The burden of unintentional drowning: Global, regional and national estimates of mortality from the Global Burden of Disease 2017 Study

__Background:__ Drowning is a leading cause of injury-related mortality globally. Unintentional drowning (International Classification of Diseases (ICD) 10 codes W65-74 and ICD9 E910) is one of the 30 mutually exclusive and collectively exhaustive causes of injury-related mortality in the Global Burden of Disease (GBD) study. This study's objective is to describe unintentional drowning using GBD estimates from 1990 to 2017. __Methods:__ Unintentional drowning from GBD 2017 was estimated for cause-specific mortality and years of life lost (YLLs), age, sex, country, region, Socio-demographic Index (SDI) quintile, and trends from 1990 to 2017. GBD 2017 used standard GBD methods for estimating mortality from drowning. __Results:__ Globally, unintentional drowning mortality decreased by 44.5% between 1990 and 2017, from 531 956 (uncertainty interval (UI): 484 107 to 572 854) to 295 210 (284 493 to 306 187) deaths. Global age-standardised mortality rates decreased 57.4%, from 9.3 (8.5 to 10.0) in 1990 to 4.0 (3.8 to 4.1) per 100 000 per annum in 2017. Unintentional drowning-associated mortality was generally higher in children, males and in low-SDI to middle-SDI countries. China, India, Pakistan and Bangladesh accounted for 51.2% of all drowning deaths in 2017. Oceania was the region with the highest rate of age-standardised YLLs in 2017, with 45 434 (40 850 to 50 539) YLLs per 100 000 across both sexes. __Conclusions:__ There has been a decline in global drowning rates. This study shows that the decline was not consistent across countries. The results reinforce the need for continued and improved policy, prevention and research efforts, with a focus on low-and middle-income countries

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Improvement of the magnetic properties for Mn–Ni–Zn ferrites by rare earth Nd3+ ion substitution

Single spinel phases of Mn0.5Ni0.1Zn0.4NdxFe2−xO4 ferrite samples (x = 0.0, 0.01, 0.02, 0.05, 0.075, and 0.1) have been prepared by ceramic method and the composition dependence of the physical and magnetic properties has been investigated. SEM micrographs and EDX analysis revealed that there is no considerable effect for the Nd3+ ion substitution on the average grain size or porosity, whereas its concentration in the grain boundaries is higher than that in the grains. Saturation magnetization (MS) increased with the Nd3+ ion concentration (x) and reached a maximum value at x = 0.05. In addition, both the initial permeability and the magnetic homogeneity increased by increasing the Nd3+ ion content. The value of Curie temperature increased due to the substitution by Nd3+ ions to record about 170 K, for the sample with x = 0.05, higher than that of the un-substituted one

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Online judges (OJ) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This meta-information could be extremely valuable to help users quickly find what types of problems they need most. To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures. Initially, these topics were used to support programming competitions and experienced learners. However, with OJs being increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors. In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus. After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%. We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs

Integration of reverse engineering and rapid technologies for rapid investment casting of gas turbine blades

The aim of this research work is to offer a unique procedure of integrated Reverse Engineering (RE) and Additive Manufacturing (AM) technologies through implementation of a comprehensive experimental study to present an extensive comparison between applicable rapid technologies for blade rapid investment casting. Different direct and indirect AM techniques were used to produce sacrificial investment casting patterns. In addition, an aluminium mould of a blade for wax injection was fabricated using Computer Numerical Control (CNC) machining to compare conventional investment casting with the proposed rapid investment casting process. Dimensional inspection of cast blades showed that the MultiJet Modelling (MJM) method has the most economic justification and compatibility with blade rapid investment casting and it can be considered as an alternative to conventional wax precision CNC machining. On the other hand, sacrificial patterns produced by the Perfactory system showed lower applicability for investment casting due to pattern flexibility and difficulties in burn-out step. As for examined rapid tooling technologies, silicone rubber moulding was not a stable technique for small batch fabrication of blade wax patterns. In contrast, the results proved that epoxy resin tooling could make possible new cost-effective approaches for low volume production of gas turbine blades