This paper analyses the impact of financial development on the environmental quality and sustainability for the group of G7 countries over the period 1990–2019 based on static panel data-fixed effect models. The objective is to explore if there exists a non-linear relationship between the whole financial system development and a wide array of measures of environmental sustainability and degradation, namely adjusted net savings, greenhouse gas, CO₂, methane, nitrous oxide emissions and ecological footprint. We define a new Financial Environmental Kuznets Curve (FEKC) by introducing the square term of financial development on the environment-finance relationship. Empirical results prove the existence of non-linear relationships between the composite index of financial development and environmental degradation for the group of advanced economies. In the case of methane, we validate the presence of an inverted-U shape association in line with the FEKC hypothesis, while for greenhouse gas and CO₂ the link follows a U-shaped pattern. The impact of financial development on environmental sustainability is monotonically positive and statistically significant while the ecological footprint is not statistically linked with the level of financial development within G7 countries. Economic growth, human capital, population density and primary energy consumption appear as significant drivers of environmental quality and sustainability.

Englacial layering reflects ice dynamics within the ice bodies, which improves understanding of ice flow variation, past accumulation rates and vertical flows transferring between the surface and the underlying bedrock. The internal layers can be observed by using Radar Echo Sounding (RES), such as the Multi-channel Coherent Radar Depth Sounder (MCoRDS) used in NASA’s Operation IceBridge (OIB) mission. Since the 1960s, the accumulation of the RES data has prompted the development of automated methods to extract the englacial layers. In this study, we propose a new automated method that combines peak detection methods, namely the CWT-based peak detection or the Automatic Phase Picker (APP), with a Hough Transform (HT) to trace boundaries of englacial layers. For CWT-based peak detection, we test it using two different wavelets. The proposed method is tested with twelve MCoRDS radio echograms, which are acquired south of the Northern Greenland Eemian (NEEM) ice drilling site, where the folding of ice layers was observed. The method is evaluated in comparison to the isochrones that were extracted in an independent study. In comparison, the proposed new automated method can restore more than 70% of the englacial layers. This new automated layer-tracing method is publicly available on github.

Aims: This study explores the mediational role of resilience, experiential avoidance and emotion regulation in the levels of anxiety, depression and posttraumatic stress disorder (PTSD) of healthcare workers during the COVID‐19 pandemic. Additionally, we explored the association of such levels with personal and professional variables.Design: Cross‐sectional study.Methods: Healthcare professionals working in Spain (N = 786) were recruited following a snowball approach in November and December 2021. Resilience, emotion regulation, experiential avoidance, depression, anxiety, PTSD and work‐related variables were measured. Mean differences and correlations were computed, and a path analysis with latent variables (PALV) model was tested.Results: In total, 18.8% of the sample scored above the cut‐off score for depression, 24.6% for anxiety and 36.4% for PTSD. Higher resilience and lower experiential avoidance and expression suppression were correlated with better mental health. The PALV model explained 42%–53% of mental health outcomes. Experiential avoidance showed the greatest explanatory power and mediated the impact that stressors had on mental health. Some work‐related variables correlated with greater psychological impact. These factors encompassed being a nurse, feeling that their job remained stressful and had not yet returned to its pre‐pandemic state and having interacted with individuals facing economic difficulties due to the pandemic, and those who had lost their lives to COVID‐19.Conclusion: Healthcare workers showed high levels of psychological impact during the COVID‐19 pandemic. Such impact was predicted from some work‐stress variables and the reliance on maladaptive strategies such as experiential avoidance and expressive suppression.Impact: Training healthcare professionals to use coping strategies incompatible with experiential avoidance may improve their mental health. Additionally, better working conditions are fundamental for reducing the impact of critical situations on healthcare workers' mental health.Patient or Public Contribution: No patient or public contribution.

The field of strength reliability is one of the critical factors restricting wider use of brittle materials in certain structural applications, like ceramics. In this area, the Weibull distribution is widely accepted for lifetime modeling. In essence, the brittleness of ceramic materials leads to poor toughness and low strength reliability. The statistical nature of these flaws results in a significant scatter of the measured macroscopic strength outcomes, which has a number of consequences both in the design and verification of components involving such materials. In this article, an analysis and evaluation of six existing estimation methods for a Weibull distribution are presented, as well as a new approach for fitting the Weibull distribution using neural networks (NNs). The major focus of this work is, however, the implementation of simulations in order to contrast how well the suggested techniques of the Weibull parameter estimation perform. Finally, an important implication of this study is that it shows how various estimators of the Weibull model work for wide-ranging sample sizes and different parameter values. The simulation results revealed that L-Moment estimator produces more accurate estimates, unlike those using NNs that are more robust with the lowest Root Mean Square Error.