Abstract
Alzheimer’s disease (AD) places a profound global challenge, driven by its escalating prevalence and the multifaceted strain it places on individuals, families, and societies. Family caregivers (FCs), who are pivotal in supporting family members with AD, frequently endure substantial emotional, physical, and psychological demands. To better understand the determinants of family caregiving strain, this study employed machine learning (ML) to develop predictive models identifying factors that contribute to caregiver burden over time. Participants were evaluated across sociodemographic clinical, psychophysiological, and psychological domains at baseline (T1; N = 130), six months (T2; N = 114), and twelve months (T3; N = 92). Results revealed three distinct risk profiles, with the first focusing on T2 data, highlighting the importance of distress, forgiveness, age, and heart rate variability. The second profile integrated T1 and T2 data, emphasizing additional factors like family stress. The third profile combined T1 and T2 data with sociodemographic and clinical features, underscoring the importance of both assessment moments on distress at T2 and forgiveness at T1 and T2, as well as family stress at T1. By employing computational methods, this research uncovers nuanced patterns in caregiver burden that conventional statistical approaches might overlook. Key drivers include psychological factors (distress, forgiveness), physiological markers (heart rate variability), contextual stressors (familial dynamics, sociodemographic disparities). The insights revealed enable early identification of FCs at higher risk of burden, paving the way for personalized interventions. Such strategies are urgently needed as AD rates rise globally, underscoring the imperative to safeguard both patients and the caregivers who support them.

Abstract
Abstract This study seeks to comprehensively analyze the multidimensional determinants underlying perceptions of safety, security, and comfort in transport mode choice, specifically focusing on private transport, public transport and walking. The research begins with an extensive literature review to identify and delve into the factors influencing perceptions of safety, security, and comfort across various transport modes. This inquiry is further enhanced by organizing two focused group sessions. A total of 35 key factors were identified, forming the basis for subsequent investigation. The study then progressed to the development and administration of a survey aimed at capturing responses from a diverse audience, with the goal of exploring the factors influencing perceptions related to different transport modes. A total of 302 responses were collected and meticulously analyzed to discern the factors impacting various relationships and to identify consistent perceptions across diverse transport modes. Additionally, a factor analysis was conducted to validate the findings derived from the data. The outcomes of this research constitute a significant contribution to the existing literature, offering valuable insights that pave the way for a more holistic understanding of the factors guiding transport mode choices.

Abstract
The lower refractive index sensitivity (RIS) of plasmonic nanoparticles (NP) in comparison to their plasmonic thin films counterparts hindered their wide adoption for wavelength-based sensor designs, wasting the NP characteristic field locality. In this context, high aspect-ratio colloidal core-shell Ag@Au nanorods (NRs) are demonstrated to operate effectively at telecommunication wavelengths, showing RIS of 1720 nm/RIU at 1350 nm (O-band) and 2325 nm/RIU at 1550 nm (L-band), representing a five-fold improvement compared to similar Au NRs operating at equivalent wavelengths. Also, these NRs combine the superior optical performance of Ag with the Au chemical stability and biocompatibility. Next, using a side-polished optical fiber, we detected glyphosate, achieving a detection limit improvement from 724 to 85 mg/L by shifting from the O to the C/L optical bands. This work combines the significant scalability and cost-effective advantages of colloidal NPs with enhanced RIS, showing a promising approach suitable for both point-of-care and long-range sensing applications at superior performance than comparable thin film-based sensors in either environmental monitoring and other fields.

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Abstract
Knowledge sharing and team dynamics are essential elements of entrepreneurial success, especially in teams that operate in innovative environments. This chapter explores how participation in an incubation program influences the formation and development of entrepreneurial team identity. It aims to understand the dynamics involved in creating entrepreneurial teams, the practices of knowledge sharing, and the role digital technologies play in supporting and sustaining these processes. The study focuses on teams that completed the CEU iLab Incubation Program, with data gathered through in-depth semi-structured interviews from twenty-five entrepreneurs across various startups. Five cases, involving entire entrepreneurial teams, were central to this research. The findings offer valuable insights for enhancing incubation programs, promoting entrepreneurial identity formation, and improving the success of new ventures. These insights are beneficial for both scholars and practitioners in the entrepreneurship field.