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  1. ABSTRACT

    Using MeerKAT, we have discovered three new millisecond pulsars (MSPs) in the bulge globular cluster M62: M62H, M62I, and M62J. All three are in binary systems, which means all ten known pulsars in the cluster are in binaries. M62H has a planetary-mass companion with a median mass Mc, med ∼ 3 MJ and a mean density of ρ ∼ 11 g cm−3. M62I has an orbital period of 0.51 d and a Mc, med ∼ 0.15 M⊙. Neither of these low-mass systems exhibit eclipses. M62J has only been detected in the two Ultra High Frequency band (816 MHz) observations with a flux density S816 = 0.08 mJy. The non-detection in the L-band (1284 MHz) indicates it has a relatively steep spectrum (β < −3.1). We also present 23-yr-long timing solutions obtained using data from the Parkes ‘Murriyang’, Effelsberg, and MeerKAT telescopes for the six previously known pulsars. For all these pulsars, we measured the second spin-period derivatives and the rate of change of orbital period caused by the gravitational field of the cluster, and their proper motions. From these measurements, we conclude that the pulsars’ maximum accelerations are consistent with the maximum cluster acceleration assuming a core-collapsed mass distribution. Studies of the eclipses of the redback M62B and the black widow M62E at four and two different frequency bands, respectively, reveal a frequency dependence with longer and asymmetric eclipses at lower frequencies. The presence of only binary MSPs in this cluster challenges models which suggest that the MSP population of core-collapsed clusters should be dominated by isolated MSPs.

     
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  2. This paper characterizes the acid and cold stress responses of the thermoacidophilic crenarchaeon Saccharolobus islandicus REY15A, showing that each stress results in impaired growth rates, altered GDGT-lipid profiles, and differences in transcriptomes and proteomes. 
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    Free, publicly-accessible full text available July 1, 2024
  3. This paper investigates the mechanical behaviour of a bi-layered panel containing many particles in one layer and demonstrates the size effect of particles on the deflection. The inclusion-based boundary element method (iBEM) considers a fully bounded bi-material system. The fundamental solution for two-jointed half spaces has been used to acquire elastic fields resulting from source fields over inclusions and boundary-avoiding multi-domain integral along the interface. Eshelby’s equivalent inclusion method is used to simulate the material mismatch with a continuously distributed eigenstrain field over the equivalent inclusion. The eigenstrain is expanded at the centre of the inclusion, which provides tailorable accuracy based on the order of the polynomial of the eigenstrain. As a single-domain approach, the iBEM algorithm is particularly suitable for conducting virtual experiments of bi-layered composites with many defects or reinforcements for both local analysis and homogenization purposes. The maximum deflection of solar panel coupons is studied under uniform vertical loading merged with inhomogeneities of different material properties, dimensions and volume fractions. The size of defects or reinforcements plays a significant role in the deflection of the panel, even with the same volume fraction, as the substrate is relatively thin. 
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  4. A longstanding goal of learner modeling and educational data min-ing is to improve the domain model of knowledge that is used to make inferences about learning and performance. In this report we present a tool for finding domain models that is built into an exist-ing modeling framework, logistic knowledge tracing (LKT). LKT allows the flexible specification of learner models in logistic re-gression by allowing the modeler to select whatever features of the data are relevant to prediction. Each of these features (such as the count of prior opportunities) is a function computed for a compo-nent of data (such as a student or knowledge component). In this context, we have developed the “autoKC” component, which clus-ters knowledge components and allows the modeler to compute features for the clustered components. For an autoKC, the input component (initial KC or item assignment) is clustered prior to computing the feature and the feature is a function of that cluster. Another recent new function for LKT, which allows us to specify interactions between the logistic regression predictor terms, is com-bined with autoKC for this report. Interactions allow us to move beyond just assuming the cluster information has additive effects to allow us to model situations where a second factor of the data mod-erates a first factor. 
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