). Structuring narratives with Orchid was easier (M = 6.0/7, p <.01) and produced better‑structured stories (M = 5.3/7, p <.05) than the alternatives, balancing author control (M = 5.5/7) with outcome diversity (M = 5/7, p <.01) and maintaining comparable usability. Finally, a case study with an artist demonstrates Orchid’s utility for interactive art.

State-of-the-art hierarchical localisation pipelines (HLoc) employ image retrieval (IR) to establish 2D-3D correspondences by selecting the top-k most similar images from a reference database. While increasing$k$improves localisation robustness, it also linearly increases computational cost and runtime, creating a significant bottleneck. This paper investigates the relationship between global and local descriptors, showing that greater similarity between the global descriptors of query and database images increases the proportion of feature matches. Low similarity queries significantly benefit from increasing k, while high similarity queries rapidly experience diminishing returns. Building on these observations, we propose an adaptive strategy that adjusts$k$based on the similarity between the query's global descriptor and those in the database, effectively mitigating the feature-matching bottleneck. Our approach reduces computational costs and processing time without sacrificing accuracy. Experiments on three indoor and outdoor datasets show that AIR-HLoc reduces feature matching time by up to 30% while preserving state-of-the-art accuracy. The results demonstrate that AIR-HLoc facilitates a latency-sensitive localisation system.

Collaborative Problem Solving (CPS) has emerged as a key competence for the 21st century. In support of this, valid assessments of CPS skills have become critical. However, limited research has designed and developed CPS assessments for young students. Based on multimodal learning analytics, we aim to develop and validate a game-based assessment of CPS for primary school students. In this study, evidence centered design approach was used to design and develop the game-based CPS assessment. Specifically, we designed and developed a mobile multiplayer online 3D role-playing game on CPS and a coding scheme for coding students’ gameplay data (i.e., game logs and voice chat) based on the ATC21S CPS framework. A total of 32 primary 5 students participated in this study to play the game in a group of four and complete a questionnaire of CPS skills. The gameplay data were coded based on our coding scheme. Correlation analysis between the coded results and the CPS questionnaire data supported the criterion validity of our game-based assessment measure. Additionally, the results of expert interview facilitated our understanding of assessment design and data use. This study will make methodological and practical contributions to the integration of MMLA into game-based CPS assessments.

Well-designed digital games can serve as the vehicle to assess and support young people’ collaborative problem solving (CPS) skills. However, there is limited research leveraging multimodal learning analytics (MmLA) to explore students’ game-based CPS processes and outcomes. Inspired by MmLA methods and approaches, this preliminary study aims to examine students’ demonstration of CPS skills through collecting and analyzing a dataset of combined game logs and verbal discourses from two groups of primary school students with contrasting performances. Based on the Assessment and Teaching of 21st Century Skills CPS framework, we iteratively coded the dataset. Results of descriptive statistics showed that the successful group exhibited cognitive skills more frequently while the unsuccessful group showcased social skills more. Results of epistemic network analysis (ENA) revealed that, in both social and cognitive dimensions, the successful group demonstrated more diverse and stronger associations among various subskills, whereas there were fewer associations in the unsuccessful group. Implications are drawn for MmLA and CPS research and teaching practices of CPS skills.

To properly define satellite lifetime so as to maximize economic net benefit, the lifetime optimization method is studied in this paper based on discrete cross entropy (CE) method. Firstly, the influences of lifetime on satellite system design, cost and revenue are studied and the disciplinary models are developed. Then the lifetime optimization problem is formulated, which is a typical discrete optimization problem with the discrete lifetime measured in years and the combination of the subsystem components as design variables. To efficiently solve this problem, the CE method is used as the optimization solver, and a convolution based smoothing strategy is proposed to enhance the space exploration capability and algorithm robustness. The efficacy of the proposed method is demonstrated in a 19 dimensional satellite lifetime design problem, which also verifies the importance of lifetime optimization in enhancing the satellite economic benefit.