10 research outputs found

    Optical Properties for Thin Film of Coumarin 334 Organic Laser Dye doped with PVA Polymer and Al2O3 Nanoparticles

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    Background: In this work, the optical properties for thin film of Coumarin 334 organic laser dye doped with PVA polymer and AL2O3 nanoparticles are determined at different concentrations, dissolved in ethanol solvent. Methods: Solutions at concentration (10-4 M) from coumarin (334) dye  in ethanol solvent at Different concentrations from dye (1, 3, 5, and 7)  ×10-5 M  were  prepared. Results: The AFM measurements show that the  grain size, root mean  square and roughness belong to the surface of the thin films rises with thickness rise for thin films  .  It has also increased the absorbance due to the increase in concentration. Conclusion: By using Gaussian beam from CW pulsed laser at 457 nm, we used Z-scan to study the nonlinear optical properties. The nonlinearity of thin film highly excels the pure dye solutions

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

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    Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

    Effect of nano-silica slurry on engineering, X-ray, and γ-ray attenuation characteristics of steel slag high-strength heavyweight concrete

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    High molar mass materials (nano-silica slurry [NSS] and aggregate of steel furnace slag [ASFS]) can improve concrete shielding properties. However, only a few studies have been reported in this regard. Hence, this paper aims to determine the effect of NSS and ASFS on the properties of the resulting steel slag heavyweight concrete (SSHWC). The use of NSS in this study is a novel contribution. Furthermore, the maximum percentage of NSS to be introduced into the concrete for maximum effect was also optimized. This study also implemented an investigation program with six concrete mixtures prepared using ASFS as the primary by-product aggregate. The engineering, X-ray, and γ-ray attenuation characteristics of the SSHWC were evaluated. The results showed that the addition of NSS in SSHWC at the optimal content of 3% by weight of cement improved the X-ray shielding by 6.4%. Besides, all the concrete’s engineering and γ-rays’ properties were enhanced correspondingly

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

    No full text
    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press

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