Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy actions to reduce the risk to human health. However, forecasting surface ozone is a difficult problem as its concentrations are controlled by a number of physical and chemical processes that act on varying timescales. We implement a state-of-the-art transformer-based model, the temporal fusion transformer, trained on observational data from three European countries. In four-day forecasts of daily maximum 8-hour ozone (DMA8), our novel approach is highly skillful (MAE = 4.9 ppb, coefficient of determination $ {\mathrm{R}}^2=0.81 $) and generalizes well to data from 13 other European countries unseen during training (MAE = 5.0 ppb, $ {\mathrm{R}}^2=0.78 $). The model outperforms other machine learning models on our data (ridge regression, random forests, and long short-term memory networks) and compares favorably to the performance of other published deep learning architectures tested on different data. Furthermore, we illustrate that the model pays attention to physical variables known to control ozone concentrations and that the attention mechanism allows the model to use the most relevant days of past ozone concentrations to make accurate forecasts on test data. The skillful performance of the model, particularly in generalizing to unseen European countries, suggests that machine learning methods may provide a computationally cheap approach for accurate air quality forecasting across Europe.

A comparison of the modelling methodologies to capture the damage onset and delamination initiation in Abaqus and LS-Dyna is presented. A quasi-isotropic carbon fibre reinforced polymer laminate is modelled under a low-energy impact scenario. Hashin, Puck and Cuntze criteria are implemented for assessing intra-laminar damage in Abaqus in the linear elastic regime without damage evolution, with Virtual Crack Closure Technique being used for inter-laminar failure. In LS-Dyna, the Chang-Chang criterion is used for the intra-lamina failure with damage evolution, whereas delamination is captured using cohesive zone model and the tiebreak contact algorithm. The implementations carried out by both finite element software result in a modelling work well set to analyse and predict the impact response at the initial stages of delamination and damage within the plies. The composite damage criteria used in both finite element codes overall predict stiffer results when compared with the experimental data, however, remain in close agreement with each other.

Surgical repair of channels between the ventricles is enhanced when the surgeon knows precisely where to place a patch, or baffle, so as to restore septal integrity. The paediatric cardiologist should provide the necessary information. Communication will be enhanced if the same words are used to account for the structures in question. Currently, however, the same term, namely “ventricular septal defect,” is used to account for markedly different areas within the heart. Closure of perimembranous defects found in hearts with concordant or discordant ventriculo-arterial connections restores the integrity of the ventricular septum, at the same time separating the systemic and pulmonary blood streams. When both arterial trunks arise from the right ventricle, in contrast, the surgeon when placing a baffle so as to separate the blood streams, does not close the channel most frequently described as the “ventricular septal defect.” In this review, we show that the perimembranous lesions as found in hearts with concordant or discordant ventriculo-arterial connections are the right ventricular entrances to the areas subtended beneath the hinges of the leaflets of the aortic or pulmonary valves. When both arterial trunks arise from the right ventricle, and the channel between the ventricles is directly subaortic, then the channel termed the “ventricular septal defect” is the left ventricular entrance to the comparable space subtended beneath the aortic root. We argue that recognition of these fundamental anatomical differences enhances the appreciation of the underlying morphology of the various lesions that reflect transfer, during cardiac development, of the aortic root from the morphologically right to the morphologically left ventricle.

This study adds to the analogic perspective-taking literature by examining whether an online perspective-taking intervention affects both antisemitic attitudes and behaviors – in particular, engagement with antisemitic websites. Subjects who were randomly assigned to the treatment viewed a 90-s video of a college student describing an experience with antisemitism and reflected on its similarity to their own experiences. In a survey, treated subjects reported greater feelings of sympathy (+29 p.p.), more positive feelings toward Jews, a greater sense that Jews are discriminated against, and more support for policy solutions (+2–4 p.p.). However, these effects did not persist after 14 days. Examining our subjects’ web browsing data, we find a 5% reduction in time spent viewing antisemitic content during the posttreatment period and some limited, suggestive evidence of effects on the number of site visits. These findings provide the first evidence that perspective-taking interventions may affect online browsing behavior.