Effect of Welding on Lateral-Torsional Buckling Resistance of I-Shaped Built-up Steel

An experimental investigation was made of the inelastic lateral-torsional buckling of built-up steel I-beams. All beams were carefully fabricated with controlled levels of initial crookedness. Nineteen beams were tested in five groups under same loading conditions with two points load applied at the top flange. The results confirmed that built-up beams of intermediate slenderness with fillet welds on one side of the web are sometimes stronger than their counterpart beams with fillet welds on both sides of the web. It was found that design loads predicted by the Australian Standard provided good lower bounds estimates to failure loads of the tested beams

Key somatic variables in young backstroke swimmers

The purpose of this study was to estimate the optimal body size, limb-segment length, girth or breadth ratios for 100-m backstroke mean speed performance in young swimmers. Sixty-three young swimmers (boys [n = 30; age: 13.98 ± 0.58 years]; girls [n = 33; age: 13.02 ± 1.20 years]) participated in this study. To identify the optimal body size and body composition components associated with 100-m backstroke speed performance, we adopted a multiplicative allometric log-linear regression model, which was refined using backward elimination. The multiplicative allometric model exploring the association between 100-m backstroke mean speed performance and the different somatic measurements estimated that biological age, sitting height, leg length for the lower-limbs, and two girths (forearm and arm relaxed girth) are the key predictors. Stature and body mass did not contribute to the model, suggesting that the advantage of longer levers was limb-specific rather than a general whole-body advantage. In fact, it is only by adopting multiplicative allometric models that the abovementioned ratios could have been derived. These findings highlighted the importance of considering somatic characteristics of young backstroke swimmers and can help swimming coaches to classify their swimmers and enable them to suggest what might be the swimmers’ most appropriate stroke (talent identification)

Key anthropometric variables associated with front-crawl swimming performance in youth swimmers: an allometric approach

This is an accepted manuscript of an article published by Lippincott, Williams & Wilkins/National Strength and Conditioning Association in Journal of Strength and Conditioning Research on 07/02/2020, available online: 10.1519/JSC.0000000000003491 The accepted version of the publication may differ from the final published version.Sammoud, S, Negra, Y, Chaabene, H, Bouguezzi, R, Attia, A, Granacher, U, Younes, H, and Nevill, AM. Key anthropometric variables associated with front-crawl swimming performance in youth swimmers: an allometric approach. J Strength Cond Res XX(X): 000-000, 2020-This study aimed to establish key anthropometric characteristics (e.g., optimal body height, limb-segment length, and girth/breadth ratios) related to 100-m front-crawl performance in young swimmers. In total, 74 swimmers (boys [n = 41; age: 18.1 ± 3.5 years]; girls [n = 33; age: 15.9 ± 3.1 years]) participated in this study. We adopted a multiplicative allometric log-linear regression model to identify key anthropometric characteristics associated with 100-m front-crawl swimming performance. The main outcomes indicated that length ratio = ([height/leg length]), foot length and ankle girth, biacromial breadth, and % of body fat were associated with 100-m front-crawl mean swimming speed performance. These findings highlight the importance of assessing anthropometric characteristics in young front-crawl swimmers for talent identification and development.Published versio

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease