The Current State-Of-The-Art In Active Region Seismology

Helioseismology is the study of the variations in the internal structure and properties of the dynamics of the Sun from measurements of its surface oscillations. With the 2010 launch of the Solar Dynamics Observatory (SDO) we are undoubtedly approaching a new dawn for local helioseismology, as the extent and quality of raw surface oscillation data has never been better. However, advances in theory and modelling are still required to fully utilise these data, especially in magnetic active regions and sunspots, where the physics is poorly understood.Comment: 6 pages. Proceedings of ESF/HELAS-5/EAST-4 Conference (Obergurgl, Austria, 20-25 May 2012), to appear in Astronomische Nachrichte

Sensitivity of helioseismic travel-times to the imposition of a Lorentz force limiter in computational helioseismology

The rapid exponential increase in the Alfv\'en wave speed with height above the solar surface presents a serious challenge to physical modelling of the effects of magnetic fields on solar oscillations, as it introduces a significant CFL time-step constraint for explicit numerical codes. A common approach adopted in computational helioseismology, where long simulations in excess of 10 hours (hundreds of wave periods) are often required, is to cap the Alfv\'en wave speed by artificially modifying the momentum equation when the ratio between Lorentz and hydrodynamic forces becomes too large. However, recent studies have demonstrated that the Alfv\'en wave speed plays a critical role in the MHD mode conversion process, particularly in determining the reflection height of the upward propagating helioseismic fast wave. Using numerical simulations of helioseismic wave propagation in constant inclined (relative to the vertical) magnetic fields we demonstrate that the imposition of such artificial limiters significantly affects time-distance travel times unless the Alfv\'en wave-speed cap is chosen comfortably in excess of the horizontal phase speeds under investigation.Comment: 8 pages, 5 figures, accepted by ApJ

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Information Literacy among Educational Academic Members of Zabol University of Medical Sciences, Zabol, Iran

Introduction: Development of information literacy is considered a required factor for instructors of higher education system due to its impact on educational and research activities, and performance of educational academic members is a main factor that affects the output of system. The aim of this study was to report and compare the information literacy among the academic members of departments of clinical and basic biomedical sciences in 2011. Methods: A cross-sectional survey was performed using a valid and reliable questionnaire distributed among 48 full-time equivalent academic members of Zabol University of Medical Sciences in both clinical (19 members) and basic biomedical departments (29 members). Data were analyzed using Fisher, Mann-Whitney and Chi-square statistics in SPSS 17. Results: Information literacy of the members was at an average level at both knowledge and attitude levels but it was low at the practice. There was a significant difference between two groups in terms of awareness about information resources; however, the difference was not significant for the utilization of information resources. Conclusion: Members of department of basic biomedical sciences were more aware than those of clinical department about the information resources but such awareness has not resulted in more use of resources in the educational and research activities. Despite positive attitude of all members towards the application of electronic information resources in both educational and research activities, their awareness of information literacy skills and practicing were not satisfying in educational and research sections. As a final point, Information literacy is hence suggested as a part of continuing medical education courses

The Effect of Preparation Method on the Activity of Bimetallic Ni-Fe Catalyst in Dry Reforming of Methane

This paper investigates the effects of Fe/Ni ratio (0.5-2) and polyvinyl alcohol to total metal ratio (PVA/ (Ni + Fe) =0.5-1.5) on the performance of bimetallic(Ni-Fe) catalyst in dry reforming of methane. The activity, stability and H2/CO ratio of the catalysts are tested. The effects of both metal ratio and shell factor on the catalyst performance have been studied. The nickel-ferrite catalysts were prepared by sol-gel method. Shell factor is employed for controlling the size of the particles. The samples were characterized by XRD, BET, SEM, and TGA. The catalysts were tested at 800˚C in the microreactor with a feed ratio of CH4/CO2=1/1. The catalyst prepared with Fe/Ni=2 and PVA/ (Ni + Fe) = 0.5 ratio shows the best activity, and stability with 30 h time on stream. It can be attributed to NiFe2O4 as only available phase after calcination and smaller crystal size at the optimum values of Fe/Ni and PVA/ (Ni + Fe) ratio