Rejection Method Research Articles

Optimum design of uniform and non-uniform infill-coated structures with discrete variables

This article introduces a novel computer-aided procedure to design optimised coated structures with precise shell thickness control using the Smallest Univalue Segment Assimilating Nucleus operator and a novel augmentation-projection technique. Structures with heterogeneous sections, or coated structures, combine two different materials for the nucleus and the shell, which are generally chosen so that the material in the infill is lighter and the material in the coating is stiffer, which in this work are supposed homogeneous. Solving the interface problem requires material properties interpolation equations that consider three material phases, accurate placement of the coating over the base material, and precise control over the coating's thickness. The formation of the coating is controlled by the Smallest Univalue Segment Assimilating Nucleus, an edge detection operator developed in Digital Image Processing. The coating's thickness is controlled by an innovative methodology consisting of the projection of an augmented contour field, which is shown to create a constant thickness coating around the material domain. The optimisation problem is solved with the Sequential Element Rejection and Admission method. The validity of the procedure has been verified by solving various numerical application examples.

Transcript analysis of uterus transplant cervical biopsies using the Banff Human Organ Transplant panel

Uterus transplantation is being more widely implemented in clinical practice. Monitoring of rejection is routinely done for cervical biopsies and is dependent on histopathological assessment, as rejections are clinically silent and nonhistological biomarkers are missing. Until this gap is filled, it is important to corroborate the histopathological diagnosis of rejection through independent methods such as gene expression analysis. In this study, we compared our previously published scoring system for grading rejection in uterus transplant cervical biopsies to the gene expression profile in the same biopsy. For this, we used the Banff Human Organ Transplant gene panel to analyze the expression of 788 genes in 75 paraffin-embedded transplant cervical biopsies with a spectrum of histologic findings, as well as in 24 cervical biopsies from healthy controls. We found that gene expression in borderline changes did not differ from normal transplants, whereas the genes with increased expression in mild rejections overlapped with previously published rejection-associated transcripts. Moderate/severe rejection samples showed a gene expression pattern characterized by a mixture of rejection-associated and tissue injury–associated genes and a decrease in epithelial transcripts. In summary, our findings support our proposed scoring system for rejection but argue against the treatment of borderline changes.

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom.

Viruses that infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood-free rejection sampling, to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available, we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R 0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

Super-twisting ADRC for maximum power point tracking control of photovoltaic power generation system based on non-linear extended state observer

This paper proposed a new method for maximum power point tracking in photovoltaic power generation systems by combining super-twisting sliding mode control and active disturbance rejection method. An incremental guidance method is used to find the point of maximum power. The non-linear extended state observer is applied to estimate the unmodeled dynamics and external disturbance. The ADRC based on a super-twisting sliding mode is designed to bring the state variables to the desired state. In the next step, the stability of NESO and ADRC are theoretically proved. Finally, the simulation results have been compared with the results of the PI controller, classical sliding mode control, and terminal sliding mode control (TSMC) presented in other articles. The results show the effectiveness and superiority of the proposed method.Also, to check the performance of the proposal method in real-time, real-time results have been compared with non-real-time results. The results obtained from the real-time and non-real-time simulations exhibited a minimal difference. This fact indicates the high accuracy of the modeling and simulations performed. Indeed, the mathematical models and non-real-time simulations have been able to accurately mimic the actual behavior of the photovoltaic system under various operating conditions.

Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen.

Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.

Sampling and filtering with Markov chains

A new continuous-time Markov chain rate change formula is proven. This theorem is used to derive existence and uniqueness of novel filtering equations akin to the Duncan–Mortensen–Zakai equation and the Fujisaki–Kallianpur–Kunita equation but for Markov signals with general continuous-time Markov chain observations. The equations in this second theorem have the unique feature of being driven by both the observations and the process counting the observation transitions. A direct method of solving these filtering equations is also derived. Most results apply as special cases to the continuous-time Hidden Markov Models (CTHMM), which have become important in applications like disease progression tracking, The corresponding CTHMM results are stated as corollaries. Finally, application of our general theorems to Markov chain importance sampling, rejection sampling and branching particle filtering algorithms is also explained and these are illustrated by way of disease tracking simulations.

Combined electrocoagulation/flotation technique and membrane desalination for textile wastewater reuse

A novel integrated system that combined electrocoagulation/flotation (ECF) technique with membrane desalination was used for textile wastewater treatment to reduce the environmental pollution of textile wastewater. Two scenarios were examined: the first is ECF followed by membrane desalination; the second is the application of membrane desalination for treatment of raw wastewater. Iron electrodes were used in batch-wise tests to examine the effects of electrolysis time and current intensity on percentage removals. The SiO2/PA(TFC) membrane was used to treat raw wastewater and ECF effluents. As the current intensity increased from 50 mA to 600 mA, the color elimination was improved from 94 % to 99 % and the COD elimination improved from 59.1 % to 81.5 %. The findings demonstrate the efficacy of ECF in removing color and COD, although the treated wastewater sample's TDS increased from 4200 mg/L to 19,400 mg/L (raw textile wastewater sample). The SiO2/PA(TFC) membrane displays the salt rejection and water flux reduction of the raw wastewater samples 95 %, and 22 (L/m2.h). Corresponding results for ECF treated wastewater were 94, 93, 91 % and 13, 11.5, and 10 (L/m2.h), respectively. The SiO2/PA(TFC) membrane displays the reduction of COD from 760 mg O2/L (raw) and 310 mg O2/L (ECF effluent) to zero%. Also, it proves the capabilities of SiO2/PA(TFC) membrane for the removal of TDS, COD and color. This integrated system can provide sustainable source for fresh water supply used for landscape, irrigation, industry and various purposes.

SECRET: Statistical Emulation for Computational Reverse Engineering and Translation with applications in healthcare

There have been impressive advances in the physical and mathematical modelling of complex physiological systems in the last few decades, with the potential to revolutionise personalised healthcare with patient-specific evidence-based diagnosis, risk assessment and treatment decision support using digital twins. However, practical progress and genuine clinical impact hinge on successful model calibration, parameter estimation and uncertainty quantification, which calls for novel innovative adaptions and methodological extensions of contemporary state-of-the-art inference techniques from Statistics and Machine Learning. In the present study, we focus on two computational fluid-dynamics (CFD) models of the blood systemic and pulmonary circulation. We discuss state-of-the-art emulation techniques based on deep learning and Gaussian processes, which are coupled with established inference techniques based on greedy optimisation, simulated annealing, Markov Chain Monte Carlo, History Matching and rejection sampling for computationally fast inference of unknown parameters of the CFD models from blood flow and pressure data. The inference task was set as a competitive challenge which the participants had to conduct within a limited time frame representative of clinical requirements. The performance of the methods was assessed independently and objectively by the challenge organisers, based on a ground truth that was unknown to the method developers. Our results indicate that for the systemic challenge, in which an idealised case of noise-free data was considered, the relative deviation from the ground-truth in parameter space ranges from 10−5% (highest-performing method) to 3% (lowest-performing method). For the pulmonary challenge, for which noisy data was generated, the performance ranges from 0.9% to 7% deviation for the parameter posterior mean, and from 35% to 570% deviation for the parameter posterior variance.

Blood stored in EDTA tubes provides accurate peanut basophil activation test results for 48 hours

BackgroundThe peanut basophil activation test (BAT) has demonstrated excellent diagnostic accuracy with heparinized blood, but its clinical utility is limited by the short stability of samples stored in this anticoagulant. ObjectiveUsing EDTA anticoagulated blood, these investigations determined if Peanut BAT sample stability can be extended to 2 days, the minimum stability requirement for diagnostic tests currently offered through American reference laboratories. MethodsPeanut non-allergic control (NAC), peanut IgE sensitized (PS), and peanut allergic (PA) children aged 6 months through 17 years were recruited from members of Kaiser Permanente Southern California. EDTA anti-coagulated blood samples were collected from participants, shipped to a centralized laboratory, and stored at 4oC for peanut BAT testing 1 and 2 days later. ResultsPeanut BAT results for 23 unblinded participants were used to establish sample rejection and interpretation criteria that were subsequently validated in a prospective double-blind study involving 112 additional children (39-NAC, 36-PS, 37-PA). Of 105 blinded blood samples tested on each study day, 88 (84%) day-1 and 90 (86%) day-2 peanut BAT results were considered interpretable, with diagnostic accuracies of 95.5% and 94.4%, respectively. Moreover, all interpretable PA results were considered positive (100% sensitivity). ConclusionUsing EDTA anti-coagulated blood samples collected remotely, 1 and 2 days before testing, study results highlight the favorable diagnostic performance characteristics of the peanut BAT and provide further evidence that the test could be readily operationalized for clinical use by interested commercial reference laboratories.

Speech Signal Enhancement with Integrated Weighted Filtering for PSNR Reduction in Multimedia Applications

This paper investigates the effectiveness of the Weighted Kalman Integrated Band Rejection (WKBR) method for enhancing speech signals in multimedia applications. Speech enhancement is crucial for improving the quality and intelligibility of audio in environments with varying noise types and levels. The WKBR method is evaluated across ten different noise scenarios, including white noise, babble noise, street noise, airplane cabin noise, and more. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Short-Time Objective Intelligibility (STOI) are used to quantify the enhancement. The results show significant improvements, with PSNR increasing from an average of 12.8 dB before enhancement to 21.9 dB after enhancement, MSE reducing from an average of 0.0179 to 0.0053, and STOI scores improving from an average of 0.58 to 0.75. These findings highlight the potential of WKBR as a powerful tool for speech signal enhancement, making it a promising solution for real-world multimedia applications where clear and intelligible speech is essential.

FPGA implementation and measurement of cusp-flattop hybrid filter shaping method for nuclear instruments

In the field of nuclear technology applications, digital multi-channel pulse amplitude analysis technology plays an indispensable role in information acquisition due to its high precision and sensitivity. However, the challenge of pulse pileup can compromise the accuracy of measurement results in this technology. While conventional filter shaping and pulse pileup processing methods can extract nuclear pulse feature information under mild pileup conditions, severe pileup can notably degrade spectral counting rate and resolution. This paper introduces a designed two-channel pulse pileup rejection method, termed the cusp-flattop hybrid filter forming algorithm, leveraging the unique features of cusp, such as high forming speed and minimal interference from pulse pileup. The two channels simultaneously process the collected signals and utilize their differences for data filtering and rejection. This approach enhances information content, improves data processing quality, and boosts the signal-to-noise ratio. Compared to three methods—three trapezoidal filter shaping algorithms, the triangle trapezoidal pulse shaping algorithm, and the pulse width discrimination algorithm—our proposed method refines the shaping threshold of pulse pileup rejection. It accurately distinguishes and extracts effective pulse amplitudes even under severe pileup conditions, generating energy spectrum results while accounting for counting rate and resolution. Building on this, we construct a digital multichannel pulse amplitude analysis system based on FPGA (Field Programmable Gate Array) and use a national standard alloy sample, YSBS41346, for testing. The system can generate energy spectrum correctly, consistent with the actual sample content. We evaluate the system's performance by comparing the peak counting rate and resolution of the energy spectrum generated by the cusp-flattop hybrid filter forming algorithm and the trapezoidal forming algorithm. A high peak counting rate represents the maximum nuclear pulse data captured by the system, while low resolution indicates more valid data after pileup rejection. Through testing, the peak counting rate reaches 12843cps, with a resolution of 6.68%, marking a significant improvement over the trapezoidal pulse forming algorithm. The system demonstrates satisfactory performance and operational effectiveness, aligning with the design objectives and presenting innovative and practical applications.

The Nonsingular Estimator for Exoplanet Orbits: An Unscented Batch Estimation Method for Direct Imaging Measurements

We present a new method for fitting exoplanet orbits to direct astrometric measurements, using nonlinear batch estimation and nonsingular orbital elements. Our estimation technique is based on the unscented transform, which approximates probability distributions using finite, deterministic sets of weighted sample points. Furthermore, we use Gaussian mixtures to account for the strong nonlinearities in the measurement model. As a fitting basis, we use a set of orbital elements developed specifically for directly observed exoplanets, combining features of the Thiele–Innes constants and the Cohen–Hubbard nonsingular elements. We validate the new method using simulated exoplanet orbits, and we demonstrate its use with real exoplanet data. Compared to state-of-the-art Markov Chain Monte Carlo and Bayesian rejection sampling techniques, the new method is found to give orbit estimates of comparable or higher accuracy but with much faster execution.

Rejection and Compensation of Periodic Disturbance in Control Systems

in this paper, a brief introduction to the disturbance rejection methods is given in general and to the periodic disturbance rejection methods in particular. Therefore in the following, methods of using only feedback technique to achieve both set point tracking and periodic disturbance rejection are presented. Also, the interaction problem between the set point design demands and the periodic disturbance rejection is discussed. Then, the interaction is minimized by using a separate feed-forward controller to reject the periodic disturbances as an add-on compensator to the pre-existing set point tracking feedback controller. Furthermore, the idea of the disturbance observer is introduced in general as well as the adaptive periodic disturbance cancelation method.

Inspection method of the corrosion rate for underwater grouting sleeves by integrating ultrasonic data augmentation and interpretable ensemble learning

To addresses the challenge of underwater grouting sleeve corrosion affecting bridge service life, we propose an innovative inspection approach by integrating a modified ultrasonic testing instrument, ultrasonic data augmentation method, and an improved ensemble learning prediction model. Firstly, the ultrasonic data was collected from grouting sleeve specimens by the modified instrument. Secondly, a data generation model, incorporating Bray Curtis distance and rejection sampling algorithm, was developed to generate samples and filter abnormal data. Then, Circle Chaotic Map improved Grey Wolf Optimizer was proposed to optimize parameters from the Random Forest (RF) algorithm for establishing the prediction model. Finally, SHapley Additive exPlanations (SHAP) method was used to interpret the model globally and locally. Experimental results validate the effectiveness of the data augmentation method, ensuring high-quality ultrasonic data for accurate corrosion rate predictions. The proposed inspection method provide technical support for assessing remaining bridge bearing capacity and service life, showcasing high accuracy, interpretability, and noise tolerance.

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging.

Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data. The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles. In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression. Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

Optimizing EEG ICA decomposition with data cleaning in stationary and mobile experiments

Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to more and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used to remove these artifacts. It is standard practice to remove artifactual samples before ICA to improve the decomposition, for example using automatic tools such as the sample rejection option of the AMICA algorithm. However, the effects of movement intensity and the strength of automatic sample rejection on ICA decomposition have not been systematically evaluated. We conducted AMICA decompositions on eight open-access datasets with varying degrees of motion intensity using varying sample rejection criteria. We evaluated decomposition quality using mutual information of the components, the proportion of brain, muscle, and 'other' components, residual variance, and an exemplary signal-to-noise ratio. Within individual studies, increased movement significantly decreased decomposition quality, though this effect was not found across different studies. Cleaning strength significantly improved the decomposition, but the effect was smaller than expected. Our results suggest that the AMICA algorithm is robust even with limited data cleaning. Moderate cleaning, such as 5 to 10 iterations of the AMICA sample rejection, is likely to improve the decomposition of most datasets, regardless of motion intensity.

The Transformed MG-Extended Exponential Distribution: Properties and Applications

Our research paper introduces a newly developed probability distribution called the transformed MG-extended exponential (TMGEE) distribution. This distribution is derived from the exponential distribution using the modified Frechet approach, but it has a more adaptable hazard function and unique features that we have explained in detail. We conducted simulation studies using two methods: rejection sampling and inverse transform sampling, to produce summaries and show distributional properties. Moreover, we applied the TMGEE distribution to three real datasets from the health area to demonstrate its applicability. We used the maximum likelihood estimation technique to estimate the distribution’s parameters. Our results indicate that the TMGEE distribution provides a better fit for the three sets of data as compared to nine other commonly used probability distributions, including Weibull, exponential, and lognormal distributions.

Optimized rejection sampling for estimating facies probabilities from seismic data

Seismic inversion for facies has nonunique solutions. There are invariably many vertical facies arrays that are consistent with both a data trace and the prior information. Stochastic sampling algorithms set within a Bayesian framework can provide an estimate of the posterior probability distribution of facies arrays by finding the arrays with relatively high posterior probabilities for each data trace. Sample-by-sample facies probabilities can be estimated by measuring the proportions of each facies type at each sample location from the set of posterior facies arrays. To enable the estimation of probabilities of facies mixtures and to obtain high-quality images of facies probability curves, facies must be modeled at high resolution. The facies arrays, or vectors, on which the sampling algorithm operates, must also be long enough to allow for vertical coupling caused by the wavelet. This results in very large sample spaces. The posterior probability distribution is highly nonconvex, which, combined with the large sample space, severely challenges conventional stochastic sampling methods in obtaining convergence of the estimated posterior distribution. The posterior sets of vectors from conventional methods tend to be either correlated or have low predictabilities, resulting in biased or noisy facies probability estimates, respectively. However, accurate estimates of facies probabilities can be obtained from a relatively small number of posterior facies vectors (about 100), provided that they are uncorrelated and have high predictabilities. Full convergence of the posterior distribution is not required. A hybrid algorithm optimized rejection sampling can be designed specifically for the seismic facies probability inversion problem by combining independent sampling of the prior, which ensures posterior vectors are uncorrelated, with an optimization step to obtain high predictabilities. Tests on both real and synthetic data demonstrate better results than conventional rejection sampling and Markov chain Monte Carlo methods.

Use of directed quasi-metric distances for quantifying the information of gene families

A large hindrance to analyzing information in genetic or protein sequence data has been a lack of a mathematical framework for doing so. In this paper, we present a multinomial probability space X as a general foundation for multicategory discrete data, where categories refer to variants/alleles of biosequences. The external information that is infused in order to generate a sample of such data is quantified as a distance on X between the prior distribution of data and the empirical distribution of the sample. A number of distances on X are treated. All of them have an information theoretic interpretation, reflecting the information that the sampling mechanism provides about which variants that have a selective advantage and therefore appear more frequently compared to prior expectations. This includes distances on X based on mutual information, conditional mutual information, active information, and functional information. The functional information distance is singled out as particularly useful. It is simple and has intuitive interpretations in terms of 1) a rejection sampling mechanism, where functional entities are retained, whereas non-functional categories are censored, and 2) evolutionary waiting times. The functional information is also a quasi-metric on X, with information being measured in an asymmetric, mountainous landscape. This quasi-metric property is also retained for a robustified version of the functional information distance that allows for mutations in the sampling mechanism. The functional information quasi-metric has been applied with success on bioinformatics data sets, for proteins and sequence alignment of protein families.

Multiobjective History Matching Using Machine Learning Proxies-Assisted Iterative Rejection Sampling

Summary The effectiveness of subsurface assessment for various field development scenarios relies on accurately quantifying uncertainties during production forecasts. This study proposes a new design-of-experiment (DoE)-based multiobjective history matching (HM) workflow using machine learning (ML) proxies-assisted iterative rejection sampling (IRS). The HM target is to improve the HM outcome of all the objectives simultaneously and attain a series of reliable simulation models for production forecasting after a predefined number of iterations. The IRS workflow takes full advantage of various proxies to efficiently explore the solution space via Monte Carlo (MC) sampling. The MC samples are iteratively rejected according to each objective function, and the new simulation designs for the next iteration are intentionally selected from the accepted MC samples to best preserve the subsurface uncertainty. The strengths of IRS over the other two optimizers commonly used in the oil and gas industry, genetic algorithm (GA) and particle swarm optimization (PSO), are validated through three applications—an analytic six-parameter three-objective minimization problem, a complex 120-parameter five-objective HM for Brugge field, and a 41-parameter three-objective HM study for an offshore asset in the Gulf of Mexico. By comparing the performance against GA and PSO, this study demonstrates that IRS is able to reduce the values of all the objectives rapidly and guarantees a better posterior distribution of simulation samples in the parameter space. Thus, IRS would yield a series of reliable simulation models with a close match to the observation data and preserve a high diversity of model parameters. These models can play an important role in modern reservoir management to facilitate the decision-making process. In addition, the IRS algorithm itself only costs 0.2–0.5% of the computational resource and 1.4–12.0% of the waiting time, according to the field study. To the best of our knowledge, the IRS workflow is the first one that integrates various types of ML proxies into the multiobjective HM process in the literature.