Abstract
The present research aims to analyse the perceptions of young university students regarding digital influencers and their role in brand communication strategies. A questionnaire was conducted using “Google Forms,” administered online between the months of February and April 2023, with a non-probabilistic convenience sample. A total of 196 questionnaires were collected and validated. The results show that young individuals recognize the role of digital influencers in brand communication to discover new products and brands. Insta- gram is the most widely used social network, and young university students follow digital influencers, primarily seeking content related to “fashion and beauty.” Respondents acknowledge when content is sponsored, but this does not diminish their trust and credibility in the influencer, especially when it aligns with their profile and lifestyle.

Abstract
At the practical level, the static stability constraint is one of the most important constraints in practical pallet loading problems, such as air cargo palletizing. Approaches to modeling static stability, which range from base support and mechanical equilibrium calculations to physical simulation, differ in workflow, focus, and assumptions, so choosing the right static stability approach has a substantial impact on the quality of the solution and, ultimately, on loading security. To date, little research has investigated the structural differences between approaches. The aim of this paper is to integrate knowledge and shed light on the applicability of the different approaches for the practical scenario of air cargo palletizing. We tackle this problem through (1) a reformulation and extension of static stability toward loading stability, (2) a conceptual analysis of current approaches, and (3) benchmarking that employs an independent multibody simulation on multiple heterogeneous datasets. Our results show that all approaches are prone to structure errors and vary significantly in their premises and information usage. Further, full base support is revealed to be the most restrictive approach by far, while physical simulation achieves the greatest accuracy. Given the trade-off between accuracy and runtime, the mechanical equilibrium approach is a good choice, while partial base support performs best for lower support values.

Abstract
The digitalization of healthcare and the widespread use of personal health technologies generate large volumes of health data, yet individuals still struggle to manage and understand their information. This paper explores the development of personas that represent typical personal health information-seeking behaviors, motivations, and needs to support the user-centered design of personal health knowledge management systems. We adopted a mixed-method approach, conducting and analyzing an online survey that collected data on personal health information needs, purposes of access, and access-related challenges, along with using affinity diagramming to group participants by key characteristics identified through data analysis. This approach derived three personas: clinical navigator, health tracker, and care planner. These personas provide a basis for designing user-centered systems that enable individuals to access, organize, and use their personal health information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

Abstract
Safety-critical deployment of machine learning models requires unambiguous specification and verified implementation of tensor operators. This paper presents a formally verified workflow based on the Why3 deductive verification platform. The workflow takes each operator from an abstract WhyML specification over mathematical tensors to extracted C code, via a concrete implementation on flat arrays linked by a machine-checked refinement proof. A reusable library supports the development, providing abstract tensor types, a row-major layout with proven bijectivity, and a refinement mapping to a C-level representation. We illustrate the workflow on several ONNX operators, developed within the SONNX Working Group. Finally, we demonstrate how the abstract specifications compose to enable contract-based verification of functional properties of complete networks. The methodology is applicable beyond ONNX to any framework requiring verified tensor implementations.

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
In the context of transport networks, synchronization techniques refer to a set of optimization approaches whose output is the timetables of different lines obtained through the minimization of transfer waiting time and vehicle bunching. Accordingly, synchronization techniques can improve the overall quality of transfers in transport systems by making them smoother for passengers. The synchronization of transport lines at strategic locations improves the system's overall connectivity and integration and, ultimately, its attractiveness to passengers. This work aimed to synchronize strategic transport lines connecting Porto to other cities, having Porto as the only focal point. The morning peak hours were selected as the analysis period. A synchronization model already proposed in the literature was adapted and applied to this case study. The results include the comparison between the real and synchronized timetables and the sensitivity analysis of the solutions obtained by changing headway and time window parameters.