Abstract
This chapter explores the innovative development and integration of tailored user research instruments to inform digital health solutions for People Living with Amyotrophic Lateral Sclerosis (PALS) exhibiting characteristics of partial Locked-In Syndrome (LIS). Addressing the complex interplay of motor, cognitive, and emotional impairments typical of this population, the study proposes a synergistic framework combining three adapted instruments: the ALS Functional Rating Scale-Revised (ALSFRS-R/EX), the User Experience Questionnaire Plus (UEQ+), and a bespoke Cognitive-Motor-Emotional (CME) Observation Grid. These instruments were tailored to detect subtle variations in user function, affect, and interaction. Results show how embodied and sensory drawing participatory methods and customisation of instruments, along with semi-structured questionnaire and interviews with caregivers, can yield actionable insights for designing a model for solutions in neurodegenerative or communication-limiting contexts beyond Augmentative and Alternative Communication (AAC).

Abstract
This study explores the complex process of entrepreneurial team identity formation and development, addressing a notable gap in the current literature. Focusing on five entrepreneurial teams affiliated with CEU iLab, the study adopts a multiple case study design drawing on semi-structured interviews with program alumni, complemented by secondary data obtained through manual web scraping. Findings reveal that entrepreneurial identity begins forming even before teams enter the incubation program and evolves through a dynamic interplay of factors. High levels of social interaction and networking, team stability, intra-team trust, effective feedback mechanisms, and perceived legitimacy all contribute to shaping this identity. The incubation setting acts as a catalyst, reinforcing these mechanisms and accelerating identity development. This research offers theoretical contributions by proposing a model of entrepreneurial team identity formation and highlighting how relational and contextual factors influence this ongoing process.

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
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.