The impact of a large cohort of Chinese students on the delivery of an Engineering degree in the UK

Undergraduate articulation programmes are common in collaborations between China and the UK. Their proliferation has resulted in a high ratio of Chinese students in some Engineering courses. This paper interrogates such a site where a ‘2+2’ agreement between a Chinese and British university has produced engineering learning contexts where Chinese students are in the majority. The paper draws upon a longitudinal ethnographic study of 50 Chinese undergraduate Engineering students, conducted over 15 months in China and the UK. In-depth interviews and participatory observations were conducted to collect data. Constructive grounded theory analytical approaches were adopted to analyse the data. Findings reveal that the Chinese students’ contribution to the revenue and internationalization of the university culture has impelled the host school to start to link with the Chinese university closely at academic level. The effort in facilitating Chinese students’ transition through early intervention and academic exchange has made the two teaching and learning contexts more connected. The presence of this large cohort of Chinese students also has motivated some of the academic staff to modify their teaching to adjust to students’ learning. However, this kind of adjustment has caused some complaints from the other students in the class. The social disintegration and unfamiliarity amongst students at the initial stage have had a negative effect on peer learning. Structured contacts have benefited the integration of the multinational class, which has enhanced the peer learning in the class. Studying with Chinese students also enables home students to reflect on past learning experiences and this highlights a gap among secondary school, college and university education, which could be a potential obstacle to young people studying Engineering

Real-time Autonomous Glider Navigation Software

Underwater gliders are widely utilized for ocean sampling, surveillance, and other various oceanic applications. In the context of complex ocean environments, gliders may yield poor navigation performance due to strong ocean currents, thus requiring substantial human effort during the manual piloting process. To enhance navigation accuracy, we developed a real-time autonomous glider navigation software, named GENIoS Python, which generates waypoints based on flow predictions to assist human piloting. The software is designed to closely check glider status, provide customizable experiment settings, utilize lightweight computing resources, offer stably communicate with dockservers, robustly run for extended operation time, and quantitatively compare flow estimates, which add to its value as an autonomous tool for underwater glider navigation.Comment: OCEANS 2023 Limeric

Anomaly Detection of Underwater Gliders Verified by Deployment Data

This paper utilizes an anomaly detection algorithm to check if underwater gliders are operating normally in the unknown ocean environment. Glider pilots can be warned of the detected glider anomaly in real time, thus taking over the glider appropriately and avoiding further damage to the glider. The adopted algorithm is validated by two valuable sets of data in real glider deployments, the University of South Florida (USF) glider Stella and the Skidaway Institute of Oceanography (SkIO) glider Angus.Comment: 10 pages, 16 figures, accepted by the International Symposium on Underwater Technology (UT23

Extension of the Semantic Sensor Network Ontology for Wireless Sensor Networks: The Stimulus-WSNnode-Communication Pattern

International audienceWireless Sensor Networks (WSN) are designed to collect large amounts of heterogeneous data to monitor environmental phenomenon. Our aim is to adapt WSN nodes communication to their context, in order to optimize the lifetime of the network. Our description of context and WSN characteristics are based on ontologies. Based upon a critical analysis of existing ontologies which formalize the WSN domain, we determine that the Semantic Sensor Network (SSN) ontology is the most suitable to represent the WSN issues. However, as the communication data policy is not characterized either by SSN or by other ontologies, we propose to enrich the SSN ontology with a new pattern describing communication. In this paper, we will first integrate the different concepts related to WSN in the SSN ontology and then we will use the resulting ontology, called Wireless Semantic Sensor Network ontology, in an agri-environmental scenario to illustrate the interest of our approach

Formation of octapod MnO nanoparticles with enhanced magnetic properties through kinetically-controlled thermal decomposition of polynuclear manganese complexes

Polynuclear manganese complexes are used as precursors for the synthesis of manganese oxide nanoparticles (MnO NPs). Altering the thermal decomposition conditions can shift the nanoparticle product from spherical, thermodynamically-driven NPs to unusual, kinetically-controlled octapod structures. The resulting increased surface area profoundly alters the NP's surface-dependent magnetism and may have applications in nanomedicine

A Chemical Abundance Study of 10 Open Clusters Based on WIYN-Hydra Spectroscopy

We present a detailed chemical abundance study of evolved stars in 10 open clusters based on Hydra multi-object echelle spectra obtained with the WIYN 3.5m telescope. From an analysis of both equivalent widths and spectrum synthesis, abundances have been determined for the elements Fe, Na, O, Mg, Si, Ca, Ti, Ni, Zr, and for two of the 10 clusters, Al and Cr. To our knowledge, this is the first detailed abundance analysis for clusters NGC 1245, NGC 2194, NGC 2355 and NGC 2425. These 10 clusters were selected for analysis because they span a Galactocentric distance range Rgc~9-13 kpc, the approximate location of the transition between the inner and outer disk. Combined with cluster samples from our previous work and those of other studies in the literature, we explore abundance trends as a function of cluster Rgc, age, and [Fe/H]. The [Fe/H] distribution appears to decrease with increasing Rgc to a distance of ~12 kpc, and then flattens to a roughly constant value in the outer disk. Cluster average element [X/Fe] ratios appear to be independent of Rgc, although the picture for [O/Fe] is more more complicated by a clear trend of [O/Fe] with [Fe/H] and sample incompleteness. Other than oxygen, no other element [X/Fe] exhibits a clear trend with [Fe/H]; likewise, there does not appear to be any strong correlation between abundance and cluster age. We divided clusters into different age bins to explore temporal variations in the radial element distributions. The radial metallicity gradient appears to have flattened slightly as a function of time, as found by other studies. There is also indication that the transition from the inner disk to the outer disk occurs at different Galactocentric radii for different age bins. (Abridged.)Comment: 35 pages, 12 figures, 18 tables; published in The Astronomical Journal (http://stacks.iop.org/1538-3881/142/59

Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.Comment: Submitted to IEEE Big Dat

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected