Abstract
This study investigates the impact of virtual agent realism in immersive Virtual Reality (iVR) on foreign-language vocabulary learning. Specifically, it compares the effectiveness of a realistic (human-like) pedagogical virtual agent versus an abstract (non-human-like) one in delivering instructional content. A between-subjects experiment was conducted with 17 participants, divided into two groups, were exposed to either the realistic or abstract agent in an iVR Search-and-Find vocabulary learning task. Learning outcomes were measured using pre- and post-tests (based on word matching translations for 10 German-Portuguese item pairs), while presence-related experiences were assessed via the Igroup Presence Questionnaire and Temple Presence Inventory. Both groups demonstrated significant vocabulary acquisition improvements. However, no significant differences were found between the realistic and abstract agent groups in either learning outcomes or presence scores. The findings suggest that the visual realism of virtual agents may not significantly influence language learning effectiveness or user presence in these iVR environments. These preliminary results imply that abstract agents could be as effective as realistic agents for this type of foreign-language instruction, potentially reducing development resources without compromising learning benefits.

Abstract
Identifying influential spreaders in temporal networks is crucial for understanding and controlling the dynamics of spreading. However, existing methods, such as temporal betweenness, closeness, pagerank, degree, and local path-based centrality, face several limitations, including high computational complexity, reliance on shortest paths, convergence issues, inability to capture influence dynamics with insufficient neighboring nodes, and a primary focus on local structural information. This paper presents PathSAGE, a novel method that addresses these problems. It integrates GraphSAGE, a deep learning model, to capture global node information while incorporating temporal local path counts as a key feature. Unlike other global feature-capturing methods, PathSAGE optimises computational complexity. Experimental results on thirteen real-world temporal networks demonstrate that PathSAGE outperforms the state-of-the-art methods in accurately identifying influential spreaders. PathSAGE exhibits a strong correlation with the Temporal Susceptible-Infected-Recovered (TSIR) model and achieves a relative improvement percentage (eta%) ranging from 0.12% to 70.70%. Additionally, PathSAGE attains the lowest average robustness value of 0.17, highlighting its effectiveness in identifying influential spreaders within temporal networks.

Abstract
The growing integration of renewable energy sources and the widespread electrification of the energy demand have significantly reduced the capacity margin of the electrical grid. This demands a more flexible approach to grid operation, for instance, combining real-time topology optimization and redispatching. Traditional expert-driven decision-making rules may become insufficient to manage the increasing complexity of real-time grid operations and derive remedial actions under the N-1 contingency. This work proposes a novel hybrid AI framework for power grid topology control that integrates genetic network programming (GNP), reinforcement learning, and decision trees. A new variant of GNP is introduced that is capable of evolving the decision-making rules by learning from data in a reinforcement learning framework. The graph-based evolutionary structure of GNP and decision trees enables transparent, traceable reasoning. The proposed method outperforms both a baseline expert system and a state-of-the-art deep reinforcement learning agent on the IEEE 118-bus system, achieving up to an 28% improvement in a key performance metric used in the Learning to Run a Power Network (L2RPN) competition.

Abstract
This study aims to investigate how specific graduate attributes relate to university students’ employability and entrepreneurial intentions, with a focus on higher education institutions in the United Arab Emirates (UAE). The research distinguishes between traditional and emerging attributes and examines their predictive value for distinct post-graduation pathways. A quantitative, cross-sectional survey design was adopted. Data were collected from 524 undergraduate students and analysed using multivariate multiple regression to assess the simultaneous effects of nine graduate attributes. The findings reveal that employability intention is significantly are associated with goal-directed behaviour, continuous learning, problem-solving, and the ability to present and apply information. Entrepreneurial intention, on the other hand, is more strongly predicted by enterprising behaviour, analytical thinking, and artificial intelligence literacy. Some attributes, such as ethical responsibility and interactive communication, were not significant predictors. University prestige had a minor but significant effect on employability intention, while the presence of a university incubator showed no significant relation. This study contributes to the theoretical development of graduate attribute frameworks by validating digital-era competencies and empirically distinguishing between employability and entrepreneurial orientations. It offers practical insights for higher education institutions seeking to develop curricula that better prepare graduates for diverse career outcomes.

Abstract
Textile-to-textile recycling is strongly constrained by upstream pre-processing, where post-consumer clothing must be identified, separated, and prepared under high variability in materials, appearance, and contamination. This paper presents a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic literature review of intelligent and automated technologies for textile recycling pre-processing covering the interval between 2015 to 2025. After screening and quality assessment, 21 primary studies published between 2020 and 2025 were included. The literature is synthesized across three task families: (i) identificationof fiber/material, composition, or color; (ii) sorting, considered only when explicit separation strategies are defined to operationalize identification outcomes into routing actions or output streams; and (iii) contaminant detection and/or removal, targeting non-recyclable items. Results show that identification dominates the field (19/21 studies), supported by Red-Green-Blue (RGB) and red-green-blue plus depth (RGB-D) imaging and material-signature sensing, including near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and Raman spectroscopy. In contrast, sorting as a defined separation stage is less frequent (4/21), and contaminant-related automation remains sparse (3/21). Most studies are validated in laboratory conditions, with limited semi-industrial evidence, highlighting a persistent perception-to-action gap. Overall, the review indicates that robust separation strategies, representative datasets, and end-to-end system integration remain key bottlenecks for scalable automated textile recycling pre-processing.

Abstract
The Industry 5.0 concept guides the industry to the premise of sustainability, resilience and human-centric solutions. The last related pillar tries to create solutions to empower the people in production line processes since solutions should be designed to be easy to use and easy to learn without discarding the working people. In this regard, it's natural that robots become closer to humans in industrial applications where it is possible to absorb human-machine qualities. Robotic grasping has widespread application with a wide range of applicability. However, engineers and shop-floor operators spend time finding a fast response solution when the production demand changes. Aiming to create a tool to help this procedure in a human-centred fashion, the current paper proposes a programming-by-demonstration solution that is easy to use, reuse, adapt, and increment with its modular design.