Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

The prospect for an australian-asian power grid: A critical appraisal

© 2018 MDPI AG. All rights reserved. Australia is an energy net self-sufficient country rich in energy resources, from fossil-based to renewable energy. Australia, a huge continent with low population density, has witnessed impressive reduction in energy consumption in various sectors of activity in recent years. Currently, coal and natural gas are two of Australia's major export earners, yet its abundant renewable energy resources such as solar, wind, and tidal, are still underutilized. The majority of Asian countries, on the other hand, are in the middle of economic expansion, with increasing energy consumption and lack of energy resources or lack of energy exploration capability becoming a serious challenge. Electricity interconnection linking two or more independent grids within a country or at cross-border or regional levels has found its way into electricity markets worldwide. This concept allows for electricity exchanges that lead to optimized use and sharing of electricity generated from different sources. The interconnection also enables the long distance exploitation of renewable energy which would otherwise be physically impossible. ASEAN (Association of Southeast Asian Nations) and other regional groupings in Asia have initiated a number of interconnections to gain economic benefits. Asian's hunger for energy for its economic development, climate change that has become a global and urgent issue to be solved, and Australia's abundant renewable energy resources have all prompted increasing interest in a super-grid interconnection linking Australia to Asian grids, the Australian-Asian (Power) Grid (AAG). This paper overviews the existing grid interconnections as well as current initiatives at domestic, sub-regional, and regional levels worldwide, with a particular focus on Asia. The paper concludes with a critical appraisal on the benefits, potential, challenges and issues to be encountered by the AAG initiative

Graphene supported plasmonic photocatalyst for hydrogen evolution in photocatalytic water splitting

It is well known that the noble metal nanoparticles show active absorption in the visible region because of the existence of the unique feature known as surface plasmon resonance (SPR). Here we report the effect of plasmonic Au nanoparticles on the enhancement of the renewable hydrogen (H2) evolution through photocatalytic water splitting. The plasmonic Au/graphene/TiO2 photocatalyst was synthesized in two steps: first the graphene/TiO2 nanocomposites were developed by the hydrothermal decomposition process; then the Au was loaded by photodeposition. The plasmonic Au and the graphene as co-catalyst effectively prolong the recombination of the photogenerated charges. This plasmonic photocatalyst displayed enhanced photocatalytic H2 evolution for water splitting in the presence of methanol as a sacrificial reagent. The H2 evolution rate from the Au/graphene co-catalyst was about 9 times higher than that of a pure graphene catalyst. The optimal graphene content was found to be 1.0 wt %, giving a H2 evolution of 1.34 mmol (i.e., 26 μmolhˉ¹), which exceeded the value of 0.56 mmol (i.e., 112 μmolhˉ¹) observed in pure TiO2. This high photocatalytic H2 evolution activity results from the deposition of TiO2 on graphene sheets, which act as an electron acceptors to efficiently separate the photogenerated charge carriers. However, the Au loading enhanced the H2 evolution dramatically and achieved a maximum value of 12 mmol (i.e., 2.4 mmolhˉ¹) with optimal loading of 2.0 wt% Au on graphene/TiO2 composites. The enhancement of H2 evolution in the presence of Au results from the SPR effect induced by visible light irradiation, which boosts the energy intensity of the trapped electron as well as active sites for photocatalytic activity

The history of disaster incidents and impact in Nepal 1900-2005: ecological, geographical, and development perspectives

The people of Nepal today are exposed to perennial local disaster events and profound vulnerability to disaster. The combined efforts of government, donors, UN agencies, NGOs, and Nepalese communities are needed to avert the impacts of disaster events. Much more can be done immediately to reduce the impacts by reviewing the scope and distribution of past disaster events. This article provides an overview of Nepal’s disaster vulnerability through an analysis of the record of disaster events that occurred from 1900 to 2005. The data were generated from historical archives and divided into incidents at the district, subnational, and national levels. Statistical and Geographical Information System (GIS) analyses were carried out to generate district level disaster vulnerability maps. It is concluded that small-scale, local disasters have a greater cumulative impact in terms of casualties than large-scale, national disasters

Insights from super-metal-rich stars: Is the Milky Way bar young?

Context. Bar formation and merger events can contribute to the rearrangement of stars within the Galaxy in addition to triggering star formation (SF) epochs. Super-metal-rich (SMR) stars found in the solar neighbourhood (SNd) can be used as tracers of such events as they are expected to originate only in the inner Galaxy and to have definitely migrated. Aims. We study a homogeneous and large sample of SMR stars in the SNd to provide tighter constraints on the epoch of the bar formation and its impact on the Milky Way (MW) disc stellar populations. Methods. We investigated a sample of 169 701 main sequence turnoff (MSTO) and subgiant branch (SGB) stars with 6D phase space information and high-quality stellar parameters coming from the hybrid-CNN analysis of the Gaia-DR3 RVS stars. We computed distances and ages using the StarHorse code with a mean precision of 1% and 11%, respectively. Of these stars, 11 848 have metallicity ([Fe/H]) above 0.15 dex. Results. We report a metallicity dependence of spatial distribution of stellar orbits shown by the bimodal distribution in the guiding radius (Rg) at 6.9 and 7.9 kpc, first appearing at [Fe/H] ~ 0.1 dex, becoming very pronounced at higher [Fe/H]. In addition, we observe a trend where the most metal-rich stars, with [Fe/H] ~ 0.4 dex, are predominantly old (9–12 Gyr), but show a gradual decline in [Fe/H] with age, reaching approximately 0.25 dex about 4 Gyr ago, followed by a sharp drop around 3 Gyr ago. Furthermore, our full dataset reveals a clear peak in the age–metallicity relationship during the same period, indicating a SF burst around 3–4 Gyr ago with slightly sub-solar [Fe/H] and enhanced [α/Fe]. Conclusions. We show that the SMR stars are good tracers of bar activity.We interpret the steep decrease in the number of SMR stars at around 3 Gyr as the end of the bar formation epoch. In this scenario the peak of bar activity also coincides with a peak in the SF activity in the disc. Although the SF burst around 3 Gyr ago has been reported previously, its origin was unclear. Here we suggest that the SF burst was triggered by the high bar activity, 3–4 Gyr ago. According to these results and interpretation, the MW bar could be young

The Gaia-ESO Survey: Probing the lithium abundances in old metal-rich dwarf stars in the solar vicinity

Context. Lithium (Li) is a fragile element that is produced in a variety of sites but can also be very easily depleted in stellar photospheres. Radial migration has been reported to explain the decrease in the upper envelope of Li measurements observed for relatively old metal-rich dwarf stars in some surveys. Aims. We test a scenario in which radial migration could affect the Li abundance pattern of dwarf stars in the solar neighbourhood. This may confirm that the Li abundances in these stars cannot serve as a probe for the Li abundance in the interstellar medium (ISM). In other words, to probe the evolution of the Li abundance in the local ISM, it is crucial that stellar intruders be identified and removed from the adopted sample. Methods. We used the high-quality data (including Li abundances) from the sixth internal Data Release of the Gaia-ESO survey. In this sample we grouped stars by similarity in chemical abundances via hierarchical clustering. Our analysis treats both measured Li abundances and upper limits. Results. The Li envelope of the previously identified radially migrated stars is well below the benchmark meteoritic value (0 should not be decreased, as recently proposed in the literature. Our study backs recent studies that claim that old dwarfs on the hot side of the dip are efficient probes of the ISM abundance of Li, provided atomic diffusion does not significantly lower the initial Li abundance in the atmospheres of metal-rich objects

Beyond Gaia DR3: tracing the [{\alpha}/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia RVS and Convolutional Neural-Networks

Gaia DR3 has provided the community with about one million RVS spectra covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200M spectra. Thus, stellar spectra are produced on an "industrial scale" with numbers well above those for current and anticipated ground based surveys. However, many of these spectra have low S/N (from 15 to 25 per pixel), such that they pose problems for classical spectral analysis pipelines and therefore alternative ways to tap into these large datasets need to be devised. We aim to leverage the versatility/capabilities of machine learning techniques for supercharged stellar parametrization, by combining Gaia RVS spectra with the full set of Gaia products and high-resolution, high-quality spectroscopic reference data sets. We develop a hybrid Convolutional Neural-Network (CNN) which combines the Gaia DR3 RVS spectra, photometry (G, Bp, Rp), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g), and overall [M/H]) and chemical abundances ([Fe/H] and [$\alpha$/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 841300 stars, that remarkably compared to external data-sets. The CNN is robust against noise in the RVS data, and very precise labels are derived down to S/N=15. We managed to characterize the [$\alpha$/M]-[M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. This work is the first to combine machine-learning with such diverse datasets (spectroscopy, astrometry, and photometry), and paves the way for the large scale machine-learning analysis of Gaia-RVS spectra from future data releases.Comment: 24 pages, 24 figures, submitted to A&