Background: Palliative care education (PCE) is increasingly recognized as a public health priority, particularly in the context of dementia and other neurocognitive disorders that place significant burdens on patients, families, and healthcare systems. While international programmes highlight key educational needs and interventions, local adaptations remain scarce in Romania. Objective: This research aimed to (1) synthesise global evidence on palliative care education needs and implemented programmes published in 2024, and (2) assess the emerging educational requirements and psychosocial well-being of healthcare providers and caregivers in multiple Romanian regional centres in 2025. Methods: A scoping review was conducted using PubMed to identify studies published in 2024, including qualitative, quantitative, and umbrella reviews. Fifty-six studies from 33 countries were analysed. Based on identified gaps, a structured questionnaire was designed and applied to 200 participants (physicians, nurses, physiotherapists, psychologists, priests, and primary home caregivers) across Romanian counties such as Calarasi, Prahova, Constanta, lasi, and Brasov. Alongside, participants were evaluated using the Beck Depression Inventory, Hamilton Anxiety Rating Scale, and Rosenberg Self-Esteem Scale. Results: The scoping review identified recurrent needs in symptom management, dementia care, communication, caregiver support, ethical decision-making, digital learning, and provider resilience. Programmes showed improvements in knowledge, confidence, and satisfaction but varied widely in scope and implementation. Romanian participants reported high interest in dementia-focused training and communication skills, aligning with global priorities. However, unique challenges emerged, including limited interdisciplinary collaboration and a lack of structured support for home-based caregivers. Screening revealed elevated levels of anxiety and depression among staff, particularly those with frequent contact with dementia patients. Conclusions: Combining international and local perspectives demonstrates that dementia education, caregiver support, and provider resilience are central needs in palliative care education. Tailored, interdisciplinary programmes are urgently required in Romania, both to align with global best practices and to address local gaps, ensuring improved outcomes for patients, families, and healthcare providers.
This study presents a novel and thermodynamically consistent framework designed to overcome key limitations of classical viscoelastic and thermoelastic theories, which fail to capture memory effects, size-dependent phenomena, and ultrafast thermal responses in micro- and nano-scale semiconductor devices. The proposed model uniquely integrates fractional calculus with nonlocal continuum mechanics by employing the Caputo–Fabrizio fractional derivative, characterized by a smooth, non-singular exponential kernel that avoids unphysical singularities while preserving realistic fading memory behavior. In addition, the model incorporates dual relaxation times to account for phase-lagged heat conduction and carrier diffusion, along with a nonlocal length-scale parameter that captures long-range atomic interactions. The framework is specifically applied to a rotating cylindrical semiconductor subjected to pulsed laser heating, a physically relevant scenario for high-speed optoelectronic and microelectromechanical systems operating under transient thermal and mechanical loads. The solution methodology combines analytical techniques based on Laplace transforms with robust numerical inversion to solve the fully coupled multiphysics problem involving thermal, mechanical, electronic, and electromagnetic fields. Key findings from parametric analyses reveal that the fractional order, nonlocal scale, angular velocity, laser pulse duration, and thermal/carrier phase lags all significantly influence the distributions of temperature, carrier density, displacement, and stress. Critically, the results demonstrate that ignoring nonlocal effects or relying on classical integer-order derivatives leads to substantial inaccuracies in predicting photothermal and thermomechanical responses. This model offers a more accurate, physically grounded, and reliable predictive tool for the design and performance assessment of next-generation semiconductor-based devices, such as high-speed rotating micro-gyroscopes, laser-driven actuators, photothermal nanosensors, and other microelectromechanical systems where precise control of coupled thermal, mechanical, and electronic behavior under ultrafast excitation is essential.
Droughts exert a profound influence on water availability, agriculture, and ecosystems worldwide, and Dobrogea, one of Romania’s driest regions, has been repeatedly affected by severe droughts over recent decades. Given the increasing frequency and intensity of such events under climate change, accurate drought prediction is vital for managing agriculture and water resources in this vulnerable region. This study evaluates the performance of statistical (SARIMA) and machine learning models (Rthe seasonal Mann-Kendallandom Forest, Gradient Boosting, Support Vector Regression) in forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple time scales (1, 3, 6, 12, and 24 months). The models were developed using in situ data from six meteorological stations and satellite-based datasets covering 1967–2021, providing both local and regional perspectives on drought variability. Lagged predictors (1–12 months) were incorporated to capture temporal memory effects and enhance forecast accuracy. Residual diagnostics confirmed the statistical adequacy and proper specification of the selected models. Results reveal that Dobrogea has experienced widespread, recurrent moderate to severe drought conditions, with extreme events becoming more frequent since 2001. In terms of SPEI prediction, model performance improved with increasing timescale. Among the predictive approaches, SVR and SARIMA achieved the best overall results, showing higher NSE ( 0.95) and lower RMSE and MAE values compared to the other models. Overall, the findings highlight Dobrogea’s persistent drought exposure and demonstrate the potential of machine learning —particularly SVR—to enhance drought forecasting precision, supporting sustainable agricultural practices and adaptive management under a changing climate.
The sustainable management of waste is a significant problem facing humanity, especially in regions with low recycling rates and a lack of infrastructure. For example, Romania has a recycling rate of only 12%, a long way from meeting the European Union's target of 42%. This article proposes a framework for sustainable waste management, called CETHTB-Chain, by combining the circular economy, Triple Helix Twins collaboration, and blockchain technology. To test the viability of this framework, a Monte Carlo simulation with 10,000 iterations and system dynamics modelling with a 10-year simulation period was conducted. The Monte Carlo simulation revealed that CETHTB-Chain can improve recycling rates by a mean of 45.6% (95% CI, 38.6-52.6%), material recovery rates by 62.7% (95% CI, 54.4-70.0%), cost savings by 18.53 euros per ton, and CO2 reduction by 629 kg per ton of waste. System dynamics modelling revealed that CETHTB-Chain is feasible for implementation, following S-curve growth, with recycling rates of 38.6% in 7-10 years. Sensitivity analysis revealed that blockchain technology adoption (rho = 0.612) and citizen participation (rho = 0.379) were key drivers of CETHTB-Chain performance. By combining Monte Carlo simulation and system dynamics modelling, this article has shown CETHTB-Chain to be a statistically significant and temporally feasible blueprint for transitioning from a linear economy to a circular economy in waste management. By engaging academia, industry, and government in a collaborative relationship facilitated by blockchain technology, CETHTB-Chain has provided valuable evidence for strategic planning in waste management in the European Union.
