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s The perforated stiffened panel is generally found as a sub-component of sophisticated structures. The fundamental purpose of this panel is to withstand against buckling under complicated loading and environmental conditions. Hence, an accurate knowledge of critical buckling behaviour of stiffened panels is very much essential for a reliable and lightweight structural design. In this paper, the focus is on quasi-laminated panels with different cutout shapes of various sizes and their responses to hygrothermal environments under nonlinearly varying edge loads and is compared with the locally stiffened panels. Towards this, the modelling of the panel and stiffener is done by adopting nine-noded heterosis plate elements and three noded beam elements respectively. The stiffener formulation is suitably modified in order to take the torsional effect also into consideration along with the effect of shear deformation. Initially, the plate and the stiffener elements are treated separately, and then the displacement compatibility is maintained between them by using the transformation matrix. For a given loading and geometric discontinuity, the stress distribution within the perforated panel is highly non-uniform in nature and hence a dynamic approach has been used to calculate buckling loads by adopting two sets of boundary conditions, one set for pre-buckling stress analysis and the second set for buckling analysis. Four different quasi-isotropic stacking sequences are deliberated in this work by varying different ply-orientation in each scheme. The study also addresses the effect of various parameters such as nonlinear loads, hygro-thermal loads, cutout size and shapes, position of cutout, stiffener parameters, stacking sequences, thickness of plate and boundary conditions.

Magnetic Resonance Imaging (MRI) is a powerful imaging modality with highly specific applications in medicine. However, certain series and sequences of MRI do not produce images with good acuity. Widely-used sharpening algorithms like Shock Filter (SF) and Unsharp Masking (UM) do not provide output images with appreciable sharpness-to-noise ratio. Output images of UM are prone to discontinuity artefact. A noise-robust sharpening algorithm to enhance acuity of MR images termed as Nonlinear Amplification of Spatial Derivative (NASD) that does not have the issue of discontinuity artefact is introduced in this paper. In the NASD, the enhanced image is estimated from the amplified spatial derivative in an inverse way. To avoid the amplification of noise, the amplification factor at any pixel location is calculated from a nonlinear function of the mean of the absolute values of the gradients along eight different orientations at that location. On hundred test images, the sharpness-to-noise ratio exhibited by NASD, SF and UM are 0.0963 ± 0.0400, 0.1642 ± 0.0967 and 0.2851 ± 0.1610. The computation time (in seconds) of NASD, SF and UM are 0.0330 ± 0.0031, 0.0452 ± 0.0390 and 0.0554 ± 0.0112. The NASD exhibits higher values of sharpness-to-noise ratio than SF and UM. NASD is able to sharpen the edges in the input image without amplifying noise and it is computationally fast like SF and UM.

This paper presents a novel method for separating sub-surface defects in cured pre- impregnated carbon fiber reinforced polimer specimens on thermal images based on edge detection. Every specimen was recorded by means of infrared thermography in reflective configuration and raw data was processed by several algorithms: Thermal Signal Reconstruction, Fast Fourier Transformation and Principal Component Analysis. Images were processed to determine damaged and non-damaged areas. Algorithm for determining area of discontinuity is based on edge detection techniques. Edge detection enables both simulated damages and boundaries between specimen and the background in the thermal image to be detected. Proposed algorithm is emphasizing edges within Sobel filter. Edges are defined as transition zone between damaged and non-damaged material. In this article edge detection algorithm is used as decision assistance for non-destructive evaluation.

Dense stereo matching based on two viewpoint images is an important step in 3D reconstruction. In this study, a new disparity estimation algorithm for depth discontinuity areas is proposed. Dense s...

The pattern of spatial contrast discontinuities in natural images has been analysed in the present work, and based on it, a new adaptive model of the bio-inspired Difference of Gaussian (DOG)-based edge detector has been designed. The distinguishing feature of the proposed filter is that the magnitude of surround suppression in receptive field of the DOG is adaptively adjusted depending on the nature of discontinuity of the edge profile. The model is based on the biological evidences indicating the possibility that human brain may be endowed with the ability to perform Fourier decomposition of visual images into its various components of spatial frequencies. It may be shown that information obtained from such a Fourier decomposition may help to measure the strength of contrast (sharpness of discontinuity) in the intensity profile across any possible edge in the natural image. In the present model, it is assumed that the magnitude of surround suppression in an excitatory–inhibitory receptive field is dependent on the sharpness of discontinuity. The suppression is strong when the edge contrast is poor, while it becomes weaker as the edge contrast is high. At a biphasic edge, the surround suppression is vanishingly small. Natural images collected from benchmark databases are used to evaluate the efficiency and robustness of the proposed model for the detection of edges. The result shows that the edge maps generated through the proposed model are at par, if not more effective as compared to the classical edge detectors like Canny. The performance of the proposed model is also compared with a number of recently proposed alternative adaptive models for edge detection.

Different numerical schemes are introduced to formulate and solve nonlinear vibration analysis of elastically supported multilayer composite plate problems. The type of elastic foundation is Winkle...

This paper presents a material relative permittivity extraction technique based on three measurements of two identical coaxial lines, terminated by a short-circuit configuration. The test cells hav...

An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure–activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein, we have investigated the question if ACs could be predicted from image data. Therefore, pairs of structural analogs were extracted from different compound activity classes that formed or did not form ACs. From these compound pairs, consistently formatted images were generated. Image sets were used to train and test convolutional neural network (CNN) models to systematically distinguish between ACs and non-ACs. The CNN models were found to predict ACs with overall high accuracy, as assessed using alternative performance measures, hence establishing proof-of-principle. Moreover, gradient weights from convolutional layers were mapped to test compounds and identified characteristic structural features that contributed to successful predictions. Weight-based feature visualization revealed the ability of CNN models to learn chemistry from images at a high level of resolution and aided in the interpretation of model decisions with intrinsic black box character.

The conventional coherence attribute is typically applied to migrated full-stack seismic data volumes to detect geologic discontinuities. Recently, multispectral, multiazimuth, and multioffset coherence attributes have been proposed and implemented with different seismic data volumes of specific frequencies, azimuths, and offsets to enhance discontinuities. Generally, geologic anomalies, such as faults and channels, will be better illuminated by a perpendicular rather than a parallel direction for computation. Therefore, we have developed a multidirectional eigenvalue-based coherence attribute by establishing multiple covariance matrices along certain different directions on a single poststack volume. We adopt two methods to compute a multidirectional coherence attribute. One is to compute multiple coherence volumes in different directions and to define the minimum as the final multidirectional coherence. This method is time-consuming, but it could provide partial and overall discontinuity simultaneously. The other method obtains one coherence volume by summing covariance matrices in different directions, which is computationally efficient, but only provides overall discontinuity. The performance of our 3D physical model and field data volumes demonstrates that multidirectional coherence can highlight subtle geologic structures with a higher resolution than conventional coherence. This suggests that a multidirectional coherence attribute may serve as an effective tool for detecting the distribution of geologic discontinuities in seismic interpretation.

Aiming to address the problems of arbitrary orientations, large aspect ratios, and dense arrangements in ship detection, an arbitrary-oriented ship detection method based on RetinaNet is proposed. Our proposed method includes a rotated RetinaNet, a refined network, a feature alignment module, and an improved loss function. First, the rotated RetinaNet achieves rotation detection by using a feature pyramid network, rotated anchors, the skew intersection-over-union (IoU), and skew nonmaximum suppression. Then, the refined network and feature alignment module are introduced to achieve better detection accuracy. Finally, to address the boundary discontinuity, the loss function is improved by introducing the IoU constant factor. Considering the problems with the HRSC2016 dataset, we establish a new dataset with more accurate labels and more images and object samples. Through an ablation study, we thoroughly analyze the validity of the proposed rotated RetinaNet, feature alignment module, and improved loss function. The experimental results show that our method is superior to other state-of-the-art methods.

Metasurface technology has become one of the promising structures for controlling and manipulating of the phase and amplitude characteristics of the electromagnetic waves in recent years. For this reason, the determination of the exact diffracted fields is also crucial especially in the edge or discontinuity points of these types of materials. In the paper, the discontinuity problem by the junction between the layers of an anomalously reflecting metasurface and an impedance half-screen is taken into consideration. These two planar layers have a common edge discontinuity where the diffracted waves occur. The scattered waves from the structure are obtained by the novel technique of the method of transition boundary, which especially offers the relation between the diffracted and scattered geometrical optical waves at the transition boundaries. The diffracted wave expressions are derived by the technique of the method of transition boundary and the uniform wave expressions are obtained by the method of uniform geometrical theory of diffraction. The diffracted and total wave results of the method of transition boundary are also compared and verified by the well-known theory of physical optics. The behaviors of the total wave and its components are analyzed numerically.

An automatic recognition of discontinuities in borehole images is a desirable way to overcome the inefficiency and inconsistency inherent in the conventional method of manual annotation. The rough borehole walls and prominent noise failed the application of existing recognition methods to borehole images taken in the coal-measure strata. This paper presents a novel approach to automatically convert coal-measure strata borehole images into identified discontinuity maps. The developed procedure formed an integrated feature representation for the borehole image through combining the color information of image and the textural features generated from multi-channel filtering. Image regions containing discontinuities are then separated from other regions by implementing fuzzy c-means clustering on the acquired feature representation. The identification of discontinuities is finally accomplished by searching for four predefined patterns (named topographic model) on the intensity transection of image regions. The proposed method is proven to be superior in the respects of noise suppression, discontinuity positioning, and recognition completeness.

In the present work, damage produced by a crack in a statically loaded beam is first evaluated. Subsequently, an attempt is made to repair the effect of the crack by attaching a piezoelectric patch to the beam as an actuator. Static analysis of PZT patched cracked beam along with rotational spring is performed using Ritz method. Subsequently, a finite element analysis is performed by using ABAQUS 6.12 to collate the analytical results. It is shown in the study that when PZT patch is subjected to external electric field, it yields a local reactive moment, which counters the crack effects. An equation is procured in order to compute the required actuation voltage for repairing of cracks. A parametric study is performed for various boundary conditions and loading patterns. It is distinctly noticed that the technique nullifies the discontinuity in slope curve which develops due to a crack.

Purpose. The purpose of the work is to determine and analyze the technical parameters of the bench for railway wheels complex tests, providing for the combination of fatigue and contact strength tests, their simultaneous performing on same test-bench. Methodology. Determination of the technical parameters of the test-bench was carried out based on comparing the characteristics of fatigue and contact strength tests of railway wheels. Requirements for fatigue tests and related equipment are determined based on an analysis of the current regulations and patent review. A model of the contact strength test-bench with the introduction of necessary changes in terms of ensuring the cyclicity of the working effort application was adopted as the basic model for the development of a complex test-bench. The criterion for the possibility of complex tests, involving the simultaneous testing of railway wheels for fatigue and contact strength, was the dependence of the test-bench roller diameter on the parameters of the tested wheel, the wheelset axle load, which includes the tested wheel and the wheel material endurance limit. Findings. The analysis of the obtained dependence showed that the roller diameter as a function of the load in the contact zone of the “wheel – roller” pair has second order discontinuity in the section where this load changes from the minimum to the maximum value. Analytical dependence is also presented and analyzed in graphical form. The generalization of the results obtained made it possible to conclude that it is impossible to carry out complex tests of railway wheels, which provide for the combination of fatigue strength tests with contact strength tests, subject to their simultaneous holding at the same test-bench. Originality. For the proposed scheme of the bench for complex tests of railway wheels, for the first time, we obtained the dependence linking the parameters of the test wheel, the parameters of the bench, the wheel pair axle load and the wheel material endurance limit. Practical value. The results obtained can be used as a basis for the development of new test-benches for railway wheels.

Carbon Nanotubes (CNTs) are considered a promising reinforcement for engineering advanced structures, such as aerospace applications. It became inevitable to tailor structures not only by conventional geometric but also by mechanical properties tailoring, to acquire the maximum load-carrying capacity. Therefore, this research presented a novel Finite Element (FE) modeling of Axially Functionally Graded CNT Reinforced Composite (AFG-CNTRC) beams, based on Timoshenko beam theory. However, an obstacle is raised due to properties discontinuity produced between consecutive elements in axial gradation. A solution is proposed based on the ladder-pattern concept. It defined a locally specific value at each element, satisfying the overall required distribution. It followed by a convergence test, static, and dynamic analysis with comprehensive parametric studies. The obtained results showed that the model is convergent, accurate, free of shear-locking, and suitable for axial gradation. Moreover, this study introduced an advanced technique for axial properties tailoring, utilizing privileges of CNT and aggregation. The longitudinal tailoring depends on both the CNT orientation angle and gradation index, which played a crucial role in response control and adjusting beam stiffness together with strength. This approach could solve enormous engineering problems, especially at critical cross-sections design and constraint response, without affecting the total weight.

Background Recently, the field of face and facial features has been progressively studied. The features of facial expression have gained increasing attention for related applications. The wrinkle is the most representative feature, and its research and applications have been topics of high interest. Wrinkles play an important role in face feature analysis. They have been widely used in applications, such as age estimation, skin texture classification, expression recognition, and simulation. Purpose Existing approaches to the image‐based analysis of wrinkles as texture not as curvilinear discontinuity and wrinkle detection mainly have focused on detecting wrinkles on forehead position, which is usually horizontal linear shapes, while the detection of the nasolabial wrinkle is not well understood due to their variety of shapes and complexity. Method In this paper, we present a nasolabial wrinkle line detecting effective algorithm based on the Active appearance model and Hessian filter to improve localization results by creating unique initial shapes of the wrinkle lines for each input face image. Results Experimental results show that the proposed method is capable of tracking curve wrinkle lines, thus allowing to detect complexly structured wrinkle lines. This work demonstrates results illustrated the competitiveness of the proposed method in detecting nasolabial wrinkle lines. Conclusion In our study, this was introduced the effectiveness of changing the structure of AAM and successfully applied in wrinkle line localizing, although competitive results are achieved by the proposed wrinkle detection method.

Lesion segmentation is a vital step in a melanoma recognition system. Many algorithms were developed for the efficient skin lesion segmentation. Most of them fails to realize a perfect segmentation. This paper proposes a novel, fully automatic system, for the lesion segmentation in dermatograms. The proposed approach executes in two steps. Selection of root seed is the first step. All the lesion pixels in the dermatogram are identified during the second step. Traversal through a predefined lesion pixel path ensures the reachability of all lesion pixels irrespective of the possible lesion discontinuity. The proposed algorithm is tested with two publically available dataset, PH2 and images of ISBI2016 challenge. Out of the six evaluation parameters, the proposed method shows the best values for specificity, accuracy, Hammuode distance and XOR. This confirms the merit of the proposal with respect to existing popular methods.

Objective. In this paper, the authors study problems of a plane strain of elastic bodies containing internal rectilinear fractures. In each case, the margins of the considered areas are supported by thin flexible coatings. The first part of the paper is devoted to the problem of an infinite elastic wedge, the faces of which are free from the outside and reinforced with a thin flexible material, and the bisector contains a rectilinear fracture with regular forces applied to the margins, and to the study of the stress concentration at the fracture vertices. In the second part of the paper, the authors consider the problem of an equilibrium radial internal fracture in the cross-section of a round pipe. The inner surface of the pipe experiences hydrostatic pressure; the outer surface is reinforced with a thin flexible coating. The purpose of the study in each of the presented tasks is to determine the values of the influence factor.Methods. Both problems are united by a single approach, in which the presence of a coating is modeled mathematically, using special marginal conditions obtained based on an asymptotic analysis of the exact solution for a strip or ring flexible coating of small relative thickness. In the first issue, the singular integral equation is derived using the Mellin transform, which allows proceeding to the solution of a system of ordinary differential equations and obtaining a singular integral equation relative to the derivative of the discontinuity function of the first kind with a Cauchy kernel. In the second issue, discontinuous solutions are constructed using the Fourier series, resulting in a singular integral equation of a similar structure. Previously, similar ideas were successfully implemented by the authors in the study of the problem of the equilibrium state of a strip with a coating weakened by an internal transverse fracture under arbitrary conditions on the lower edge of the strip.Conclusion. Singular integral equations for the considered problems are obtained. The collocation method is used to construct solutions of singular integral equations for various combinations of geometric and physical characteristics of issues. In all the considered cases, the values of the influence factor were calculated. The analysis of changes in the influence factor depending on various combinations of geometric parameters and mechanical characteristics of problems is carried out. It is noted that with increasing rigidity of the coating and increasing its thickness, the values of the influence factor decrease; the increase in the value of the influence factor is provided by approaching the fracture to the body margin and increasing its relative length.

A computationally efficient method for discrete modelling of cracks in laminated composite structures using explicit time integration is proposed. Discrete crack modelling involves formation of strong discontinuities and the integration domain of elements must be transformed to include this discontinuity. In explicit time integration, this results in a reduction in the stable time increment of an analysis and the corresponding increase in computational cost limits its application to small scale meshes. Also, remapping of integration domains and initiation of cohesive segments introduces numerical errors in the solution. To solve these two problems, a mixed time integration or subcycling is performed adaptively following element splitting and cohesive segments are initiated with minimal disturbances to the surrounding stress field by nodal force balance. The challenges in the implementation, the effects of assumptions involved in subcycling and its computational benefits are discussed in the context of modelling tensile failure in composite laminates. Modelling of transverse cracks are performed with linear solid elements in unstructured meshes where crack geometry does not align with the initial mesh. To demonstrate its effectiveness, the method is applied to laminates containing an open-hole and a laminate with embedded wrinkles arising from manufacturing defects.

Cracks are accounted as the most destructive discontinuity in the rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity (UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning (ML) for analyzing wave signals and predict rock properties such as crack density (CD)- the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid (RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2 > 96% with mean square error (MSE) < 25e-4 (normalized values). Overall, we found that: (i) performance of both CNN and LSTM is highly promising, (ii) accuracy of the transmitted signals is slightly higher than the reflected signals, (iii) accuracy of 2D signals is marginally higher than 1D signals, (iv) accuracy of horizontal and vertical component signals are comparable, (v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling.

Introduction Patients with cancer require specialized care from different care providers, challenging continuity of care in terms of information, relationships, and/or management. The recognition of discontinuity of care has led to different initiatives by the healthcare system over the years. Yet, making use of the theory on boundary objects and brokers, this research explores the active role of patients themselves in resolving discontinuity along their care trajectories. Methods Semi-structured interviews were conducted with 33 patients to unravel the discontinuities that they experience and their attempts to resolve these. Interview data were analyzed using directed-content analysis informed by concepts from boundary crossing literature (i.e., data were searched for potential boundary objects and brokers). Results To re-establish continuity of care, patients actively use the objects and people provided by the healthcare system when these meet their needs. Patients also introduce own objects and people into the care trajectory. As such, information and management discontinuity can typically be resolved. Relational continuity appears to be more difficult to resolve, in some cases leaving patients to take drastic measures, such as changing care providers. Discussion The use of boundary crossing theory in improving care from a patient perspective is relatively novel. When patients and providers together address the objects and people that support establishing continuity of care, a continuous care process may be encouraged. We advocate an integrated approach, rather than provider or healthcare system initiatives exclusively, to patient care and continuity. Supplementary Information The online version contains supplementary material available at 10.1007/s40271-021-00535-x.

The rolling sphere method for air termination placement on a structure is easy to apply, but it cannot be used to determine the probability of lightning strike to different points on the structure. The dynamic electro-geometrical model (DEGM) using numerical methods can be implemented to evaluate the interception efficiency of various air termination systems. The DEGM is implemented using the probability function of lightning current distribution which has a discontinuity at 20 kA. This study proposes a way to remove the discontinuity by the introduction of a continuous step function. The effectiveness of various centrally positioned air terminations on structures was also examined. Mesh air terminations are typically installed directly on the roof of protected structures. Using DEGM concept, result shows that lifting the mesh network by the penetration depth does not significantly improve the interception efficiency of the air termination network for a gable roof structure.

This paper presents a methodology to predict the fatigue lifetime in notched geometries subjected to multiaxial loading based on the effective strain energy density concept. The modus operandi consists of defining a fatigue master curve that relates the strain energy density with the number of cycles to failure from standard cylindrical specimens tested under low-cycle fatigue conditions. After that, the multiaxial loading history at the geometric discontinuity is reduced to an equivalent uniaxial loading scenario via the calculation of an averaged value of the strain energy, which is done by combining the equivalent strain energy density concept along with the theory of critical distances. Then, this energy is inserted into the fatigue master curve to estimate the fatigue lifetime. The method is tested in solid round bars with lateral notches subjected to in-phase bending-torsion loading. Overall, the comparison between the experimental and predicted fatigue lives shows a very good agreement. Additionally, the proposed approach enables the determination of the most likely initiation sites as well as the crack angles at the early stage of crack growth. This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.

Predicting scattering of elastic guided waves in multi-layered solid plates with geometrical and/or material discontinuities is of great interest to many fields, including ultrasonic-based Non-Destructive Testing (NDT) and health monitoring of critical structural components (SHM). The problem is complicated by the multimode and dispersive behaviour of the guided waves. This paper describes a unified Global-Local (GL) approach that is computationally efficient in cases that can be very complex in terms of geometry and/or material properties. One example of this is a composite built-up structure. The proposed GL procedure discretizes the “local” region with the scattering discontinuity by regular finite elements, and utilizes the efficient Semi-Analytical Finite Element solutions in the “global” region away from the scatterer. The GL formulation that is presented includes the dispersive unforced solutions for each applicable mode, the mode tracking, the scattered spectra (reflection and transmission), and the energy balance calculations. The algorithm is applied to the case of a composite skin-to-stringer assembly used in modern aircraft construction. Various representative defects in this assembly are modelled, and transmission spectra are calculated for both axial or flexural guided wave modes used in excitation. The resulting scattered spectra (which are the broadband transfer functions of the structure) can be useful to either select suitable wave mode-frequency combinations or to identify specific defects in guided-wave NDT or SHM tests of these components.

Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas. To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas. We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods.

The design of nuclear civil structures based on rules in European standards makes extensive use of the Finite Element Method (FEM). The size and complexity of the models are continuously increasing. Lately, the post-processing of the FEM results has centered the engineers’ contribution on analyzing the reinforcement density produced using automated methods dealing with shell or plate models, which often leads to excessive plate use even in D-regions (discontinuity or disturbance region). This practice is particularly problematic for nuclear structures, which exhibit a large set of massive parts due to radiation protection requirements in many areas such as the reactor pit, the raft, or the containment gusset. Furthermore, these substructures are generally not well aligned with thin structural elements (such as slabs and walls) due to heavy equipment, piping, and HVAC (heating, ventilation, and air conditioning) constraints. This is contrary to what should be done to achieve the best engineering design. To address these issues, an overall modeling and post-processing approach is proposed herein. The objective is to enhance the implementation of the finite element model and the post-processing phase to improve the quality of the modeling and reinforcement of B (non-disturbed region) and D regions without significantly impacting the time needed for the design. A second objective of the proposed methods is to improve the constructability of reinforced concrete nuclear structures. Indeed, the current modeling approach, which often misuses shell elements, often leads to a significant overestimation of the required reinforcement density and consequently complicates the fabrication of the reinforced concrete structures. To illustrate the approach used here, an example based on a building typical of a nuclear structure is presented. The distinct benefits of both the overall modeling and the reinforcement post-processing are highlighted. This approach exhibits a saving of nearly 25% on the bending reinforcement sections of the outer walls compared to a practice that doesn’t account for the improvements presented in the paper. Moreover, it reduces the maximum values of reinforcement due to bending of a plate by almost 40% without changing the reinforcement calculation process. It also provides a smoothing method of the reinforcement ratio fringes based on energy conservation. The method is automated and replaces the common, time-consuming practice of averaging the reinforcement ratios for arbitrary defined sets of finite elements.

The inevitable formation of a Mach disk at the central axis of a convergent conical shock wave may suffer from fundamental changes when the flow deviates from the axisymmetric condition. In this paper, the behaviours of near-conical shocks, which are generated by a circular ring wedge of $10^{\circ }$ at typical angles of attack (AoAs), are investigated at a free stream Mach number of 6 in a shock tunnel. To reveal the characteristics and mechanism of the flow, numerical analyses are carried out under the same conditions. The results indicate that when the flow deviates from axial symmetry, the circumferential non-uniformity is remarkably intensified as the shock converges downstream. The converging centre shifts against the inclination of the incoming flow and moves to the leeward side. For a sufficiently small AoA, the formation of a Mach disk remains similar to that in the axisymmetric case, although the Mach disk shrinks in size and is slightly flattened. As the circumferential non-uniformity of the shock increases at an AoA of approximately $3^{\circ }$ , a pair of kinks separate the shock surface into two discontinuous segments with the stronger shock segment on the windward side and the weaker shock segment on the leeward side. When the AoA increases further, the shrinkage of the Mach disk continuously occurs, and the Mach disk is eventually replaced by a regular reflection. The discontinuity of a convergent shock with flattening on the separated shock segments and the insufficient strength increase during the subsequent convergence are responsible for the appearance of regular reflection.

Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between −π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications.

Rank and cardinality penalties are hard to handle in optimization frameworks due to non-convexity and discontinuity. Strong approximations have been a subject of intense study and numerous formulations have been proposed. Most of these can be described as separable, meaning that they apply a penalty to each element (or singular value) based on size, without considering the joint distribution. In this paper we present a class of non-separable penalties and give a recipe for computing strong relaxations suitable for optimization. In our analysis of this formulation we first give conditions that ensure that the globally optimal solution of the relaxation is the same as that of the original (unrelaxed) objective. We then show how a stationary point can be guaranteed to be unique under the RIP assumption (despite non-convexity of the framework).

This paper presents a broadband 3 dB directional coupler based on a multi-branch hybrid waveguide. By placing a two-stage stepped waveguide-height discontinuity in the middle of the coupler symmetrically, a controllable ripple is introduced to achieve better overall amplitude imbalance. The simulation results show that over the most of the operating frequency bands from 385 GHz to 481 GHz, the amplitude imbalance is less than 0.037dB. The return loss of the operating frequency band is less than −17 dB and the phase imbalance is better than ± 1.85°.

A wide range of process systems engineering problems involve an optimisation formulation that is difficult to solve due to sources of discontinuity and non-convexity and a high number of constraints to satisfy. Differential Evolution algorithm (DE) has proven to be robust for the solution of highly non-convex and mixed-integer problems; nevertheless, its performance greatly depends on the constraint-handling technique used. In this study, numerical comparisons of some state-of-the-art constraint-handling techniques are performed: static penalty function, stochastic ranking, feasibility rules, e constrained method and gradient-based repair. The obtained results show that the gradient-based repair technique deserves a special attention when solving highly constrained problems. This technique enables to efficiently satisfy both inequality and equality constraints, which makes it particularly adapted for the solution of process engineering optimization problems.

A W-band TE□ 40 mode converter with a compact structure based on three T-junction power dividers for measuring high-order overmoded mode slow-wave structures is presented. Since the T-junction discontinuity causes the port reflection, a series of matching steps were introduced in the converter. The proposed converter possessed a bandwidth of 12 GHz with the transmission coefficient S 21 higher than -3 dB and port reflection S 11 lower than -15 dB. More importantly, the transmission coefficient of the unwanted modes is lower than -15 dB within the bandwidth of interest.

The refraction of an oblique shock wave on a tangential discontinuity dividing two gas flows with different properties is considered. It is shown that its partial reflection occurs with the exception of the geometrical diffraction of an oblique shock. Another oblique shock, expansion wave or weak discontinuity that coincides with the Mach line can act as a reflected disturbance. This study focuses on the relationships that define the type of reflected discontinuity and its parameters. The domains of shock wave configurations with various types of reflected discontinuities, including characteristic refraction and refraction patterns with a reflected shock and a reflected rarefaction wave, are analyzed. The domains of existence of various shock wave structures with two types of reflected disturbance, and the boundaries between them, are defined. The domains of parameters with one or two solutions exist for the characteristic refraction. Each domain is mapped by the type of refraction with regard to the Mach number, the ratio of the specific heat capacities of the two flows and the intensity of a refracted oblique shock wave. The conditions of the regular refraction and the Mach refraction are formulated, and the boundaries between the two refraction types are defined for various types of gases. Refraction phenomena in various engineering problems (hydrocarbon gaseous fuel and its combustion products, diatomic gas, fuel mixture of oxygen and hydrogen, etc.) are discussed. The result can be applied to the modeling of the shock wave processes that occur in supersonic intakes and in rotating and stationary detonation engines. The solutions derived can be used by other researchers to check the quality of numerical methods and the correctness of experimental results.

An advanced modeling strategy for notched ceramic matrix composite coupons with patch reinforcement was proposed to investigate the failure mechanisms. This model considered the tailored fiber–placed (TFP) yarn details obtained from the design phase and the embedded element concept which was used to successfully overcome the meshing difficulties. Inter-ply “glue” layers were simulated using the surface-based contact cohesive element method, so the delamination due to interfacial material discontinuity and damage can be well reproduced and analyzed. For composite ply, the energy-based composite progressive damage model that is independent of the mesh size was applied. Virtual test campaign was performed with a variety of geometrical and material parameters, and the damage and failure mechanisms based on the stress analysis can be revealed to support the design optimization of patch reinforcement.

This paper proposes a novel pretext task for self-supervised video representation learning by exploiting spatiotemporal continuity in videos. It is motivated by the fact that videos are spatiotemporal by nature and a representation learned to detect spatiotemporal continuity/discontinuity is thus beneficial for downstream video content analysis tasks. A natural choice of such a pretext task is to construct spatiotemporal (3D) jigsaw puzzles and learn to solve them. However, this task turns out to be intractable. We thus propose Constrained Spatiotemporal Jigsaw (CSJ) whereby the 3D jigsaws are formed in a constrained manner to ensure that large continuous spatiotemporal cuboids exist in a shuffled clip to provide sufficient cues for the model to reason about the continuity. With the constrained jigsaw puzzles, instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable but meanwhile still ensure that the learned representation is sensitive to spatiotemporal continuity at both the local and global levels. Extensive experiments show that our CSJ achieves state-of-the-art on two downstream tasks across various benchmarks.

Time of flight and photometric stereo are two three-dimensional (3D) imaging techniques with complementary properties, where the former can achieve depth accuracy in discontinuous scenes, and the latter can reconstruct surfaces of objects with fine depth details and high spatial resolution. In this work, we demonstrate the surface reconstruction of complex 3D fields with discontinuity between objects by combining the two imaging methods. Using commercial LEDs, a single-photon avalanche diode camera, and a mobile phone device, high resolution of surface reconstruction is achieved with a RMS error of 6% for an object auto-selected from a scene imaged at a distance of 50 cm.

The cable-stayed bridge (CSB) can be considered as a masterpiece of architecture. Relatively longer spans and distinctive pylon shapes make it suitable for rapid rail transit. Pylon shape of CSB is an important consideration for modern CSB designing; lots of research has been done in the same area, but the pylon shape influence for high-speed trains remains limited. The modelling and analysis of CSB under high-speed train loads involves heavy traction force and repetitive heavy rail axle load (impact axle load), which necessitates a precise and fast analysis approach. In this research article, nonlinear dynamic analysis is performed for four distinct pylon shapes: diamond shape, H shape, A shape, and inverted Y shape for the same bridge geometry and different speed variant. A catenary cable element is used for cable modelling. The dynamic equations have been derived for the rail vehicle, bridge, and rail–bridge interaction and solved via the time integration Newton–Raphson method. Dynamic rail wagon model is formulated with a 10-degree-of-freedom system. The advanced MATLAB Simulink programme has been developed to predict the excitation experienced by the wagon for any discontinuity. The pretension in cables and the dynamic impact factor for acceleration and displacement are used to represent the bridge behaviour. The analysis results prove that the pylon with the least amount of discontinuity and the higher flexibility performs best for high-speed rail movement and resonance criteria.

Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number or the infamous derivative discontinuity at integer particle numbers. Here we propose a solution to these problems by training a neural network as the universal functional of density-functional theory that (a) depends explicitly on the number of particles with a piece-wise linearity between the integer numbers and (b) reproduces the derivative discontinuity of the exchange-correlation energy. This is achieved by using an ensemble formalism, a training set containing fractional densities, and an explicitly discontinuous formulation.

Highly transparent and reflective objects are prevalent in life, yet their existence severely degrades the performance of existing object detectors. In this paper, we propose a novel Edge-aware Context-aggregation Network (ECNet) for transparent and reflective object detection, with three well-designed modules introduced to enhance the ability of extracting distinctive features. Specially, we develop the Edge Detection Module (EDM) to make the network pay more attention to the boundary areas; present the Depth Feature Fusion Module (DFFM) to extract content discontinuity in depth maps; and introduce the Multi-respective-field Feature Extraction Module (MFEM) which fusing multi-receptive-field features to reveal the texture discontinuity. Extensive experiments show the proposed ECNet surpasses the state-of-the-art detectors by at least 7.4% mAP in transparent and reflective object detection, which well demonstrates the effectiveness of the proposed method.

Purpose This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods. Methods A deep learning network called PHU-NET was designed for MR phase unwrapping. In this network, a novel training data generation method was proposed to simulate the wrapping patterns in MR phase images. The wrapping boundary and wrapping counts were explicitly estimated and used for network training. The proposed method was quantitatively evaluated and compared to other methods using a number of simulated datasets with varying signal-to-noise ratio (SNR) and MR phase images from various parts of the human body. Results The results showed that our method performed better in the simulated data even under an extremely low SNR. The proposed method had less residual wrapping in the images from various parts of human body and worked well in the presence of severe anatomical discontinuity. Our method was also advantageous in terms of computational efficiency compared to the traditional methods. Conclusion This work proposed a robust and computationally efficient MR phase unwrapping method based on a deep learning network, which has promising performance in applications using MR phase information.

Novel approach of modeling with recurrent neural networks is proposed and tested for de-embedding of discontinuities (e.g. connectors, probes, etc.) in microwave test fixtures. Very high numbers of parameters of the test fixture are used for generating the model to cover the actual hardware and measurement variation. Excellent results are demonstrated when de-embedding with DUT, compared to the well-known TRL method. It is shown that by taking advantage of the high dimensional regression capability of machine learning, one can take into consideration and mitigate the uncertainty of the discontinuity, as well as of the substrate structure on which the test fixture is built on, arises from variation of calibration kits and repeated measurements. The machine learning approach is very successful in addressing the issues of time consuming, error prone, and computational stability of the very widely used measurement technique of TRL.

In this paper, a high efficiency D-band 32-channel power divider/combiner is proposed. The design incorporates rectangular waveguide, coaxial line, radial waveguide, and three matching transitions. The electromagnetic wave is fed by a center rectangular waveguide port and then transmits to the radial waveguide through a coaxial line. Next, the electromagnetic wave is transferred from the radial waveguide to 32 rectangular waveguide ports. Note that three matching transitions are utilized to overcome the discontinuity in transport nodes. Based on such direct power divider configuration, this radial power divider/combiner has the merit of high efficiency with a large number of channels. To verify the mechanism, a D-band 32-channel radial power divider/combiner is fabricated and measured. The measured minimum insertion loss of two power dividers with back to back configuration is 2.74 dB, which is equivalent to the combination efficiency of 72.9 %.

In self-supervised monocular depth estimation, the depth discontinuity and motion objects' artifacts are still challenging problems. Existing self-supervised methods usually utilize two views to train the depth estimation network and use one single view to make predictions. Compared with static views, abundant dynamic properties between video frames are beneficial to refining depth estimation, especially for dynamic objects. In this work, we improve the self-supervised learning framework for depth estimation using consecutive frames from monocular and stereo videos. The main idea is to exploit an implicit depth cue extractor which leverages dynamic and static cues to generate useful depth proposals. These cues can predict distinguishable motion contours and geometric scene structures. Moreover, a new high-dimensional attention module is proposed to extract a clear global transformation, which effectively suppresses the uncertainty of local descriptors in high-dimensional space, resulting in a more reliable optimization in the learning framework. Experiments demonstrate that the proposed framework outperforms the state-of-the-art on KITTI and Make3D datasets.

Two-dimensional phase unwrapping is the task of recovering the true phase values, given the wrapped phase values in an image. Phase unwrapping arises in several branches of applied optics, physics, medicine and engineering, such as homomorphic signal processing, solid-state physics, optical interferometry, adaptive or compensated optics, magnetic resonance imaging, synthetic aperture radar interferometry, and optical and electron holography (Volkov & Zhu, 2003). In these applications, the measured information is denoted by a two-dimensional phase distribution called the wrapped phase image. In wrapped phase images, the phase is the interval (-π, π] or (0, 2π] due to the use of the mathematical arctangent function (Bone, 1991). Since this wrapped phase suffers from 2-π phase jumps, it is unusable until the phase discontinuities are removed. In order to recovering the true continuous phase values to denote real physical quantity, a phase unwrapping process is needed to recovering the true phase values. The procedure of phase unwrapping is performed by either adding or subtracting integer multiples of 2π to all successive pixels when a phase discontinuity encounters, which are based on some kind of threshold mechanism (Ghiglia et al., 1987). However, many factors, such as surface discontinuities, noise, under-sampling, or shadow, would produce unreliable phase data, which make the recovery of the wrapped phase challenging. To solve the problem, many phase unwrapping algorithms have been developed during the last three decades. These phase unwrapping algorithms can be found in a very good reference book (Ghigli & Pritt, 1998) and review papers such as (Baldi et al., 2002; Jenkinson, 2003; Su & Chen, 2004; Zappa & Busca, 2008). In many phase unwrapping algorithms, a quality map, which evaluated the quality or the reliability of the phase data, is used for completing the phase unwrapping process. In wrapped phase images, the quality of a pixel is low if it is located in areas where the surface discontinuities, noise or undersampling exists. On the contrary, the quality of a pixel is high if it is located in areas where the variation of phase value is low. From the mathematical point of view, quality map is a matrix of the same size of phase image that assign a quality value to each pixel. The quality values are usually normalized in the range [0, 1], where a large value means high reliability of the corresponding pixel. In most phase unwrapping algorithms, a quality map is necessary to guide the phase unwrapping process for achieving desire results. Furthermore

This paper presents a coaxial-to-microstrip transition with improved transmission characteristics for use in 5G millimeter wave communication band. When a coaxial cable and a microstrip line are connected, a discontinuous portion exists at the connection, and the reflection, transmission, and radiation of the incident electromagnetic waves occur due to the discontinuity. To prevent degradation of the transition characteristics due to the discontinuity, metal legs in the coaxial cable are introduced. The proposed coaxial-to-microstrip transition has a higher transmission coefficient than conventional coaxial-to-microstrip transitions.

A finite-difference algorithm is proposed for numerical modeling of hydrodynamic flows with rarefaction shocks, in which the fluid undergoes a jump-like liquid-gas phase transition. This new type of flow discontinuity, unexplored so far in computational fluid dynamics, arises in the approximation of phase-flip (PF) hydrodynamics, where a highly dynamic fluid is allowed to reach the innermost limit of metastability at the spinodal, upon which an instantaneous relaxation to the full phase equilibrium (EQ) is assumed. A new element in the proposed method is artificial kinetics of the phase transition, represented by an artificial relaxation term in the energy equation for a “hidden” component of the internal energy, temporarily withdrawn from the fluid at the moment of the PF transition. When combined with an appropriate variant of artificial viscosity in the Lagrangian framework, the latter ensures convergence to exact discontinuous solutions, which is demonstrated with several test cases.

Electronic speckle pattern interferometry (ESPI) is a kind of full-field, non-contact nondestructive measurement technology, which is suitable for deformation measurement and nondestructive testing of optical rough surface. Fringe phase is an important information of interference fringe image, and the accuracy of phase estimation plays an important role in the extraction of fringe information. However, because of the discontinuity of fringe image and the influence of noise, phase extraction has always been a challenging problem. In this paper, we propose a fringe image phase extraction technique based on convolutional neural network, and experimental results show that this method has a good effect on phase estimation of the interference fringe.

In this paper, the robustness of tessellation images segmentation using Convolutional Neural Network (CNN) is presented. Particularly, this paper aims to study the effect of the quality and number of the images used for training on the robustness of the edge segmentation and completion. Three kinds of image deterioration are considered: the absence of a part of the tessellation, the discontinuity of the edges constituting the tessellation and the presence of a background noise. Five CNNs are trained with several kinds of deteriorated images and the trained models are then compared according to the results obtained with four classes of images.

The reliable detection of defects is one of the most important tasks in nondestructive tests, mainly in the radiography test (RT) and ultrasonic test (UT). Non-Destructive Testing (NDT) is the most efficient as internal and external discontinuities are inspected while maintaining the target’s original structure (form and function). Therefore, in this paper, the Phased Array Ultrasonic Testing (PAUT) method was applied to propose inspection method that can address certain key PAUT Parameters and image processing to enhance the precision of discontinuities and the reliability of calculations of discontinuity size and characterization and also transfer 2D S-Scan images into 3D images (volumetric scan) which is more reliable and easily interpretation.

This article outlines the objectives, activities, and achievements of using intuitive logics scenarios to improve strategic decision-making in situations of discontinuity. Pierre Wack's work at Royal Dutch/Shell illustrates the use of exploratory, reframing, and decision scenarios to change the mental models of decision-makers in advance of the 1973 oil crisis. Thomas S. Kuhn's The Structure of Scientific Revolutions offers a promising theoretical account of paradigm shifts in science, but its application is limited by the ambiguity of the concept of paradigm. Margaret Masterman's distinction of three senses of paradigm – introduced as construct paradigm, worldview paradigm, and action paradigm – clarifies and extends these accounts to support a preliminary framework and systematic approach. Overall, this investigation expands our understanding of strategic decision-making as guided not only by analytic achievements and developmental investigations but also by discontinuous breaks that can be explored and addressed using scenario methodology. Section 1 provides an introduction and literature review. Section 2 examines the scenarios developed at Royal Dutch/Shell. Section 3 summarizes Kuhn's new image of science, clarified by Masterman's three senses of paradigm in Section 4. Section 5 presents implications for scenario methodology, followed by conclusions and areas for research in Section 6.