Rejection Method Research Articles

Study of pre-analytical errors in clinical biochemistry laboratory in Dhiraj General hospital, Piparia

Introduction: Pre-analytical errors is the major source of mistakes in laboratory diagnostics. It includes from order of test by physician to sample is ready for analysis. Preanalytical errors are upto 70 % of errors of total diagnostic process. Objectives: 1. To evaluate the pre-analytical errors during pre-analytical testing process; 2. To formulate the possible corrective measures to be taken to minimise such errors. Materials and Methods: A prospective study was done for a period of 2 months from January 2022 to February 2022 in clinical biochemistry laboratory, central laboratory, Dhiraj general hospital, Piparia. All types of preanalytical errors were recorded. Total 15189 specimens were received during this period. Out of which 569 were sorted with pre analytical errors. Results: They were categorised as follows: Improper test request forms (n-91), Improper labelling (n-118), Insufficient sample (n-121), invalid samples (n-127), sample mix ups (n-18), Delay in sample transportation (n-32), Wrong timing for Collection (n-37), sample not received (SNR) (n-17), Sample from IV running area (n-08). Conclusion: The overall percentage of rejection found is 3.75 %. Hemolysis was the most common cause for sample rejection in clinical laboratory.

A New Secure Crowdfunding Transaction Scheme Based on Blockchain

Online crowdfunding, an innovative model based on “[Formula: see text]”, is a hot spot for financing via Internet. Crowdfunding based on blockchain is an emerging economic phenomenon and becomes one of the most advanced risk financing strategies. However, crowdfunding transactions face security threats due to identity leaks, quantum attacks and the untraceable nature of blind signatures, which facilitate criminal activity. Different from the previous works, which ignored the importance of traceability, in this paper, we establish a blockchain-empowered secure crowdfunding architecture and propose an anti-quantum partially blind signature algorithm based on the verifiable identity of both sides. Specially, for one thing, the private key decided by user identity is generated by lattice-based sample matrix, and the privacy of user identity can be ensured and traced by the rejection sampling theorem. For another thing, we design an improved krill herd algorithm (IKHA) to increase the credit factor of fundraisers for dealing with project investment issues. The simulation evaluates the correctness and effectiveness of our theoretical analyses. Compared with the current popular schemes, the proposed IKH algorithm has a higher convergence speed and can optimize investment efficiency.

Resolving icteric interference by a novel photoisomerization device: Bilibox

Icterus, a phenomenon caused by bilirubin elevation in the blood, is a common endogenous interference in chemistry testing, occurring either spectrally or through chemical reactivity with assay reagents. Often, laboratories have few options other than to dilute or reject samples exceeding icteric thresholds. However, recent studies have optimized in vitro photoisomerization of bilirubin to a 17-minute bilirubin half-life using 500 nm light at 37 °C. Using an enzymatic creatinine assay as a model, due to its prevalence in routine laboratory testing and susceptibility to icteric interference, our study explores the usage of in vitro photoisomerization by replicating these conditions in a device, the Bilibox, as means of resolving icterus in laboratory testing.Left-over icteric and non-icteric clinical samples, collected by lithium heparin vacutainer (n = 10), were analyzed for baseline creatinine, diluted creatinine (1:4 0.9 % NaCl), total bilirubin, direct bilirubin, and hemolysis, icterus and lipemia (HIL) indices. Samples were then placed in the Bilibox in two intervals of 30 min with repeat measurements of the aforementioned analytes.On average, icteric-index, total bilirubin (TBIL), and direct bilirubin (DBIL) decreased by 33.5, 39.1 and 39.9 % respectively following 30 min of Bilibox treatment; and by 47.6 %, 63.7 % and 59.8 % following 60 min. The average percent difference between the pre-exposure diluted and undiluted creatinine was 5.8 %, demonstrating the icterus interference. Following Bilibox treatment, the difference between undiluted (post-exposure) and diluted (pre-exposure) creatinine decreased to 0.02 % (p = 0.0232) and 2.2 % (p = 0.0021) at 30 and 60 min of treatment respectively, demonstrating resolution of interference. Consequently, photoisomerization can be utilized as an additional and reasonably quick method for resolving icterus when dilutional methods cannot be applied.

Integral, mean and covariance of the simplex-truncated multivariate normal distribution.

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution. In this paper, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution. For computations in low-dimensional distributions, the semi-analytical method is fast and thus should be considered. As the dimension increases, the Gessner et al. method becomes the only practically efficient approach of the methods tested here.

Guía de práctica clínica para toma de muestra de gases en sangre en México

In order to promote preanalytic blood gas sample quality and to standardize sample collection, transport, and preservation protocols, a multidisciplinary experts group developed Guía de práctica clínica para toma de muestra de gases en México (Clinical practice guidelines for blood gas sample collection in Mexico). It includes recommendations for request, identification and patient´s preparation, sampling indications, contraindications, simple type selection, anatomical site, use and selection of the optimal sampling materials, operating room sampling, complications, handling, transport, and simple rejection criteria.

HatchFrac: A fast open-source DFN modeling software

This paper introduces a comprehensive C++ software package, HatchFrac, for stochastic modeling of fracture networks in two and three dimensions. The inverse cumulative distribution function (CDF) and acceptance–rejection methods are applied to generate random variables from the stochastic distributions commonly used in discrete fracture network (DFN) modeling. The multilayer perceptron (MLP) machine learning approach, combined with the inverse CDF method, generates random variables following any sampling distribution. We extend the Newman–Ziff algorithm to determine clusters in the fracture networks and make the code faster. When combined with the block method, the coding efficiency is further enhanced. The software generates the T-type fracture intersections in the network by simulating a fracture growth process, which can be used in applications involving fracture growth or incorporating geomechanics. Three applications of HatchFrac are introduced to demonstrate the versatility of our software: percolation analysis, fracture intensity analysis, and flow and connectivity analysis.

The Computational Cost of Blocking for Sampling Discretely Observed Diffusions

Many approaches for conducting Bayesian inference on discretely observed diffusions involve imputing diffusion bridges between observations. This can be computationally challenging in settings in which the temporal horizon between subsequent observations is large, due to the poor scaling of algorithms for simulating bridges as observation distance increases. It is common in practical settings to use a blocking scheme, in which the path is split into a (user-specified) number of overlapping segments and a Gibbs sampler is employed to update segments in turn. Substituting the independent simulation of diffusion bridges for one obtained using blocking introduces an inherent trade-off: we are now imputing shorter bridges at the cost of introducing a dependency between subsequent iterations of the bridge sampler. This is further complicated by the fact that there are a number of possible ways to implement the blocking scheme, each of which introduces a different dependency structure between iterations. Although blocking schemes have had considerable empirical success in practice, there has been no analysis of this trade-off nor guidance to practitioners on the particular specifications that should be used to obtain a computationally efficient implementation. In this article we conduct this analysis and demonstrate that the expected computational cost of a blocked path-space rejection sampler applied to Brownian bridges scales asymptotically at a cubic rate with respect to the observation distance and that this rate is linear in the case of the Ornstein–Uhlenbeck process. Numerical experiments suggest applicability both of the results of our paper and of the guidance we provide beyond the class of linear diffusions considered.

Exact simulation of the first passage time through a given level of jump diffusions

Continuous-time stochastic processes play an important role in the description of random phenomena. It is therefore important to study particular stopping times dependent on the trajectories of these processes. Two approaches are possible: introducing an explicit expression of their probability distribution, and evaluating values generated by numerical models. Choosing the second alternative, we propose an algorithm to generate the first passage time through a given level. The stochastic process under consideration is a one-dimensional jump diffusion that satisfies a stochastic differential equation driven by a Brownian motion. It is subject to random shocks that are characterised by an independent Poisson process. The proposed algorithm belongs to the family of rejection sampling procedures: the outcome of the algorithm and the stopping time under consideration are identically distributed. This algorithm is based on both the exact simulation of the diffusion value at a fixed time and on the exact simulation of the first passage time for continuous diffusion processes (see Herrmann and Zucca (2019)). At a fixed point in time, the main challenge is to generate the position of a continuous diffusion that is conditioned not to reach a given level before that time. We present the construction of the algorithm and numerical examples. We also discuss specific conditions leading to the recurrence of a jump diffusion process.

Quality indicators to quality reports

Background - Good laboratory practice is based on the simple doctrine which states, “in a laboratory what moves, needs to be trained, what does not move needs to be calibrated, whatever happens, needs to be documented, and whatever is not documented has never happened’. These principles can be used to improve the quality indicators (QIs) and quality reports (QRs) and can potentiate the total testing process (TTP). Methods – We did a cross-sectional study where annual sample rejection rates from clinical biochemistry laboratories of two quaternary care hospitals in Southern India were compared with similar studies from developing and developed countries. Results – Annual sample rejection rate from laboratory 1 was 0.912%, and 1.69% from laboratory 2. The overall median rejection rate worldwide was 1.3. Hemolysis was the most common cause of rejection from both laboratories. Two criteria, inadequate volume (Quantity not sufficient) and wrong vacutainer, were consistent with 90% of the studies worldwide. Conclusion – To overcome the widespread disparity in sample rejection criteria, this study proposes adopting standard rejection criteria and formulating the national average for rejection.

A complete computer-based approach for data generation patterning to a pdf in \(\mathbb{R}\) and application to gamma and gig data

Here, we present an automatic data generation method which is fully computer-based for a variate $X$ with an absolutely continuous probability density function (<b>pdf</b>) $f$ exactly computable. The method uses computer-based on calculations of integrals (trapezoidal and/or the Monte-Carlo method) for approximating the cumulative distribution function and next, the dichotomy algorithm to get the quantile function from which we obtain data from \(f\). We apply the method to generate <i>gig(a,b,c)</i> data. The comparison with analogues, as in \textbf{R} Software is very successful. The method may work where the rejection method fails because of a lack of \textit{pdf} bound which can be generated. The method might be slower but the area of more and more powerful computer is favorable to it. The implementation for <i>gamma<\i> and/or <i>gig<\i> laws in <b>R</b> codes are presented.

Efficient reliability-based design optimization for hydraulic pipeline with adaptive sampling region

In engineering practice, the active learning Kriging (AK) model is always embedded in the computation of reliability-based design optimization (RBDO) problem. Traditionally, with the iteration of the design points, the candidate sample pool for establishing the Kriging model must be constructed repeatedly. However, there are overlaps in the sample space, which affect the efficiency of the RBDO. In this study, a novel method combining the rejection sampling (RS) with AK is proposed to further ensure the accuracy of the Kriging model. The proposed method aims to reduce the number of candidate sample pools and improve the efficacy of sample utilization. In the optimization process, RS can capture the variation of the sampling region using the rejection algorithm, which avoids the need to update the Kriging model in the entire overlapping sample space, and focuses on improving the model accuracy in the additional sample space with the new design point. Thus, the Kriging model continuously updates with the iteration of the design variable in the optimization process, which significantly reduces the computational cost. Several examples were employed to validate the performance of the proposed method. Lastly, the proposed method was applied to the RBDO of a hydraulic pipeline system.

Research on a Monte Carlo Simulation Method of Neutron Coded-Aperture Imaging

Pinhole imaging is an important test technique to obtain information on the spatial distribution of the radiation field in the target region and has been widely used in nuclear physics and inertial confinement fusion (ICF). Coded-aperture, able to maintain good resolution as well as enhancing signal strength, has become a more frequently used method than pinhole imaging in experimental studies. Thus, implementing Monte Carlo simulations of coded-aperture imaging will improve coded-aperture design, image reconstruction, and other related works. However, the current international mainstream Monte Carlo transport simulation programs do not possess the ability to directly simulate coded-aperture imaging. This paper develops a relatively complete coded-aperture imaging simulation function on the Neutron Photon Transport System code based on the next-event estimation method. With the application of Monte Carlo simulation techniques, such as variance reduction and rejection sampling, it is capable of simulating coded-aperture accurately, flexibly, and efficiently, including problems of multiple shapes and even irregular geometry. The results are consistent with combined pinhole imaging, and the computational efficiency has been improved significantly.

A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform

A great challenge for credit-scoring models in online peer-to-peer (P2P) lending platforms is that credit-scoring models simply discard rejected applicants. This selective discard can lead to an inability to increase the number of potentially qualified applicants, ultimately affecting the revenue of the lending platform. One way to deal with this is to employ reject inference, a technique that infers the state of a rejected sample and incorporates the results into a credit-scoring model. The most popular approach to reject inference is to use a credit-scoring model built only on accepted samples to directly predict the status of rejected samples. However, the distribution of accepted samples in online P2P lending is different from the distribution of rejected samples, and the credit-scoring model on the original accepted sample may no longer apply. In addition, the acceptance sample may also include applicants who cannot repay the loan. If these applicants can be filtered out, the losses to the lending platform can also be reduced. Therefore, we propose a global credit-scoring model framework that combines multiple feature selection methods and classifiers to better evaluate the model after adding rejected samples. In addition, this study uses outlier detection methods to explore the internal relationships of all samples, which can delete outlier applicants in accepted samples or increase outlier applicants in rejected samples. Finally, this study uses four data samples and reject inference to construct four different credit-scoring models. The experimental results show that the credit-scoring model combining Pearson and random forest proposed in this study has significantly better accuracy and AUC than other scholars. Compared with previous studies, using outlier detection to remove outliers in loan acceptance samples and identify potentially creditworthy loan applicants from loan rejection samples is a good strategy. Furthermore, this study not only improves the accuracy of the credit-scoring model but also increases the number of lenders, which in turn increases the profitability of the lending platform.

Pair-switching rerandomization.

Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance-rejection sampling method used in rerandomization is computationally inefficient. As a result, it is time-consuming for rerandomization to draw numerous independent assignments, which are necessary for performing Fisher randomization tests and constructing randomization-based confidence intervals. To address this problem, we propose a pair-switching rerandomization (PSRR) method to draw balanced assignments efficiently. We obtain the unbiasedness and variance reduction of the difference-in-means estimator and show that the Fisher randomization tests are valid under PSRR. Moreover, we propose an exact approach to invert Fisher randomization tests to confidence intervals, which is faster than the existing methods. In addition, our method is applicable to both nonsequentially and sequentially randomized experiments. We conduct comprehensive simulation studies to compare the finite-sample performance of the proposed method with that of classical rerandomization. Simulation results indicate that PSRR leads to comparable power of Fisher randomization tests and is 3-23 times faster than classical rerandomization. Finally, we apply the PSRR method to analyze two clinical trial datasets, both of which demonstrate the advantages of ourmethod.

Development of unstructured mesh tally capability in cosRMC and application to shutdown dose rate analysis

Accurate estimation of shutdown dose rate (SDDR) is one of the key issues in the neutronics calculation of fusion reactors. cosRMC R2S was developed based on the Rigorous two-step (R2S) method using a mesh tally for shutdown dose rate calculation, which requires higher transport calculation accuracy in complex geometry. However, the traditional method in this approach was based on the orthogonal structured mesh. The orthogonal structured mesh does not necessarily coincide with geometry and material boundaries, which can introduce large errors in activation calculation and reduce the calculation accuracy of shutdown dose rate. To solve this problem, the unstructured mesh tally capability was developed in the Monte Carlo code cosRMC. On this basis, cosRMC R2S was further developed, using unstructured meshes to replace structured meshes. Due to the geometry particularity of the unstructured tetrahedral mesh, the rejection sampling strategy was adopted to sample the position of the decay gamma source. In order to verify the reliability and validity of the code, the verification analyses were carried out on the ITER port plug benchmark and the FNG ITER shutdown dose rate benchmark respectively. The results obtained with unstructured meshes were compared with those obtained with structured meshes, and the reliability of the code was preliminarily verified. The results showed that the maximum discrepancy between experimental results was 10.1% with unstructured meshes, which was closer to experimental results than that with structured meshes, indicating that the computational accuracy of the result was improved effectively. It is demonstrated that the code developed in this paper is capable of accurate shutdown dose field calculations and has the potential to be applied to complex shielding devices.

Preparing many copies of a quantum state in the black-box model

We describe a simple quantum algorithm for preparing $K$ copies of an $N$-dimensional quantum state whose amplitudes are given by a quantum oracle. Our result extends a previous work of Grover, who showed how to prepare one copy in time $O(\sqrt{N})$. In comparison with the naive $O(K\sqrt{N})$ solution obtained by repeating this procedure~$K$ times, our algorithm achieves the optimal running time of $\theta(\sqrt{KN})$. Our technique uses a refinement of the quantum rejection sampling method employed by Grover. As a direct application, we obtain a similar speed-up for obtaining $K$ independent samples from a distribution whose probability vector is given by a quantum oracle.

Adaptive FRESH filter with vicarious reference for interference rejection: An application to signal detection in NOMA system

AbstractAdaptive frequency shift (FRESH) filter is one of the most suggested methods for interference rejection. This filter extracts the desired cyclostationary signal by exploiting its spectral correlation through the adaptive periodically time‐varying filters. The reference signal in this filter has an essential role in the quality of the desired signal estimation. In this article, a vicarious signal is generated as a reference of an adaptive FRESH structure. Using the cyclostationary characteristics of the desired signal (eg, modulation, cyclic frequencies, etc.), the generated signal exhibits all feasible cyclostationary behaviors of the desired signal. The overall system significantly improves the performance of the FRESH filter. The advantage of using the vicarious signal is demonstrated with theoretical analysis. Furthermore, a multistage form of the proposed method is designed for severe noise and interference conditions. The method is used for signal detection in non‐orthogonal multiple access (NOMA) system. Simulation results validate its efficiency in this case and in different interference scenarios which include pulse chirp radar signal, orthogonal frequency division multiplexing (OFDM), and quadrature amplitude modulation (QAM).

Development of a Magnet Power Supply with a Common-mode Rejection Method Based on Automatic Calibration of Driving Pulses

Development of a Magnet Power Supply with a Common-mode Rejection Method Based on Automatic Calibration of Driving Pulses

The bounded coalescent model: Conditioning a genealogy on a minimum root date

The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability density of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.

Don’t Reject This: Key-Recovery Timing Attacks Due to Rejection-Sampling in HQC and BIKE

Well before large-scale quantum computers will be available, traditional cryptosystems must be transitioned to post-quantum (PQ) secure schemes. The NIST PQC competition aims to standardize suitable cryptographic schemes. Candidates are evaluated not only on their formal security strengths, but are also judged based on the security with regard to resistance against side-channel attacks. Although round 3 candidates have already been intensively vetted with regard to such attacks, one important attack vector has hitherto been missed: PQ schemes often rely on rejection sampling techniques to obtain pseudorandomness from a specific distribution. In this paper, we reveal that rejection sampling routines that are seeded with secretdependent information and leak timing information result in practical key recovery attacks in the code-based key encapsulation mechanisms HQC and BIKE.Both HQC and BIKE have been selected as alternate candidates in the third round of the NIST competition, which puts them on track for getting standardized separately o the finalists. They have already been specifically hardened with constant-time decoders to avoid side-channel attacks. However, in this paper, we show novel timing vulnerabilities in both schemes: (1) Our secret key recovery attack on HQC requiresonly approx. 866,000 idealized decapsulation timing oracle queries in the 128-bit security setting. It is structurally different from previously identified attacks on the scheme: Previously, exploitable side-channel leakages have been identified in the BCH decoder of a previously submitted HQC version, in the ciphertext check as well as in the pseudorandom function of the Fujisaki-Okamoto transformation. In contrast, our attack uses the fact that the rejection sampling routine invoked during the deterministic re-encryption of the decapsulation leaks secret-dependent timing information, which can be efficiently exploited to recover the secret key when HQC is instantiated with the (now constant-time) BCH decoder, as well as with the RMRS decoder of the current submission. (2) From the timing information of the constant weight word sampler in the BIKE decapsulation, we demonstrate how to distinguish whether the decoding step is successful or not, and how this distinguisher is then used in the framework of the GJS attack to derive the distance spectrum of the secret key, using 5.8 x 107 idealized timing oracle queries. We provide details and analyses of the fully implemented attacks, as well as a discussion on possible countermeasures and their limits.