Rejection Method Research Articles

Backscattering of Ultrasonic Waves as the Basis of the Method of Control of Structure and Physicо-Mechanical Properties of Cast Irons

Increasing the reliability of control of cast iron structure and its physical and mechanical characteristics is an important scientific and technical task of the machine-building industry. The paper studies the possibilities of controlling the structure of cast irons using structural noise created by ultrasonic scattering on graphite inclusions of different shapes. The subject of the present studies was such characteristics of structural noise as amplitude-temporal A(t) and as root mean square value of the amplitude of the ultrasonic waves backscattering field AN, compared with the data on ultrasonic velocity and strength or tensile strength of cast iron samples. As a result of the studies, a significant difference between the amplitude parameters of the AN structural noise obtained for samples with different shapes of graphite inclusions at 5 MHz was revealed for the first time. So, for example, for samples of gray cast iron (Russian: СЧ10, СЧ15, СЧ20, СЧ25), having predominantly plate-like form of graphite inclusions, the value of AN on 14–15 dB exceeds that measured in high-strength specimens of the cast iron with the prevailing form of spherical graphite inclusions ВЧ50 (Russian), etc. At the same time growth of longitudinal ultrasonic velocity amounted to 20–25 %. The method of rejection of gray cast iron from high-strength cast iron according to the data of amplitude parameters of structural noise AN at unilateral and local sounding of the object without using an additional reference signal reflected from its oppositional wall is suggested.

DNA barcodes, ISSR, RAPD and SCAR markers as potential quality control tools for molecular authentication of black and white mulberry

Morus species, especially black mulberry (Morus nigra L.), are well known for their nutritive and economic values. Black mulberry is often a subject for deliberate and undeliberate substitution with the morphologically similar white mulberry (Morus alba L.). In the current study, the genetic diversity of Morus nigra L. and Morus alba L. was assessed using different genetic profiling and fingerprinting techniques namely namely DNA barcoding, Random Amplified Polymorphic DNA (RAPD) and Inter Simple Sequence Repeat (ISSR). DNA barcoding was implemented using 2 plastid loci (matK, and rbcL-1) and 2 nuclear spacers (ITS and ITS2) aiming at accurate authentication of the studied species. DNA barcoding, targeting the selected loci succeeded to identify and authenticate the studies Morus species but failed to differentiate them, owing to their close genetic resemblance. Out of 52 screened RAPD primers, 27 were able to produce reproducible and clear profiles with 115 amplified fragments of which 37 were polymorphic accounting for 32.17% polymorphism. The number of amplified bands varied from 2 to 7 ranging in size from 100 to 700 bp. Moreover, 55 ISSR primers were evaluated, 108 fragments were produced, using 20 primers, of which 28 were polymorphic accounting for 25.92% polymorphism with size range of 200–800 bp. Sequence Characterized Amplified Region (SCAR) marker was developed based on reproducible banding pattern obtained by RAPD primer (OPG-03), for the purpose of accelerating the initial screening of Morus batches and enabling the preventative rejection of adulterated samples. The polymorphic band obtained was sequenced and specific primers were designed to develop a SCAR marker of approximately 558 bp exclusively for M. nigra, allowing detection of down to 1 ng, of M. nigra. The SCAR marker showed 100% success in differentiating authentic M. nigra and M. alba samples. In addition, the application of the developed SCAR marker was extended to distinguish M. nigra from its four common adulterants namely Ficus carica L., Fragaria vesca L., Ficus racemosa L. and Vitis vinifera L.. The proposed protocol was successfully implemented for the evaluation of 47 commercial M. nigra samples collected from different Egyptian locations.

Comparison of a Pre-Trained AI Contouring Model to Unseen Institutional Data

To compare contours generated by a pre-trained, commercial AI model to those manually drawn in house. Previously treated radiotherapy patients (N = 20 per site) for several sites (brain, head & neck, thorax, abdomen, pelvis) with approved structure sets were selected for this retrospective analysis. For the planning CT of each patient, a pre-trained AI model auto-contoured several OARS: bladder, brain, eyes, femurs, kidneys, lenses, mandible, and parotids, etc. A two-step rejection method filtered the results based on unmatched structure names (i.e., contours existing in only one of the two structure sets causing incorrect auto-matching of structure names by the comparison algorithm), and structures with different superior and/or inferior extents. From the remaining contours, DSC (Dice Similarity Coefficient) and HD95% (95th percentile of Hausdorff Distance) were calculated between auto and manually generated contours using vendor-supplied software; median values were then calculated. The entire data set contained 592 structures at the on-set of analysis. After applying the rejection filters, the remaining data had 294 structures; a large portion of filtering was due to unmatched names. Out of these, OARs with contours from at least 10 patients (N>9) were further analyzed to include the 25th and 75th percentile for DSC and HD95%. Results from this analysis (structures with N>9) are presented in the table below, where results of left-right paired structures are combined into one row. The submandibular glands, larynx, and optical nerves (5 < N < 10) all had median DSC < 0.77 while lungs had median DSC > 0.98 (N = 6). Parotids and lenses had poor DSC and HD95% scores and may require significant contour editing to achieve agreement with our clinical conventions. This study highlights the difficulty with retrospective analysis of contours with an external trained model due to variations in the superior/inferior extent of tubular structures such as rectum, cord, esophagus, etc. CONCLUSION: Results are encouraging, given that the pre-trained commercial model has not seen our institutional data. The pre-trained AI contouring model matched very well to manual contours for large volume, higher contrast structures but did not match well for parotids and lenses. Variability in conventions regarding the superior/inferior extent of some structures hinders retrospective comparison with a pre-trained AI model.

Examining the influence of sample rejection rates on the carbon footprint of clinical laboratories: a retrospective analysis

Aims: Clinical laboratories play a vital role in healthcare, yet their operations contribute to resource consumption, waste generation, and greenhouse gas emissions. The need for sustainable practices in laboratories has led to guidelines for reducing their carbon footprint. This study aims to assess the impact of sample rejection rates (SRRs) on laboratory sustainability by calculating the carbon footprint and medical waste generated due to rejected samples.&#x0D; Methods: This retrospective, single-center study obtained data from the Hospital Information Management System for two years (2021 and 2022). SRRs were calculated for different sample tube types. The carbon footprint caused by rejected samples was calculated using CO2 emission (CO2e) conversion factors. The weight of medical waste generated due to rejected samples was evaluated. Statistical analysis was performed using appropriate tests.&#x0D; Results: In 2021 and 2022, SRRs for different sample tubes were calculated, with statistically significant differences observed. The total CO2e value resulting from rejected samples over two years was 12.3 tons, and the medical waste generated was 3.7 tons. The highest SRR was observed in Blue top tubes, while yellow top tubes showed a significant reduction in SRR in 2022.&#x0D; Conclusion: This study highlights the impact of SRRs on laboratory sustainability. The calculated CO2e and medical waste values underscore the need to minimize sample rejections. While these values seem minor compared to global emissions, they reflect only a portion of the potential environmental impact. Reducing sample rejections not only improves patient safety and laboratory efficiency but also aligns with the larger goal of creating environmentally conscious and sustainable healthcare practices.

A-193 Effects of Delayed Centrifugation and Delayed Testing on the Stability of Comprehensive Metabolic Profile Analytes

Abstract Background The centralization of clinical laboratory testing has become increasingly common in recent years. While centralization allows for consolidation of resources, expanded test menus, procedural standardization, and improved efficiencies, central laboratories can face significant logistical challenges related to sample transport. In particular, problems may arise when samples are received from distant sites or if the courier services used to transport samples are infrequent or unreliable. Policies aimed to mitigate pre-analytical issues such as the time-to-centrifugation, the time-to-testing, and storage temperature, are best devised when driven by empiric data. Objective In this study, we provide stability studies for the Comprehensive Metabolic Panel (CMP) analytes in paired serum and lithium heparin (LiHep) plasma samples. Our findings not only complement preexisting literature data but also help guide the current sample acceptance criteria and policy for receiving satellite clinic samples in our laboratory. Methods Serum and LiHep plasma were collected from healthy volunteers in gel separator tubes. In one set of experiments, samples of both specimen types were all centrifuged within 30 min of collection and tested at pre-determined time intervals (2, 4, 6, 12, and 24 h post collection) using Roche Cobas 8000 system. In a second set of experiments, samples of both specimen types collected from another set of volunteers were kept at room temperature and only centrifuged prior to testing at pre-determined time intervals (2, 4, 6, 12, and 24 h post collection). Sample storage prior to testing was at room temperature. The percent recovery of each result was calculated relative to the first sample of the series. The CMP includes the following analytes: glucose, sodium, potassium, chloride, carbon dioxide, urea nitrogen, creatinine, calcium, total bilirubin, total protein, albumin, alkaline phosphatase, aspartate aminotransferase (AST), and alanine aminotransferase (ALT). Results Glucose in promptly centrifuged (i.e., within 30 min of collection) LiHep plasma samples showed acceptable recovery (≥90%) when tested within 12 h of collection in LiHep gel separator tubes, whereas glucose stability in serum stored at room temperature exceeded 24 h. In contrast, delayed centrifugation beyond 3 h led to unacceptably low recoveries in both LiHep plasma and serum sample types. All other analytes included in the CMP were stable beyond 24 h irrespective of whether the sample type is LiHep plasma or serum, or even if the time-to-centrifugation was up to 24 h. Conclusions Based on the study, our laboratory has updated our policy to accept pre-centrifuged (i.e., within 30 min of collection) LiHep plasma from satellite clinics up to 8 h from blood collection, which is a 2-hour increase as compared to our prior cut-off acceptability. Given the location of our satellite clinics and unpredictability of sample transport, this increase represents a positive change in reducing outpatient sample rejections while ensuring reliable laboratory results.

Correlation between HER2/neu protein overexpression on Immunohistochemistry and Fluorescent in Situ Hybridization (FISH) in breast carcinoma: Problems in developing countries.

To correlate the results of HER2/neu protein overexpression on immunohistochemistry (IHC) and gene amplification on fluorescence in situ hybridization (FISH) and to document the problems faced in performing FISH procedure. This was an observational retrospective study covering five years from January 1st, 2015 - December 31st, 2019 at Histopathology Department of Shifa International Hospital (SIH), Islamabad. All cases of breast cancer that underwent florescence in situ hybridization (FISH) were retrieved. Correlation between HER2/neu overexpression on IHC and its amplification on FISH was analyzed. Problems in application of FISH were recorded. Out of 451 cases submitted for HER2/neu testing by FISH, 68 cases (15%) were rejected. Gene amplification was seen in 139 (36.29%) cases. Total cases with HER2/neu IHC score of 2+ were 330 cases and out of which gene amplification was seen in 98 cases (29.69%) whereas 93.1% (41/44) 3+ IHC cases were amplified. Poor fixation, inadequate amount of tumor with crushing artefacts and dye application to the biopsy fragments were causes of sample rejection. Her2/neu amplification was seen in most Her2/neu 3+ cases and approximately one-third of Her2neu 2+ cases. Proper fixation, adequate biopsy material with standardized processing is required to yield useful results on FISH.

FACTORS OF INTERFERENCE IN LABORATORY INVESTIGATIONS: HEMOLYSIS, ICTERUS, LIPEMIA

The accuracy of laboratory investigation results depends on various factors, including interfer- ence factors. Additionally, it is worth noting that a significant number of samples are photometrical- ly measured in the wavelength range of 350 to 600 nm, which corresponds to the absorption peak of many interferents.&#x0D; In laboratories where the process of sample rejection is established based on serum indices (color scale) and specific cutoff points for samples showing hemolysis, icterus, and lipemia, the issuance of falsely elevated/decreased investigation results to the clinic is avoided. The declared criteria for sample rejection should comply with the recommendations of CLSI CA-56A and CLSI EP 07 standards.&#x0D; The majority of errors in the preanalytical stage are caused by hemolysis. All hemolyzed sam- ples with a concentration of free hemoglobin equal to or greater than 10.0 g/L should be rejected, and the laboratory report should indicate the reason for rejection as "Hemolyzed sample. Resampling is required." Each laboratory establishes its own cutoff point for free hemoglobin, bili- rubin and triglicerol concentration in the sample. This is because an increase in hemolysis index in serum/plasma leads to falsely increased levels of certain analytes (such as inorganic phosphorus, potassium, folic acid, magnesium, iron, urea, LDH, AST, ALT). Conversely, the concentration of albumin, bilirubin, glucose, sodium, and chloride in such samples is falsely decreased. The presence of icterus in samples is always indicative of a pathological process. The elevation of bilirubin con- centration in the sample will be reflected in the increase/decrease of analytes such as creatinine, total protein, triglycerides, cholesterol, phosphorus, magnesium, urea, hormones (free triiodothyro-nine, testosterone, estradiol), and others. An increase in triglyceride and lipoprotein concentration in the sample significantly affects the levels of analytes such as phosphorus, creatinine, total protein and calcium.&#x0D; The presence of hemolysis, icterus, and lipemia in samples affects the measurement of various analytes and, consequently, the results of laboratory investigations and patient safety. Laboratories should have a developed procedure for systematic identification of interfering agents and decision- making regarding the handling of such samples to ensure the reliability of obtained data.

Improving the Classification Ability of Delegating Classifiers Using Different Supervised Machine Learning Algorithms

Cancer Classification &amp;amp; Prediction Is Vitally Important for Cancer Diagnosis. DNA Microarray Technology Has Provided Genetic Data That Has Facilitated Scientists Perform Cancer Research. Traditional Methods of Classification Have Certain Limitations E.G. Traditionally A Proposed DSS Uses A Single Classifier at A Time for Classification or Prediction Purposes. To Increase Classification Accuracy, Reject Option Classifiers Has Been Proposed in Machine Learning Literature. In A Reject Option Classifier, A Rejection Region Is Defined and The Samples Fall in That Region Are Not Classified by The Classifier. The Unclassifiable Samples Are Rejected by Classifier in Order to Improve Classifier&amp;rsquo;s Accuracy. However, These Rejections Affect the Prediction Rate of The Classifier as Well. To Overcome the Problem of Low Prediction Rates by Increased Rejection of Samples by A Single Classifier, the &amp;ldquo;Delegating Classifiers&amp;rdquo; Provide Better Accuracy at Less Rejection Rate. Different Classifiers Such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K Nearest Neighbor (KNN) Etc. Have Been Proposed. Moreover, Traditionally, Same Learner Is Used As &amp;ldquo;Delegated&amp;rdquo; And &amp;ldquo;Delegating&amp;rdquo; Classifier. This Research Has Investigated the Effects of Using Different Machine Learning Classifiers in Both of The Delegated and Delegating Classifiers, And the Results Obtained Showed That Proposed Method Gives High Accuracy and Increases the Prediction Rate.

Repetitive sampling inspection plan for cancer patients using exponentiated half-logistic distribution under indeterminacy

This piece of work deals with a time truncated sampling scheme for cancer patients using exponentiated half-logistic distribution (EHLD) based on indeterminacy. We have studied time truncated schemes like repetitive acceptance sampling plan (RASP) under indeterminacy. We have estimated the projected scheme parameters such as sample size and acceptance and rejection sample numbers for known indeterminacy parameters. In addition to the projected sampling scheme quantities, the corresponding tables are generated for various values of indeterminacy parameters. The results of a sampling scheme show that the average sample number (ASN) decreases as indeterminacy values increase. It leads that the indeterminacy parameter is played a crucial portrayal in ASN. A comparative study is carried out with existing sampling schemes based on indeterminacy and classical sampling schemes. The evaluated sampling schemes are exemplified with the help of cancer data. From tables and exemplification, we wind up that the projected RSP scheme under indeterminacy desired a smaller sample size than the existing schemes.

Rejection methods for vegetation mapping using hyperspectral airborne data

ABSTRACT Vegetation mapping from remote sensing data has proven useful for monitoring ecosystems at local, regional and global scales. Generally based on supervised classification methods, ecosystem mapping needs representative and consistent labelling. Such completeness is often difficult to achieve and requires the exclusion of minority species poorly represented in the studied scene in the training base. This exclusion leads to wrong predictions in the resulting map. In this study, the use of a posteriori classification rejection methods to limit the errors associated with minority species was evaluated in three different mapping scenarios: classification according to vegetation layers, prediction of genera from various vegetation types from low vegetation to trees and mapping of habitat (assemblages of species). For this purpose, several supervised classification methods based on Support Vector Machines (SVM), Random Forests (RF) and Regularized Logistic Regression (RLR) algorithms were first applied to hyperspectral images covering the reflective domain. On these classifications, the usual evaluation methods (confusion matrix and its derivatives calculated on an independent test set composed of the majority species) showed performances similar to those of the state-of-the-art. However, the introduction of a new score taking into account minority species demonstrated the need to include them in the evaluation process to provide robust performance quantification representing map effectiveness. Three a posteriori rejection methods, based on simple thresholding, K-means and SVM algorithms, were well suited to monitor minority species. The performance gain depended on the mapping scenario, the machine learning model and the rejection method. An increase in performance with the inclusion of minority species of up to 12% could be observed through the new score. These methods also detected a similar proportion of prediction errors associated with predominant species

Patch area and uniform sampling on the surface of any ellipsoid

Algorithms for generating uniform random points on a triaxial ellipsoid are non-trivial to verify because of the non-analytical form of the surface area. In this paper, a formula for the surface area of an ellipsoidal patch is derived in the form of a one-dimensional numerical integration problem, where the integrand is expressed using elliptic integrals. In addition, analytical formulae were obtained for the special case of a spheroid. The triaxial ellipsoid formula was used to calculate patch areas to investigate a set of surface sampling algorithms. Particular attention was paid to the efficiency of these methods. The results of this investigation show that the most efficient algorithm depends on the required coordinate system. For Cartesian coordinates, the gradient rejection sampling algorithm of Chen and Glotzer is best suited to this task, when paired with Marsaglia’s method for generating points on a unit sphere. For outputs in polar coordinates, it was found that a surface area rejection sampler is preferable.

Scattering direction sampling methods for polarized Monte Carlo simulation of oceanic lidar.

Monte Carlo techniques have been widely applied in polarized light simulation. Based on different preconditions, there are two main types of sampling strategies for scattering direction: one is the scalar sampling method; the others are polarized sampling approaches, including the one- and two-point rejection methods. The polarized simulation of oceanic lidar involves a variety of mediums, and an efficient scattering sampling method is the basis for the coupling simulation of the atmosphere and ocean. To determine the optimal scattering sampling method for oceanic lidar simulation, we developed a polarized Monte Carlo model and simulated Mie scattering, Rayleigh scattering, and Petzold average-particle scattering experiments. This simulation model has been validated by comparison with Ramella-Roman's program [Opt. Express13, 4420 (2005)OPEXFF1094-408710.1364/OPEX.13.004420], with differences in reflectance and transmittance Stokes less than 1% in Mie scattering. The simulation results show these scattering sampling methods differ in runtime, scattering angle distributions, and reflectance and transmittance Stokes. Considering the current simulation accuracy of oceanic lidar, the differences in reflectance and transmittance Stokes are acceptable; thus, the runtime becomes the main evaluation factor. The one-point rejection method and scalar sampling method are preferable for the oceanic lidar polarized simulation. Under complex atmosphere-ocean coupling systems, scalar sampling methods may be a better choice since the calculation process of the sampling is independent of the incident Stokes vector.

Two-stage deterioration model updating of RC structures in marine environment using long-term field inspection data

For the deterioration of reinforcement concrete (RC) structures in marine environment at various stages, models on chloride penetration, crack initiation and crack propagation have been proposed and studied in literature. However, to be applied to real existing structures, these models, usually associated with significant uncertainties, have to be calibrated and modified with long-term in-situ inspection data. Toward this goal, this paper develops a two-stage updating framework to incorporate measured data into existing models to achieve a more realistic modeling of deterioration process. To improve the computation efficiency, the framework can be integrated with simple rejection sampling and other advanced sampling techniques such as the modified sequential adaptive importance sampling (SAIS). A real case study of high-pile wharf structure in Guangzhou Port is carried out to demonstrate the updating effect of the proposed framework. The observed data of chloride content profile and steel bar corrosion depths from one field inspection are used to construct the likelihood functions in the two updating stages. Comparison between the results of the first and second updating stages is made, and the sensitivities of different parameters to the data are analyzed, subsequently the year of reinforcement corrosion initiation and the evolution of corrosion depth are inferred using these results. At last, the data from an early inspection are also incorporated to investigate the impact of two inspections on updating effects.

A method to construct statistical prior models of geology for probabilistic inversion of geophysical data

In probabilistic inversion of geophysical data, one must describe the expected noise in the system, and any prior information. In a geoscience context, prior information can provide a quantitative description of the expected spatial variability and correlations of the geology. But in practical inversion cases, driven by difficulty in quantifying geological information and computational complexity, an analytical mathematical smooth prior model is often chosen to describe the spatial variability. This is one of the primary reasons that realistic geological structures are difficult to resolve in geophysical models. Thus, there is currently a need for investigating and proposing practical ways of capturing complex (and often qualitative) geological information in statistical prior models that can be used in probabilistic inversion, which satisfies both the geologist, geophysicist, engineer and the geostatistician. In this research we show how a 1D statistical prior model can be designed that emulates the spatial distribution found in 188 boreholes with Miocene and Quaternary deposits from a study area (approx. 177,5 km2) near Horsens, Denmark. The prior model is built in two major steps 1) a multidimensional distribution describing the sub-division of major geological elements (here represented by lithologies from defined geological periods) and 2) a truncated pluri-Gaussian distribution describing the internal structure of lithologies within each element. The presented prior model can both be used to generate independent realizations, which can be used as part of the extended rejection sampler, as well as allowing the possibility of doing a “random walk” in the model space, as required by the extended Metropolis algorithm. We demonstrate, as an example, how the developed prior model can be used in a probabilistic inversion of airborne transient electromagnetic (AEM) data and discuss the implications the use of such informed prior models can have.

Fast Uplink Bandwidth Testing for Internet Users

Access bandwidth measurement is crucial to emerging Internet applications for network-aware content delivery. However, today’s bandwidth testing services (BTSes) are slow and costly—the tests take a long time to run, consume a great deal of data usage, and usually require large-scale test server deployments. The inefficiency and high cost of BTSes root in their methodologies that use excessive temporal/spatial redundancies for combating noises in Internet measurement. In particular, compared to downlink BTSes, uplink BTSes are subject to more severe performance problems and technical challenges. This paper presents FastUpBTS to make uplink BTS fast and cheap while maintaining high accuracy. The key idea is to strategically accommodate and exploit the noise rather than repetitively and exhaustively suppress the impact of noise. This is achieved by a novel statistical sampling framework termed fuzzy rejection sampling. We build FastUpBTS as an end-to-end BTS that implements fuzzy rejection sampling based on memorization-reinforced throughput denoising, data-driven server selection, and informed multi-homing support. Our evaluation shows that with only 30 test servers, FastUpBTS achieves the same level of accuracy compared to the state-of-the-art BTS () that deploys <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 16,000 servers. Most importantly, FastUpBTS makes bandwidth tests 5.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> faster and 6.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> more data-efficient.

An Accurate Outlier Rejection Network With Higher Generalization Ability for Point Cloud Registration

Feature-based point cloud registration algorithms have gained more attention recently for their high robustness. Outlier rejection is a key step of such algorithms. With the development of deep learning, some of the learning-based outlier rejection methods have been proposed and implemented in various scenes. However, generalization ability and accuracy of the existing methods in complex scenes still need to be improved. In this paper, we construct a neural network for removing outlier correspondences. Particularly, we propose a novel seed selection method based on feature consistency (FC) and a new loss function based on second order feature consistency (FC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> ). Experimental results on various datasets show the proposed network achieves better accuracy and stronger generalization ability than the state-of-the-art learning-based algorithms.

"Fractionalization" A new approach for comparing different approximation methods of fractional order systems and disturbances Rejection in PID Control

"Fractionalization" A new approach for comparing different approximation methods of fractional order systems and disturbances Rejection in PID Control

Statistical Analysis and Countermeasures for Specimen Rejection in the Emergency Department

The rejected specimens from the Emergency Department of the Center of Clinical Laboratory from January 1, 2022 to January 1, 2023 were analyzed to reduce the specimen rejection rates and to improve the quality of inspection. The results showed that there were 1488 samples of rejected specimens and the non-conforming rate was 0.58%. The departments involved were mainly the Emergency Department, the Hematology Department, the Cardiology Department, the Intensive Care Department, and the Brain Surgery Department. Among the reasons for rejection, blood hemolysis accounted for 43.15%, blood coagulation accounted for 26.61%, and the rate of insufficient specimens was 17.14%. Among them, the sample rejection rate for arterial blood gas analysis was the highest, which accounted for 1.74%; followed by specimens for coagulation test, which was 1.18%. These results indicate the main reason for producing rejected specimens is mainly due to not following the standard operating procedure. Specimen rejection can largely be avoided if the standards for specimen collection are strictly followed.

Estimating the information content of genetic sequence data

Abstract A prominent problem in analysing genetic information has been a lack of mathematical frameworks for doing so. This article offers some new statistical methods to model and analyse information content in proteins, protein families, and their sequences. We discuss how to understand the qualitative aspects of genetic information, how to estimate the quantitative aspects of it, and implement a statistical model where the qualitative genetic function is represented jointly with its probabilistic metric of self-information. The functional information of protein families in the Cath and Pfam databases are estimated using a method inspired by rejection sampling. Scientific work may place these components of information as one of the fundamental aspects of molecular biology.

Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields

The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines.