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.
Twin-wire directed energy deposition-arc (TW-DED-arc) presents a transformative route for fabricating large-scale NiTi-based shape memory alloys (SMAs). However, accurate prediction of phase-transformation temperatures in TW-DED-arc NiTi-X (Cu/Nb/Hf) alloys remains challenging because of the scarcity of process-specific data. Therefore, this study proposes a materials-knowledge-guided transfer-learning framework for the accurate prediction of phase-transformation temperatures, including Af and Ms. A hybrid dataset integrating literature data and experimental data was constructed. By incorporating materials-knowledge-guided feature engineering and an instance-weighted transfer learning strategy, the framework effectively optimizes the predictive performance of traditional machine learning models on the nine-sample TW-DED-arc target-domain evaluation set. Comparative analysis reveals that the optimized Extra Trees (ET) regressor outperforms eleven other algorithms, achieving R2 values of 0.958 for Af and 0.986 for Ms. Experimental verification on NiTiCu, NiTiNb, and NiTiHf ternary alloy systems confirmed the model's versatility. Furthermore, SHapley Additive exPlanations (SHAP) analysis identified the principal feature contributions to the ET model predictions across the three NiTi-X alloy systems. This work establishes a data-efficient framework for predicting the phase-transformation temperatures of TW-DED-arc-fabricated SMAs by leveraging historical materials data for an emerging manufacturing process.
Biogenic amines (BAs) are biologically active nitrogen compounds formed during food spoilage and are closely associated with food quality, safety, and freshness. Their accumulation at high levels poses health risks, motivating the development of new analytical detection strategies. Herein, native /3-cyclodextrin (/3-CD) and two sulfobutylether-functionalized /3-CDs (DS 4 and 6.5) were combined with a pH-sensitive dye (4 '-hydroxy-10-methylpyranoflavylium) to investigate fluorescence modulation induced by structurally diverse BAs (putrescine, cadaverine, spermine, spermidine, histamine, 2-phenylethylamine, and tyramine). Distinct, structure-and pH-dependent photophysical behaviors were observed as a function of host functionalization. Native /3-CD complex with dye exhibited responses similar to the free dye, showing fluorescence enhancement with aliphatic and heterocyclic amines and negligible response toward aromatic amines. In contrast, functionalized /3-CDs produced markedly different profiles: SBECD4 displayed mixed enhancement and quenching depending on the amine, whereas SBECD6.5 showed dominant quenching, particularly for aromatic amines. At acidic pH, response amplitudes decreased overall, while at neutral pH, aromatic amines induced the strongest quenching in functionalized systems. These results demonstrate that fluorescence modulation arises from the interplay of host functionalization, amine structure, and pH-dependent dye speciation, highlighting cyclodextrin modification as a powerful strategy to tune supramolecular photophysical responses.
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 survivalanalysis models, with Random Survival Forests as our primary specification, to estimate, for each booking, a dayby-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.
