Whistler’s Mothers: Painters, Models, and Uncanny Arrangements

Well test analysis using a composite reservoir model has become popular because of the flexibility provided by a composite reservoir model in visualizing numerous reservoir situations. A composite reservoir model is useful for analysing transient pressure data from acidization and fluid injection projects, oil or gas reservoirs with rock or fluid property contrasts) and geothermal reservoirs with thermal discontinuities. In the simplest case, a composite reservoir model is helpful in all reservoir situations, where we can consider the reservoir to be composed of two regions of different properties. Therefore, well test analysis based on a composite reservoir model is aimed at obtaining estimates of reservoir properties and skin factors corresponding to bath the inner and outer region data. Introduction Though linear, spherical(3), and elliptical(1–6) composite models have been discussed in the literature a radial(7–17) composite model has been a popular model to analyse well test data from a variety of reservoir situations. Generally, a two-region, radial composite reservoir model is used in practice (see Figure 1). In a two-region, composite reservoir idealization the reservoir is considered to be composed of two regions of different properties. The well may be producing (or injecting) at a constant rate and may exhibit wellbore storage and skin effects. The variable R denotes the discontinuity (or front) radius. There may also be a skin at the discontinuity. The dimensionless pressure solution, in Laplace space, for such a situation has been presented in References (9) and (11). Title Laplace space solution is inverted to the real space using Stehfest algorithm (1–8). Well test analysis based on a radial, two-region composite reservoir model is aimed at obtaining estimates of discontinuity radius, and reservoir properties and skin factors corresponding to both the inner and outer region data. Problems associated with estimating discontinuity radius (or swept volume for fluid injection projects) have been discussed before(9.10,14.19.20). This paper addresses the issues related to data misinterpretation, and consequent misinterpretations of reservoir characteristics and skin factors, while analysing well test data using a radial, two-region composite reservoir model. All results presented in this paper have been generated using the solution presented in Reference (11). Ideal Model Response Figure 2 shows drawdown semi-log pressure derivative (PwD = d Pwd I d In tD) responses for a well located in an infinite, two-region, radial composite reservoir. The pressure derivative responses are graphed as a function of tRD with M and Fs as parameters. All variables are identified in the Nomenclature. Figure 2 applies for CD = S = sr = 0 situations. Pressure derivative responses are shown for several possible combinations of mobility and storativity ratios, which may be equal to, less than, or greater than unity. For two-region composite reservoir situations, Figure 2 shows that at early times an infinite-acting radial flow regime corresponding to the inner region mobility appears. The early time infinite-acting radial flow regime is identified as that portion of the responses where PWD has a constant value equal to one-half. Dimensionless pressure response during the early-time infinite-acting radial flow regime is expressed as:

ABSTRACTComposite models have a long history in actuarial science because they provide a flexible method of curve-fitting for heavy-tailed insurance losses. The ongoing research in this area continuously suggests methodological improvements for existing composite models and considers new composite models. A number of different composite models have been previously proposed in the literature to fit the popular data set related to Danish fire losses. This paper provides the most comprehensive analysis of composite loss models on the Danish fire losses data set to date by evaluating 256 composite models derived from 16 parametric distributions that are commonly used in actuarial science. If not suitably addressed, inevitable computational challenges are encountered when estimating these composite models that may lead to sub-optimal solutions. General implementation strategies are developed for parameter estimation in order to arrive at an automatic way to reach a viable solution, regardless of the specific head and/or tail distributions specified. The results lead to an identification of new well-fitting composite models and provide valuable insights into the selection of certain composite models for which the tail-evaluation measures can be useful in making risk management decisions.

Towards a deterministic composite collision outcome model for surface-tension dominated droplets

PurposeThis paper aims to examine the valuation precision of composite models in each of six key industries in South Africa. The objective is to ascertain whether equity-based composite multiples models produce more accurate equity valuations than optimal equity-based, single-factor multiples models.Design/methodology/approachThis study applied principal component regression and various mathematical optimisation methods to test the valuation precision of equity-based composite multiples modelsvis-à-visequity-based, single-factor multiples models.FindingsThe findings confirmed that equity-based composite multiples models consistently produced valuations that were substantially more accurate than those of single-factor multiples models for the period between 2001 and 2010. The research results indicated that composite models produced up to 67 per cent more accurate valuations than single-factor multiples models for the period between 2001 and 2010, which represents a substantial gain in valuation precision.Research implicationsThe evidence, therefore, suggests that equity-based composite modelling may offer substantial gains in valuation precision over single-factor multiples modelling.Practical implicationsIn light of the fact that analysts’ reports typically contain various different multiples, it seems prudent to consider the inclusion of composite models as a more accurate alternative.Originality/valueThis study adds to the existing body of knowledge on the multiples-based approach to equity valuations by presenting composite modelling as a more accurate alternative to the conventional single-factor, multiples-based modelling approach.

Predictors of same-day discharge following benign minimally invasive hysterectomy

A dataflow network consists of nodes that communicate by passing data over unbounded FIFO channels. For dataflow networks containing only deterministic nodes, Kahn has presented a simple and elegant semantic model. However, the generalization of this model is not compositional for nondeterministic networks. Past work has shown that compositionality can be attained by models based on traces. In the paper, we investigate trace models of dataflow networks, with the aim of characterizing compositional and non-compositional models. We study several compositional trace models, which differ in whether they model liveness, termination or divergence. We relate the models into a hierarchy, according to their capability to distinguish networks. A hierarchy is called complete if any gap between two models in the hierarchy contains no compositional models. Our main contribution is to prove that most of the gaps in our hierarchy do not contain compositional models. Several full abstraction results in the literature follow directly from the gaps in our hierarchy. We also show that by restricting the networks to contain less powerful nondeterministic processes, additional models become compositional. This means that additional models are added to the hierarchy.

In this paper, the strip synthetic aperture radar (SAR) image of a composite vehicle-ground model is simulated by combining the electromagnetic scattering algorithm and radar imaging algorithm. A linear frequency modulated wave is incident on the composite model, which is partitioned into a mass of triangular patches. In the "stop-and-go" radar mode, for each patch, the amplitude of scattered echo in the frequency domain is solved by a hybrid method of physical optics (PO)-shooting and bouncing ray (SBR)-physical theory of diffraction (PTD). For the composite model, the total scattered echo in terms of range frequency and azimuth time is obtained by the vector superposition of echo on patches. Then the SAR image of the composite model is generated by the range-Doppler algorithm. In numerical simulations, both the electromagnetic scattering of a target by the SBR-PTD method and composite scattering by the PO-SBR-PTD method are validated and evaluated by comparing with the multilevel fast multipole method (MLFMM) in FEKO software. Moreover, the SAR image of the composite vehicle-ground model is also compared with the real image in Moving and Stationary Target Acquisition and Recognition database, which verifies the feasibility of the proposed method. SAR images of the composite model for different incident angles are also presented and analyzed.

Estimation of the modulus of elasticity of slag concrete by using composite material models

Iron overload is commonly encountered during the course of aplastic anemia (AA), but no composite animal model has been developed yet, which hinders drug research. In the present study, the optimal dosage and duration of intraperitoneal iron dextran injection for the development of an iron overload model in mice were explored. A composite model of AA was successfully established on the principle of immune-mediated bone marrow failure. Liver volume, peripheral hemogram, bone marrow pathology, serum iron, serum ferritin, pathological iron deposition in multiple organs (liver, bone marrow, spleen), liver hepcidin, and bone morphogenetic protein 6 (BMP6), SMAD family member 4 (SMAD4) and transferrin receptor 2 (TfR2) mRNA expression levels were compared among the normal control, AA, iron overload and composite model groups to validate the composite model, and explore the pathogenesis and features of iron overload in this model. The results indicated marked increases in iron deposits, with significantly increased liver/body weight ratios as well as serum iron and ferritin in the iron overload and composite model groups as compared with the normal control and AA groups (P<0.05). There were marked abnormalities in iron regulation gene expression between the AA and composite model groups, as seen by the significant decrease of hepcidin expression in the liver (P<0.01) that paralleled the changes in BMP6, SMAD4, and TfR2. In summary, a composite mouse model with iron overload and AA was successfully established, and AA was indicated to possibly have a critical role in abnormal iron metabolism, which promoted the development of iron deposits.

Prediction models for same-day discharge following benign minimally invasive hysterectomy

A Composite Surface Model is a structure in which streams of information from diverse sensory sources are integrated into a unified model of the immediate environment. The composite surface model then serves as the basis for planning and executing actions, for learning about objects, and for interpreting the world in terms of known objects. This paper reviews current progress in developing a composite surface model using geometric information. Principles for composite modeling are described, and the role of the composite model in a task oriented robotic system is presented. A set of geometric primitives for surfaces patches, contours and vertices are then defined. A family of interface functions are presented which permit the composite surface model to be used by other processes within a task-oriented robotic system. The use of these interface functions is illustrated by a procedure for the task of finding an object.

To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis (PCA). In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume (GTV) and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration. PCA was used to model the target and lung variability during treatment. Four PCA models were generated for each specific patient: (1) Individual models for the GTV and each lung from one image per week (week to week, W2W); (2) a W2W composite model of all structures; (3) individual models using all images (weekly plus repeat intrafraction images, allscans); and (4) composite model with all images. Models were reconstructed retrospectively (using all available images acquired) and prospectively (using only data acquired up to a time point during treatment). Dominant modes representing at least 95% of the total variability were used to reconstruct the observed anatomy. Residual reconstruction error between the model-reconstructed and observed anatomy was calculated to compare the accuracy of the models. An average of 3.4 and 4.9 modes was required for the allscans models, for the GTV and composite models, respectively. The W2W model required one less mode in 40% of the patients. For the retrospective composite W2W model, the average reconstruction error was 0.7 +/- 0.2 mm, which increased to 1.1 +/- 0.5 mm when the allscans model was used. Individual and composite models did not have significantly different errors (p = 0.15, paired t-test). The average reconstruction error for the prospective models of the GTV stabilized after four measurements at 1.2 +/- 0.5 mm and for the composite model after five measurements at 0.8 +/- 0.4 mm. Retrospective PCA models were capable of reconstructing original GTV and lung shapes and positions within several millimeters with three to four dominant modes, on average. Prospective models achieved similar accuracy after four to five measurements.

The neurobiological mechanisms driving the ictal-interictal fluctuations and the chronification of migraine remain elusive. We aimed to construct a composite genetic-microRNA (miRNA) model that could reflect the dynamic perturbations of the disease course and inform the pathogenesis of migraine. We prospectively recruited four groups of participants, including interictal episodic migraine (i.e. headache free for >72 h, apart from prior and subsequent attacks), ictal episodic migraine (i.e. during moderate to severe migraine attacks), chronic migraine and controls in the discovery cohort. Next-generation sequencing was used for miRNA profiling. The candidate miRNAs were validated with quantitative PCR in an independent validation cohort. Biological pathways associated with the microRNA regulome and interaction networks were explored. In addition, all participants received genotyping with the Axiom Genome-Wide Array TWB chip. A composite model was established, combining disease-associated miRNAs and genetic risk scores indicative of genetic susceptibility, with the objective of differentiating migraine from controls using a binary outcome. From a total of 120 participants in the discovery cohort and 197 participants in the validation cohort, we identified disease-state miRNA signatures (including miR-183, miR-25 and miR-320) that were ubiquitously higher or lower in patients with migraine compared with controls. We have also validated four disease-activity miRNA signatures (miR-1307-5p, miR-6810-5p, let-7e and miR-140-3p) that were differentially expressed only during the ictal stage of episodic migraine. Functional analysis suggested that prolactin and oestrogen signalling pathways might play important roles in the pathogenesis. Moreover, the composite miRNA-genetic risk score model differentiated patients from controls, achieving a positive predictive value of >90%. To conclude, we developed a composite miRNA-genetic risk score model, which might serve as a predictive tool for identifying high-risk individuals. Our findings might help to illuminate potential pathogenic mechanisms underlying the dysfunctional allostasis of migraine and pave the way for future precision medicine.

A validated composite model to predict risk of curve progression in adolescent idiopathic scoliosis

Compositional models represent visual patterns as hierarchies of meaningful and reusable parts. They are attractive to vision modeling due to their ability to decompose complex patterns into simpler ones and resolve the lowlevel ambiguities in high-level image interpretations. However, current compositional models separate structure and part discovery from parameter estimation, which generally leads to suboptimal learning and fitting of the model. Moreover, the commonly adopted latent structural learning is not scalable for deep architectures. To address these difficult issues for compositional models, this paper quests for a unified framework for compositional pattern modeling, inference and learning. Represented by And-or graphs (AOGs), it jointly models the compositional structure, parts, features, and composition/sub-configuration relationships. We show that the inference algorithm of the proposed framework is equivalent to a feed-forward network. Thus, all the parameters can be learned efficiently via the highlyscalable back-propagation (BP) in an end-to-end fashion. We validate the model via the task of handwritten digit recognition. By visualizing the processes of bottom-up composition and top-down parsing, we show that our model is fully interpretable, being able to learn the hierarchical compositions from visual primitives to visual patterns at increasingly higher levels. We apply this new compositional model to natural scene character recognition and generic object detection. Experimental results have demonstrated its effectiveness.