Tool For Refinement Research Articles

Simulação de Reservatórios de Óleo Leve com Poço Horizontal Hidraulicamente Fraturado

<p>Since the 1960s, because of the relevance to the oil industry, the numerical simulation of hydrocarbon reservoirs has received special attention and has been the subject of extensive studies. The main goal of computational modeling and the use of numerical methods for reservoir simulation is to allow better placement and control of wells, so that there is a optimized oil recovery. In this work, production of hydraulically fractured horizontal wells in light tight oil reservoirs will be studied. In this case, fractures do not form a continuous conductive network and can communicate hydraulically with only the horizontal producer well. In order to do that, a simulator for three-dimensional oil flow in reservoirs, suitable for applications in the field scale, already developed, using the Cartesian coordinate system and a finite difference approach, will be applied for the study of hydraulically fractured horizontal wells. Originally, this simulator and its grid refinement tools had been used only on the simulation of naturally fractured reservoirs. The nonlinear partial differential equation resulting from physical-mathematical modeling, written in terms of pressure, will be solved numerically after discretization and linearization using the Preconditioned Conjugate Gradient method. The main objective is to study the combined effects of hydraulic fractures and horizontal well on the wellbore pressure profile, considering different light tight oil production scenarios. Numerical simulations displayed the influence of important parameters on the well-reservoir system in study, such as fracture permeability and matrix porosity. A study of this type is relevant on the discussion of reservoir production strategies, helping on the decisions about a hydraulic fracturing operation in order to obtain economic viability for the hydrocarbons recovery project.</p><p><strong>Keywords</strong>: reservoir simulation, light tight oil, horizontal well, hydraulic fracturing, nite diferences method.</p>

Discussion on the Implementation of the Patient Centred Medical Home model - Experiences from Australia

Introduction: Different practitioners and academics have been working on the application of the Patient Centred Medical Home (PCMH) model within the Australian context for many years. In early 2016, the Commonwealth government of Australia announced plans to establish Health Care Homes throughout the country based off the PCMH model, beginning with trial sites focused on the bundling of payments. As a result, the number of Primary Health Networks, policy makers and general practices receptive to establishing Health Care Homes is growing rapidly. The time is ripe to identify how best the elements of the model translate into the Australian context and how to implement its elements with success. As a contribution to the opportunity for a widespread implementation, the North Coast Primary Health Network is engaged in a project to build capacity in general practices to transition into Health Care Homes. The main outcomes of this project include: 1. Preparing “The Australian Handbook for Transitioning to Health Care Homes” A resource which will provide a rationale for transitioning to a HCH, milestones for transitioning along a continuum and tools for practice and practice support for establishing the model in general practice. Thus developing capacity to train ‘change facilitators’ to work to accompany transitioning practices. 2. Establishment of a National Network of Patient Centred HCH Collaborators Made up of PHN representatives, experts and policy makers working in the PCMH development space. Focused on improving advocacy effectiveness, knowledge sharing and keeping stakeholders up to date with unfolding developments. 3. Increasing local preparedness and interest for establishing HCHs Focused on propagation of development of interest locally for transitioning practices into HCHs. A local network of practitioners and collaborators informed of project updates and HCH learning and development opportunities in the region. 4. Local trial and adaptation of PCMH model to Australian context Trial and evaluation of tools for transitioning practices to HCHs using an action based research approach, leading to improvements in the handbook. Workshop objective: To share insights and facilitate a structured discussion about practical experience in implementing the elements of the Patient Centred Medical Home, tools that have been effective, challenges that have emerged and approaches used to overcome these challenges. Discussion generated through the workshop will be used to inform further work on the Australian Handbook for Transitioning to the Health Care Home. Workshop outcome: As a result of the workshop it is anticipated that participants will have an increased understanding of the medical home model, hear rich implementation experiences from different contexts and become aware of further tools for model implementation and refinement. Workshop structure: - Brief overview of model implementation taking place in Australia - Smaller group discussion about medical home implementation challenges and successes - Structured workshop group discussion drawing together key insights, common challenges and practical steps, resources or further research needed for address these challenges. Target audience: Policy makers, Primary Health Networks/Organisations, clinicians, practice staff and anyone interested in how to support practices to transition into Medical Homes

Optimizing Click-Through in Online Rankings with Endogenous Search Refinement

Consumers engage in costly searches to evaluate the increasing number of product options available from online retailers. Presenting the best alternatives at the beginning reduces search costs associated with a consumer finding the right product. We use rich data on consumer click-stream behavior from a major web-based hotel comparison platform to estimate a model of search and click. We propose a method of determining the ranking of search results that maximizes consumers’ click-through rates (CTRs) based on partial information available to the platform at the time of the consumer request, its assessment of consumers’ preferences, and the expected consumer type based on request parameters from the current visit. Our method has two distinct advantages. First, we endogenize a consumer response to the ranking using search refinement tools, such as sorting and filtering of product options. Accounting for these search refinement actions is important since the ranking and consumer search actions together shape the consideration set from which clicks are made. Second, rankings are targeted to anonymous consumers by relating price sensitivity to request parameters, such as the length of stay, number of guests, and day of the week of the stay. We find that predicted CTRs under our proposed ranking are almost double those of the platform’s default ranking. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1036 .

Image-based clothes changing system

Current image-editing tools do not match up to the demands of personalized image manipulation, one application of which is changing clothes in usercaptured images. Previous work can change single color clothes using parametric human warping methods. In this paper, we propose an image-based clothes changing system, exploiting body factor extraction and content-aware image warping. Image segmentation and mask generation are first applied to the user input. Afterwards, we determine joint positions via a neural network. Then, body shape matching is performed and the shape of the model is warped to the user’s shape. Finally, head swapping is performed to produce realistic virtual results. We also provide a supervision and labeling tool for refinement and further assistance when creating a dataset.

ICoVeR \u2013 an interactive visualization tool for verification and refinement of metagenomic bins

BackgroundRecent advances in high-throughput sequencing allow for much deeper exploitation of natural and engineered microbial communities, and to unravel so-called “microbial dark matter” (microbes that until now have evaded cultivation). Metagenomic analyses result in a large number of genomic fragments (contigs) that need to be grouped (binned) in order to reconstruct draft microbial genomes. While several contig binning algorithms have been developed in the past 2 years, they often lack consensus. Furthermore, these software tools typically lack a provision for the visualization of data and bin characteristics.ResultsWe present ICoVeR, the Interactive Contig-bin Verification and Refinement tool, which allows the visualization of genome bins. More specifically, ICoVeR allows curation of bin assignments based on multiple binning algorithms. Its visualization window is composed of two connected and interactive main views, including a parallel coordinates view and a dimensionality reduction plot. To demonstrate ICoVeR’s utility, we used it to refine disparate genome bins automatically generated using MetaBAT, CONCOCT and MyCC for an anaerobic digestion metagenomic (AD microbiome) dataset. Out of 31 refined genome bins, 23 were characterized with higher completeness and lower contamination in comparison to their respective, automatically generated, genome bins. Additionally, to benchmark ICoVeR against a previously validated dataset, we used Sharon’s dataset representing an infant gut metagenome.ConclusionsICoVeR is an open source software package that allows curation of disparate genome bins generated with automatic binning algorithms. It is freely available under the GPLv3 license at https://git.list.lu/eScience/ICoVeR. The data management and analytical functions of ICoVeR are implemented in R, therefore the software can be easily installed on any system for which R is available. Installation and usage guide together with the example files ready to be visualized are also provided via the project wiki. ICoVeR running instance preloaded with AD microbiome and Sharon’s datasets can be accessed via the website.

Pre-buckling responses of Timoshenko nanobeams based on the integral and differential models of nonlocal elasticity: an isogeometric approach

Differential form of Eringen’s nonlocal elasticity theory is widely employed to capture the small-scale effects on the behavior of nanostructures. However, paradoxical results are obtained via the differential nonlocal constitutive relations in some cases such as in the vibration and bending analysis of cantilevers, and recourse must be made to the integral (original) form of Eringen’s theory. Motivated by this consideration, a novel nonlocal formulation is developed herein based on the original formulation of Eringen’s theory to study the buckling behavior of nanobeams. The governing equations are derived according to the Timoshenko beam theory, and are represented in a suitable vector–matrix form which is applicable to the finite-element analysis. In addition, an isogeometric analysis (IGA) is conducted for the solution of buckling problem. Construction of exact geometry using non-uniform rational B-splines and easy implementation of geometry refinement tools are the main advantages of IGA. A comparison study is performed between the predictions of integral and differential nonlocal models for nanobeams under different kinds of end conditions.

Structural features embedded in G protein-coupled receptor co-crystal structures are key to their success in virtual screening.

Structure based drug discovery on GPCRs harness atomic detail X-ray binding pockets and large libraries of potential drug lead candidates in virtual screening (VS) to identify novel lead candidates. Relatively small conformational differences between such binding pockets can be critical to the success of VS. Retrospective VS on GPCR/ligand co-crystal structures revealed stark differences in the ability of different structures to identify known ligands, despite being co-crystallized with the same ligand. When using the OpenEye toolkit and the ICM modeling package, we identify criteria associated with the predictive power of binding pockets in VS that consists of a combination of ligand/receptor interaction pattern and predicted ligand/receptor interaction strength. These findings can guide the selection and refinement of GPCR binding pockets for use in SBDD programs and may also provide a potential framework for evaluating the ability of computational GPCR binding pocket refinement tools in improving the predictive power of binding pockets.

Guiding Double Electron Electron Resonance Experiments using Information Theory and Molecular Dynamics Simulations

Flexible proteins present a unique set of challenges for refinement with either computational or experimental techniques: because these proteins occupy a large conformational space, standard molecular dynamics (MD) simulations can be prohibitively expensive, and high-resolution experimental techniques like x-ray crystallography intentionally capture only small set of low energy states. A new approach is therefore needed to capture the conformational ensembles of flexible proteins. Double electron-electron resonance (DEER) spectroscopy, an experimental technique that measures distance distributions between pairs of spin-labeled residues on a protein, provides quantitative information about less populated but still potentially important conformational states. However, because DEER experiments are costly and time consuming, only a small subset of pairs can be measured, often chosen arbitrarily. If instead of selecting an arbitrary or random subset, we select a subset of pairs such that they provide the maximum amount of information about the conformational ensemble, DEER could become an even more powerful tool for refinement. We have therefore developed 1) a new information-theoretic metric that ranks pairs of residues based on how well they refine a conformational ensemble and 2) a new methodology which identifies a set of highly informative pairs that perform well under this metric. Specifically, we incorporate DEER distance distributions into restrained-ensemble MD simulations, utilize the data from these simulations to identify a set of maximally-informative and minimally-redundant pairs, measure the pairs with DEER, and demonstrate the use of these pairs in refining the conformational ensemble of a flexible protein.

Construction Marketing: Developing a Reference Framework

Successful companies are strongly committed to marketing management. However, marketing is either misunderstood or completely neglected in many construction companies, mainly due to the difficulty of applying conventional marketing in the industry, accompanied by the lack of sufficient research on the nature of marketing and tailored marketing theories and strategies for the construction. This study attempts to fill a part of this gap by examining the nature of the construction industry from the marketing viewpoint and developing a comprehensive framework. A systematic investigation into the nature via a combination of Kotler’s product classification system and Lovelock’s classification criteria reveals that construction is an “industrial, project-based, and primarily service-oriented” (IPS) product with specific characteristics from the marketing perspective. Based on this nature, a reference framework for strategic marketing planning is developed through a literature review based on grounded theory and using the focus group discussion as a refinement tool. The framework indicates that construction companies are involved in and should plan for three working fields—project-based activities, relationship marketing, and marketing mix-related functions. The findings provide a fundamental basis that helps researchers and practitioners gain a true understanding of the concepts and scope of construction marketing and draw a clear and practical roadmap for future work.

Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems.

Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.

Refinement of Intraocular Pressure Measurements made by Ocular Response Analyzer following Laser in Situ Keratomileusis using M2 90 Moria Microkeratome for Egyptian Myopic and Astigmatic Patients.

IntroductionThe Goldmann applanation tonometer readings were noted to be markedly reduced after Lasik surgery using a thin flap technique which is widely used nowadays, to correct a wide range of myopia and astigmatism. The Ocular Response Analyzer (ORA) is considered one of the important tools for refinement of Intraocular Pressure (IOP) measurements. The aim of this study is to evaluate and refine ORA measurements for IOP changes post- M2 90 Moria microkeratome Lasik procedure for Egyptian myopic and astigmatic patients trying to aid in glaucoma diagnosis and management.MethodsThirty-five eyes of nineteen Egyptian patients with myopia or myopic astigmatism who had undergone Lasik procedure using M2 90 Moria microkeratome were included in this consecutive case series study. All cases were subjected to full ophthalmological examination including uncorrected and best corrected visual acuity, refraction, slit lamp examination, Scheimpflug imaging and ORA (Reichert Ophthalmic Instruments, Inc., Buffalo, NY, USA). The refractive errors were measured using an auto refractometer and were confirmed by trial. For the ocular response analyzer, the Goldmann-correlated IOP measurement (IOPg) which simulates IOP measured by Goldmann tonometer and the Corneal-Compensated Intraocular Pressure (IOPcc) that takes corneal biomechanical properties into consideration were reported as well as the Corneal Hysteresis (CH) and the Corneal Resistance Factor (CRF). Cases were examined preoperatively and again about one month after Lasik, after stabilization of eye condition. The collected data were tabulated and analysed with the suitable statistical methods. The mean values and standard deviation were calculated for quantitative data. Comparison tests (t-test) and correlation tests (Pearson) were also performed.ResultsIn our study, involving M2 90 Moria microkeratome Lasik procedure, a highly significant post-Lasik reduction in IOPg (t-test = 8.62 (p<0.01), and a statistically significant reduction in IOPcc (t-test = 3.37 (p<0.05) by ocular response analyzer. The average post-Lasik reduction in IOP in mmHg was 4.84±2.82 for IOPg and 2.23±3.51for IOPcc. A statistically significant correlation (p<0.05) existed between post-Lasik reduction of IOPg and ablation depth as well as preoperative spherical equivalent. A non-significant correlation existed between post-Lasik reduction of IOPcc and both of ablation depth and preoperative spherical equivalent.ConclusionsFollowing Lasik procedure using M2 90 Moria microkeratome, the IOPcc values by ORA were closer to the preoperative values than the IOPg values and the reduction of IOPcc values was not affected by ablation depth (i.e. preoperative spherical equivalent). Therefore, it is advisable to use the IOPcc values by ORA when evaluating post-Lasik patients for glaucoma diagnosis and management, but with the addition of the average value for post-Lasik reduction which in the study was 2.23±3.51mmHg.

Crystallographic computing system Jana2006: solution and refinement of twinned structures

Abstract Twinning is a phenomenon complicating structure analysis of single crystals of standard as well as modulated structures. Jana2006 as a software for advanced structure analysis contains tools for recognition and refinement of twins including most complicated cases of modulated and magnetic structures. In order to efficiently use the tools of Jana2006 related to twinning, we explain the basic terminology and the underlying theory, especially the symmetry of the diffraction patterns affected by twinning. We present typical diffraction patterns of twins and show how twinning can be recognized or detected by various tools and described with twinning matrices. Data processing of twins and ways of how they can be imported to Jana2006 are also discussed. Two examples demonstrate the solution of typical twins: twinning by metric merohedry and twinning by reticular merohedry, followed by the third example demonstrating twinning in a commensurately modulated structure. The relationship between the dimensionality of the structure and twinning is discussed, too.

Erratum: Evaluation of the tool "Reg Refine" for user-guided deformable image registration.

"Reg Refine" is a tool available in the MIM Maestro v6.4.5 platform (www.mim-software.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground-truth models, the Pixel-based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck. Image noise matched in both magnitude and texture to clinical CBCT scans was also added to each model to simulate the use case of CBCT-CT alignment. For lung, the results showed Reg Refine effective at improving registration accuracy when controlled by an expert user within the context of large lung deformation. CBCT noise was also shown to have no effect on DIR performance while using the MIM algorithm for this site. For head and neck, the results showed CBCT noise to have a large effect on the accuracy of registration, specifically for low-contrast structures such as the brain-stem and parotid glands. In these cases, the Reg Refine tool was able to improve the registration accuracy when controlled by an expert user. Several strategies for how to achieve these results have been outlined to assist other users and provide feedback for developers of similar tools.

Accurate refinement of docked protein complexes using evolutionary information and deep learning.

One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work, we introduced AccuRefiner, a machine learning based tool for refining protein-protein complexes. Given a docked complex, the refinement tool produces a small set of refined versions of the input complex, with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work, we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 Å error margin on average, by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures.

Computational Methodologies for Real-Space Structural Refinement of Large Macromolecular Complexes.

The rise of the computer as a powerful tool for model building and refinement has revolutionized the field of structure determination for large biomolecular systems. Despite the wide availability of robust experimental methods capable of resolving structural details across a range of spatiotemporal resolutions, computational hybrid methods have the unique ability to integrate the diverse data from multimodal techniques such as X-ray crystallography and electron microscopy into consistent, fully atomistic structures. Here, commonly employed strategies for computational real-space structural refinement are reviewed, and their specific applications are illustrated for several large macromolecular complexes: ribosome, virus capsids, chemosensory array, and photosynthetic chromatophore. The increasingly important role of computational methods in large-scale structural refinement, along with current and future challenges, is discussed.

Orders of Magnitude Faster cryo-EM Refinement with GPUs

Direct-electron detectors, increased computational power and advances in sample preparation have rapidly made single particle cryogenic electron microscopy (cryo-em) a leading structural determination method. However, the growing number of images collected with constantly increasing resolution raises the demand on the computational processing capabilities, rendering the refinement step an increasingly notable bottleneck. For large macromolecular complexes, like ribosomes, this can require up to weeks of time on computer clusters using RELION, the currently fastest growing refinement tool. In recognition of this issue we have, in close collaboration with the original developers, optimized a significant portion of RELION for execution on consumer streaming multiprocessors (GPUs). The massive parallelism inherent in the algorithm enables an efficient utilization of such hardware layout, where the large number of processors can be assigned isolated small tasks. Here we present a new implementation yielding a 100-fold speed-up of the most time consuming step (3D classification) using GPUs, with comparable prices to conventional hardware. The resulting increase in overall cost efficiency, and removal of the need for large clusters, drastically increases the accessibility of the method to the field. Whereas, the reduced computational time makes it feasible to further strengthen the capabilities of the method by expanding the biophysical analysis, with other computationally intensive methods. Cryo-em is undoubtedly becoming a dominant tool within structural biology, why the acquired leap in throughput and applicability will have a major overall impact on the field in the coming years.

Adjoint Optimization of Multistage Axial Compressor Blades with Static Pressure Constraint at Blade Row Interface

AbstractAdjoint method is an important tool for design refinement of multistage compressors. However, the radial static pressure distribution deviates during the optimization procedure and deteriorates the overall performance, producing final designs that are not well suited for realistic engineering applications. In previous development work on multistage turbomachinery blade optimization using adjoint method and thin shear-layer N-S equations, the entropy production is selected as the objective function with given mass flow rate and total pressure ratio as imposed constraints. The radial static pressure distribution at the interfaces between rows is introduced as a new constraint in the present paper. The approach is applied to the redesign of a five-stage axial compressor, and the results obtained with and without the constraint on the radial static pressure distribution at the interfaces between rows are discussed in detail. The results show that the redesign without the radial static pressure distribution constraint (RSPDC) gives an optimal solution that shows deviations on radial static pressure distribution, especially at rotor exit tip region. On the other hand, the redesign with the RSPDC successfully keeps the radial static pressure distribution at the interfaces between rows and make sure that the optimization results are applicable in a practical engineering design.

Os Precedentes da Corte Europeia de Direitos Humanos como Instrumento de Refinamento das Normas de Direitos Humanos: Decisão Judicial e Norma de Direito Fundamental Adscrita /Derivada

Essa pesquisa possui o objetivo de demonstrar importância das decisões dos Tribunais Internacionais, especificamente a Corte Europeia de direitos Humanos, para o refinamento das normas de direitos humanos/fundamentais. Para tanto, se dedicará, em um primeiro momento, a identificar o conceito semântico de norma jurídica, o que colocará a hermenêutica jurídica em evidência; bem como, pontuará a questão da abertura da identificação da norma em decorrência dos conteúdos ético-morais inseridos nos enunciados normativos que prescrevem direitos humanos/fundamentais. Em um segundo momento, apresentaremos aquilo que Robert Alexy denomina de norma de direito fundamental atribuída ou adstrita, o que permitirá identificar como normas de direitos fundamentais todas as manifestação legislativas ou jurisdicionais que guardem correntes de fundamentação com as normas primárias de direitos humanos/fundamentais. Com isso, analisaremos duas decisões do Tribunal Europeu de Direitos Humanos como instrumento de refinamento, delimitação e, mais, como mecanismo de efetiva concretização das normas de direitos humanos/fundamentais, a partir das correntes racionais de argumentação.

Stochastic nonlinear mixed effects: a metformin case study.

In nonlinear mixed effect (NLME) modeling, the intra-individual variability is a collection of errors due to assay sensitivity, dosing, sampling, as well as model misspecification. Utilizing stochastic differential equations (SDE) within the NLME framework allows the decoupling of the measurement errors from the model misspecification. This leads the SDE approach to be a novel tool for model refinement. Using Metformin clinical pharmacokinetic (PK) data, the process of model development through the use of SDEs in population PK modeling was done to study the dynamics of absorption rate. A base model was constructed and then refined by using the system noise terms of the SDEs to track model parameters and model misspecification. This provides the unique advantage of making no underlying assumptions about the structural model for the absorption process while quantifying insufficiencies in the current model. This article focuses on implementing the extended Kalman filter and unscented Kalman filter in an NLME framework for parameter estimation and model development, comparing the methodologies, and illustrating their challenges and utility. The Kalman filter algorithms were successfully implemented in NLME models using MATLAB with run time differences between the ODE and SDE methods comparable to the differences found by Kakhi for their stochastic deconvolution.

Deformable complex network for refining low-resolution X-ray structures.

In macromolecular X-ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low-resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network-based restraint with the DEN model in the target function. Testing of DCN on a wide range of low-resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low-resolution structural determination.