NOVA University Lisbon (Universidade NOVA de Lisboa - pronounced [univɨɾsiˈðad(ɨ) ˈnɔvɐ ðɨ liʒˈbo.ɐ]), NOVA, is a Portuguese university whose rectorate is located in Campolide, Lisbon. Founded in 1973, it is the newest of the public universities in the Portuguese capital city, earning its name as the "New" (NOVA) University of Lisbon. The institution has more than 20,000 students, 1,800 professors and staff members distributed through five faculties, three institutes and one school, providing a variety of courses in several fields of knowledge.
This paper presents an efficient probabilistic framework for evaluating the failure probability of eccentrically loaded shallow foundations on undrained soils with spatially variable shear strength. The approach combines Random Finite Element Limit Analysis (RFELA) with a reduced-dimension Monte Carlo formulation in which uncertainty in load magnitude and effective eccentricity is formulated analytically, with the resulting integrals evaluated using Gauss–Hermite quadrature, while soil variability is sampled numerically. This reduces the computational burden associated with conventional Monte Carlo simulation, which requires simultaneous sampling of both loads and soil properties.The method explicitly accounts for the influence of load eccentricity induced by random load combinations and incorporates a discretized representation of the ultimate bearing capacity derived from precomputed failure mechanisms. The proposed framework is validated against brute-force Monte Carlo simulations using up to 109 realizations, showing excellent agreement across a wide range of failure probabilities.Results show that the proposed approach achieves accurate estimates of probabilities as low as 10−9 while reducing the computational cost by several orders of magnitude. The framework is therefore particularly suitable for large-scale reliability assessments of footings under spatial variability and variable load eccentricity.
Population ageing and demographic shifts in the European Union are placing increasing strain on traditional pension systems, shifting greater responsibility to individuals to ensure their own retirement preparedness. In this context, identifying factors that strengthen individuals’ confidence in their retirement readiness has become increasingly important. This study examines the role of trust in financial advisors in shaping retirement preparedness and investigates a serial mediation mechanism through financial behavior and financial resilience. Drawing on the financial capability framework, the study emphasizes the importance of behavioral and capacity-building processes that translate trust into improved retirement outcomes. Using a large-scale dataset from European Union member states, the proposed relationships are empirically tested using PROCESS Model 6. The results show that trust in financial advisors is positively associated with retirement preparedness, both directly and indirectly through financial behavior and financial resilience. By integrating trust, financial behavior, and resilience into a unified analytical framework, this study contributes to the literature on household finance and retirement planning. The findings also offer practical implications for policymakers and financial service providers seeking to improve retirement readiness in ageing societies.
Booking cancellations distort hotel demand forecasts, pricing, and overbooking decisions. The dominant approach in the literature predicts whether a reservation will cancel. We move the question forward by asking when a cancellation is most likely to occur. Using reservation data from four Portuguese hotels, we fit survival-analysis models, with Random Survival Forests as our primary specification, to estimate, for each booking, a day-by-day cancellation risk profile over the booking horizon. We translate this profile into two operational outputs: a Predicted Cancellation Day (PCD), the single day on which cancellation is most likely, and a Predicted Cancellation Window (PCW), a compact interval around it. The best PCW setting captures 33–72% of cancellations within a median window of 5–7 days. The approach complements existing classifiers by indicating when interventions are most likely to matter, supporting retention, overbooking, and short-term staffing decisions.
Multi-omics is the coordinated acquisition, integration, and interpretation of multiple datasets generated from diverse molecular layers of a biological system, intending to capture a comprehensive understanding of interactions between molecular hierarchies and reveal the complex regulatory architecture underlying cellular states, physiological processes, and disease phenotypes. Multi-omics integration signifies a transformative approach in cancer research, facilitating a systems-level comprehension of tumor biology that goes beyond the analysis of individual data layers. Through the combined analysis of data from genomics, transcriptomics, epigenomics, proteomics, and metabolomics, this method reveals the intricate molecular networks that influence tumorigenesis and its variability. High-throughput technologies play a crucial role in this context, enabling the identification of new biomarkers, the detection of actionable therapeutic targets, and the classification of unique cancer subtypes. Integrative omics is essentially transforming precision oncology by enhancing patient risk assessment and forecasting treatment responses, thus guiding personalized diagnosis and therapy approaches. The effectiveness of this method increases when molecular data are integrated with clinical and imaging information, resulting in stronger predictive models for personalized patient treatment. Nonetheless, considerable obstacles remain, such as the integration of diverse data, the adjustment of batch effects, and the clinical understandability of intricate computational models. Tackling these challenges requires sophisticated machine learning methods, uniform data processing workflows, and ongoing cross-disciplinary teamwork. With the advancement of these methodologies, multi-omics integration will act as the essential link between large-scale data and precision medicine, providing unmatched chances to unravel the intricacies of cancer and produce effective, tailored treatments. This review highlights the latest advancements, ongoing challenges, and future pathways that are influencing the next wave of cancer research and clinical applications.
The use of wood in mobility applications is attracting growing interest due to its environmental benefits, light weight, and good specific strength. This review presents the current state of research on wood and its derivatives—natural wood, treated wood, technical plywood, sandwich composites, and 3D-printed wood-based materials—examining their mechanical behavior, durability, and potential for integration into mobile structures. The work reviewed shows significant advances for plywood and wood-composite sandwiches, but studies on solid wood, dynamics, cracking, and fatigue remain limited and scattered. The main obstacles concern the natural variability of the material, industrial reproducibility, hygro-mechanical modeling, and the lack of test protocols adapted to real-world mobility conditions. The analysis also emphasizes that the favorable mechanical properties of densified or treated wood should not overshadow environmental sustainability requirements. Future research should focus on conducting more tests under variable environments, in vacuum conditions, and at high strain rates, as well as on characterizing interfaces and mixed-mode cracking behavior. Particular attention should be paid to wood-based sandwich structures, which have been identified as particularly promising solutions. Finally, the development of a reference guide listing the mechanical and environmental properties of different wood species and forms would be a key step toward the design and standardization of wood materials for sustainable mobility.
