131,224 research outputs found

    The JCMT BISTRO Survey: Multi-wavelength polarimetry of bright regions in NGC 2071 in the far-infrared/submillimetre range, with POL-2 and HAWC+

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    Polarized dust emission is a key tracer in the study of interstellar medium and of star formation. The observed polarization, however, is a product of magnetic field structure, dust grain properties and grain alignment efficiency, as well as their variations in the line of sight, making it difficult to interpret polarization unambiguously. The comparison of polarimetry at multiple wavelengths is a possible way of mitigating this problem. We use data from HAWC+/SOFIA and from SCUBA-2/POL-2 (from the BISTRO survey) to analyse the NGC 2071 molecular cloud at 154, 214 and 850 μm. The polarization angle changes significantly with wavelength over part of NGC 2071, suggesting a change in magnetic field morphology on the line of sight as each wavelength best traces different dust populations. Other possible explanations are the existence of more than one polarization mechanism in the cloud or scattering from very large grains. The observed change of polarization fraction with wavelength, and the 214-to-154 μm polarization ratio in particular, are difficult to reproduce with current dust models under the assumption of uniform alignment efficiency. We also show that the standard procedure of using monochromatic intensity as a proxy for column density may produce spurious results at HAWC+ wavelengths. Using both long-wavelength (POL-2, 850 μm) and short-wavelength (HAWC+, ≲200μm) polarimetry is key in obtaining these results. This study clearly shows the importance of multi-wavelength polarimetry at submillimeter bands to understand the dust properties of molecular clouds and the relationship between magnetic field and star formation

    Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting

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    Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications

    Really proper dangerous one: Aboriginal responses to the first wave of COVID-19 in the Kimberley

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    https://researchonline.nd.edu.au/nulungu_reports/1002/thumbnail.jp

    Journal and disciplinary variations in academic open peer review anonymity, outcomes, and length

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    This is an accepted manuscript of an article published by SAGE in Journal of Librarianship and Information Science on 01/03/2022, available online: https://doi.org/10.1177/09610006221079345 The accepted version of the publication may differ from the final published version.Understanding more about variations in peer review is essential to ensure that editors and reviewers harness it effectively in existing and new formats, including for mega-journals and when published online. This article analyses open reviews from the MDPI suite of journals to identify commonalities and differencesfrom a simplistic quantitative perspective, focusing on reviewer anonymity, review length and review outcomes. The sample contained 45,385 first round open reviews from published standard journal articles in 288 MDPI journals classified into one or more Scopus disciplinary areas (Health Sciences; Life Sciences; Physical Sciences; Social Sciences). The eight main findings include substantial differences between journals and disciplines in review lengths, reviewer anonymity, review outcomes, and the use of attachments. In particular, Physical Sciences journal reviews tended to be stricter and were more likely to be anonymous. Life Sciences and Social Sciences reviews were the longest overall. Signed reviews tend to be 15% longer (perhaps to be more careful or polite) but gave similar decisions to anonymous reviews. Finally, reviews with major revision outcomes tended to be 68% longer than reviews with for minor revision outcomes, except in a few journals. In conclusion, signing reviews does not seem to threaten the validity of peer review outcomes and authors, editors and reviewers of multidisciplinary articles should be aware of substantial field differences in what constitutes an appropriate review

    Evolutionary Computation Based Real-time Robot Arm Path-planning Using Beetle Antennae Search

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