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

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
Context. Direct observations of exoplanet and brown dwarf companions with near-infrared interferometry, first enabled by the dualfield mode of VLTI/GRAVITY, provide unique measurements of the objects' orbital motions and atmospheric compositions. Aims. Here we compile a homogeneous library of all exoplanet and brown dwarf K-band spectra observed by GRAVITY thus far. This ExoGRAVITY Spectral Library is made publicly available online. Methods. We re-reduced all the available GRAVITY dual-field high-contrast data in a uniform and highly automated way and, where companions were detected, extracted their similar to 2.0-2.4 mu m K-band contrast spectra. We then derived stellar model atmospheres for all the employed flux references (either the host star or the swap calibrator), which we used to convert the companion contrast into companion flux spectra. Solely from the resulting GRAVITY K-band flux spectra, we extracted spectral types, spectral indices, and bulk physical properties for all the companions. Finally, and with the help of age constraints from the literature, we also derived isochronal masses for most of the companions using evolutionary models. Results. The resulting library contains R similar to 500 GRAVITY K-band spectra of 39 substellar companions from late M to late T spectral types, including the entire L-T transition. Throughout this transition, a shift from CO-dominated late M- and L-type dwarfs to CH4-dominated T-type dwarfs can be observed in the K-band. The GRAVITY spectra alone constrain the objects' bolometric luminosity to typically within +/- 0.15 dex. The derived isochronal masses agree with dynamical masses from the literature where available, except for HD 4113 c for which we confirm its previously reported potential underluminosity. Conclusions. Medium-resolution spectroscopy of substellar companions with GRAVITY provides insight into the carbon chemistry and the cloudiness of these objects' atmospheres. It also constrains these objects' bolometric luminosities, which can yield measurements of their formation entropy if combined with dynamical masses, for instance from Gaia and GRAVITY astrometry.

Abstract
Healthcare systems are complex adaptive environments in which clinical work, digital technologies, and organizational routines interact continuously, often challenging the integration of artificial intelligence (AI) into everyday practice. Although explainable AI (xAI) has been proposed to address concerns related to algorithmic opacity and professional trust, explainability is still frequently approached. Grounded in General Systems Theory, sociotechnical systems theory, and complexity science, this study conceptualizes explainability as an emergent system-level property of healthcare systems. Using Design Science Research as a systems-oriented inquiry methodology, a human-centered conceptual framework for AI-supported clinical decision-making was developed through iterative cycles of problem framing, design, demonstration, and evaluation. The framework was explored in rehabilitation nursing, a domain characterized by multidimensional patient data, longitudinal decision processes, and close professional-patient interaction. Iterative engagement with Rehabilitation Nursing Specialists informed design principles related to user participation, contextualized explanations, and workflow alignment. An exploratory evaluation with 144 Specialists assessed perceived usefulness, comprehensibility of explanations, and acceptance of AI-supported recommendations in realistic scenarios. The findings indicate that explainability is experienced not as a property of the algorithm alone, but as an outcome emerging from interactions between AI behavior, human interpretation, and organizational context. The framework shows potential to meaningfully support clinical decision-making in Rehabilitation Nursing by providing contextually aligned, human-centered explanatory outputs.

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.