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

Regression and Learning to Rank Aggregation for User Engagement Evaluation

User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems. In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain. For this task, we define several features which can be extracted from the meta-data of each tweet. The features are partitioned into three categories: user-based, movie-based, and tweet-based. We show that in order to obtain good results, features from all categories should be considered. We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance. We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.Comment: In Proceedings of the 2014 ACM Recommender Systems Challenge, RecSysChallenge '1

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

Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game

Dynamic Difficulty Adjustment (DDA) is a viable approach to enhance a player's experience in video games. Recently, Reinforcement Learning (RL) methods have been employed for DDA in non-competitive games; nevertheless, they rely solely on discrete state-action space with a small search space. In this paper, we propose a continuous RL-based DDA methodology for a visual working memory (VWM) game to handle the complex search space for the difficulty of memorization. The proposed RL-based DDA tailors game difficulty based on the player's score and game difficulty in the last trial. We defined a continuous metric for the difficulty of memorization. Then, we consider the task difficulty and the vector of difficulty-score as the RL's action and state, respectively. We evaluated the proposed method through a within-subject experiment involving 52 subjects. The proposed approach was compared with two rule-based difficulty adjustment methods in terms of player's score and game experience measured by a questionnaire. The proposed RL-based approach resulted in a significantly better game experience in terms of competence, tension, and negative and positive affect. Players also achieved higher scores and win rates. Furthermore, the proposed RL-based DDA led to a significantly less decline in the score in a 20-trial session

Fracture Resistance of Root Canals Obturated with Gutta-Percha versus Resilon with Two Different Techniques

Introduction: Dentin removal during root canal instrumentation creates a weaker root structure and increases its potential to fracture. The aim of this in vitro experimental study was to compare fracture resistance of teeth filled with gutta-percha, and Resilon using two different techniques. Materials and Methods: This study was performed on 105 single-canal extracted maxillary incisors. Samples were divided into seven groups of 15 each. Three groups were prepared with K-files; three groups with Race rotary files and in one group no preparation was carried out. Of all samples prepared either manually or with rotary instruments, 15 teeth were obturated using gutta-percha and AH26 sealer, 15 teeth were filled with Resilon and 15 teeth remained unfilled. Loading force to fracture was measured and ANOVA and Tukey tests were used for statistical analysis. Results: No statistically significant differences were observed between different preparation techniques. The intact roots showed significantly greater fracture resistance compared to both instrumented groups (P<0.01). Resilon Group showed significantly higher resistance than gutta-percha Group (P<0.01); however the difference between Resilon and intact teeth was not statistically significant. Conclusion: Accoding to the results of this in vitro study, root canal filling using Resilon may increase the fracture resistance of treated teeth