Estimating the mode I through-thickness intralaminar R-curve of unidirectional carbon fibre-reinforced polymers using a micromechanics framework combined with the size effect method

A three-dimensional micromechanics framework is developed to estimate the mode I through-thickness intralaminar crack resistance curve of unidirectional carbon fibre-reinforced polymers. Finite element models of geometrically-scaled single edge notch tension specimens were generated. These were modelled following a combined micro-/meso-scale approach, where the region at the vicinity of the crack tip describes the microstructure of the material, while the regions far from the crack tip represent the mesoscopic linear-elastic behaviour of the composite. This work presents a novel methodology to estimate fracture properties of composite materials by combining computational micromechanics with the size effect method. The size effect law of the material, and consequently the crack resistance curve, are estimated through the numerically calculated peak stresses. In-depth parametric analyses, which are hard to conduct empirically, are undertaken, allowing for quantitative and qualitative comparisons to be successfully made with experimental and numerical observations taken from literature

Fast object detection in pastoral landscapes using a multiple expert colour feature extreme learning machine

Fast and accurate object detection is a desire of many vision-guided robotics based systems. Agriculture is an area where detection accuracy is often sacrificed for speed, especially in the pursuit of real time results. Pastoral landscapes are especially challenging with varying levels of complexity, as competing objects are rarely textually smooth or visibly different from surroundings. This study presents a machine learning algorithm designed for object detection called the Multiple Expert Colour Extreme Learning Machine (MEC-ELM). The MEC-ELM is a multiple expert implementation of a Colour Feature Extreme Learning Machine (CF-ELM). The CF-ELM is itself a modification of the Extreme Learning Machine (ELM) with a partially connected hidden layer and a fully connected output layer, taking 3 inputs. The inputs can be utilised by multiple colour systems, including, RGB, Y'UV and HSV. Colour inputs were chosen, as colour is not sensitive to adjustments in scale, size and location and provides information not available in the standard grey-scale ELM. In the MEC-ELM algorithm, feature extraction and classification techniques were implemented simultaneously making a fully functional object detection algorithm. The algorithm was tested on weed detection and cattle detection from a video feed, delivering 0.89 (cattle) to 0.98 (weeds) accuracy in tuning and a precision of 0.61 to 0.95 in testing, with classification times between 0.5s to 1s per frame. The algorithm has been designed with complex and unpredictable terrain in mind, making it an ideal application for agricultural or pastoral landscapes

Atmospheric Pressure Plasma-Synthesized Gold Nanoparticle/Carbon Nanotube Hybrids for Photothermal Conversion

In this work, a room-temperature atmospheric pressure direct-current plasma has been deployed for the one-step synthesis of gold nanoparticle/carboxyl group-functionalized carbon nanotube (AuNP/CNT-COOH) nanohybrids in aqueous solution for the first time. Uniformly distributed AuNPs are formed on the surface of CNT-COOH, without the use of reducing agents or surfactants. The size of the AuNP can be tuned by changing the gold salt precursor concentration. UV–vis, ζ-potential, and X-ray photoelectron spectroscopy suggest that carboxyl surface functional groups on CNTs served as nucleation and growth sites for AuNPs and the multiple potential reaction pathways induced by the plasma chemistry have been elucidated in detail. The nanohybrids exhibit significantly enhanced Raman scattering and photothermal conversion efficiency that are essential for potential multimodal cancer treatment applications

The safety of isoniazid tuberculosis preventive treatment in pregnant and postpartum women: systematic review and meta-analysis

BACKGROUND: The World Health Organization (WHO) recommends tuberculosis (TB) preventive treatment for high-risk groups. Isoniazid preventive therapy (IPT) has been used globally for this purpose for many years, including in pregnancy. This review assessed current knowledge about the safety of IPT in pregnancy. METHODS: We searched PubMed, Embase, CENTRAL, Global Health Library and HIV and TB-related conference abstracts, until May 15, 2019, for randomised controlled trials (RCTs) and non-randomised studies (NRS) where IPT was administered to pregnant women. Outcomes of interest were: 1) maternal outcomes, including permanent drug discontinuation due to adverse drug reactions, any grade 3 or 4 drug-related toxic effects, death from any cause and hepatotoxicity; and 2) pregnancy outcomes, including in utero fetal death, neonatal death or stillbirth, preterm delivery/prematurity, intrauterine growth restriction, low birth weight and congenital anomalies. Meta-analyses were conducted using a random-effects model. RESULTS: After screening 1342 citations, nine studies (of 34 to 51 942 participants) met inclusion criteria. We found an increased likelihood of hepatotoxicity among pregnant women given IPT (risk ratio 1.64, 95% CI 0.78-3.44) compared with no IPT exposure in one RCT. Four studies reported on pregnancy outcomes comparing IPT exposure to no exposure among pregnant women with HIV. In one RCT, adverse pregnancy outcomes were associated with IPT exposure during pregnancy (odds ratio (OR) 1.51, 95% CI 1.09-2.10), but three NRS showed a protective effect. CONCLUSIONS: We found inconsistent associations between IPT and adverse pregnancy outcomes. Considering the grave consequences of active TB in pregnancy, current evidence does not support systematic deferral of IPT until postpartum. Research on safety is needed

#ArsonEmergency and Australia's "Black Summer": Polarisation and misinformation on social media

During the summer of 2019-20, while Australia suffered unprecedented bushfires across the country, false narratives regarding arson and limited backburning spread quickly on Twitter, particularly using the hashtag #ArsonEmergency. Misinformation and bot- and troll-like behaviour were detected and reported by social media researchers and the news soon reached mainstream media. This paper examines the communication and behaviour of two polarised online communities before and after news of the misinformation became public knowledge. Specifically, the Supporter community actively engaged with others to spread the hashtag, using a variety of news sources pushing the arson narrative,while the Opposer community engaged less, retweeted more, and focused its use of URLs to link to mainstream sources, debunking the narratives and exposing the anomalous behaviour. This influenced the content of the broader discussion. Bot analysis revealed the active accounts were predominantly human, but behavioural and content analysis suggests Supporters engaged in trolling, though both communities used aggressive language.Derek Weber, Mehwish Nasim, Lucia Falzon, and Lewis Mitchel

Promoting and countering misinformation during Australia’s 2019–2020 bushfires: a case study of polarisation

During Australia’s unprecedented bushfires in 2019–2020, misinformation blaming arson surfaced on Twitter using #ArsonEmergency. The extent to which bots and trolls were responsible for disseminating and amplifying this misinformation has received media scrutiny and academic research. Here, we study Twitter communities spreading this misinformation during the newsworthy event, and investigate the role of online communities using a natural experiment approach—before and after reporting of bots promoting the hashtag was broadcast by the mainstream media. Few bots were found, but the most bot-like accounts were social bots, which present as genuine humans, and trolling behaviour was evident. Further, we distilled meaningful quantitative differences between two polarised communities in the Twitter discussion, resulting in the following insights. First, Supporters of the arson narrative promoted misinformation by engaging others directly with replies and mentions using hashtags and links to external sources. In response, Opposers retweeted fact-based articles and official information. Second, Supporters were embedded throughout their interaction networks, but Opposers obtained high centrality more efciently despite their peripheral positions. By the last phase, Opposers and unaffliated accounts appeared to coordinate, potentially reaching a broader audience. Finally, the introduction of the bot report changed the discussion dynamic: Opposers only responded immediately, while Supporters countered strongly for days, but new unafiliated accounts drawn into the discussion shifted the dominant narrative from arson misinformation to factual and official information. This foiled Supporters’ efforts, highlighting the value of exposing misinformation. We speculate that the communication strategies observed here could inform counter-strategies in other misinformation-related discussions.Derek Weber, Lucia Falzon, Lewis Mitchell, Mehwish Nasi

A method to evaluate the reliability of social media data for social network analysis

In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.Derek Weber, Mehwish Nasim, Lewis Mitchell, Lucia Falzo