Starspots on the fastest rotators in the Beta Pic moving group

Aims: We carried out high-resolution spectroscopy and BV(I)_C photometric monitoring of the two fastest late-type rotators in the nearby Beta Pictoris moving group, HD199143 (F7V) and CD-641208 (K7V). The motivation for this work is to investigate the rotation periods and photospheric spot patterns of these very young stars, with a longer term view to probing the evolution of rotation and magnetic activity during the early phases of main-sequence evolution. We also aim to derive information on key physical parameters, such as rotational velocity and rotation period. Methods: We applied maximum entropy (ME) and Tikhonov regularizing (TR) criteria to derive the surface spot map distributions of the optical modulation observed in HD199143 (F7 V) and CD-641208 (K7V). We also used cross-correlation techniques to determine stellar parameters such as radial velocities and rotational velocities. Lomb-Scargle periodograms were used to obtain the rotational periods from differential magnitude time series. Results: We find periods and inclinations of 0.356 days and 21.5deg for HD199143, and 0.355 days and 50.1deg for CD-641208. The spot maps of HD199143 obtained from the ME and TR methods are very similar, although the latter gives a smoother distribution of the filling factor. Maps obtained at two different epochs three weeks apart show a remarkable increase in spot coverage amounting to ~7% of the surface of the photosphere over a time period of only ~20 days. The spot maps of CD-641208 from the two methods show good longitudinal agreement, whereas the latitude range of the spots is extended to cover the whole visible hemisphere in the TR map. The distributions obtained from the first light curve of HD199143 show the presence of an extended and asymmetric active longitude with the maximum filling factor at longitude ~325degree.Comment: Accepted by A&A. 13 pages, 13 figures (4 online included), 5 Table

Mapping the Shores of the Brown Dwarf Desert III: Young Moving Groups

We present the results of an aperture masking interferometry survey for substellar companions around 67 members of the young (~8-200Myr) nearby (~5-86pc) AB Doradus, Beta Pictoris, Hercules-Lyra, TW Hya, and Tucana-Horologium stellar associations. Observations were made at near infrared wavelengths between 1.2-3.8 microns using the adaptive optics facilities of the Keck II, VLT UT4, and Palomar Hale Telescopes. Typical contrast ratios of ~100-200 were achieved at angular separations between ~40-320mas, with our survey being 100% complete for companions with masses below 0.25\msolar across this range. We report the discovery of a $0.52 \pm 0.09$\msolar companion to HIP14807, as well as the detections and orbits of previously known stellar companions to HD16760, HD113449, and HD160934. We show that the companion to HD16760 is in a face-on orbit, resulting in an upward revision of its mass from $M_2 \sin i \sim 14$\mjupiter to $M_2 = 0.28 \pm 0.04$\msolar. No substellar companions were detected around any of our sample members, despite our ability to detect companions with masses below 80\mjupiter for 50 of our targets: of these, our sensitivity extended down to 40\mjupiter around 30 targets, with a subset of 22 subject to the still more stringent limit of 20\mjupiter. A statistical analysis of our non-detection of substellar companions allows us to place constraints on their frequency around ~0.2-1.5\msolar stars. In particular, considering companion mass distributions that have been proposed in the literature, we obtain an upper limit estimate of ~9-11% for the frequency of 20-80\mjupiter companions between 3-30AU at 95% confidence, assuming that their semimajor axes are distributed according to $d\mathcal{N}/da \propto a^{-1}$ in this range.Comment: Accepted by Ap

The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data. Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysi

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Big Data Analytics for Wireless and Wired Network Design: A Survey

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks

Chitin and Chitosan as Sources of Bio‐Based Building Blocks and Chemicals

Chitin and chitosan polymers are a valuable source of functional chemicals and materials. Chemical and/or enzymatic depolymerisation processes have been developed for the production of chitooligosaccharides (COS), N‐acetylglucosamine (GlcNAc) and glucosamine (GlcN), which have a wide variety of applications. New technologies are now emerging to convert chitin and its derivatives into platform chemicals. Chemical liquefaction of chitin can lead to bulk chemicals like acetic acid and platform chemicals like hydroxymethylfurfural (HMF) and amine‐containing monomers for polymers, in low yield. The monomers GlcNAc and GlcN can be converted into N‐containing HMF derivatives, opening a pathway for furan‐based monomers for polyamides. Selective catalytic oxidation of GlcN results in the production of D‐glucosaminic acid (DGA). This acid is a valuable building block for the synthesis of various amino acids for biomedical applications and bio‐based chiral polyamides. Further technological improvements are necessary to increase the selectivity and efficiency of reactions, particularly for the conversion of polymeric chitin and chitosan into building blocks.<br/