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The power-sharing literature lacks a review that synthesizes its findings, despite spanning over 50 years since Arend Lijphart published his seminal 1969 article ‘Consociational Democracy’. This review article contributes to the literature by introducing and analysing an original dataset, the Power Sharing Articles Dataset, which extracts data on 23 variables from 373 academic articles published between 1969 and 2018. The power-sharing literature, our analysis shows, has witnessed a boom in publications in the last two decades, more than the average publication rate in the social sciences. This review offers a synthesis of how power sharing is theorized, operationalized and studied. We demonstrate that power sharing has generally positive effects, regardless of institutional set-up, post-conflict transitional character and world region. Furthermore, we highlight structural factors that are mostly associated with the success of power sharing. Finally, the review develops a research agenda to guide future scholarly work on power sharing.
Individuals with autism spectrum disorder (ASD) are challenged not only by the defining features of social-communication deficits and restricted repetitive behaviors, but also by a myriad of psychopathology varying in severity. Different cognitive deficits underpin these psychopathologies, which could be subjected to intervention to alter the course of the disorder. Understanding domain-specific mediating effects of cognition is essential for developing targeted intervention strategies. However, the high degree of inter-correlation among different cognitive functions hinders elucidation of individual effects.
In the Philadelphia Neurodevelopmental Cohort, 218 individuals with ASD were matched with 872 non-ASD controls on sex, age, race, and socioeconomic status. Participants of this cohort were deeply and broadly phenotyped on neurocognitive abilities and dimensional psychopathology. Using structural equation modeling, inter-correlation among cognitive domains were adjusted before mediation analysis on outcomes of multi-domain psychopathology and functional level.
While social cognition, complex cognition, and memory each had a unique pattern of mediating effect on psychopathology domains in ASD, none had significant effects on the functional level. In contrast, executive function was the only cognitive domain that exerted a generalized negative impact on every psychopathology domain (p factor, anxious-misery, psychosis, fear, and externalizing), as well as functional level.
Executive function has a unique association with the severity of comorbid psychopathology in ASD, and could be a target of interventions. As executive dysfunction occurs variably in ASD, our result also supports the clinical utility of assessing executive function for prognostic purposes.
Student's t test is valid for statistical inference under the normality assumption or asymptotically. By contrast, although the bootstrap t test was proposed in 1993, it is seldom adopted in medical research. We aim to demonstrate that the bootstrap t test outperforms Student's t test under normality in data. Using random data samples from normal distributions, we evaluated the testing performance, in terms of true-positive rate (TPR) and false-positive rate and diagnostic abilities, in terms of the area under the curve (AUC), of the bootstrap t test and Student's t test. We explore the AUC of both tests with varying sample size and coefficient of variation. We compare the testing outcomes using the COVID-19 serial interval (SI) data in Shenzhen and Hong Kong, China, for demonstration. With fixed TPR, the bootstrap t test maintained the equivalent accuracy in TPR, but significantly improved the true-negative rate from the Student's t test. With varying TPR, the diagnostic ability of bootstrap t test outperformed or equivalently performed as Student's t test in terms of the AUC. The equivalent performances are possible but rarely occur in practice. We find that the bootstrap t test outperforms by successfully detecting the difference in COVID-19 SI, which is defined as the time interval between consecutive transmission generations, due to sex and non-pharmaceutical interventions against the Student's t test. We demonstrated that the bootstrap t test outperforms Student's t test, and it is recommended to replace Student's t test in medical data analysis regardless of sample size.
This chapter describes the basic mechanisms that give rise to subsurface excess fluid pore pressure. These mechanisms include rapid deposition where the fluid cannot escape as the sediments are deposited, giving rise to excess pore pressure. This is known as disequilibrium compaction. Other mechanism are also described, including chemical diagenesis, kerogen maturation, aquathermal pressuring, and hydrocarbon buoyancy effects.
This chapter discusses several techniques for detection and analysis of geopressure in real time. This deals with real-time updates of a predrill pore pressure model during drilling. This is an important phase during which real-time data are acquired, processed, and integrated with the model so that an updated look-ahead and look-around (drill bit) image of geopressure is made. This way, as drilling continues, the cone of uncertainty will reduce. All real-time sensors such as the logging-while-drilling (LWD) tools are at least a hundred or more feet behind the bit. Therefore, calibration must be done in three steps - behind the bit (using all processed logs, including wireline), at the bit (mostly real-time logs), and look-ahead of and around the bit (mostly calibrated seismic model).
The chapter attempts to produce a few guidelines for pressure prediction. These guidelines for best practices are divided into three broad categories: subsurface geological habitat for pore pressure (geology), physics of pore pressure generation (models), and technology for subsurface prediction (tools). An integrated workflow should satisfy four important criteria: (1) the model should be geologically consistent, conforming to the geologic history; (2) model variability should be constrained by bounds derived from physical understanding of the rocks; (3) the model should agree with all available data from wells and seismic measurement; and (4) modeling uncertainty should be rigorously quantified. The chapter describes various approches for uncertainty quantification for pore pressure prediction.
This chapter describes examples of basin modeling in 1D and 2D and shows the power of basin models to predict pore pressure. The fundamental conservation laws and partial differential equations used in basin modeling are presented. Mathematical derivations for 1D shale-on-shale and sand-on-shale compaction and pore pressure models are given in detail. While seismic models for pore pressure prediction predict pore pressure given present-day geology, basin modeling provides an alternate technique that describes the pore pressure distribution using transient models that depend on fluid and heat flow during geologic times along with other phenomena, such as changes in facies and geologic structures in paleotimes. So this approach not only tells us what the pore pressure is today but allows us to investigate the effect of uncertainties in geology in the past and their imprint on the present-day pressure distribution.
Seismic velocity is a very useful tool for pore pressure prediction prior to drilling a well. This chapter identifies various sources of velocity data - checkshot, VSP, well logs, laboratory, and seismic measurements. The goal is to obtain velocity variations in 3D that not only reflect the subsurface structures in depth but also convey the expected range of velocity variations that is compliant with rock physics principles, structural geology, and stratigraphy of formations and geopressure. This chapter discusses various ways to obtain velocity data for pore pressure analysis and points out how to establish a link between seismic traces that are recorded in "space and time" and the "space and depth" that are required by the drilling community. It stresses that the seismic model building step must deal with separating imaging velocity from the velocity that is close to rock velocity.
This chapter highlights nonseismic potential field methods so that exploration geoscientists can include these for quantitative evaluation of geopressure. That nonseismic methods such as gravity and E&M methods are useful for exploration is beyond question. These methods are also applicable to geopressure investigation. Given an understanding of how the tools behind the potential field technologies work in their current forms, we believe that these methods will add to seismic technology for geopressure analysis. Despite being comparatively low resolution, they have some advantages. For example, at a comparatively low cost, airborne potential field surveys can provide coverage of large areas.
This chapter describes seafloor and subsurface geologic hazards that pose significant drilling and environental challenges. Of all subsurface geologic hazards, shallow-water flow (SWF) sands pose the most significant drilling challenge. The chapter describes the geological environments for occurrence of shallow-water flow sands and provides guidelines for identification of shallow-water flow sands from seismic data. Other geohazards, including shallow gas and gas hydrates, are also discussed.
In this chapter we discuss various methods to quantify pore pressure, fracture pressure, and overburden stress using a host of geophysical methods, such as velocity, density, and resistivity, from well logs and seismic data. Most of these methods are based on approaches that work for shales, where it is easier to define a trend based on normal compaction. We compare and contrast some of the well-established models in the literature.
This chapter focuses on some of the wireline logging tools that can be used to estimate pore pressure. These measurements include sonic velocity, resistivity, and density. In addition, various drilling paramters, such as rate of penetration, torque, and drag, that can be used as pore pressure indicators are described. These are either qualitative or semiqualitative indicators of geopressured zones.
This chapter conjectures on some of the upcoming technologies and how they might impact pore pressure prediction in the future. It gives a schematic of a workflow that integrates basin modeling derived pore pressure to rock physics and seismic anisotropic velocity modeling and imaging, executed in a grand loop. Some recent applications of machine learning applied to pore pressure prediction are discussed. The chapter concludes that the future is almost here, and it is bright indeed!
The second chapter deals with the fundamental of continuum mechanics. It describes stresses and strains in a continuum. This is followed by fundamental laws of mechanics and constitutive relations. Elasticity and Hooke's law are described. Next, the chapter describes poroelasticity as well as poroplasticity, followed by fracture mechanics. Finally, rock physics models as applied to pore pressure detection are described.