Prevalence and determinants of substance use among a sample of Iranian adolescents with ease of access to drugs: an application of Social Development Model

The aim of this study was to investigate the prevalence rate and determinants of SU in adolescents based on the social development model (SDM). In 2018, applying a cross-sectional design, cluster multistage random sampling was employed to recruit 600 adolescents in Bam County, Iran, to participate in the study. A valid and reliable SDM-based instrument was used to collect data. The prevalence rate of using at least one substance was 42 (in girls 33.6 and in boys 50.3). Adjusted for covariates, having close friends with SU was found as the factor with the highest risk. Higher scores in involvement in prosocial activities and interactions (OR: 0.47; 95 Confidence interval (CI): 0.33�0.66, p < 0.001), attachment and commitment to prosocial others (family and school) (OR: 0.73; 95 CI: 0.58�0.93, p < 0.05), and skills for interaction/involvement (OR: 0.51; 95CI: 0.39�0.67, p < 0.001) reduced the odds of ever use of SU among adolescents. Also, higher levels of perceived rewards for antisocial interaction/involvement (OR: 2.22; 95 Confidence interval (CI): 1.53�3.22, p < 0.001) and belief in antisocial values (OR: 2.24; 95 CI: 1.67�2.94, p < 0.001) increased the odds of ever use SU among the respondents. In community-based interventions to prevent SU among adolescents, a great focus should be firstly on identifying the probability of SU in close friends. Moreover, the involvement of adolescents in prosocial activities and interactions, attachment and commitment to prosocial others (family and school), and skills for interaction/involvement should be core categories while designing community-based interventional studies. © 2020 Institute of Health Promotion and Education

The RANSAC method for generating fracture networks from micro-seismic event data

Fracture network modeling is an essential part of the design, development and performance assessment of Enhanced Geothermal Systems. These systems are created from geothermal resources, usually located several kilometers below the surface of the Earth, by establishing a network of connected fractures through which fluid can flow. The depth of the reservoir makes it impossible to make direct measurements of fractures and data are collected from indirect measurements such as geophysical surveys. An important source of indirect data is the seismic event point cloud generated by the fracture stimulation process. Locations of these points are estimated from recorded micro-seismic signals generated by fracture initiation, propagation and slip. This point cloud can be expressed as a set of three-dimensional coordinates with attributes, for example Seijk={(x,y,z); a{pipe}x,y,z∈R, a∈I}. We describe two methods for reconstructing realistic fracture trace lines and planes given the point cloud of seismic events data: Enhanced Brute-Force Search and RANSAC. The methods have been tested on a synthetic data set and on the Habanero data set of Geodynamics' geothermal project in the Cooper Basin of South Australia. Our results show that the RANSAC method is an efficient and suitable method for the conditional simulation of fracture networks. © 2013 International Association for Mathematical Geosciences.Younes Fadakar Alghalandis, Peter A. Dowd, Chaoshui X

A spatial clustering approach for stochastic fracture network modelling

Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach followed by a real 3D case study of the Habanero reservoir in the Cooper Basin, Australia. © 2013 Springer-Verlag Wien.S. Seifollahi, P. A. Dowd, C. Xu, A. Y. Fadaka