Abstract
Brain connectivity analysis based on stereo-electroencephalography (SEEG) has been increasingly explored as a tool for studying Epileptogenic Networks and supporting therapies for Refractory Epilepsy. In this Review, we summarize recent methodologies and findings on SEEG-based Functional and Effective Connectivity, which aim to define the dependence and influence between spatially distributed neurophysiological events. After briefly outlining the network analysis measurements used to interpret connectivity, we detail the mathematical principles underlying the 11 reviewed connectivity methods, along with the main findings characterizing the epileptogenic network for each method. Based on their principles, these methods can be organized into four classes: Signal Interaction, Signal Synchronization, Causality, and Other Nonlinear methods. Across these methods, consistent network signatures emerge, including functional isolation of the Epileptogenic Zone during interictal periods and complex hypersynchronization dynamics during seizures. As there is no single best method, we present a methodological framework that summarizes the relationship assumptions, computational costs, signal-processing requirements, and clinical use cases for each method. Finally, we discuss current challenges and future trends in this field.

Abstract
Within the scope of the Mobilizing Agenda for the Development of Intelligent Green Mobility Products and Systems (A-MoVeR), PPS2 defined the presentation of a new electric motorcycle, with high autonomy, aimed at promoting comfortable, efficient and efficient urban mobility. green. In this context, the need to develop interfaces that meet the expectations of end users, promoting user experience and security are crucial. Therefore, following a User-Centered Design (DCU) methodology, a co-design perspective and UX data collection methods, this article presents the steps and preliminary results of the preparation, face-to-face session and subsequent analysis of results of a preliminary moment of acquiring knowledge on how to optimize motorcycle user interfaces. Specifically: script planning, requirements and analysis of user feedback collected through audiovisual recording, in a focus group, are described.

Abstract
This chapter is grounded in the results of the ERASMUS+ funded project SmartAgeCare, which investigated active and healthy ageing strategies across eight European countries. The project aims to foster digital inclusion, civic participation, and psychosocial well-being among older adults by exploring innovative models of engagement. This chapter introduces the concept of 'Ikigai Play' as a transdisciplinary framework rooted in a meta-narrative review and inspired by the Japanese philosophy of Ikigai—meaning 'reason for being'. Synthesising evidence from national studies and digital ageing strategies, the chapter identifies regional disparities, psychosocial drivers, and the transformative potential of inclusive technologies. 'Ikigai Play' is proposed as a culturally adaptive model to support autonomy, well-being, and digital health equity.

Abstract
Background: Biliary tract cancers (BTCs), encompassing cholangiocarcinoma and gallbladder carcinoma, are aggressive malignancies with poor prognosis and increasing incidence in selected regions worldwide. Advances in imaging, biomarker profiling, immunotherapy, and targeted therapies have improved treatment options but have also increased the economic pressure on health systems. Understanding the economic evidence on BTC is therefore important for resource allocation and health technology assessment. Methods: We systematically searched PubMed/MEDLINE, Embase, Scopus, and Web of Science for peer-reviewed economic studies of BTC published from January 2010 to March 2025. Eligible studies included cost-effectiveness, cost-utility, cost-benefit, cost-of-illness, and resource-use analyses. The review followed PRISMA reporting principles. Reporting completeness was assessed using CHEERS 2022, and methodological credibility was appraised using the Drummond framework. Results: Twenty studies were included: 13 cost-effectiveness or cost-utility analyses and seven cost-of-illness or resource-use studies. Conventional chemotherapy strategies, including gemcitabine plus cisplatin in some settings and other cytotoxic combinations in selected jurisdictions, generally produced more favorable economic results than newer systemic therapies, although findings varied by country, threshold, comparator, and price assumptions. First-line immunotherapy combinations and biomarker-directed targeted therapies frequently produced ICERs above jurisdiction-specific willingness-to-pay thresholds at current prices, often requiring substantial price reductions to approach cost-effectiveness. Real-world studies showed high resource use and costs, particularly with hospitalizations and later treatment lines. Evidence on screening and prevention was limited, with one study suggesting that ultrasound surveillance may be cost-effective in a liver fluke-endemic region of Thailand. Discussion: The available economic evidence suggests that affordability and jurisdiction-specific value assessment are central to BTC policy decisions. Current prices for several immunotherapy and targeted agents limit cost-effectiveness in published models, while evidence on prevention, early detection, and care-pathway interventions remains sparse and context-specific.

Abstract
Peer-reviewed paper 7-108-26 The energy transition creates significant challenges for long-term planning in the buildings sector, which accounts for around 35% of EU greenhouse gas emissions. Frameworks such as the Energy Performance of Buildings Directive promote electrification and the deployment of heat pumps, yet the representation of domestic hot water (DHW) technologies in long-term energy models remains challenging due to limited data. This paper examines the integration of DHW heat pumps into the OSeMOSYS-based national energy system model, using Portugal as a case study. Key modelling gaps are identified in the DHW demand, technology performance, and economic parameters. National statistics provide aggregated residential energy data, while detailed end-use information and hourly DHW profiles remain scarce. To address these limitations, the modelling framework reconstructs DHW demand from national energy balances, derives stylised demand profiles from literature proxies, and represents heat pump performance using seasonal coefficients of performance (COP). Technology costs, including capital investment, maintenance, and electricity prices, are parameterised using market data and European literature benchmarks. Instead of exploring numerous scenarios, this study quantifies how key modelling assumptions, particularly cost parameters and COP variations, affect heat pump deployment, system costs, and electricity demand. Results show that cost assumptions influence heat pump deployment, while variations in COP affect electricity demand, with implications for grid planning. The outcomes demonstrate that modelling assumptions can lead to substantial variations in system outcomes, even under consistent decarbonisation constraints. The study emphasises the importance of transparency when modelling under data scarcity and illustrates a pragmatic approach to representing DHW heat pumps in national energy system planning models.

Abstract
Accurate time series forecasting is crucial across various domains, yet traditional models that rely solely on numerical data often struggle to capture complex patterns in dynamic environments. This work proposes a novel multimodal forecasting framework that integrates visual and numerical data to enhance predictive performance. The framework leverages a FT-Transformer for temporal data processing and a TIMM-based convolutional network for visual data extraction. A hybrid fusion strategy combines these modalities, enabling the model to capture complementary information that improves forecasting accuracy. Empirical evaluations on the M4 dataset demonstrate that the multimodal model consistently outperforms unimodal approaches, achieving up to a 7.0% reduction in Normalized Root Mean Squared Error across multiple forecast horizons. The proposed framework also incorporates an automated training pipeline powered by Optuna, ensuring efficient hyperparameter tuning and scalability across diverse datasets. These results highlight the effectiveness of multimodal integration in advancing time series forecasting performance. © 2026 IEEE.