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Your article about the problems that some practices experience with their electronic records ["IT Implementation: Why EHRs falter," April 7] emphasizes the importance of selecting the right system and implementing [...]

ABSTRACT Restaurant foods are typically higher in calories than meals consumed at home. A goal of the 2010 Patient Protection and Affordable Care Act is to encourage healthier food choices at restaurants by providing consumers with information about the calorie content of menu items. However, doesn't a well-informed consumer already know that a 12-piece bucket of chicken has more calories than a salad? Following research in marketing science and behavioral economics, we evaluate a representative consumer's ability to discriminate between low-calorie and high-calorie menu items using only some basic 'rules of thumb' nutrition knowledge. Results reveal the extent to which menu labeling may increase consumers' knowledge about the calories in restaurant foods. [ABSTRACT FROM AUTHOR]

We provide a description and computational investigation of an efficient method to stochastically generate realistic pore structures. Smolarkiewicz and Winter introduced this specific method in pores resolving simulation of Darcy flows (Smolarkiewicz and Winter, 2010 [1] ) without giving a complete formal description or analysis of the method, or indicating how to control the parameterization of the ensemble. We address both issues in this paper. The method consists of two steps. First, a realization of a correlated Gaussian field, or topography, is produced by convolving a prescribed kernel with an initial field of independent, identically distributed random variables. The intrinsic length scales of the kernel determine the correlation structure of the topography. Next, a sample pore space is generated by applying a level threshold to the Gaussian field realization: points are assigned to the void phase or the solid phase depending on whether the topography over them is above or below the threshold. Hence, the topology and geometry of the pore space depend on the form of the kernel and the level threshold. Manipulating these two user prescribed quantities allows good control of pore space observables, in particular the Minkowski functionals. Extensions of the method to generate media with multiple pore structures and preferential flow directions are also discussed. To demonstrate its usefulness, the method is used to generate a pore space with physical and hydrological properties similar to a sample of Berea sandstone. [ABSTRACT FROM AUTHOR]

Animal migrations frequently cross international boundaries, and conservation of migratory species requires the collaborative efforts of multiple nations. The Convention on Migratory Species (CMS) has overseen such conservation efforts for over twenty-five years by encouraging member Parties to conclude daughter agreements focused on protecting individual migratory species or groups of migratory species. In the past twenty-five years, CMS members have concluded twenty-six agreements that protect a wide range of migratory species. Many of these agreements provide targeted actions to offer immediate protection for critically endangered or threatened migratory species, but a handful are much broader in scope, providing protection for large classes of migratory species, regardless of endangerment status. This Article examines the structure of the CMS and its daughter agreements to identify key challenges for international migratory species protection, and draws on international environmental policy literature to identify potential strategies for overcoming these problems in future agreements. [ABSTRACT FROM AUTHOR]

• Combined effects of flow variability and retention due to matrix-diffusion. • We perform a set of three-dimensional discrete fracture network simulations. • We develop an upscaled continuous time random walk (CTRW) approach. • Advective transport uses an Ornstein-Uhlenbeck model for the particle velocities. • Fracture-matrix coupling is represented using a compound Poisson process. We investigate the combined effects of network scale flow variability and retention due to matrix-diffusion on the scaling behavior of transport through fractured media. Two of the principal mechanisms controlling the transport of solutes through fractured low-permeability media are broad distributions of flow velocities and retention times in the solid matrix. We study the relative impact of these two processes under different initial conditions using a set of three-dimensional discrete fracture network simulations. We use these simulations to develop and calibrate an upscaled continuous time random walk (CTRW) approach for advective transport based on an Ornstein-Uhlenbeck model for the particle velocities that accounts for the fracture-matrix coupling using a compound Poisson process. This CTRW model can be conditioned on the initial solute distribution and allows to observe late-time scaling behavior at distances beyond what is feasible to observe using high-fidelity direct numerical simulations. We determine that the initial distribution of particles leads to marked differences in the persistent long-term scale behavior in the solute travel time distributions, even those undergoing retention due to matrix diffusion through implementation and analysis of the model. [ABSTRACT FROM AUTHOR]

Exploratory data analysis (EDA) for functional data—data objects where observations are entire functions—is a difficult problem that has seen significant attention in recent literature. This surge in interest is motivated by the ubiquitous nature of functional data, which are prevalent in applications across fields such as meteorology, biology, medicine and engineering. Empirical probability density functions (PDFs) can be viewed as constrained functional data objects that must integrate to one and be nonnegative. They show up in contexts such as yearly income distributions, zooplankton size structure in oceanography and in connectivity patterns in the brain, among others. While PDF data are certainly common in modern research, little attention has been given to EDA specifically for PDFs. In this paper, we extend several methods for EDA on functional data for PDFs and compare them on simulated data that exhibit different types of variation, designed to mimic that seen in real‐world applications. We then use our new methods to perform EDA on the breakthrough curves observed in gas transport simulations for underground fracture networks. [ABSTRACT FROM AUTHOR]

The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and necessitates horizontal wells with multi-stage engineered fractures to efficiently extract hydrocarbons resources. These artificially-generated and naturally-occurring fractures form complex networks that create complex flow regimes which control oil production. These fractures are neither identical nor equally-spaced, which leads to a production profile with a masked onset of the boundary-dominated flow. The combination of the extended linear flow with the indeterminate onset of the boundary-dominated flow challenges the current deterministic analytic approaches to forecast the estimated ultimate recovery (EUR). Herein, we propose a novel machine-learning approach which overcomes these challenges and provides reliable EUR estimates based on field-wide analyses. We implement a novel unsupervised machine learning (ML) methodology, which allows for automatic identification of the optimal number of features (signals) present in the data based on non-negative matrix/tensor factorization coupled with k -means clustering incorporating regularization and physics constraints. In the presented analyses, the input data to the ML algorithm is the available (public) production history from the field collected at existing unconventional reservoirs. We validate our approach through hindcasting of the production data, where we achieved an excellent agreement. In addition, our approach is able to identify the poorly-performing wells, which could benefit from early refracing. Our approach provides fast and accurate estimations of the well performance without presumptions about the state of the well or the flow regime. • Novel unsupervised machine learning (ML) methodology to predict the shale well performance. • Automatic identification of the optimal number of features present in the database. • Excellent agreement is observed when validating the proposed approach. [ABSTRACT FROM AUTHOR]

Evaluates a model that has proven useful in studies of maximum security facilities for adult males with data obtained from a sample of male juvenile delinquents who were committed to a custodially oriented training school. Discussion on contextual powerlessness as an aspect of alienation; Attitudes toward the law and delinquent self-identification; Application of multivariate analysis.

Reports the negotiation over occupational pensions and employee participation in the management of pension schemes in Great Britain. Reasons for a substantial rise in pensions as a component of labor cost; Absence of occupational pensions from mainstream industrial relations; Role of labor unions in improving current living standards.

• Flow and solute transport simulations were conducted in sparse fracture networks. • Continuous time random walk (CTRW) approach was used to quantify transport behavior. • Generated networks were near the representative elementary volume (REV) for flow. • In most networks, effective permeability slightly decreased and then increased with scale increase. • Solute transport and CTRW model parameters were scale-dependent. Understanding the effect of fracture network heterogeneity on flow and transport in tight formations with low permeability values is of great importance in a wide variety of applications, such as groundwater pollution and storage as well as water production. Although numerous studies have investigated solute transport in fractured media, investigation about scale effects on flow and transport in low-permeability aquifers is limited. More specifically, we are not aware of any study that has addressed how the continuous time random walk (CTRW) model parameters vary with scale and fracture network properties. To this end, we performed a set of numerical simulations in sparse fracture networks of sizes L = 20, 35, and 50 m under two different volumetric fracture counts (p30 = 0.05 and 0.1) using the discrete fracture network approach. The fractures in the networks were elliptical in shape, and their radii followed a truncated power-law distribution with exponents α = 1.5, 2, and 2.5. We simulated fluid flow using the Reynolds equation, and solute transport was simulated using particle tracking. The solute transport behavior was quantified by fitting the solution of the CTRW transport equation to the arrival time distributions. Results showed that as the truncated power law exponent α increased, the permeability of the networks decreased. In all the fracture networks studied, we found non-Fickian solute transport behavior deduced from small values of β (0.69 ≤ β ≤ 1.25), the exponent in the truncated power-law arrival time distribution in the CTRW model. In our fracture networks, the aperture size spanned less than one order of magnitude meaning that the fracture permeability distribution was relatively narrow. Therefore, the observed non-Fickian behavior should be attributed to sparse fracture networks and low connectivity. We found that the effective permeability, k eff , at p30 = 0.05 was more scale-dependent compared to that at p30 = 0.1 because the fracture networks with p30 = 0.05 were less connected and, thus, more heterogeneous. Our simulation also disclosed the scale dependence of the CTRW model parameters. We conducted multiple linear regression analysis to link the CTRW model parameters to the network size as well as the geometrical and topological properties of the network. Results of regression analysis showed that all the CTRW model parameters except the average solute velocity were significantly dependent on the system size L (p-value < 0.05). Further investigations using a broader range of fracture network properties, such as broader aperture size distributions, smaller and larger network sizes and volumetric fracture counts are still required. [ABSTRACT FROM AUTHOR]

The article focuses on alienation and prisonization. It views prison as being similar to other types of formal organizations that are characterized by a system of rules, a rigid hierarchy of authority, a reliance of coercive power, and a low degree of client autonomy. One product of such characteristics is the alienation of the inmates. Two measures of alienation were investigated: societal alienation and alienation from the formal organization of the prison. Initially, the effect of duration of confinement on levels of alienation was evaluated. The expectation was that there would be a positive relationship between duration of exposure to the type of coercive organization that prisons typically represent and the extent to which inmates become alienated. Further, because alienation is also an important independent variable, the extent to which alienation was predictive of prisonization was assessed. A model was developed in order to account for the hypothesized relationships. The conceptual model tested in the article predicted that: as length of time incarcerated increases, contextual alienation, societal alienation, and prisonization increase; as contextual alienation increases, societal alienation and prisonization increase; as societal alienation increases, prisonization increases.

We performed a set of numerical simulations to characterize the interplay of fracture network topology, upscaling, and mesh refinement on flow and transport properties in fractured porous media. We generated a set of generic three-dimensional discrete fracture networks at various densities, where the radii of the fractures were sampled from a truncated power-law distribution, and whose parameters were loosely based on field site characterizations. We also considered five network densities, which were defined using a dimensionless version of density based on percolation theory. Once the networks were generated, we upscaled them into a single continuum model using the upscaled discrete fracture matrix model presented by Sweeney et al. (2019). We considered steady, isothermal pressure-driven flow through each domain and then simulated conservative, decaying, and adsorbing tracers using a pulse injection into the domain. For each simulation, we calculated the effective permeability and solute breakthrough curves as quantities of interest to compare between network realizations. We found that selecting a mesh resolution such that the global topology of the upscaled mesh matches the fracture network is essential. If the upscaled mesh has a connected pathway of fracture (higher permeability) cells but the fracture network does not, then the estimates for effective permeability and solute breakthrough will be incorrect. False connections cannot be eliminated entirely, but they can be managed by choosing appropriate mesh resolution and refinement for a given network. Adopting octree meshing to obtain sufficient levels of refinement leads to fewer computational cells (up to a 90% reduction in overall cell count) when compared to using a uniform resolution grid and can result in a more accurate continuum representation of the true fracture network. [ABSTRACT FROM AUTHOR]