Estimating stellar rotation from starspot detection during planetary transits

A new method for determining the stellar rotation period is proposed here, based on the detection of starspots during transits of an extra-solar planet orbiting its host star. As the planet eclipses the star, it may pass in front of a starspot which will then make itself known through small flux variations in the transit light curve. If we are lucky enough to catch the same spot on two consecutive transits, it is possible to estimate the stellar rotational period. This method is successfully tested on transit simulations on the Sun yielding the correct value for the solar period. By detecting two starspots on more than one transit of HD 209458 observed by the Hubble Space Telescope, it was possible to estimate a period of either 9.9 or 11.4 days for the star, depending on which spot is responsible for the signature in the light curve a few transits later. Comparison with period estimates of HD209458 reported in the literature indicates that 11.4 days is the most likely stellar rotation period.Comment: 13 pages, 5 figure

Time evolution and rotation of starspots on CoRoT-2 from the modelling of transit photometry

CoRoT-2, the second planet-hosting star discovered by the CoRoT satellite, is a young and active star. A total of 77 transits were observed for this system over a period of 135 days. Small modulations detected in the optical light curve of the planetary transits are used to study the position, size, intensity, and temporal evolution of the photospheric spots on the surface of the star that are occulted by the planetary disk. We apply a spot model to these variations and create a spot map of the stellar surface of CoRoT-2 within the transit band for every transit. From these maps, we estimate the stellar rotation period and obtain the longitudes of the spots in a reference frame rotating with the star. Moreover, the spots temporal evolution is determined. This model achieves a spatial resolution of 2\circ. Mapping of 392 spots vs. longitude indicates the presence of a region free of spots, close to the equator, reminiscent of the coronal holes observed on the Sun during periods of maximum activity. With this interpretation, the stellar rotation period within the transit latitudes of -14.\circ 6 \pm 10 \circ is found to be 4.48 days. This rotation period is shorter than the 4.54 days as derived from the out-of-transit light modulation. Since the transit data samples a region close to the stellar equator, while the period determined from out-of-transit data reflects the average rotation of the star, this is taken as an indication of a latitudinal differential rotation of about 3% or 0.042 rad/d.Comment: 8 pages, 12 figure

AS DOENÇAS CRÔNICAS E AS REPERCUSSÕES DO TRATAMENTO HEMODIALÍTICO PARA OS PACIENTES RENAIS CRÔNICOS E SUAS FAMÍLIAS

RESUMO: O estudo tem como objetivo discutir as repercussões para os envolvidos no cuidado de pessoa que realiza tratamento hemodialítico. O aumento do número de doenças crônicas é uma realidade no Brasil e, diante do fato das duas doenças crônicas mais prevalentes no país (o diabetes e a hipertensão arterial) serem as principais causas da doença renal crônica e consequente tratamento de diálise, o trabalho é fundamental para desvelar como está sendo realizado o cuidado dessas pessoas e se este cuidado tem custos – seja financeiro, de tempo, de trabalho ou emocional para os familiares e pacientes. Os principais resultados apontam que o custo do cuidado despendido para os pacientes em tratamento hemodialítico pelos familiares e/ou cuidadores vai além do gasto monetário perpassando por aspectos da vida antes não imaginados. Estes acontecimentos interferem no cotidiano de todos os envolvidos no cuidado

Reducing Jamming Attack effects

Jamming as a form of denial-of-service is a commonly-used attack initiated against security at the physical layer of a wireless system. A new paradigm, known as the Internet of Things (IoT), has an extensive applicability in numerous are-as, including healthcare. The full application of this paradigm in healthcare area is a mutual hope because it allows medical centers to function more competently and patients to obtain better treatment

Distributed Denial of Service Attack in Networks

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. Our aim is to provide a tutorial to introduce DDoS attack and its working knowledge as well as rectifications. We will address its issues and suggest how it can overcome

Wireless Communications and Mobile Computing using Machine learning

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not re-place, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Artificial iIntelligence for Big Data: issues and challenges

Artificial intelligence (AI) concerns the study and development of intelligent ma-chines and software. The associated ICT research is highly technical and specialized, and its focal problems include the developments of software that can reason, gather knowledge, plan intelligently, learn, communicate, perceive and manipulate objects. AI also allows users of big data to automate and enhance complex descriptive and predictive analytical tasks that, when performed by humans, would be extremely la-bour intensive and time consuming. Thus, unleashing AI on big data can have a sig-nificant impact on the role data plays in deciding how we work, how we travel and how we conduct business. This paper explores how Artificial Intelligence, in conjunc-tion with Big Data technologies, can help organizations to bring about operational and business transformation.Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Internet of Things and WBAN: Attacks Presentations

What we are approaching is a world where basic healthcare would become out of reach to most people, a large section of society would go unproductive owing to old age and people would be more prone to chronic disease. A new paradigm, known as the Internet of Things (IoT), has an extensive applicability in numerous areas, including healthcare. The full application of this paradigm in healthcare area is a mutual hope because it allows medical centers to function more competently and patients to obtain better treatment

Fluctuation dynamics of a single magnetic chain

"Tunable" fluids such as magnetorheological "MR" and electrorheological "ER" fluids are comprised of paramagnetic or dielectric particles suspended in a low-viscosity liquid. Upon the application of a magnetic or electric field, these fluids display a dramatic, reversible, and rapid increase of the viscosity. This change in viscosity can, in fact, be tuned by varying the applied field, hence the name "tunable fluids". This effect is due to longitudinal aggregation of the particles into chains in the direction of the applied field and the subsequent lateral aggregation into larger semisolid domains. A recent theoretical model by Halsey and Toor "HT" explains chain aggregation in dipolar fluids by a fluctuation-mediated long-range interaction between chains and predicts that this interaction will be equally efficient at all applied fields. This paper describes video-microscopy observations of long, isolated magnetic chains that test HT theory. The measurements show that, in contrast to the HT theory, chain aggregation occurs more efficiently at higher magnetic field strength (H0) and that this efficiency scales as H0½. Our experiments also yield the steady-state and time-dependent fluctuation spectra C(x,x')≡ [h(x)-h(x')]²>½ and C(x,x',t,t')≡ ½ for the instantaneous deviation h(x,t) from an axis parallel to the field direction to a point x on the chain. Results show that the steady-state fluctuation growth is similar to a biased random walk with respect to the interspacing ͉ |x-x'| along the chain, C(x,x')≈|x-x'| α, with a roughness exponent α =0.53±0.02. This result is partially confirmed by Monte Carlo simulations. Time-dependent results also show that chain relaxation is slowed down with respect to classical Brownian diffusion due to the magnetic chain connectivity, C(x,x',t,t')≈|t-t'|β, with a growth exponent β=0.35±0.05<½. All data can be collapsed onto a single curve according to C(x,x',t,t')≈|x-x'| α ψ (|t-t'| / |x-x'| z ), with a dynamic exponent z= α /β≅ 1.42