Rejection Method Research Articles

Rates and Reasons of Laboratory Sample Rejection due to Pre-analytical Errors in Clinical Settings.

Laboratory analysis errors in procedure or interpretation may be seen during the process of completing physician test orders. They may also result in rejection of the requests due to some applicability reasons. Hence, this study was carried out to determine the rate and reasons for such rejections in clinical settings. This cross-sectional comparative study was performed on 104008 laboratory tests in a one-year period in terms of the percentage and type of errors that occurred in Shahid Bahonar Hospital in Kerman, Iran, in 2018. The types of studied errors included hemolysis, sample clotting, insufficient sample size, and mistakes in labels or absence of labels on the sample. In this study, 104008 laboratory tests were performed, with 2299 (2.21%) sample rejections, 456 (32.31%) complete blood count (CBC) sample clotting; 417 (29.38 %) hemolysis; and 150 (17.47 %) inadequate sample volume as the majority of errors. There was no statistically significant relationship between pre-analysis errors and clinical aspects (P=0.124). According to the results, it may be concluded that considering the high prevalence of laboratory errors in comparison with the majority of other studies, continuous training courses and determination of the causes of these errors are crucial to attaining better function and basic knowledge.

Novel antibacterial and antifouling PES nanofiltration membrane incorporated with green synthesized nickel-bentonite nanoparticles for heavy metal ions removal

• The nickel-bentonite nanoparticles (NBNPs) were synthesized using plant extract. • The NBNPs were used to modifying the polyethersulfone membrane. • The modified membranes exhibited significant water flux and antifouling properties. • The NBNPs/PES membranes showed high rejection of heavy metal ions than PES. • The NBNPs/PES membranes exhibited desirable antibacterial activity. Lack of water resources and water pollution by heavy metal ions and microorganisms has made it necessary to utilize efficient water treatment methods. In this study, for the first time, nickel-bentonite nanoparticles (NBNPs) were synthesized using Scrophularia striata extract. NBNPs were used as novel nanofillers to fabricate antifouling and antibacterial NBNPs/polyethersulfone (PES) nanofiltration membranes via the phase inversion method for heavy metal ions rejection. NBNPs were characterized by FT-IR, XRD, zeta potential, EDS, FESEM, and TEM. The fabricated membranes were characterized using FT-IR, XRD, EDS, SEM, water contact angle, zeta potential, and porosity analyses. The results showed improvement of hydrophilicity, surface charge, and porosity for the modified membranes. The blended membranes had substantially less fouling and higher heavy metal ions rejection than the unmodified membrane. Pure water flux of the unfilled PES membrane was enhanced from 15 to 34.4 and 58.8 kg/m2h for 0.1 and 0.5 wt% of NBNPs, respectively. The flux recovery ratio was increased from 38.3% for the unmodified membrane to 98.2% for 0.5 wt% NBNPs/PES membrane. Also, 0.5 wt% NBNPs/PES membrane exhibited the best rejection performance (Zn2+: 98.62%, Cu2+: 97.88% and Pb2+: 97.03%). Evaluation of antibacterial properties of the membranes against Escherichia coli and Staphylococcus aureus was performed. The modified membranes had significant antibacterial capability and 1 wt% NBNPs/PES membrane exhibited the highest antibacterial rate. According to the findings, NBNPs could be used as nanofillers to fabricate modified nanofiltration membranes for heavy metal ions rejection and antibacterial ability.

Practical Improvements to Generator Parameter Validation Using Stator-Decrement Test

This article discusses the accuracy issue in applying the commonly used stator-decrement test (load rejection) method for validating generator d-axis parameters. Theoretical analysis and actual examples are provided to illustrate how the accuracy issue is related to the rotor field winding current as well as hysteresis, which has not been analyzed in detail for the second-order generator models widely used in power system studies. A generic, practical method for validating generator synchronous inductance is proposed and proven with actual field measurements and model validation study results using the GENQEC model. The generator field current compensation factor of the GENQEC model improves the field current accuracy independently of magnetic saturation. Its advantages are demonstrated in model parameter validation using field measurement results from in-service salient-pole and round-rotor synchronous generators. The proposed method with the GENQEC dynamic model can be used to establish a baseline for accurately validating generator direct-axis parameters using stator-decrement test results. The innovative method of validating generator synchronous d-axis reactance proposed in this article can be a practical and reliable alternative to the test methods provided by IEEE standards.

Deep learning based optimization under uncertainty for surfactant-enhanced DNAPL remediation in highly heterogeneous aquifers

The optimization for surfactant-enhanced aquifer remediation (SEAR) of dense non-aqueous phase liquid (DNAPL)-contaminated aquifers is usually accompanied by uncertainties, which may arise from the characterization of complex aquifer heterogeneity and DNAPL source zone architecture (SZA) due to measurement sparsity. Optimization under uncertainty is computationally expensive as it involves an enormous number of model runs. The huge computational burden can be alleviated by utilizing a surrogate model for repeated model evaluations. However, most of the developed surrogates are often limited to low-dimensional optimization problems that only consider simplified aquifer heterogeneity. In this study, we developed a multi-objective simulation-optimization framework to optimize the SEAR schemes considering the characterization uncertainties from both highly heterogeneous aquifer permeability and complex SZA. A fast-to-run convolutional neural network (CNN)-based surrogate model was developed to approximate the high-dimensional and highly complex input-output mapping of the DNAPL multiphase flow simulation model. We first used the rejection sampling strategy to generate random realizations of permeability and SZA conditioning on their limited measurements and then formulated a multi-objective optimization under uncertainty problem based on these realizations. The developed 3-D CNN was trained and used as the surrogate for repeated model runs in optimization to identify the optimal SEAR schemes under uncertainty. A 3-D numerical experiment was used to test the performance of the CNN-based simulation-optimization framework. Comprehensive analysis on the obtained Pareto fronts demonstrates that the proposed framework can efficiently identify reliable Pareto-optimal solutions with a 99.8% speedup compared to the traditional optimization coupled with the forward model. Moreover, the optimization considering multiple realizations enables us to perform the risk assessment to locate the risk zone where the NAPL phase possibly exists after remediation, which provides useful information for decision-making.

Electron Microscopy Reveals Evidence of Perinuclear Clustering of Mitochondria in Cardiac Biopsy-Proven Allograft Rejection.

Acute cellular rejection is a major complication in heart transplantation. We focus on the analysis of new ultrastructural findings in cardiac biopsy rejection based on mitochondrial intracellular organization. This study includes heart transplanted patients from a single center who were referred for endomyocardial biopsies as a scheduled routine screening. Participants were divided into two groups: patients transplanted without allograft rejection (Grade 0R), and patients with biopsy-proven allograft rejection (Grade ≥ 2R). Using electronic microscopy, we detected a significant increase in the volume density of mitochondria (p < 0.0001) and dense bodies (p < 0.01) in the rejection group. The most relevant finding was the presence of local accumulations of mitochondria close to the nuclear envelope, pressing and molding the morphology of this membrane in all rejection samples (100%). We identified this perinuclear clustering of mitochondria phenomenon in a 68 ± 27% of the total cardiac nucleus observed from rejection samples. We did not observe this phenomenon in any non-rejection samples, reflecting excellent sensitivity and specificity. We have identified a specific phenomenon affecting the architecture of the nuclear membrane—perinuclear clustering of mitochondria—in endomyocardial biopsies from patients with cardiac rejection. This ultrastructural approach might complement and improve the diagnosis of rejection.

Algorithms for inference in SVARs identified with sign and zero restrictions

Summary I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms proposed in Amir-Ahmadi and Drautzburg (2021) to check whether the identified set is nonempty and to sample from the identified set without rejection sampling. I compare the new algorithms to alternatives by applying them to variations of the model considered by Arias et al. (2019a), who estimate the effects of US monetary policy using sign and zero restrictions on the monetary policy reaction function. The new algorithms are particularly useful when a rich set of sign restrictions substantially truncates the identified set given the zero restrictions.

Evaluation of the performance of the pre-analytical phase of the testing process in medical laboratory accreditation

Aim: For the accreditation of a medical laboratory, it is necessary to evaluate the quality indicators (QI) used to evaluate pre-analytical process performance and establish an infrastructure to prevent errors arising from outside the laboratory. We aimed to present the quality indicators to prove the pre-analytical process performance of a medical (clinical) laboratory that has a large workforce prepared for medical laboratory accreditation.Methods: The sample rejection criteria were defined for the pre-analytical process. QIs, which are the requirements of the ISO15189 standard, was determined. QIs were estimated both as percentages and process Sigma levels. Pareto charts presented the distribution of errors.Results: QI values calculated as “%” and “Sigma” levels consistently demonstrated performances. According to 80% cumulated percentages, the Pareto charts rankings were “haemolysed,” “coagulated,” “barcode error,” and “insufficient” samples. In addition, when Pareto charts were evaluated, it was seen that the first 2 reasons in the 6-month period were “hemolysis” and “clotted samples” in all months. Still, the third most common reason was found to vary between “barcode error” and “insufficient” samples.Conclusion: Because of the consistency between % and sigma values, QIs can be presented with one of these in showing laboratory pre-analytical processes. However, sigma values give a more general view, and performance can be easily monitored between months. Pareto charts help illustrate error distribution and provide information for continuous improvement in laboratory-related healthcare.

Power Transformer Voltages Classification with Acoustic Signal in Various Noisy Environments.

Checking the stable supply voltage of a power distribution transformer in operation is an important issue to prevent mechanical failure. The acoustic signal of the transformer contains sufficient information to analyze the transformer conditions. However, since transformers are often exposed to a variety of noise environments, acoustic signal-based methods should be designed to be robust against these various noises to provide high accuracy. In this study, we propose a method to classify the over-, normal-, and under-voltage levels supplied to the transformer using the acoustic signal of the transformer operating in various noise environments. The acoustic signal of the transformer was converted into a Mel Spectrogram (MS), and used to classify the voltage levels. The classification model was designed based on the U-Net encoder layers to extract and express the important features from the acoustic signal. The proposed approach was used for its robustness against both the known and unknown noise by using the noise rejection method with U-Net and the ensemble model with three datasets. In the experimental environments, the testbeds were constructed using an oil-immersed power distribution transformer with a capacity of 150 kVA. Based on the experimental results, we confirm that the proposed method can improve the classification accuracy of the voltage levels from 72 to 88 and to 94% (baseline to noise rejection and to noise rejection + ensemble), respectively, in various noisy environments.

Exploring Chemical Markers Related to the Acceptance and Sensory Profiles of Concentrated Liquid Coffees: An Untargeted Metabolomics Approach.

In this study, we aimed to apply an untargeted LC/QTOF-MS analysis for the identification of compounds that positively and negatively affect the acceptance of coffee beverages from liquid coffee concentrates (CLCs) before and after storage. The metabolomic results were integrated with physicochemical and sensory parameters, such as color, pH, titratable acidity, and oxygen contents, by a bootstrapped version of partial least squares discriminant analysis (PLS-DA) to select and classify the most relevant variables regarding the rejection or acceptance of CLC beverages. The OPLS-DA models for metabolite selection discriminated between the percent sensory acceptance (the Accepted group) and rejection (the Rejected group). Eighty-two molecular features were considered statistically significant. Our data suggest that coffee sample rejection is associated with chlorogenic acid hydrolysis to produce ferulic and quinic acids, consequently generating methoxybenzaldehydes that impact the perceived acidity and aroma. Furthermore, acceptance was correlated with higher global scores and sweetness, as with lactones such as feruloyl-quinolactone, caffeoyl quinolactone, and 4-caffeoyl-1,5-quinolactone, and significant oxygen levels in the headspace.

BUAK-AIS: Efficient Bayesian Updating with Active learning Kriging-based Adaptive Importance Sampling

Bayesian updating provides a sound mathematical framework for probabilistic calibration as new information emerges. Bayesian Updating with Structural reliability methods (BUS) reformulates the acceptance domain in rejection sampling as a failure domain in reliability analysis, offering considerable potential for higher efficiency and accuracy. Kriging-based Monte Carlo Simulation has been studied to facilitate the application of BUS for problems with expensive-to-evaluate likelihood functions. Nevertheless, as the implementation of BUS often involves a rare event, the number of required Monte Carlo samples can become unaffordable. This gap is addressed here through Bayesian Updating with Active learning Kriging-based Adaptive Importance Sampling (BUAK-AIS). An importance sampling density based on Gaussian mixture distribution is introduced, and the discrepancy between the adopted and theoretically best sampling densities is measured through the Kullback–Leibler cross entropy. The proposed method includes an active learning framework that adaptively extends the training set and optimizes the parameters of the Gaussian mixture distribution based on the cross entropy and the current Kriging model. As BUS uses accepted samples to estimate the posterior distribution, the present work discusses the estimate for the first moment of the posterior distribution, and proposes a criterion to check the sufficiency of the number of accepted samples to guarantee robust estimations. A new stopping criterion is also developed by quantifying the error introduced by Kriging. Three numerical examples and an engineering application concerning model updating of cable-stayed bridges in the construction process are investigated, demonstrating the efficiency and accuracy of the proposed method.

Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy

Machine learning supports prediction and inference in multivariate and complex datasets where observations are spatially related to one another. Frequently, these datasets depict spatial autocorrelation that violates the assumption of identically and independently distributed data. Overlooking this correlation result in over-optimistic models that fail to account for the geographical configuration of data. Furthermore, although different data split methods account for spatial autocorrelation, these methods are inflexible, and the parameter training and hyperparameter tuning of the machine learning model is set with a different prediction difficulty than the planned real-world use of the model. In other words, it is an unfair training-testing process. We present a novel method that considers spatial autocorrelation and planned real-world use of the spatial prediction model to design a fair train-test split. Demonstrations include two examples of the planned real-world use of the model using a realistic multivariate synthetic dataset and the analysis of 148 wells from an undisclosed Equinor play. First, the workflow applies the semivariogram model of the target to compute the simple kriging variance as a proxy of spatial estimation difficulty based on the spatial data configuration. Second, the workflow employs a modified rejection sampling to generate a test set with similar prediction difficulty as the planned real-world use of the model. Third, we compare 100 test sets' realizations to the model's planned real-world use, using probability distributions and two divergence metrics: the Jensen-Shannon distance and the mean squared error. The analysis ranks the spatial fair train-test split method as the only one to replicate the difficulty (i.e., kriging variance) compared to the validation set approach and spatial cross-validation. Moreover, the proposed method outperforms the validation set approach, yielding a minor mean percentage error when predicting a target feature in an undisclosed Equinor play using a random forest model. The resulting outputs are training and test sets ready for model fit and assessment with any machine learning algorithm. Thus, the proposed workflow offers spatial aware sets ready for predictive machine learning problems with similar estimation difficulty as the planned real-world use of the model and compatible with any spatial data analysis task.

A point-wise artifact rejection method for estimating transient-evoked otoacoustic emissions and their group delay.

Due to its low intensity, measurement of transient-evoked otoacoustic emission (TEOAE) requires repeated stimulation. When any acoustic artifact occurs, an entire click interval is typically abandoned. Here, a point-wise artifact rejection strategy is proposed, and it partially preserves the data when artifacts occur in an interval. At the noisiest setting (-46 dB signal-to-noise ratio) the proposed strategy retains four times more data and thereby reduces the root mean square signal estimation error by over 60%. Consequently, the group delay can be calculated more accurately. These findings might facilitate TEOAE measurement at home or in other noisy environments in the future.

A constant-time sampling algorithm for binary Gaussian distribution over the integers

Discrete Gaussian sampling over the integers is one of fundamental operations in lattice-based cryptography. The binary Gaussian distribution DZ+,σ2 is a special discrete Gaussian distribution with σ2=1/(2ln⁡2) and μ=0 over the set of non-negative integers Z+, and a sampling algorithm for DZ+,σ2 can be used as the base sampler in a generic algorithm based on rejection sampling for any discrete Gaussian distribution over the integers. We present a constant-time algorithm for sampling from the binary Gaussian distribution DZ+,σ2. It requires no precomputation storage and mainly relies on bitwise operations, which could be more hardware-friendly. Its computational complexity is lower than that of the algorithm based on the full-tree Knuth-Yao method, and its entropy consumption is smaller than that of the full-table access algorithm based on a cumulative distribution table. The Rényi-divergence based security analysis of our constant-time algorithm can also be simplified.

Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach.

A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural good, obtaining global information of its interior. While deep learning (DL) methods have outperformed state-of-the-art benchmarks in various applications, the reason for lacking adaptation of DL algorithms such as convolutional neural networks (CNNs) can be traced back to its high data inefficiency and the absence of annotated data. Active learning is a framework that has been heavily used in machine learning when the labelled instances are scarce or cumbersome to obtain. This is specifically of interest when the DL algorithm is highly uncertain about the label of an instance. By allowing the human-in-the-loop for guidance, a continuous improvement of the DL algorithm based on a sample efficient manner can be obtained. This paper seeks to study the applicability of active learning when grading ‘Galia’ muskmelons based on its shelf life. We propose k-Determinantal Point Processes (k-DPP), which is a purely diversity-based method that allows to take influence on the exploration within the feature space based on the chosen subset k. While getting coequal results to uncertainty-based approaches when k is large, we simultaneously obtain a better exploration of the data distribution. While the implementation based on eigendecomposition takes up a runtime of , this can further be reduced to based on rejection sampling. We suggest the use of diversity-based acquisition when only a few labelled samples are available, allowing for better exploration while counteracting the disadvantage of missing the training objective in uncertainty-based methods following a greedy fashion.

Analysis of the kinematic boundaries of the quasielastic neutrino-nucleus cross section in the superscaling model with a relativistic effective mass

In this work we obtain the analytical expressions for the boundaries of the charged current quasielastic (CCQE) double differential cross section in terms of dimensionless energy and momentum transfers, for the Relativistic Fermi Gas (RFG) and the Superscaling approach with relativistic effective mass (SuSAM*) models, within the scaling formalism. In addition, we show that this double differential cross section in the scaling formalism has very good properties to be implemented in the Monte Carlo (MC) neutrino event generators, particularly because its peak is almost flat with the (anti)neutrino energy. This makes it especially well suited for the event generation by the acceptance-rejection method usually used in the neutrino generators. Finally, we analyze the total CCQE cross section $\ensuremath{\sigma}({E}_{\ensuremath{\nu}})$ for both models and attribute the enhancement observed in the SuSAM* total cross section to the high-momentum components which are present, in a phenomenological way, in its scaling function, while these are absent in the RFG model.

Pre-Analytical Error Cost Analysis of 10,732 Contaminated Urine Culture Samples.

Unlike other pre-analytical (PA) phase errors, in our country Social Security Institution (SSI) reimburses urine samples rejected due to contamination in the form of a finalized test. The primary purpose was to estimate the direct financial loss created by contaminated urine sampling. We want to encourage its prioritization among corrective and preventive actions of laboratory PA management. We examined the urine samples which were sent to Istanbul Anatolian North Central Laboratory retrospectively. The central laboratory serves the community of 6,400-bed hospitals. We made direct financial loss calculations with material-, labor-, and waste-related financial elements. We rejected 10,732 of 138,834 samples due to contamination. With the 2019 Euro Index, the cost for each urine sample rejected due to contamination was calculated as € 2.98. It is the first cost study where the prices are in human resources, material, waste cost, and the amount reimbursed by SSI as direct cost elements per rejection sample. Highlighting the cost elements in establishing health policies predicted due to their striking feature may contribute to their prioritization in corrective and preventive actions. The urine culture test is the most commonly performed bacterial culture test. The determination of financial loss predicted that it would also reduce antibiotic resistance in healthcare by providing financial resource threshold information to develop improved methods. In addition, it will inspire new additions to the existing few detailed pre-analytical error cost studies.

Policy-Based Group Signature Scheme from Lattice

Although the existing group signature schemes from lattice have been optimized for efficiency, the signing abilities of each member in the group are relatively single. It may not be suitable for complex applications. Inspired by the pioneering work of Bellare and Fuchsbauer, we present a primitive called policy-based group signature. In policy-based group signatures, group members can on behalf of the group to sign documents that meet their own policies, and the generated signatures will not leak the identity and policies of the signer. Moreover, the group administrator is allowed to reveal the identity of signer when a controversy occurs. Through the analysis of application scenarios, we concluded that the policy-based group signature needs to meet two essential security properties: simulatability and traceability. And we construct a scheme of policy-based group signature from lattice through techniques such as commitment, zero-knowledge proof, rejection sampling. The security of our scheme is proved to be reduced to the module short integer solution (MSIS) and module learning with errors (MLWE) hard assumptions. Furthermore, we make a performance comparison between our scheme and three lattice-based group signature schemes. The result shows that our scheme has more advantages in storage overhead and the sizes of key and signature are decreased roughly by 83.13%, 46.01%, respectively, compared with other schemes.

The anomalous measurements selection in primary processing for the orbit determination from the «Kobalt-R» ground station trajectory information.

Algorithms for deciphering measurements, celestial-mechanical interpretation of the requested measurements of the range and radial velocity are presented. The method of the anomalous measurements rejection during their preliminary processing is presented.

Agglomerative clustering to improve the resolution of pseudo well stochastic seismic inversion: A case study

The Monte Carlo rejection and Monte Carlo Markov Chain methods are two common sampling techniques in Bayesian seismic inversion. Conventional methods of pseudo well stochastic seismic inversion for reservoir properties uses Monte Carlo rejection sampling of pseudo-wells to select those that best match seismic traces. The selected pseudo-wells do not influence the selection/rejection of subsequent pseudo-wells. The process requires a very large number of prior pseudo-wells to draw a reasonable outcome, which consumes a major part of the computation time. In conventional methods, the best-matched pseudo-wells often have dissimilarly shaped volume of shale logs, which can degrade the resolution of the outcome. Though Agglomerative clustering is well-known but the idea of grouping pseudo-wells based on similarly shaped volume of shale logs in pseudo well stochastic seismic inversion is unique. We propose an agglomerative clustering technique to group the prior pseudo-wells according to the shape of their volume of shale logs, before matching to seismic. The centroids of the pseudo-well clusters summarize the large number of prior pseudo-wells. The best-matched pseudo-well cluster centroid with seismic gives the most likely outcome while the two next best-matched cluster centroids give alternative results. The alternative results are useful to understand the uncertainty in the outcome. It has been observed that, the proposed method has reduced computation time (50–55% less than conventional pseudo well stochastic inversion). Meanwhile, the quantification of sand volume from the proposed method shows improvement over the conventional method. A comparison between the proposed and the conventional method has been shown on a synthetic wedge model and real seismic. In the wedge model (volume of sand is 0.65), the resultant volume of sand predicted in the modified method (0.7) is better than that predicted using the conventional method (0.8). The modified pseudo-well stochastic seismic inversion method has been applied on seismic data from deepwater Krishna-Godavari basin. The result shows an improvement in the characterization of thin shale layers within the reservoir.

CALCULATING PROBABILITY DENSITIES WITH HOMOTOPY AND APPLICATIONS TO PARTICLE FILTERS

We explore a homotopy sampling procedure and its generalization, loosely based on importance sampling, known as annealed importance sampling. The procedure makes use of a known probability distribution to find, via homotopy, the unknown normalization of a target distribution, as well as samples of the target distribution. We propose a reformulation of the method that leads to a rejection sampling alternative. Estimates of the error as a function of homotopy stages and sample averages are derived for the algorithmic version of the method. These estimates are useful in making computational efficiency decisions on how the calculation should proceed, given a computer architecture. Consideration is given to how the procedure can be adapted to Bayesian stationary and nonstationary estimation problems. The connection between homotopy sampling and thermodynamic integration is made. Emphasis is placed on the non-stationary problems, and in particular, on a sequential estimation technique known as particle filtering. It is shown that a modification of the particle filter framework to include the homotopy process can improve the computational robustness of particle filters.