Boundary Correction Research Articles

Effective Boundary Correction for Deterministic Lateral Displacement Microchannels to Improve Cell Separation: A Numerical and Experimental Study.

Particle separation and sorting techniques based on microfluidics have found extensive applications and are increasingly gaining prominence. This research presents the design and fabrication of a microfluidic device for separating cells using deterministic lateral displacement (DLD), enabling accuracy and continuity while being size-based. Nevertheless, it remains demanding, to completely reverse the detrimental effects of the boundaries that disturb the fluidic flow in the channel and reduce particle separation efficiency. This study introduces a novel approach to enhance the boundary structure of channels. By using this design, separation efficiency is boosted, and the fluid behavior around the walls is improved. The boundary correction (BC) enhances the operation of the microchannel and is very effective in microchannels. With boundary correction, the device exhibited improved separation efficiencies, but in its absence, separation efficiencies dropped. The collected microscopic images of the isolation of prostate cancer cell lines and red blood cells revealed promising outcomes. The efficiency of circulating tumor cell (CTC) throughput in the microfluidic channel, quantified as the ratio or proportion of tumor cells exiting the channel to cells entering it, exceeds 93%. Moreover, the efficiency of CTC isolation, expressed as the proportion of tumor cells from the upper outlet of the microfluidic channel to all cells, is over 89%. Additionally, the efficiency of red blood cell isolation, evaluated as the ratio of red blood cells from the lower outlet of the microfluidic channel to all cells, surpasses 77%. While using the same DLD separator without boundary correction reduced the separation efficiency by around 5%.

Accurate backside boundary recognition of girth weld beads

Visual recognition of weld beads is essential for post-weld robotic grinding. The recognition of thin-walled weld bead boundary, especially the backside boundary, remains challenging due to the diverse features such as debris, misalignment, and deformation. Based on point cloud from a laser scanner, we present a robust and accurate backside boundary recognition method for girth weld beads of thin-walled pipes. A boundary point extraction method is designed based on an adaptive sliding window model. Without prior morphology features, the influence of misalignment and deformation on the accuracy of boundary point recognition is greatly reduced by the local model matching strategy. Leveraging the correlation among overall weld bead features, an anomalous boundary point recognition and correction method based on DBSCAN clustering is proposed to further enhance robustness. A series of validation experiments were conducted by the obtained backside point cloud data inside a girth weld pipe, and our proposed method showed a high accuracy and a high robustness to misalignment, deformation and debris features.

Discrete element model for effective electrical conductivity of spark plasma sintered porous materials

AbstractThis paper aims to analyse electrical conduction in partially sintered porous materials using an original resistor network model within discrete element framework. The model is based on sintering geometry, where two particles are connected via neck. Particle-to-particle conductance depends on neck size in sintered materials. Therefore, accurate evaluation of neck size is essential to determine conductance. The neck size was determined using volume preservation criterion. Additionally, grain boundary correction factor was introduced to compensate for any non-physical overlaps between particles, particularly at higher densification. Furthermore, grain boundary resistance was added to account for the porosity within necks. For numerical analysis, the DEM sample was generated using real particle size distribution, ensuring a heterogeneous and realistic microstructure characterized by a maximum-to-minimum particle diameter ratio of 15. The DEM sample was subjected to hot press simulation to obtain geometries with different porosity levels. These representative geometries were used to simulate current flow and determine effective electrical conductivity as a function of porosity. The discrete element model (DEM) was validated using experimentally measured electrical conductivities of porous NiAl samples manufactured using spark plasma sintering (SPS). The numerical results were in close agreement with the experimental results, hence proving the accuracy of the model. The model can be used for microscopic analysis and can also be coupled with sintering models to evaluate effective properties during the sintering process.

The crucial role of Lagrange multipliers in a space-time symmetry preserving discretization scheme for IVPs

In a recently developed variational discretization scheme for second order initial value problems [1], it was shown that the Noether charge associated with time translation symmetry is exactly preserved in the interior of the simulated domain. The obtained solution also fulfils the naively discretized equations of motions inside the domain, except for the last two grid points. Here we provide an explanation for the deviations at the boundary as stemming from the effect of Lagrange multipliers used to implement initial and connecting conditions. We show explicitly that the Noether charge including the boundary corrections is exactly preserved at its continuum value over the whole simulation domain, including the boundary points.

Quasi-Interpolation on Chebyshev Grids with Boundary Corrections

Quasi-interpolation is a powerful tool for approximating functions using radial basis functions (RBFs) such as Gaussian kernels. This avoids solving large systems of equations as in RBF interpolation. However, quasi-interpolation with Gaussian kernels on compact intervals can have significant errors near the boundaries. This paper proposes a quasi-interpolation method with Gaussian kernels using Chebyshev points and boundary corrections to improve the approximation near the boundaries. The boundary corrections use a linear approximation of the function beyond the interval to estimate the truncation error and add correction terms. Numerical studies on test functions show that the proposed method reduces errors significantly near boundaries compared to quasi-interpolation without corrections, for both equally spaced and Chebyshev points. The convergence and accuracy with the boundary corrections are generally better with Chebyshev points compared to equally spaced points. The proposed method provides an efficient way to perform quasi-interpolation on compact intervals while controlling the boundary errors. This study introduces a novel approach to quasi-interpolation modification, which significantly enhances convergence rates and minimizes errors at boundary points, thereby advancing the methods for boundary approximation.

Study on fire temperature fields of air-supported membrane structures considering thermal–fluid–solid coupling and boundary correction

The study analyzed and compared the effects of thermal–fluid–solid coupling dynamic boundary conditions and traditional fixed-boundary conditions on the distribution of the temperature field under fires. The feasibility of simulating the temperature field distribution was confirmed by using thermal–fluid–solid coupling dynamic boundary conditions. Then, the traditional temperature field analysis methods were corrected by including a flow-based heat exchange coefficient, and the feasibility of this approach was also confirmed. The fire temperature field in a rectangular air-supported membrane structure was analyzed through parameterization. The fire temperature field model of the air-supported membrane structure was established using temperature field data fitting. Furthermore, the study explored the high-temperature material properties of P-type membranes and analyzed and compared the temperature field of the air-supported membrane structure with time-varying mechanical properties of the membrane material to that without time-varying properties.

Semi-parametric Spatio-Temporal Hawkes Process for Modelling Road Accidents in Rome

AbstractWe propose a semi-parametric spatio-temporal Hawkes process with periodic components to model the occurrence of car accidents in a given spatio-temporal window. The overall intensity is split into the sum of a background component capturing the spatio-temporal varying intensity and an excitation component accounting for the possible triggering effect between events. The spatial background is estimated and evaluated on the road network, allowing the derivation of accurate risk maps of road accidents. We constrain the spatio-temporal excitation to preserve an isotropic behaviour in space, and we generalize it to account for the effect of covariates. The estimation is pursued by maximizing the expected complete data log-likelihood using a tailored version of the stochastic-reconstruction algorithm that adopts ad hoc boundary correction strategies. An original application analyses the car accidents that occurred on the Rome road network in the years 2019, 2020, and 2021. Results highlight that car accidents of different types exhibit varying degrees of excitation, ranging from no triggering to a 10% chance of triggering further events.

Process control of chemical dynamic system based on multi-strategy mayfly optimization algorithm

Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem.

Misaligned RGB-Depth Boundary Identification and Correction for Depth Image Recovery

Misaligned RGB-Depth Boundary Identification and Correction for Depth Image Recovery

SG-MIAN: Self-guided multiple information aggregation network for image-level weakly supervised skin lesion segmentation

Nowadays, skin disease is becoming one of the most malignant diseases that threaten people’s health. Computer aided diagnosis based on deep learning has become a widely used technology to assist medical professionals in diagnosis, and segmentation of lesion areas is one of the most important steps in it. However, traditional medical image segmentation methods rely on numerous pixel-level labels for fully supervised training, and such labeling process is time-consuming and requires professional competence. In order to reduce the costs of pixel-level labeling, we proposed a method only using image-level label to segment skin lesion areas. Due to the lack of lesion’s spatial and intensity information in image-level labels, and the wide distribution range of irregular shape and different texture on skin lesions, the algorithm must pay great attention to the automatic lesion localization and perception of lesion boundary. In this paper, we proposed a Self-Guided Multiple Information Aggregation Network (SG-MIAN). Our backbone network MIAN utilizes the Multiple Spatial Perceptron (MSP) solely using classification information as guidance to discriminate the key classification features of lesion areas, and thereby performing more accurate localization and activation of lesion areas. Additionally, adjunct to MSP, we also proposed an Auxiliary Activation Structure (AAS) and two auxiliary loss functions to further self-guided boundary correction, achieving the goal of accurate boundary activation. To verify the effectiveness of the proposed method, we conducted extensive experiments using the HAM10000 dataset and the PH2dataset, which demonstrated superior performance compared to most existing weakly supervised segmentation methods.

Hölder estimate for a tug-of-war game with $1\lt p\lt 2$ from Krylov–Safonov regularity theory

We propose a new version of the tug-of-war game and a corresponding dynamic programming principle related to the p -Laplacian with 1\lt p\lt 2 . For this version, the asymptotic Hölder continuity of solutions can be directly derived from recent Krylov–Safonov type regularity results in the singular case. Moreover, existence of a measurable solution can be obtained without using boundary corrections. We also establish a comparison principle.

A Novel Fission-Matrix Based Radial Boundary Correction Methodology and its Implementation into the RAPID Code System

The RAPID code system utilizes a novel combined fission matrix methodology to rapidly solve the whole-core eigenvalue problem. To represent axial and radial reflector regions, the fission density distribution in the boundary assemblies, a fission density correction factor methodology was devised and demonstrated. Although, accurate results were achieved, because of the need for a full core Monte Carlo calculation, this technique requires significant computing resources. To address this issue, this paper introduces a novel FM based methodology, which achieves accurate results, however at very small computing expense. The methodology has been implemented into the RAPID code system, and successfully demonstrated for the Watts Bar whole core OECD/NEA

Exploring the user guidance for more accurate building segmentation from high-resolution remote sensing images

In recent years, the computer vision domain has witnessed a surge of interest in interactive object segmentation, an area of study that seeks to expedite the annotation process for pixel-wise segmentation tasks through user guidance. Despite this growing focus, existing methods mainly focus on a single type of pre-annotation and neglect the quality of boundary prediction, which significantly influences subsequent manual adjustments to segmentation boundaries. To address these limitations, we introduce a novel end-to-end network to facilitate more precise building segmentation using diverse types of user guidance. In our proposed method, a centroid map is generated to provide foreground prior information crucial to the subsequent segmentation procedure, and the boundary correction module automatically refines the segmentation mask from existing segmentation networks. Extensive experiments on two popular building extraction datasets demonstrate that our method outperforms all existing approaches given various user guidance (bounding boxes, inside-outside points, or extreme points), achieving the IoU scores of over 95% on SpaceNet-Vegas dataset and over 93% on Inria-building dataset. The remarkable performance of our method further demonstrates its immense potential to alleviate the labor-intensive annotation process associated with remote sensing datasets. The code of our proposed method is available at https://github.com/StephenDHYang/UGBS-pytorch.

Symmetric teleparallel cosmology with boundary corrections

We investigate the geometrodynamical effects of introducing the boundary term in symmetric teleparallel gravity. Specifically, we consider a homogeneous and isotropic universe in fQ,B, where Q is the non-metricity scalar, and B is the boundary term that relates the non-metricity and Ricci scalars. For the connection in the coincidence gauge, we find that the field equations are of fourth-order, and the fluid components introduced by the boundary are attributed to a scalar field. In the coincidence gauge, the cosmological field equations are equivalent to those of teleparallelism with a boundary term. Nevertheless, for the connection defined in the non-coincidence gauge, the geometrodynamical fluid consists of three scalar fields. We focus on the special case of fQ,B=Q+FB theory, and we determine a new analytic cosmological solution that can explain the late-time acceleration of the universe and provide a geometric mechanism for the unification of dark energy with dark matter.

Boundary corrections on multi-dimensional PDEs

Two new boundary correction techniques are proposed in order to mitigate the order reduction phenomenon associated with the numerical solution of initial boundary value problems for parabolic partial differential equations in arbitrary spatial dimensions with time-dependent Dirichlet boundary conditions. The new techniques are based on the idea of discretizing the PDE problem at the boundary points as similar as possible to that of the interior points of the domain. These new techniques are considered for the time integration with W-methods based on approximate matrix factorization. By suitably modifying the internal stages of the methods on the boundary points, it is illustrated by numerical testing with time-dependent boundary conditions that the new boundary correction techniques are able to keep the same accuracy and order of convergence that the method reaches in the case of homogeneous boundary conditions.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

Online correction of the transient voltage security region boundary based on load parameter variations in power systems

The transient voltage security region (TVSR) is an essential part of the dynamic safety region of a power system, which represents the safe operating region of the transient voltage where there is no loss in stability. The power system load significantly affects the transient voltage stability. However, the load model in the existing power systems, which considers the security region of the dynamic process, is too simple to comprehensively characterize the load characteristics of the dynamic process. Furthermore, it neglects the influence of the load model parameters on the dynamic process security region. In this study, a composite load model with distributed photovoltaic power is used as the research object. The main load parameters affecting the voltage stability and the generator nodes that are sensitive to the load parameters are calculated using the trajectory sensitivity method. The TVSR of the system is constructed based on the load and generator’s active powers. The correlations between different load-leading parameter combination scenarios and TVSR boundary points are mined using the CatBoost learning framework. Thus, the TVSR boundary can be promptly corrected online based on the changes in the load parameters, and the system security boundary can be described more accurately. The proposed method is verified using the IEEE39 and IEEE118 node systems. It is observed that the proposed method can correct the TVSR boundary online with high precision corresponding to real-time changes in the load model parameters. This provides a more accurate TVSR boundary for the power system operation scheduler, which helps in guiding it to control the system more accurately.

Populations structure and role of Hedysarum grandiflorum in the spatial organization of plant communities of petrophyte steppes

Comprehensive monitoring and assessment of the state of vegetation cover of Samara Trans-Volga show that steppe landscapes are the most vulnerable, and areas of petrophytic steppes. Rare species include Hedysarum grandiflorum Pall. This species is protected in the territory of Russia. Structural features of cenopopopulations were studied in more than 50 natural-territorial complexes. The number of individuals in populations of specific locations is usually sufficient to make a comprehensive assessment of the species at different levels of organization (organismal, population, species, and phytocoenotic). The study of cenopopopulations was carried out in 1998-2020. During the field studies, we used the method of laying test sites (stationary plots). Data on the spatial distribution of individuals were processed in the R environment (version 3.3.2) using the SPATSTAT package. The intensity of the location of individuals of the studied species was estimated using the Gaussian kernel function with boundary correction. Most of the H. grandiflorum populations known to us are (or have already been) degraded or even critical due to irrational economic exploitation of natural complexes. The random type of placement of individuals indicates favorable habitat conditions for the studied species at stationary plots. Perhaps, some tendency of pregenerative individuals to cluster is due to the mosaic location of sites suitable for seed germination (small depressions with sufficient moisture, areas not occupied by vegetation, formed under the action of exogenous factors).

Effect of Residual Stress on Mode-I Stress Intensity Factor: A Quantitative Evaluation and a Suggestion of an Estimating Equation

An extensive literature review was conducted primarily to develop a comprehensive understanding of the quantitative behavior of residual stress (RS) distribution in weld joints. Based on prior data, various levels of the peak RS and distribution profiles were applied to a finite element analyses (FEA) model as an initial loading to evaluate the effect of RS on the Mode-I stress intensity factor (SIF), KI. The RS was not found to have a significant effect on the SIF values when the peak RS was less than 300 MPa. The enhanced effect of RS on the KI values was found to be more pronounced at a lower crack ratio, while the analytical form of the RS distribution had a minor effect. The effective boundary corrections function, FResidual, was derived under the effect of RS for 1T CT specimen for various crack geometries of the crack ratio, (a/W) of 0.2, 0.40, 0.50, 0.60, and 0.75, and with peak RS varying from 100 MPa to 600 MPa. The obtained effective function of KI can be employed to quantitatively evaluate the effect of RS on the fatigue performance of welded joints.

Nonparametric Threshold Estimation for Drift Function in Jump–Diffusion Model of Interest Rate Using Asymmetric Kernel

The existing estimators for the drift coefficient in the diffusion model with jumps involve jump components and possess larger boundary error. How to effectively estimate the drift function is an important issue that faces challenges and has theoretical significance. In this paper, the gamma asymmetric kernel for boundary correction and threshold function eliminating jump impacts are combined to estimate the unknown drift coefficient in the jump diffusion process of interest rate. The asymptotic large sample property and the better finite sample property through the Monte Carlo numerical simulation experiment and the empirical analysis of SHIBOR and LIBOR for the corresponding estimator are considered in detail. It is found that the estimator proposed in this paper can correct the estimation error near or far away from the origin point, which provides a more asymptotic unbiased estimator for the drift function in diffusion models with jumps.