Smoothed particle hydrodynamics (SPH) and the finite element method (FEM) are often combined with the scope to model the interaction between structures and the surrounding fluids (FSI). There is the case, for instance, of aircrafts crashing on water or speedboats slamming into waves. Due to the high computational complexity, the influence of air is often neglected, limiting the analysis to the interaction between structure and water. On the contrary, this work aims to specifically investigate the effect of air when merged inside the fluid–structure interaction (FSI) computational models. Measures from experiments were used as a basis to validate estimations comparing results from models that include or exclude the presence of air. Outcomes generally showed a great correlation between simulation and experiments, with marginal differences in terms of accelerations, especially during the first phase of impact and considering the presence of air in the model.

Rationale Estimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention. Objectives We aimed to develop machine learning (ML) models which estimate ITE of an intervention using data from randomised controlled trials and illustrate this approach with prediction of ITE on annual chronic obstructive pulmonary disease (COPD) exacerbation rates. Methods We used data from 8151 patients with COPD of the Study to Understand Mortality and MorbidITy in COPD (SUMMIT) trial (NCT01313676) to address the ITE of fluticasone furoate/vilanterol (FF/VI) versus control (placebo) on exacerbation rate and developed a novel metric, Q-score, for assessing the power of causal inference models. We then validated the methodology on 5990 subjects from the InforMing the PAthway of COPD Treatment (IMPACT) trial (NCT02164513) to estimate the ITE of FF/umeclidinium/VI (FF/UMEC/VI) versus UMEC/VI on exacerbation rate. We used Causal Forest as causal inference model. Results In SUMMIT, Causal Forest was optimised on the training set (n=5705) and tested on 2446 subjects (Q-score 0.61). In IMPACT, Causal Forest was optimised on 4193 subjects in the training set and tested on 1797 individuals (Q-score 0.21). In both trials, the quantiles of patients with the strongest ITE consistently demonstrated the largest reductions in observed exacerbations rates (0.54 and 0.53, p<0.001). Poor lung function and blood eosinophils, respectively, were the strongest predictors of ITE. Conclusions This study shows that ML models for causal inference can be used to identify individual response to different COPD treatments and highlight treatment traits. Such models could become clinically useful tools for individual treatment decisions in COPD.

Steel of the mild-strength S355J2+N steel grade is the most often used steel for manufacturing carrying sections of constructions exposed to fatigue loads. The use of high-strength steels, such as S690QL, allows for the creation of structures that are light and simple to construct. However, increasing the yield strength of high-strength steels does not result in a corresponding increase in fatigue resistance. As a result, using high-strength steels for constructions subjected to fatigue loading can be a major design concern, raising the question of whether high-strength steels should be used at all. Most of the experimental investigations regarding the hot work tool steel X37CrMoV5-1 found in the literature are focused on its machining and wear resistance, with insufficient attention paid to the cyclic loads. This article evaluates the fatigue properties of mild-strength S355J2+N, high-strength S690QL, and X37CrMoV5-1 steel grades. A SHIMADZU servo-hydraulic testing machine is used to perform uniaxial tensile tests under uniaxial fatigue stress-controlled, fully reversed conditions (tensile–compression testing with R = −1 stress ratio) in accordance with EN ISO and ASTM standards. The aim of this paper is to highlight the fatigue characteristics of these three steels that are among the most used in their respective groups. Steel S355J2+N belongs to the group of hot-rolled normalized steels, S690QL belongs to the group of improved (quenched + tempered) steels with increased strength, and X37CrMoV5-1 belongs to the group of high-alloyed tool steels for hot work. This choice was made as the tested steels can be considered typical representatives of their groups. Based on the test results of these three steels, which are organized in S–N curves, the fatigue behavior of the entire mentioned groups of steels can be foreseen.

Crawler travel gear is a type of heavy vehicle propulsion that is commonly found in tanks, excavators, and specialized off-road vehicles. They have an advantage over wheels when it comes to robust vehicle weight distribution over soft terrain, and some disadvantages as well. They can damage paved roads and have complex design so, considering the enormous weight they must carry, their reliability must be determined and verified. The main parts of the assembly are the drive wheels, which move the crawler, and the supporting structure that holds four-wheel bogies and two-wheel bogies. In this paper, we present a methodology for FEM analysis of parts of an eight-wheel bogie according to DIN 22261-2 standard.