Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of hydraulic fracture deformation hysteresis on CO_2 huff-n-puff performance in shale gas reservoirs

目的 在页岩气藏CO_2吞吐过程中, 水力裂缝处于循环载荷作用下时, 极易发生不可逆变形(变形滞后), 影响吞吐效果。 本文旨在建立考虑水力裂缝变形滞后的页岩气藏CO_2吞吐流固耦合模型, 形成相应的高效求解方法, 并开展流固耦合数值模拟研究, 以揭示变形滞后对CO_2吞吐的影响规律。 创新点 1. 建立考虑水力裂缝变形滞后、复杂裂缝系统和特殊流动机理的页岩气藏多组分流固耦合模型, 并形成相应的三维高效数值模拟技术; 2. 揭示水力裂缝变形滞后对页岩气藏CO_2吞吐的影响规律。 方法 1. 建立考虑水力裂缝变形滞后、复杂裂缝系统和特殊流动机理的页岩气藏多组分流固耦合模型; 2. 基于结构化网格构造高效稳定的多组分流固耦合模型数值求解算法; 3. 通过流固耦合数值模拟, 揭示水力裂缝变形滞后对页岩气藏CO_2吞吐的影响规律。 结论 1. 水力裂缝变形滞后会阻碍 CO_2注入期间裂缝渗透率的恢复, 对CO_2吞吐有负面影响; 2. 较低的初始水力裂缝导流能力和生产压力、较晚的吞吐启动时间、较高的注入压力和较多的循环次数均会增强变形滞后的负面影响; 3. CO_2吞吐效果与初始水力裂缝导流能力、吞吐启动时间、注入压力和循环次数呈正相关, 与生产压力呈负相关。 As a promising enhanced gas recovery technique, CO_2 huff-n-puff has attracted great attention recently. However, hydraulic fracture deformation hysteresis is rarely considered, and its effect on CO_2 huff-n-puff performance is not well understood. In this study, we present a fully coupled multi-component flow and geomechanics model for simulating CO_2 huff-n-puff in shale gas reservoirs considering hydraulic fracture deformation hysteresis. Specifically, a shale gas reservoir after hydraulic fracturing is modeled using an efficient hybrid model incorporating an embedded discrete fracture model (EDFM), multiple porosity model, and single porosity model. In flow equations, Fick’s law, extended Langmuir isotherms, and the Peng-Robinson equation of state are used to describe the molecular diffusion, multi-component adsorption, and gas properties, respectively. In relation to geomechanics, a path-dependent constitutive law is applied for the hydraulic fracture deformation hysteresis. The finite volume method (FVM) and the stabilized extended finite element method (XFEM) are applied to discretize the flow and geomechanics equations, respectively. We then solve the coupled model using the fixed-stress split iterative method. Finally, we verify the presented method using several numerical examples, and apply it to investigate the effect of hydraulic fracture deformation hysteresis on CO_2 huff-n-puff performance in a 3D shale gas reservoir. Numerical results show that hydraulic fracture deformation hysteresis has some negative effects on CO_2 huff-n-puff performance. The effects are sensitive to the initial conductivity of hydraulic fracture, production pressure, starting time of huff-n-puff, injection pressure, and huff-n-puff cycle number.
Block--diagonal and indefinite symmetric preconditioners for mixed finite element formulations

We are interested in the numerical solution of large structured indefinite symmetric linear systems arising in mixed finite element approximations of the magnetostatic problem; in particular, we analyze definite block--diagonal and indefinite symmetric preconditioners. Relating the algebraic characteristics of the resulting preconditioned matrix to the properties of the continuous problem and of its finite element discretization, we show that the considered preconditioning strategies make the used Krylov subspace solver insensitive to the mesh refinement parameter, in terms of number of iterations. In order to achieve computational efficiency, we also analyze algebraic approximations to the optimal preconditioners, and discuss their performance on real two and three dimensional application problems.
Offshore Landward Motion Shortly After a Subduction Earthquake Implies Rapid Relocking of the Shallow Megathrust

Geodetic observations after large subduction earthquakes reflect multiple postseismic processes, including megathrust relocking. The timing of relocking and the observational constraints on it are unclear. Relocking was inferred to explain some observed landward motion that occurs within months. It was also considered unable to explain other, greater landward motion, including that off the coast of Japan beginning weeks after the 2011 Tohoku earthquake, attributed to postseismic relaxation. We use generic, 3D numerical models to show that relocking, particularly of the shallow interface, is needed for postseismic relaxation to produce landward motion on the tip of the overriding plate. We argue that this finding is consistent with previous simulations that implicitly relock the megathrust where afterslip is not included. We conclude that the Tohoku megathrust relocked within less than 2 months of the earthquake. This suggests that the shallow megathrust probably behaves as a true, unstably sliding asperity.
Optimal Control in Wall Bounded Flows

Optimal control of transition in channel flow and boundary layer flow is attempted. First the optimization problem is stated and the corresponding adjoint equations used to compute the gradient of the objective function are derived for both the channel flow and boundary layer flow problems. Implementation and numerical issues are discussed. The governing equations are the incompressible Navier– Stokes equations with appropriate boundary conditions for the two cases. The boundary condition on the wall normal velocity at the walls of the channel, or at the single wall in the boundary layer case, is used as control and is determined in the iterative optimization procedure. The objective function used for the optimization problem contains the perturbation energy and a regularization term containing the control. The optimization problem is formulated using the primitive variables — velocity and pressure — and is then rewritten in a formulation containing only the wall normal velocity and the wall normal vorticity. An existing solver for the incompressible Navier–Stokes equations using this formulation can then also be used to solve the associated adjoint problem. The implementation is straightforward using this formulation and the efficiency of the original solver is maintained. To test the performance of the solver of the optimization problem, it is applied on different stages of the oblique transition scenario in the channel flow case. In a parallel Falkner–Skan–Cooke flow successful control of an inviscid instability is reported, and in the spatial Blasius flow the energy growth of a Tollmien–Schlichting wave is efficiently inhibited.
Image Segmentation via an Iterative Algorithm of the Mean Shift Filtering for Different Values of the Stopping Threshold

Image segmentation is a fundamental process of many image, video, and computer vision applications. Image segmentation is a critical step towards visual pattern recognition and image understanding. In this work we carried out a study on standard images of a segmentation algorithm based on iterative algorithm of the mean shift filtering for different values of the stopping threshold. A comparison of the obtained results for different values of the stopping threshold was carried out, taking into consideration the number of iterations and the degree of homogenization of the segmented images. With this study it was possible to obtain an optimum value of the stopping threshold for images with a content of low frequencies and high frequencies.
Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence.

BACKGROUND AND AIMS High-resolution esophageal manometry (HREM) is the gold standard procedure used for the diagnosis of esophageal motility disorders (EMD). Artificial intelligence (AI) might provide an efficient solution for the automatic diagnosis of EMD by improving the subjective interpretation of HREM images. The aim of our study was to develop an AI-based system, using neural networks, for the automatic diagnosis of HREM images, based on one wet swallow raw image. METHODS In the first phase of the study, the manometry recordings of our patients were retrospectively analyzed by three experienced gastroenterologists, to verify and confirm the correct diagnosis. In the second phase of the study raw images were used to train an artificial neural network. We selected only those tracings with ten test swallows that were available for analysis, including a total of 1570 images. We had 10 diagnosis categories, as follows: normal, type I achalasia, type II achalasia, type III achalasia, esophago-gastric junction outflow obstruction, jackhammer oesophagus, absent contractility, distal esophageal spasm, ineffective esophageal motility, and fragmented peristalsis, based on Chicago classification v3.0 for EMDs. RESULTS The raw images were cropped, binarized, and automatically divided in 3 parts: training, testing, validation. We used Inception V3 CNN model, pre-trained on ImageNet. We developed a custom classification layer, that allowed the CNN to classify each wet swallow image from the HREM system into one of the diagnosis categories mentioned above. Our algorithm was highly accurate, with an overall precision of more than 93%. CONCLUSION Our neural network approach using HREM images resulted in a high accuracy automatic diagnosis of EMDs.
Especialización trófica de dos morfoespecies de Chloridea virescens (Fabricius, 1777) en el cultivo de tabaco negro en Pinar del Río, Cuba (Lepidoptera: Noctuidae, Heliothinae)

Mediante la identificación taxonómica integrativa se describen dos morfoespecies del género Chloridea Duncan [& Westwood], 1841 que afectan al tabaco negro en la provincia de Pinar del Río, Cuba. Se recolectaron larvas de último instar en dos poblaciones con diferente hábito alimenticio: la primera afectando la yema terminal del tallo y la segunda a los órganos reproductivos del tabaco. Las larvas fueron caracterizadas por la coloración y ornamentación cuticular, así como por 11 caracteres morfológicos de la quetotaxia. La confirmación de la taxonomía molecular se realizó mediante secuenciación del gen mitocondrial de la citocromo oxidasa I (mtCOI), identificando a Chloridea virescens (Fabricius, 1777) como única especie para ambas poblaciones, con variabilidad morfológica y divergencia genética asociadas al nicho trófico, sugiriendo una especiación simpátrica.
Numerical analysis of a general elliptic variational-hemivariational inequality

This paper is devoted to the numerical analysis of a general elliptic variational-hemivariational inequality. After a review of a solution existence and uniqueness result, we introduce a family of Galerkin methods to solve the problem. We prove the convergence of the numerical method under the minimal solution regularity condition available from the existence result and derive a C´ea’s inequality for error estimation of the numerical solutions. Then, we apply the results for the numerical analysis of a variational-hemivariational inequality in the study of a static problem which models the contact of an elastic body with a reactive foundation. In particular, under appropriate solution regularity conditions, we derive an optimal order error estimate for the linear ﬁnite element solution
Nonlinear set‐membership state estimation based on the Koopman operator

In this study, the Koopman operator is applied to solve the challenging nonlinear set‐membership (SM) state estimation problem. The basic idea is to lift the nonlinear system into a linear one with a higher dimension, and then linear SM estimation methods can be adopted. The Koopman operator is an infinite‐dimensional linear decomposition of nonlinear dynamics. This linearized system can fully describe the state evolution of the nonlinear dynamics with input, output, and noises. To apply SM state estimation algorithms, a finite‐dimensional approximation is obtained by using the data‐driven method. Meanwhile, the statistical properties of the approximation errors are also utilized to reduce the conservativeness of the filter. The probability distributions of approximation errors are described by sample particles. Coupled with the unknown but bounded noises, the linearized system is associated with two types of uncertainties. To this end, the merging SM and stochastic strategy is adopted to construct a class of confidence state set whose level approaches 1. After that, the optimal filter gain is given to minimize the size of the confidence state set. Finally, three numerical examples are given to demonstrate the effectiveness of the proposed method.
Boosting bone regeneration using augmented melt-extruded additive-manufactured scaffolds

ABSTRACT Bone tissue engineering (BTE) is in active search of the ideal scaffold to give a clinical solution for bone regeneration in non-union fractures. During the last decades, the use of additive manufacturing (AM), and, in particular, melt extrusion AM (ME-AM), has been investigated towards this aim. ME-AM enables the fabrication of personalized 3D scaffolds, with a controlled and highly interconnected porosity, through the solvent-free processing of biodegradable and mechanically robust polymers. In addition to these properties matching the requirements for BTE scaffolds, the polymers used to fabricate these constructs are also more amenable for further functionalization than metals or ceramics, to influence cell behaviour, making thermoplastic materials a preferred choice for BTE. This review provides a comprehensive analysis of various ME-AM scaffolds developed for BTE, along with approaches used to augment their bioactivity, which includes architectural, surface physical and chemical modifications, the incorporation of secondary fibrous or hydrogel networks within the scaffold pores, and the use of composites for ME-AM scaffold fabrication.
Observational Tests for the Evolution of Massive Stars in Nearby Galaxies

Population synthesis calculations applicable to the massive stellar content in nearby galaxies are presented. Stellar evolution calculations are combined with mass loss, model atmospheres with line blanketing, and a spectral type calibration to compute observable parameters of massive stars as a function of the star formation rate and the initial mass function slope. The number of O stars of given spectral types, the number of W-R stars, supernova rates, and fluxes of ionizing photons are predicted. Important constraints for the theories of stellar atmospheres and stellar evolution can be derived from observations if stellar number counts and ionizing flux data are available. 94 refs.
The imaging principle of IVIM and its value in the diagnosis and treatment of ischemic stroke

: Ischemic stroke (IS) is the most common cerebrovascular disease in clinical practice, with high morbidity, high mortality and high disability rate. It is the leading cause of death and disability among Chinese adults. Intravoxel incoherent motion (IVIM) enables simultaneous acquisition of tissue diffusion and perfusion information in a single scan. In 1988, Le Bihan first proposed the concept of IVIM. With the emergence of high-field MR and the improvement of computer post-processing speed,IVIM imaging technology has gradually developed and matured, and has been widely used in the diagnosis of various systemic diseases, especially in the application of IS.This article aims to review the imaging principle of IVIM and the value of IVIM in the diagnosis and treatment of ischemic stroke, and discuss the clinical application value and research prospect of IVIM in IS.
A theoretical argument for complex-valued convolutional networks

A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with several complex-valued vectors followed by (2) taking the absolute value of every entry of the resulting vectors followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as “data-driven multiscale windowed power spectra,” “data-driven multiscale windowed absolute spectra,” “data-driven multiwavelet absolute values,” or (in their most general configuration) “data-driven nonlinear multiwavelet packets. ” Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (for example, logistic or tanh) nonlinearities, max. pooling, etc., do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). This note develops “data-driven multiscale windowed spectra” for certain stochastic processes that are common in the modeling of time series (such as audio) and natural images (including patterns and textures). We motivate the construction of such multiscale spectra in the form of “local averages of multiwavelet absolute values” or (in the most general configuration) “nonlinear multiwavelet packets” and connect these to certain “complex-valued convolutional networks. ” A textbook treatment of all concepts and terms used above and below is given by [12]. Further information is available in the original work of [7], [15], [5], [4], [19], [16], [9], [20], and [18], for example. The work of [8], [13], [17], [2], and [3] also develops complex-valued convolutional networks (convnets). Renormalization group theory and its connection to convnets is discussed by [14]; this connection is incredibly insightful, though we leave further discussion to the cited work. Our exposition relies on nothing but the basic signal processing treated by [12]. For simplicity, we first limit consideration to the special case of a doubly infinite sequence of nonnegative random variables Xk, where k ranges over the integers. This input data will be the result of convolving an unmeasured independent and identically distributed (i.i.d.) sequence Zk, where k ranges over the integers, with an unknown sequence of real numbers fk, where k ranges over the integers (this latter sequence is known as a “filter,” whereas the i.i.d. sequence is known as “white noise”):
Detecting small moving target in image sequences using optical flow based on the discontinuous frame difference

A new concept that is the Discontinuous Frame Difference in image sequences is proposed in this paper, and is applied to the Optical Flow Algorithm. The modified Optical Flow Algorithm overcomes the shortcoming of the traditional Optical Flow Algorithm that cannot detect the small moving target whose moving displacement is less than one pixel between two continuous frames. The infinite norm of the Discontinuous Frame Difference Vector is used to preprocess the image sequences to get rid of most of pixels taht are not the pixels of the moving target. And then the instantaneous velocities of the pixels remained by preprocessing is calculated by the Optical Flow Algorithm. If the pixels in an area have the moving continuity and consistency, a moving object is determined. For the preprocessing is able to get rid of most of pixels, the calculation quantity of the Optical Flow is reduced greatly. But the preprocessing is probably to lose some candidate pixels of the moving target, so the Gray Intensity Analysis is used to find these pixels back again. The Discontinuous Frame Difference Optical Flow Field Algorithm can be composed of the parallel structure system, which can detect different kinds of moving objects with different velocities. The experiment result proves the effectiveness of the method.
GCR‐Net: A deep learning‐based bare hand detection and gesticulated character recognition system for human‐computer interaction

Precisely detecting bare hands and recognizing the characters are two major stages in gesticulated character recognition systems. It is very challenging to implement them in an uncontrolled environment. Additional variations, particularly (i) background feature domination (BFD) effect and motion blur in detection, (ii) gesturing style, pattern, and case sensitivity in recognition, make the system more complex. To address these challenges, a gesticulated character recognition (GCR‐Net) model is designed. To detect the bare hand precisely, a pixel‐wise segmentation approach, HandSNet, is presented, which is able to overcome the BFD effect. To handle the motion blur in the frames, a tracking module comprised of a point‐tracker and Kalman filter is applied. To reduce the computational time, a mini‐SqueezeNet network is designed, which is used in HandSNet and recognition models as the backend network. It has 0.39 million parameters only. Four separate deep convolutional neural networks (DCNNs) are connected with the network section module at the recognition end. This network selection module activates one DCNN at a time to recognize the gesticulated characters accurately. The proposed GCR‐Net reduces the complexity between similar characters and provides a high precision rate compared to the existing approaches.
G0. 9 + 0. 1 and the emerging class of composite supernova remnants

High-resolution, multifrequency maps of a bright extended radio source near the Galactic center have revealed it to be a classic example of a composite supernova remnant. A steep-spectrum shell of emission, about 8 arcmin in diameter, surrounds a flat-spectrum, highly polarized Crab-like core about 2 arcmin across. The two components have equal flux densities at about 6 cm, marking this source as having the highest core-to-shell ratio among the about 10 composite remnants identified to date. X-ray and far-infrared data on the source are used to constrain the energetics and evolutionary state of the remnant and its putative central pulsar. It is argued that the total energy contained in the Crab-like components requires that the pulsars powering them were all born with periods shorter than 50 ms, and that if a substantial number of neutron stars with slow initial rotation rates exist, their birthplaces have not yet been found. 58 references.
Synthesis of sum and difference patterns for monopulse antennas by an hybrid real/integer-coded differential evolution method

The synthesis of sum and difference patterns of monopulse antennas is considered in this paper. The synthesis problem is recast as an optimization problem by defining a suitable cost function based on the constraints on the side lobe levels. A subarray configuration is considered and the excitations of the difference pattern are approximately determined. The optimization problem is efficently solved by a differential evolution algorithm, wich is able to contemporarly handle real and integer unknowns. Numerical results are reported considering classic array configurations previusly assumed in the literature.
Spectroscopic Characteristics of Dual - Color CdSe/CdS/CdSe1-xSx/CdS Quantum Dot - Quantum Wells

In this work, preparation and spectroscopic characteristics of CdSe/CdS/CdSe1-xSx/CdS core/shell 1/well/shell 2 structure were presented. The obtained quantum dot - quantum well (QDQW) samples exhibited two emission peaks at 1.97 and 2.2 eV. Excitation power dependent photoluminescence (PL) of the QDQWs indicated that the integrated emission intensities of these peaks increase linearly with excitation power densities up to 1.3×102 mW/cm2. The temperature dependence of the bandgap energies of the QDQW’s core and well layer was well described using the Varshni expression. Evidence of the existence of “local wells” in the QDQW’s well layer that leads to a more significant change in the maximum position and full width at half maximum of the well’s emission peak compared to the core’s counterpart was discovered. Annealing samples resulted in the considerable increase of the well layer’s emission efficiency. Keywords: Quantum dot - quantum wells, dual - color, spectroscopic characteristics.
A proposal for rotating machineries analysis by considering the unknown equal potential condition on FEM

This paper presents a technique for a magnetic field analysis using the Finite Element Method (FEM) which is applied to the rotatory movement of rotating machinery. The proposed technique uses only a couple of meshes because the nodes on an interface are joined under the unknown equal potential condition. This technique is examined by the analysis of the characteristics of brushless DC machines. Calculated results by this technique agree well with the measured ones, so that the utility of this technique is confirmed. >