Abstract
The deep ocean is vast and challenging to observe; however, it is key to knowledge of the sea and its impact on global climate. Fixed sea observing points (such as the EMSO observing nodes) provide a limited view and are complemented by expensive oceanographic campaigns with systems demanding high logistical requirements such as deep-sea ROVs.  These costs not only limit our capability for key ocean data collection in the deep but also introduce their own environmental costs.Emerging challenges in knowledge and pressure on the exploration of the deep ocean demand new technological solutions for monitoring and safeguarding the marine ecosystem.Innovative robotic technologies such as the TURTLE robotic deep-sea landers can combine long-term permanence at the seabed with mobility and dynamic reconfigurability in spatial and temporal deep-sea observation.Robotic systems of a heterogeneous nature (from conventional gliders, AUVs, or robotic landers) can be combined with standard and new sensing systems, such as bottom-deployed sensor nodes, moored systems, and cabled points when feasible.These systems can provide underwater localization services for the different assets, energy supply and high bandwidth data transfer with robotic docking stations for other mobile elements. An example of the synergy obtained with these new systems is the possibility of using robotic landers as carriers of EGIM (EMSO Generic Instrument Module) sensor payloads, providing power and data storage and flexibility in the deployment and recovery process.This approach, partly taken in the EU-funded Trident project to develop technical solutions for cost-effective and efficient observation of environmental impacts on deep seabed environments, allows for a substantial reduction in the operational and logistic requirements for deep-sea observation, greatly reducing the need for costly oceanographic campaigns or the use of expensive (economic and logistical) deep sea ROV systems.In this work, we present some of the new developments and discuss the transition from existing technological solutions to new ones integrating these recent developments.

Abstract
[No abstract available]

Abstract
For the upcoming 6G wireless networks, reconfigurable intelligent surfaces are an essential technology, enabling dynamic beamforming and signal manipulation in both reflective and transmissive modes. It is expected to utilize frequency bands in the millimeter-wave and THz, which presents unique opportunities but also significant challenges. The selection of switching technologies that can support high-frequency operation with minimal loss and high efficiency is particularly complex. In this work, we demonstrate the potential of advanced components such as Schottky diodes, memristor switches, liquid metal-based switches, phase change materials, and RF-SOI technology in RIS designs as an alternative to overcome limitations inherent in traditional technologies in D-band (110-170 GHz).

Abstract
PurposeThis study aims to assess how envy, both directly and indirectly, through negative behaviors such as ostracism, negative word-of-mouth and alignment with the negative behaviors of superiors, influences innovative behavior based on the cultural dimension of individualism versus collectivism.Design/methodology/approachThe data was collected using a survey applied to 305 individuals between October 2022 and June 2023. The model developed was analyzed and validated using partial least squares estimation with structural equation modeling (PLS-SEM) and PLS-SEM multigroup analysis techniques.FindingsThe results suggest that for individualistic individuals, negative word-of-mouth exerts a greater positive mediating influence on the relationship between envy and ostracism, and that envy exerts a greater positive influence on both alignment with the negative behaviors of superiors and on ostracism. In addition, the results indicate that negative word-of-mouth and ostracism together negatively influence the relationship between envy and innovative behavior.Practical implicationsThis research provides empirical evidence that envy triggers negative behavior in both individualistic and collectivist individuals. Thus, in practical terms, envy can be considered as something more primitive that goes beyond the accepted values of sociability, especially in the organizational environment.Originality/valueThe main contribution of this research is to understand the effects of envy on innovative behavior, based on a conceptual model that considers the mental programs that differentiate one group of individuals from another. In addition, it presents theoretical and empirical implications that provide descriptive evidence of behaviors, making it possible to broaden the psychological understanding of them. In this specific sense, this research differs from other organizational studies, whose objectives are to standardize behavior.

Abstract
This article explores the optimization of Battery Energy Storage Systems (BESS) in energy markets, emphasizing their role in decarbonization by storing excess renewable energy and mitigating grid constraints. BESS enables energy transition by facilitating energy arbitrage, frequency regulation, and grid stabilization, essential for integrating variable renewable sources. Focusing on the UK energy market, the study highlights the favorable policies and investments driving BESS deployment. It examines revenue streams, including Day-Ahead and Intraday markets, ancillary services, and balancing mechanisms, particularly dynamic services like frequency regulation. Challenges such as gas market volatility and regulatory hurdles are also discussed. The proposed market optimization model simulates BESS operations, revealing consistent revenue potential influenced by market conditions and regulatory frameworks. The study underscores BESSs critical role in stabilizing grids, supporting renewables, and enhancing energy security while calling for further research on equipment degradation and broader impacts on energy systems and pricing.

Abstract
Medical case retrieval plays a crucial role in clinical decision-making by enabling healthcare professionals to find relevant cases based on patient records, diagnostic images, and textual descriptions. Given the inherently multimodal nature of medical data, effective retrieval requires models that can bridge the gap between different modalities. Traditional retrieval approaches often rely on unimodal representations, limiting their ability to capture cross-modal relationships. Recent advances in dense model-based techniques have shown promise in overcoming these limitations by encoding multimodal information into a shared latent space, facilitating retrieval based on semantic similarity. This paper investigates the potential of dense models to enhance multimodal search systems. We evaluate various dense model-based approaches to assess which model characteristics have the greatest impact on retrieval effectiveness, using the medical case-based retrieval task from ImageCLEFmed 2013 as a benchmark. Our findings indicate that different dense model approaches substantially impact retrieval effectiveness, and that applying the CombMAX fusion methodto combine their output results further improves effectiveness. Extending context length, however, yielded mixed results depending on the input data. Additionally, domain-specific models—those trained on medical data—outperformed general models trained on broad, non-specialized datasets within their respective fields. Furthermore, when text is the dominant information source, text-only models surpassed multimodal models