A Qualitative Analysis of Online Gaming:

The popularity of Massively Multi-Player Online Role-Playing Games (MMORPGs) has risen dramatically over the last decade. Some gamers spend many hours a day in these virtual environments interacting with others gamers, completing quests, and forming social groups. The present study set out to explore the experiences and feelings of online gamers. The study comprised 71 interviews with online gamers (52 males and 19 females) from 11 different countries. Many themes emerged from the analyses of the interview transcripts including (i) relationship with social networking, (ii) social interaction, (iii) the community, (iv) learning real-life skills, (v) reinforcement schedules and operant conditioning, (vi) game design and content, (vii) escaping from real life, (viii) playing longer than intended, and (ix) gamers’ obligations towards others in online worlds. These findings specifically showed the social networking capabilities of online gaming, the community aspects and the psychological mechanisms within MMORPGs that can lead to excessive online gaming. The implications of these findings are discussed in relation to previous qualitative and quantitative research in the area

Female Gamers:

International evidence indicates that the number of females involved in video-gaming is increasing. Within the context of this increase, there is a need to explore the experiences of this group of gamers in detail. This study explored female experiences of playing video-games. Data were collected from an online discussion forum dedicated to video-gaming; the sample comprised of posts drawn from 409 discussion threads. Thematic analysis of the discussions suggests that gaming is a key element of the female gamers’ identity, with females discussing the integration of gaming into their daily lives on a number of different levels. Similar to previous research, social elements of gaming is highlighted with simultaneous difficulties with online interaction emphasised. These themes are discussed in relation to relevant research in the area, along with recommendations for future research and consideration of possible explanations for the themes observed

File-Sharing and the Darknet

Today the darknet is a term most often is used for privacy networks like TOR. Around the year 2000 at the high of the mp3 file sharing debate between internet activists and copyright holders, it was a term commonly used for file sharing networks allowing the transfer of digital files, especially music and movies. These former darknets were subject of multiple investigations with the aim to identify and sue users who traded copyrighted martial via these networks. Therefore, a technical development started trying to achieve privacy for file sharing users. In this article, the authors show how file sharing networks evolved into the privacy networks we know today and which impact the privacy mechanisms have on file sharing

Estimation of minimum miscibility pressure of varied gas compositions and reservoir crude oil over a wide range of conditions using an artificial neural network model

Minimum miscibility pressure (MMP) is a key variable for monitoring miscibility between reservoir fluid and injection gas. Experimental and non-experimental methods are used to estimate MMP. Available miscibility correlations attempt to predict the minimum miscibility pressure for a specific type of gas. Here an artificial neural network (ANN) model is applied to a dataset involving 251 data records from around the world in a novel way to estimate the gas-crude oil MMP for a wide range of injected gases and crude oil compositions. This approach is relevant to sequestration projects in which injected gas compositions might vary significantly. The model is correlated with the reservoir temperature, concentrations of volatile (C1 and N2) and intermediate (C2, C3, C4, CO2 and H2S) fractions in the oil (Vol/Inter), C5+ molecular weight fractions in the oil and injected gas specific gravity. A key benefit of the ANN model is that MMP can be determined with reasonable accuracy for a wide range of oil and gas compositions. Statistical comparison of predictions shows that the developed ANN model yields better predictions than empirical-correlation methods. The ANN model predictions achieve a mean absolute percentage error of 13.46%, root mean square error of 3.6 and Pearson's correlation coefficient of 0.95. Sensitivity analysis reveals that injected gas specific gravity and temperature are the most important factors to consider when establishing appropriate miscible injection conditions. Among the available published correlations, the Yellig and Metcalfe correlation demonstrates good prediction performance, but it is not as accurate as the developed ANN model