Abstract
Local flexibility markets have emerged as a promising market-based approach to accommodate the increasing penetration of distributed energy resources in distribution grids in a cost-efficient manner. This paper presents a novel fast datadriven methodology for the segmentation of medium-voltage distribution networks into flexibility zones based on historical operation data. Each flexibility zone groups nodes that affect network constraints in the same way, so that activating active power flexibility at any node within a zone produces the same effect on the network. This allows the DSO to assess and procure flexibility needs at zonal level, while enabling aggregators to manage and optimize their flexibility portfolios by zone. A case study is conducted to validate the methodology and the performance of the proposed data-driven grid segmentation by comparing with the dynamic grid segmentation approach. The results demonstrate the advantages of the proposed grid segmentation in terms of computational efficiency in real-time flexibility procurement. © 2026 IEEE.

Abstract
In CPU-FPGA systems, C/C++ applications are commonly accelerated by offloading selected code regions to the FPGA using HighLevel Synthesis (HLS). Despite the growing capacity of modern FPGAs to support large designs, those code regions are typically limited to kernels of small regions. This is primarily due to the limitations of the HLS tools, such as the lack of support for dynamic memory allocation and multiple levels of pointer dereferencing. This paper presents an automated approach that uses source-tosource transformations, such as function inlining, memory allocation hoisting, and struct flattening, to expand code regions beyond standard kernels and function-based hotspots in complex multifunction applications. We present a method that, by leveraging information from the entire program, enhances communication within each region and mitigates shared-memory data communication bottlenecks, mapping the most promising buffers (partially or fully) to FPGA BRAMs. We evaluate our approach using four applications from the CortexSuite-Vision benchmark suite. As a baseline, we consider the code regions that can be offloaded without transformations, which are severely limited by HLS restrictions and capable of capturing at most 3% of the program's CPU execution time. Then, we examine how lifting each restriction, through code transformations, can extend code regions that capture over similar to 80% of the execution time. Finally, we examine the impact of these regions on FPGA resources and latency. Our approach yields speedups of up to 31x for the application hotspots, and speedups ranging from 2x to 5x for the entire CPU-FPGA applications when compared to their CPU execution time baselines.

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
Virtual reality (VR) has emerged as a powerful promotional tool in tourism, providing consumers immersive and engaging experiences. However, its specific impact on the wine tourism sector remains underexamined. This study aims to both investigate and convincingly highlight the promotional influence of VR on the intention to visit wine tourism destinations. By providing an immersive VR experience to 405 participants, our research revealed that the quality of VR experiences is essential for generating consumer satisfaction. More crucially, we found that wine tourists' satisfaction with VR experiences plays a crucial role in motivating them to visit a destination. Our results not only fill a gap in understanding the impact of VR on wine tourist behaviour but also offer valuable insights for marketing professionals and companies in the sector. This study emphasises the critical need for enjoyable, high-quality and satisfying VR experiences to catalyse the intention to visit. In doing so, we contribute to academic knowledge and provide practical guidance for the industry, highlighting VR's effectiveness as a promotional strategy in wine tourism. This research is not merely an exploration but a compelling defense of VR's transformative influence on wine tourist behaviour.

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.