Abstract
Telecommunications and computer vision have evolved separately so far. Yet, with the shift to sub-terahertz (sub-THz) and terahertz (THz) radio communications, there is an opportunity to explore computer vision technologies together with radio communications, considering the dependency of both technologies on Line of Sight. The combination of radio sensing and computer vision can address challenges such as obstructions and poor lighting. Also, machine learning algorithms, capable of processing multimodal data, play a crucial role in deriving insights from raw and low-level sensing data, offering a new level of abstraction that can enhance various applications and use cases such as beamforming and terminal handovers. This paper introduces CONVERGE, a pioneering vision-radio paradigm that bridges this gap by leveraging Integrated Sensing and Communication (ISAC) to facilitate a dual View-to-Communicate, Communicate-to-View approach. CONVERGE offers tools that merge wireless communications and computer vision, establishing a novel Research Infrastructure (RI) that will be open to the scientific community and capable of providing open datasets. This new infrastructure will support future research in 6G and beyond concerning multiple verticals, such as telecommunications, automotive, manufacturing, media, and health.

Abstract
This study investigates how grapevines (Vitis vinifera L.) respond to shading induced by artificial nets, focusing on physiological and metabolic changes. Through a multidisciplinary approach, grapevines' adaptations to shading are presented via biochemical analyses and hyperspectral data that are then combined with systems biology techniques. In the study, conducted in a 'Moscatel Galego Branco' vineyard in Portugal's Douro Wine Region during post-veraison, shading was applied and predawn leaf water potential (Psi pd) was then measured to assess water stress. Biochemical analyses and hyperspectral data were integrated to explore adaptations to shading, revealing higher chlorophyll levels (chlorophyll a-b 117.39% higher) and increased Reactive Oxygen Species (ROS) levels in unshaded vines (52.10% higher). Using a self-learning artificial intelligence algorithm (SL-AI), simulations highlighted ROS's role in stress response and accurately predicted chlorophyll a (R2: 0.92, MAPE: 24.39%), chlorophyll b (R2: 0.96, MAPE: 17.61%), and ROS levels (R2: 0.76, MAPE: 52.17%). In silico simulations employing flux balance analysis (FBA) elucidated distinct metabolic phenotypes between shaded and unshaded vines across cellular compartments. Integrating these findings provides a systems biology approach for understanding grapevine responses to environmental stressors. The leveraging of advanced omics technologies and precise metabolic models holds immense potential for untangling grapevine metabolism and optimizing viticultural practices for enhanced productivity and quality.

Abstract
Virtual Reality (VR) has the capacity to increase tourists' responses, compared with other marketing tools. In tourism, it can play a decisive role in its promotion, since it can generate impactful information that will increase the visit intention. However, there are few reviews that focus on VR as a promotional tool in tourism. To overcome this limitation, this work provides a bibliometric analysis of papers from the Web of Science and Scopus databases. The analysis allows us to conclude that although its potential is recognized, the use of VR is infrequent in tourism. We also identified three main avenues for future research: presence and devices, promotional strategies, and segments to explore.

Abstract
PurposeThis study aims to identify and explore the factors affecting social entrepreneurial intentions considering an educational institution in Portugal. It also intends to determine the relevance of moderating factors in the antecedents and entrepreneurial intention of these students. Design/methodology/approachA panel of 177 undergraduate students enrolled in a social entrepreneurship course between the academic years 2018 and 2021 is considered. The data is explored quantitatively considering descriptive analysis techniques, correlational analysis and hypothesis testing. FindingsThe findings reveal that entrepreneurial intention depends on multiple individual, organizational and contextual dimensions. Students' entrepreneurial intention remains unchanged regardless of the student's profile. However, students' professional experience is a more relevant factor for the identification of organizational dimensions related to curriculum and critical pedagogy, while previous involvement in volunteer activities contributes to a higher prevalence of individual factors. Originality/valueTo the best of the authors' knowledge, this study is original in exploring the role of entrepreneurial intention and its antecedents considering a heterogeneous students' profile. It offers theoretical and practical contributions by extending the literature on social entrepreneurial intention that can be used by higher education institutions to offer specific training more focused on the student's profile.

Abstract
DAS technology has emerged as a transformative technology with a vast range of applications, both on land and at sea. These applications span from oil and gas exploration to geophysical data collection, infrastructure monitoring, security, and environmental hazard monitoring, including earthquake and tsunami early warning systems (Landrø et al., 2022; Gorshkov et al., 2022). The unique properties of DAS systems can bring high benefits to the demanding field of seismology, as it provides a significant increment in the spatial information that can be obtained from a seismic event. Moreover, the widespread deployment of optical fiber across the Earth's surface, coupled with the relatively low cost per monitoring point for extended distances, has rendered DAS an appealing alternative to traditional seismographs (Li et al., 2023). This is especially true for subsea applications, where the capability of remote sensing is particularly attractive. Remote sensing enables the placement of systems far from harsh environments, often difficult to access, enhancing the feasibility and effectiveness of monitoring efforts. In this work, it was employed a DAS equipment on a dark telecommunication fiber was installed exclusively for research purposes, named GEOLAB, located on the island of Madeira. This fiber spans approximately 50 km, where the initial tests were conducted using a DAS from January 31 to February 14, 2023. The equipment utilized is the HDAS provided by the IO-CSIC. The signal of the fiber was collected with a spatial resolution (or gauge length) of 10 m, resulting in total of 5000 channels, with a temporal acquisition with a frequency of 50 Hz. The DAS system has a chirped pulsed laser as the optical source, generating pulses with a width of 100 ns. These pulses were then amplified using a semiconductor optical amplifier to mitigate intra-band coherent noise. A total of 19 seismic events were detected, and then characterized by performing two-dimensional linear bandpass filtering. We will present the initial findings, particularly the seismic activity resulting from the earthquakes with epicenters near the city of Gaziantep, located in Turkey. These events occurred on February 6, 2023, with magnitudes of 7.5 and 7.8 on the Richter scale.

Abstract
This paper addresses a critical gap in applying semantic enrichment for online news text classification using large language models (LLMs) in fast-paced newsroom environments. While LLMs excel in static text classification tasks, they struggle in real-time scenarios where news topics and narratives evolve rapidly. The dynamic nature of news, with frequent introductions of new concepts and events, challenges pre-trained models, which often fail to adapt quickly to changes. Additionally, the potential of ontology-based semantic enrichment to enhance model adaptability in these contexts has been underexplored. To address these challenges, we propose a novel supervised news classification system that incorporates semantic enrichment to enhance real-time adaptability. This approach bridges the gap between static language models and the dynamic nature of modern newsrooms. The system operates on an adaptive prequential learning framework, continuously assessing model performance on incoming data streams to simulate real-time newsroom decision-making. It supports diverse content formats - text, images, audio, and video - and multiple languages, aligning with the demands of digital journalism. We explore three strategies for deploying LLMs in this dynamic environment: using pre-trained models directly, fine-tuning classifier layers while freezing the initial layers to accommodate new data, and continuously fine-tuning the entire model using real-time feedback combined with data selected based on specified criteria to enhance adaptability and learning over time. These approaches are evaluated incrementally as new data is introduced, reflecting real-time news cycles. Our findings demonstrate that ontology-based semantic enrichment consistently improves classification performance, enabling models to adapt effectively to emerging topics and evolving contexts. This study highlights the critical role of semantic enrichment, prequential evaluation, and continuous learning in building robust and adaptive news classification systems capable of thriving in the rapidly evolving digital news landscape. By augmenting news content with third-party ontology-based knowledge, our system provides deeper contextual understanding, enabling LLMs to navigate emerging topics and shifting narratives more effectively.