Forty-four snake and spider phobic 5s, selected from a large pool of undergraduates were exposed to either (a) a form of systematic desensitization treatment, (6) a technique, called T-scope therapy, which embodies most of the expectancy-manipulating features of desensitization, but does not contain the technical elements of the procedure (i.e., relaxation, visualization, and the construction of an anxiety hierarchy), (c) T-scope therapy, presented as an "incomplete" and probably ineffective form of treatment, or (d) no treatment. There were no significant differences (on self-rating, runway, or interview measures) between the effects of the systematic desensitization procedure and T-scope therapy, although 5s receiving either of these treatments improved significantly more than those who received no treatment or T-scope therapy administered under the "low-expectancy" condition.

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Unconventional shale gas reservoirs require stimulation via hydraulic fracturing of pre-existing fracture networks for practical exploitation, creating a stimulated reservoir volume (SRV). Within the SRV, gas flow from the nano-Darcy shale to the complex stimulated fracture network has been modeled in reservoir simulators using a variety of techniques which upscale/simplify the fracture network. The simulation techniques used in the past were normally not compared with reference solutions.This work investigates using finely-gridded single well reference solutions (approximately 6-14 million cells) for simulating Darcy and non-Darcy flow within an explicitly modeled SRV complex fracture network, in 2-D, with and without primary hydraulic fractures, as well as scenarios which model stress sensitive permeability and later re-stimulation of a horizontal well. The network fractures use cells which are only 0.001 ft. wide.The reference solutions are compared with standard dual permeability and MINC (multiple interacting continua) dual continua models as well as novel models which simulate flow inside of the SRV using coarse, logarithmically spaced, locally refined, dual permeability grids, and simulate flow outside of the SRV using unrefined dual permeability grids. These coarse models can be run in minutes on standard hardware, where as the reference models can take days to run on the same hardware.We will show that excellent matches to the reference solutions are possible using a modest number of refinements to simulate the flow within the SRV when the fracture permeability and the fracture Forchheimer number (for non-Darcy flow) are scaled as described in the paper. These techniques allow the use of 2.0 ft. wide fracture conduits to mimic non-Darcy flow in 0.001 ft. wide fractures.Good agreement between the reference and coarse models are observed even during the early flow period of the reservoir.

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For over two decades, policy makers and economic development professionals have stressed the importance of encouraging and supporting industry clusters to promote job creation and regional competitiveness. Several researchers-Michael Porter and Christian Ketels, among others-have developed the study of cluster-based economic development and touted the employment and competitive benefits of cluster-based development strategies (Ketels, 2013; Ketels & Memedovic, 2008; Porter, 2008). Several studies have quantified the benefits of clusters in terms of employment growth and competitiveness as it relates to patent rates, with Delgado, Porter, and Stern (2014) being the most comprehensive. Yet other economic benefits along the dimensions of productivity, wage, and income growth have been largely overlooked. In this study, we assess the benefits of clusters across several traditional measures of economic performance. This study is organized as follows: We first discuss key concepts related to clusters in a brief literature review, including a discussion on the role of related and unrelated variety as they are connected to cluster definitions. Second, we present the data, analytical methods, and the rationale and construction for three measures of regional cluster characteristics, namely strength, diversity, and growth. These three measures are applied to both traded and local/nontraded clusters. The third section reports the empirical results, with the fourth section discussing the findings and conclusions about the usefulness of our measures. Review of the Literature Industry clusters are agglomerations of closely related industries (Delgado, Porter, & Stern, 2010). One might say that clusters are a network of economic relationships that create a competitive advantage for the related firms in a particular region. This advantage then becomes an enticement for similar industries to develop or relocate to a region. Developing industry clusters has become a key goal for regional economic development, as clusters have been shown to strengthen competitiveness by increasing productivity, stimulating innovative new partnerships (even among competitors), and presenting opportunities for new businesses.