A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing

The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.Comment: accepted in DCOSS 201

Modeling membrane nanotube morphology: the role of heterogeneity in composition and material properties.

Membrane nanotubes are dynamic structures that may connect cells over long distances. Nanotubes are typically thin cylindrical tubes, but they may occasionally have a beaded architecture along the tube. In this paper, we study the role of membrane mechanics in governing the architecture of these tubes and show that the formation of bead-like structures along the nanotubes can result from local heterogeneities in the membrane either due to protein aggregation or due to membrane composition. We present numerical results that predict how membrane properties, protein density, and local tension compete to create a phase space that governs the morphology of a nanotube. We also find that there exists a discontinuity in the energy that impedes two beads from fusing. These results suggest that the membrane-protein interaction, membrane composition, and membrane tension closely govern the tube radius, number of beads, and the bead morphology

SurvJamda: an R package to predict patients' survival and risk assessment using joint analysis of microarray gene expression data

Summary: SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients' survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and to improve survival prognosis. The prognosis performance derived from the combined datasets can be assessed to determine which feature selection approach, joint analysis method and bias estimation provide the most robust prognosis for a given set of datasets. Availability: The survJamda package is available at the Comprehensive R Archive Network, http://cran.r-project.org. Contact: [email protected]

Developing A Machine Learning Based Approach For Fractured Zone Detection By Using Petrophysical Logs

Oil reservoirs are divided into three categories: carbonate (fractured), sandstone and unconventional reservoirs. Identification and modeling of fractures in fractured reservoirs are so important due to geomechanical issues, fluid flood simulation and enhanced oil recovery.Image and petrophysical logs are individual tools, run inside oil wells, to achieve physical characteristics of reservoirs, e.g. geological rock types, porosity, and permeability. Fractures could be distinguished using image logs because of their higher resolution. Image logs are an expensive and newly developed tool, so they have run in limited wells, whereas petrophysical logs are usually run inside the wells. Lack of image logs makes huge difficulties in fracture detection, as well as fracture studies. In the last decade, a few studies were done to distinguish fractured zones in oil wells, by applying data mining methods over petrophysical logs. The goal of this study was also discrimination of fractured/non-fractured zones by using machine learning techniques and petrophysical logs. To do that, interpretation of image logs was utilized to label reservoir depth of studied wells as 0 (non-fractured zone) and 1 (fractured zone). We developed four classifiers (Deep Learning, Support Vector Machine, Decision Tree, and Random Forest) and applied them to petrophysics logs to discriminate fractured/non-fractured zones. Ordered Weighted Averaging was the data fusion method that we utilized to integrate outputs of classifiers in order to achieve unique and more reliable results. Overall, the frequency of non-fractured zones is about two times of fractured zones. This leads to an imbalanced condition between two classes. Therefore, the aforementioned procedure relied on the balance/imbalance data to investigate the influence of creating a balanced situation between classes. Results showed that Random Forest and Support Vector Machines are better classifiers with above 95 percent accuracy in discrimination of fractured/non-fractured zones. Meanwhile, making a balanced situation in the wells by a higher imbalance index helps to distinguish either non-fractured or fractured zones. Through imbalance data, non-fractured zones (dominant class) could be perfectly distinguished, while a significant percentage of fractured zones were also labeled as non-fractured ones

Factors related to disturbed eating patterns amongst normal adolescents.

Health in adult life has been associated with health during early and late adolescence. Investigations here also show that a number of factors such as family environment, sociocultural norms, stress and self perception can affect one's health at any age. As the prevalence of eating disorders has increased among women and especially among female adolescents, this study set out to look at the role of factors which are influential in the onset and maintenance of eating problems among normal weight adolescents who have high scores on the Eating Disorder Inventory (EDI). These adolescents reported symptoms of atypical and abnormal eating habits, even though they did not have a history of chronic eating disorders or weight problems. A six months longitudinal study was conducted with pupils of four age groups (third to sixth formers) divided into two groups of high EDI scorers and low EDI scorers. They were assessed on three occasions at three month intervals. A battery of eight instruments was employed, assessing the role of family members and environment, relationship with significant others such as peers and friends; and sociocultural norms on the type and intensity of stress experienced by the adolescents. How stress affects their self perception and self-esteem, as well as their perception and appraisal of stressors was also assessed in relation to their eventual effects on the adolescents' eating patterns. Overall the results showed that the adolescents who showed high EDI scores, initially, were considerably different from those with low EDI scores in their self-esteem level, level of stress they experienced, the coping strategies they employed, their family environment, their perception of themselves and their ideal self, as well as of significant others. Based on the findings and previous literature, a model is proposed, arguing that conflicts and discords arose in the family environment or in the adolescents' relationships with others; or caused by sociocultural factors can lead to stress which in turn can affect the adolescents' self-esteem and self perception. The way the adolescents perceive themselves can influence their perception and appraisal of potential stressors and hence their choice of strategies to cope with them, one of which is manipulating eating habits, such as overeating. The results of the findings, implications, methodological and limitations and suggestions for future research are also discussed