Abstract
Highlights What are the main findings? Envelope methods (convex hull and alpha shape) are generally more sensitive to point density loss than voxel-based grids, which maintain a relative stability, although they were not always the closest to field-based volume estimations. Methods parameters (alpha and voxel size) influence accuracy and should be adapted to point cloud density, canopy structure, and growth stage. What are the implications of the main findings? UAV photogrammetry provides dense, low-cost 3D canopy data suitable for vineyard monitoring at the row or plant level. Multi-temporal 3D measurements can support vineyard management and integration with decision support systems.Highlights What are the main findings? Envelope methods (convex hull and alpha shape) are generally more sensitive to point density loss than voxel-based grids, which maintain a relative stability, although they were not always the closest to field-based volume estimations. Methods parameters (alpha and voxel size) influence accuracy and should be adapted to point cloud density, canopy structure, and growth stage. What are the implications of the main findings? UAV photogrammetry provides dense, low-cost 3D canopy data suitable for vineyard monitoring at the row or plant level. Multi-temporal 3D measurements can support vineyard management and integration with decision support systems.Abstract Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates.

Abstract
Purpose This study aims to explore how technology-enabled services influence the overall experience of participants in running events by applying a structured service design methodology. Specifically, it examined how recreational runners engage with technology-enabled services throughout the customer journey of a running event, and how the application of the MINDS method contributes to enhancing the runners' experience. Design/methodology/approach Thirty-nine running event participants were interviewed to explore their experiences. The interviews took place in Greece in 2023, across various mass-participation events from marathons to 5K city races. Using the Management and INteraction Design for Service (MINDS) method, qualitative data were thematically analyzed. Findings The study identified how recreational runners interact with technology-enabled services across the pre-, during-, and post-event stages. Using the MINDS method, participants' experiences were mapped to reveal emotional touchpoints, service gaps, and opportunities to enhance the event experience. These findings were translated into service design proposals through the MINDS method, resulting in visual outputs that illustrate how technology-enabled services could be better integrated across the event journey. Originality/value This study is among the first to examine running event experiences from the participants' perspective using a service design methodology. It also contributes to the advancement of the MINDS by introducing customer journey and emotional journey extensions, offering richer insights into how participant experiences can be optimized across the event lifecycle.

Abstract
This paper aims to clarify the characteristics of Digital Twins (DTs) in their most advanced conceptual development, Cognitive Digital Twins (CDTs), and analyze their support for the implementation of the Circular Economy (CE). A systematic literature review was conducted using a specially developed five-dimensional analytical framework to characterize DT proposals and their potential for CE based on an established framework for circularity strategies. The study indicates that cognitive and hybrid DT approaches tend to cover high levels of interoperability, data flow, system levels, and cognitive processes. However, CDT use in CE demands harmonizing different strategies to cover the complete product lifecycle, which recent research on DTs has not fully addressed. This study is the first to systematically review cognitive digital twins and their relation to circularity, offering an analytical framework that can be expanded for future research in various application areas of Industry 5.0.

Abstract
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Abstract
This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. This research evaluates a range of predictive models to identify the most effective techniques for forecasting demand across different routes and times. Two modeling strategies were implemented: a generalistic approach and a specialized one. The latter was designed to capture route-specific characteristics and variability. A real-world case study from a medium-sized metropolitan region in Brazil was used to assess model performance. Results indicate that ensemble-tree-based models, particularly XGBoost, achieved the highest accuracy and robustness in handling nonlinear relationships and complex interactions within the data. Compared to the generalistic approach, the specialized approach demonstrated superior adaptability and precision, making it especially suitable for long-term and strategic planning applications. It reduced the average RMSE by 19.46% (from 13.84 to 11.15) and the MAE by 17.36% (from 9.60 to 7.93), while increasing the average R2 from 0.289 to 0.344. However, these gains came with higher computational demands and mean Forecast Bias (from 0.002 to 0.560), indicating a need for bias correction before operational deployment. The findings highlight the practical value of predictive modeling for transit authorities, enabling data-driven decision making in fleet allocation, route planning, and service frequency adjustment. Moreover, accurate demand forecasting contributes to cost reduction, improved passenger satisfaction, and environmental sustainability through optimized operations.

Abstract
Disinformation is an ancient social phenomenon that has found a favourable environment for dissemination in internet-based social networks. While the scientific community seeks to address the problem by creating specific tools to detect and classify the various types of false information, we argue that systems thinking is necessary to understand and holistically address this major threat. The works that directly cite Disinformation Systems treat this term as a grouping of concepts, mechanisms, objectives and institutions in a large multidisciplinary repository that finds a self-explanation in the term systems. Through a qualitative and theoretical basis, this research proposes that the generation of disinformation can be defined as a system model, theorizing that the entire process of creating, producing and disseminating disinformation can be defined systematically. Thus, we define an initial descriptive model and affirm that the generation of disinformation can be characterized in terms of a sociotechnical work system. We tested the model in historical disinformation scenarios showing that it fits the components and flows of the system. Although initial, this work has the potential to enable the development of new systemic insights and research in the area of disinformation.