Error Identification Research Articles

Maintaining taxonomic accuracy in genetic databases: A duty for taxonomists—Reanalysis of the DNA sequences from Mercan et al. (2024) on the genus Potamothrix (Annelida, Clitellata) in Turkish lakes

Public DNA sequence databases such as GenBank are widely used for identification of organisms in ecological and taxonomic studies. It is important that these public databases contain as few mistakes as possible and that any errors detected in these databases are reported. Here, we reanalyzed the COI sequences of Mercan et al. (2024) and showed that they were mistakenly considered by these authors as belonging to different populations (haplotypes) within the species Potamothrix hammoniensis (Tubificinae). We found that they corresponded to four distinct Tubificinae lineages (species), Pothamothrix alatus paravanicus, Potamothrix bavaricus, Tubifex sp. and Potamothrix sp. Despite these identification errors, the data from Mercan et al. (2024) remain interesting as they provide new information on the diversity of the genus Potamothrix in Turkey. Prompt measures must be taken to correct these errors and prevent them from being detrimental to future studies.

Evaluating ChatGPT's Chinese-English Translation Quality of Tender Documents: A Research of MTPE with MQM Scoring Models

Since Google introduced the Transformer model into natural language processing (NLP) in 2017, AI-aided translation has rapidly advanced. At the same time, translation is evolving from a solitary endeavor into a cooperative activity between human translators and machine translation systems, epitomized by the emergency of platforms with the Machine Translation Post Editing (MTPE) function. The advent of new translation modes also leads to increased research evaluating the effectiveness and quality of machine translation, for example, studies on the translation quality under the Multidimensional Quality Metrics (MQM) error typology framework. Involving AI-based translators and MTPE in their translation enables human translators to prepare the engineering documents efficiently. However, researchers notice that it is difficult for most machine translators to figure out the semantic and cultural differences in the source language and generate coherent structural translation in the target language. This research opens up ChatGPTs application in tender document translation under the MQM framework, hoping to cast light on assessment on ChatGPT's translation quality, identification of ChatGPT's errors in translating such documents and suggestions on human translators' performance throughout MTPE.

Using informative priors to account for identifiability issues in occupancy models with identification errors

Using informative priors to account for identifiability issues in occupancy models with identification errors

Classification of Scanning Errors of Digital Scans Recorded by Using Intraoral Scanners.

The different scanning errors that can be caused by the operator handling an intraoral scanner (IOS) or the intraoral conditions of the patient being scanned have not been described. The purpose of this review was to describe and classify the scanning errors that can be identified in digital scans recorded by using IOSs. The identification of scanning errors in an intraoral scan and understanding the cause of these scanning errors are fundamental procedures for successfully handling an IOS and integrating these digital data acquisition technologies in dental practices. There are two main types of scanning errors: the ones created by the operator and the ones caused by the intraoral conditions of the patient. There are seven operator-related scanning errors: mesh hole, stitching, tissue, reliability, umbrella, implant scan body geometry, and scanning noise errors. Additionally, there are four patient-related scanning errors: humidity, bridge, fuzzy finish line, and scanability noise errors. The identification of scanning errors is fundamental for assessing the quality of an intraoral digital scan. The comprehensive reading of these scanning errors allows the dental professional to understand if the scanning error can be corrected or if it is related to hardware/software limitations of IOSs.

Robust discrimination between closely related species of salmon based on DNA fragments.

Closely related species of Salmonidae, including Pacific and Atlantic salmon, can be distinguished from one another based on nucleotide sequences from the cytochrome c oxidase sub-unit 1 mitochondrial gene (COI), using ensembles of fragments aligned to genetic barcodes that serve as digital proxies for the relevant species. This is accomplished by exploiting both the nucleotide sequences and their quality scores recorded in a FASTQ file obtained via Next Generation (NextGen) Sequencing of mitochondrial DNA extracted from Coho salmon caught with hook and line in the Gulf of Alaska. The alignment is done using MUSCLE (Muscle 5.2) (Edgar in Nat Commun 13:6968, 2022), applied to multiple versions of each fragment perturbed according to the nucleobase identification error probabilities underlying the quality scores. The Damerau-Levenshtein distance was used to determine the genetic barcode of the candidate species that is closest to each aligned, perturbed fragment. The "votes" that the sampled fragments cast for the different candidate species are then pooled and converted into identification probabilities, using weights determined by the entropy of the fragment-specific identification probability distributions. This novel approach to quantify the uncertainty associated with measurements made using NextGen Sequencing can be applied to discriminate closely related species, hence to value-assignment for reference materials supporting determinations of the authenticity of seafood, for example, NIST Reference Materials 8256 and 8257 (Coho salmon) (Ellisor et al., 2021).

Identification and correction of phase delay errors for hemispherical resonator gyroscopes

The phase-delay error of the circuit system is the primary source of the output error observed in the hemispherical resonator gyroscope (HRG). Additionally, the temperature-dependent nature of the phase-delay error results in a deterioration of the initial calibration parameters, which, in turn, significantly impairs the performance of the gyroscope in its intended application. This paper proposes a self-calibration method to effectively suppress the impact of phase-delay error on the application performance of gyroscopes. Firstly, based on the analysis of the influence of phase-delay error on the performance of the whole-angle mode HRG, a sinusoidal interference signal with a small amplitude is introduced into the quadrature control loop of the gyroscope. Then, the angular rate signal is demodulated and calculated to obtain the phase-delay error of the system, which is employed as the compensation value of the reference phase of the PLL to offset the phase-delay error of the circuit system in real-time. The experimental results demonstrate that after the phase-delay error is self-calibrated, the zero-bias output error of the gyroscope is reduced by 5 times, the bias instability is improved by 37.7%, and the self-calibration method of the phase-delay error presented is more strongly robust to temperature changes.

Adaptive Recognition of English Translation Errors Based on Improved Machine Learning Methods

The accurate identification of errors in machine-translated English textual content is both necessary and tough, with massive implications for natural language processing packages. current device translation structures, while an increasing number of sophisticated, nevertheless fall prey to quite a number of errors that may compromise which means and fluency. This paper addresses these shortcomings through offering a greater system mastering method for the adaptive recognition of translation errors. We gift a singular framework that integrates the Transformer version, famed for its efficacy in shooting contextual relationships within text sequences, with a robust attention mechanism that prioritizes salient data at some stage in translation. The innovation of this studies lies in the incorporation of meta-getting to know techniques, allowing the model to self-modify in response to various classes of mistakes, therefore refining the precision of errors recognition. We outline the technique in detail, emphasizing the systematic steps taken to enrich the model’s adaptability. The efficacy of our approach is substantiated through comparative experiments, which demonstrate extremely good upgrades in errors identification over existing techniques. The results suggest the capacity of our adaptive mechanism to decorate the excellent of machine translation, paving the method for greater, reliable and nuanced language translation tools.

Adaptive Friction Compensation for Tracking Control With Position Error Constraints of Servo Control Systems: A Switched System Point of View

ABSTRACTThis paper will investigate the adaptive backstepping friction compensator for a class of servo control systems from a switched system point of view. Firstly, by introducing a convergence‐invariant unbounded transformation, the position error bounds are incorporated into the original system. Secondly, an integral term is introduced to increase the robustness and the ‐backstepping technique is utilized to deal with the sign‐indefinite term. Then, an adaptive controller with a Coulomb friction estimator is obtained and the generated closed‐loop error system has the upper block‐triangle structure, which can be treated as a pair of cascade‐connected systems. Further, the common Lyapunov function (CLF) is obtained. The objects of asymptotical tracking with position error constraints and identification of the Coulomb friction coefficient can be realized. By similar methods, we propose adaptive friction compensators for the viscous friction model and the Coulomb & viscous model. Finally, the simulation results demonstrate the advantages of the proposed methods.

Quantitative Analysis of Pseudogene-Associated Errors During Germline Variant Calling.

A pseudogene is a non-functional copy of a protein-coding gene. Processed pseudogenes, which are created by the reverse transcription of mRNA and subsequent integration of the resulting cDNA into the genome, being a major pseudogene class, represent a significant challenge in genome analysis due to their high sequence similarity to the parent genes and their frequent absence in the reference genome. This homology can lead to errors in variant identification, as sequences derived from processed pseudogenes can be incorrectly assigned to parental genes, complicating correct variant calling. In this study, we quantified the occurrence of variant calling errors associated with pseudogenes, generated by the most popular germline variant callers, namely GATK-HC, DRAGEN, and DeepVariant, when analysing 30x human whole-genome sequencing data (n = 13,307). The results show that the presence of pseudogenes can interfere with variant calling, leading to false positive identifications of potentially clinically relevant variants. Compared to other approaches, DeepVariant was the most effective in correcting these errors.

Damage Identification in Large‐Scale Structures Using Time Series Analysis and Improved Sparse Regularization

The academic community has paid much attention to the research topic of structural health monitoring (SHM) and damage identification for several decades, which flourishes the development of diverse damage identification approaches. The sensitivity‐based damage identification enjoys popularity due to its clear physical meaning and long development history. However, this type of method faces challenges of fewer applications on the large‐scale structure, slow convergence rate, and poor performance based on the sensitivity of the modal parameters. Aiming at the existing obstacles, this study enables to propose a novel method based on time series analysis model and improved sparse regularization technique for damage identification of the large‐scale structure. Firstly, the model reduction technique is introduced to condense the unconcerned degrees of freedom (DOFs) of the finite element model (FEM) of the large‐scale structure, and then the sensitivity of the autoregression (AR) coefficient with respect to the damage coefficient for the large‐scale structure has been deduced, which can reduce the uncertainty, which comes from modal identification, in modal parameter sensitivity. Furthermore, the mean‐value normalization strategy is incorporated into the sparse regularization solving process to improve the computational efficiency. The proposed method has been validated based on a numerical continuous rigid frame bridge and an experimental steel truss bridge. Compared to the moth‐flame optimization (MFO) algorithm and traditional regularization methods, for both noise‐free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. Furthermore, regarding the numerical example, the damage identification results show that the max identification errors of the proposed method are 6% among the three cases under noise‐free and noise‐polluted data, while the max identification errors for MFO and the regularization method are 15% and 10%, respectively. And for the experimental example, based on the limited sensors, the proposed method can accurately detect the damage location and quantify damage severity with a good accuracy, which means this method has good potential for actual engineering structure. To summarize, the findings highlight that the AR coefficient sensitivity combined with improve sparse regularization can effectively detect damage of large‐scale structure, providing a feasible way to expand the application of the sensitivity‐based method.

An error compensation method for on-machine measuring blade with industrial robot

To achieve the seamless integration of precision grinding blade with timely measured data, an on-machine measurement system employing industrial robot is developed. The limitation to its widespread application lies in the accuracy of measurement, prompting the proposal of an error compensation method to address the measurement error within the implemented blade on-machine measurement system. To address the pre-travel error in the measuring device of the on-machine measurement system, an analysis of the factors influencing the pre-travel error of the contact probe is conducted. Subsequently, pre-travel error data is gathered utilizing the experimental calibration method involving a standard ball, and a prediction model employing the bilinear interpolation algorithm is established to facilitate error compensation. Employing pre-travel error compensation for the analysis of the operating body error in the on-machine measurement system, a spherical center distance constraint error model is derived through closed-loop kinematic calibration of the six-degrees-of-freedom industrial robot. Kinematic parameter error identification is conducted using a hybrid algorithm combining the L-M algorithm and the adaptive factor double-variable DE algorithm. This approach diminishes the spherical center distance measurement error, reducing it from 0.081 mm to 0.016 mm. Subsequently, experiment is conducted to measure blade machining allowances using an on-machine robotic measurement system, comparing the obtained data with measurement from a blue light scanner and a coordinate measuring machine (CMM). The results reveal average absolute deviations of 0.020 mm, 0.015 mm, and 0.016 mm in the three blade cross sections for the on-machine robotic measurement system and the blue light scanner, respectively. Correspondingly, the average absolute deviations for the on-machine robotic measurement system and the CMM in the three blade sections are 0.028 mm, 0.029 mm, and 0.029 mm. Moreover, the on-machine measurement system demonstrates commendable measurement repeatability, with a standard deviation of 0.003 mm.

드론 영상 활용 딥러닝 기반 피플 카운팅 알고리즘 개발

This study proposes an algorithm that can be utilized in various drone-related fields. The algorithm builds training data based on drone images and applies deep learning to detect and count the objects. The real-time detection algorithm YOLO was used for training, and the results obtained were used to validate object detection performance. To address identification errors, the labeling area was refined, and additional training data containing shadows were collected, whereby an accuracy of more than 90% was achieved. Furthermore, by developing a people counting algorithm, the proposed method can be applied to various fields, such as crowd crush prevention, tracking people’s movements, and access management. In addition, if diverse environments and types of training data are secured, this algorithm can be widely applied to disaster management fields such as searching for missing persons

СОВРЕМЕННЫЕ СРЕДСТВА И МЕТОДЫ МОНИТОРИНГА ПЛАВАЮЩЕГО МОРСКОГО МАКРОМУСОРА И ВНЕДРЕНИЕ ТЕХНОЛОГИЙ МАШИННОГО ОБУЧЕНИЯ

Marine litter pollution is currently recognized as global problem at the level of all international organizations and conventions related to the marine protection. This review examines modern methods and approaches for detecting floating marine macrolitter. The task of detecting marine litter on the water surface is complicated by a large variety of objects, various degrees of their degradation, predominantly small size, partial immersion in the subsurface layer, colorlessness, disguising within the water, difficult observation conditions. The main approaches today include visual observations (from ships, aircraft), trawling, and remote sensing, especially using radar systems. In the last decade, deep learning methods have made significant progress, which has allowed error recognition and identification to be brought to a new level due to various modifications of artificial neural networks. In this review, we analyze the main research on the presented topic and significant achievements and prospects for the application of artificial intelligence to improve methods for detecting and classifying marine litter larger than 2.5 cm.

Identification and evaluation of human errors in the care of pregnant mothers using the extensive CREAM technique

Background Care of pregnant mothers is one of the most important services provided in the healthcare system. Objective This study aims to identify and evaluate human errors in the care process of pregnant mothers using the extensive Cognitive Reliability and Error Analysis Method (CREAM). Methods In this study, tasks and activities related to the care of pregnant mothers in the health center and hospital were first analyzed using the HTA (hierarchical task analysis). Then, using extensive CREAM, human errors in tasks related to the care of pregnant mothers in the health center and hospital were identified and evaluated. Results In general, 34 main tasks and 106 subtasks were identified in the process of caring for pregnant Mothers. Thirty seven activities are related to midwifery associates or experts, and 19 activities are related to gynecologists. The highest Cognitive Failure Probability total (CFPt) was related to the duties of measures related to the infant, preparing the pregnant mother for transfer to the operating room, routine obstetric measures, taking delivery, measures related to resuscitation, and measures related to the control of the condition after delivery. Conclusions The process of caring for pregnant mothers is very vital and prone to errors due to different levels of service. The presence of experienced and capable personnel can largely prevent the occurrence of human errors. Increasing the number of personnel can be effective in reducing workload and preventing errors.

Research on Fine-Grained Phenotypic Analysis of Temporal Root Systems - Improved YoloV8seg Applied for Fine-Grained Analysis of In Situ Root Temporal Phenotypes.

Root systems are crucial organs for crops to absorb water and nutrients. Conducting phenotypic analysis on roots is of great importance. To date, methods for root system phenotypic analysis have predominantly focused on semantic segmentation, integrating phenotypic extraction software to achieve comprehensive root phenotype analysis. This study demonstrates the feasibility of instance segmentation tasks on in situ root system images. An improved YoloV8n-seg network tailored for detecting elongated roots is proposed, which outperforms the original YoloV8seg in all network performance metrics. Additionally, the post-processing method introduced reduces root identification errors, ensuring a one-to-one correspondence between each root system and its detection box. The experiment yields phenotypic parameters for fine-grained roots, such as fine-grained root length, diameter, and curvature. Compared to traditional parameters like total root length and average root diameter, these detailed phenotypic analyses enable more precise phenotyping and facilitate accurate artificial intervention during crop cultivation.

LEXICAL COLLOCATIONAL ERRORS MADE BY EFL HIGH SCHOOL STUDENTS

This research paper addresses various lexical collocational errors made by EFL private school students, aiming to investigate their writings (essays and paragraphs) through detailed analysis. The primary objectives of this study are to identify various lexical collocation errors in the written compositions of students at the school level and to provide the correct forms of these lexical collocations to enhance their writing accuracy. The study utilizes both qualitative and quantitative methods, focusing on a sample of 50 randomly selected students (males and females), who participated voluntarily. By using qualitative and quantitative methods and adopting the classification of lexical collocational errors by Benson et al. (1997), for data analysis and to check the acceptance of word combinations, these errors were manually identified in the target written tasks. Then, the researchers used the HASK collocation database (www.pelcra.pl/hask_en/), a reliable online resource containing a large number of collocations. The extracted errors were analyzed and interpreted, with their frequencies and percentages tabulated using Excel bar charts. The findings of the study conclude that EFL students make diverse errors in using lexical collocations in their writing when they try to express their thoughts and ideas. In the analysis of 50 academic pieces of writing, 58 lexical collocation errors were found. It was also found that two specific patterns, (verb + noun) and (verb + adverb) were rarely or almost never used by the participants of the study. The study also found that literal translation from the native language to the target language and limited knowledge of collocations were the main reasons behind these errors. The current study is valuable since collocations play an essential role in improving writing skills. Particularly, the identification of common lexical collocation errors helps educators target specific instructional areas and understand their impact on students' long-term language development. The findings also aid curriculum developers in addressing lexical challenges faced by EFL high school students.

Differential Diagnosis of a Pharyngeal Fricative and Therapeutic Monitoring of Velopharyngeal Function Using Magnetic Resonance Imaging.

Speech disorders associated with velopharyngeal dysfunction (VPD) are common. Some require surgical management, while others are responsive to speech therapy. This is related to whether the speech error is obligatory (passive) or compensatory (active). Accurate identification of speech errors is necessary to facilitate timely and appropriate intervention. Recent studies have supported the role of magnetic resonance imaging (MRI) in the assessment process for VPD. The purpose of this study was to utilize MRI to support differential diagnosis and treatment planning in a child presenting with inconsistent nasal air escape, mild hypernasality, and compensatory speech errors. A nonsedated, fully awake, velopharyngeal (VP) MRI protocol was implemented to acquire anatomic data at rest and during phonation. Segmentations and visualization of the tongue, palate, adenoids, and nasopharyngeal airway were completed. Anatomic linear measurements were obtained for VP variables to assess VP function, establish a baseline, and monitor change over time. VP anatomy was successfully visualized on MRI in multiple imaging planes. All anatomic measurements fell within normative expectations. Elevation and retraction of the soft palate occurred against the adenoid pad. A pharyngeal fricative was documented, resulting a small VP gap during speech. In contrast, adequate VP closure was obtained for vowels and other oral consonant sounds. Quantitative assessment and visualization of the anatomy demonstrated adequate VP closure capabilities and a pharyngeal fricative substitution that had not been adequately perceived during routine clinical assessments. This study suggests a promising additive role for VP MRI for enhanced differential diagnosis and therapeutic monitoring in children with VPD and concomitant speech disorders. https://doi.org/10.23641/asha.27905451.

Feasibility study of structural similarity index for patient-specific quality assurance.

The traditional gamma evaluation method combines dose difference (DD) and distance-to-agreement (DTA) to assess the agreement between two dose distributions. However, while gamma evaluation can identify the location of errors, it does not provide information about the type of errors. The purpose of this study is to optimize and apply the structural similarity (SSIM) index algorithm as a supplementary metric for the quality evaluation of radiation therapy plans alongside gamma evaluation. By addressing the limitations of gamma evaluation, this study aims to establish clinically meaningful SSIM criteria to enhance the accuracy of patient-specific quality assurance (PSQA). We analyzed the relationship between the gamma passing rate (GPR) and the SSIM index with respect to distance and dose errors. For SSIM analysis corresponding to gamma evaluation criteria of 3%/2mm, we introduce the concept of SSIM passing rate (SPR). We determined a valid SSIM index that met the gamma evaluation criteria and applied it. Evaluations performed for 40 fields measured with an electronic portal imaging device (EPID) were analyzed using the GPR and the applied SPR. The study results showed that distance errors significantly affected both the GPR and the SSIM index, whereas dose errors had some influence on the GPR but little impact on the SSIM index. The SPR was 100% for distance error of 2mm but began to decrease for distance errors of 3mm or more. An optimal SSIM index threshold of 0.65 was established, indicating that SPR fell below 100% when distance errors exceeded 2mm. This study demonstrates that the SSIM algorithm can be effectively applied for the quality evaluation of radiation therapy plans. The SPR can serve as a supplementary metric to gamma evaluation, offering a more precise identification of distance errors. Future research should further validate the efficacy of SSIM algorithm across a broader range of clinical cases.

Analysis of Preanalytical Errors in Clinical Biochemistry Laboratory of a Tertiary Care Hospital: A Retrospective Study

Introduction: Pre-analytical phase of laboratory testing is the most susceptible phase, as errors in this phase leads to more than 50% of erroneous results and often breaches the trust of the stakeholders on the quality of the laboratory results. Many pre-analytical errors occur during the pre-analytical phase, encompassing sample collection, labelling and transportationfactors often beyond the laboratory’s direct control. Aim: To determine the type and frequency of pre-analytical errors leading to sample rejection in clinical biochemistry laboratory. Materials and Methods: Being a retrospective descriptive study, convenient sampling was used to analyse sample rejection due to pre-analytical errors in clinical biochemistry laboratory of a tertiary care teaching hospital- PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India for a period of six months from May 2023 to October 2023. All the cases/blood samples from Outpatient Department (OPD) and Inpatient Department (IPD), received and rejected during this period were included under study. The data collection and analysis was done over a period of five months using the sample rejection and resample description from Laboratory Information System (LIS). Using Statistical Package for the Social Sciences (SPSS) version 28.0, data were summarised using descriptive statistics such as numbers and percentages. Results: During the six months period out of the total of 667454 samples, 1505 (0.23%) samples were rejected due to pre-analytical errors. The majority of the samples which were rejected were from IPD than OPD. Among the pre-analytical errors, haemolysis accounted for 806 (53.6%), clotted samples 256 (17%), delta check 217 (14.4%), insufficient sample 129 (8.6%), contamination 74 (4.9%), identification error 14 (0.9%), sample without request form 3 (0.2%) while missing samples, billing error, inappropriate tube, delay in transport and wrong test selection accounted for <3 (0.1%). Conclusion: Haemolysis and clotted samples were the most common pre-analytical causes for sample rejection in the laboratory. The samples from IPD were rejected more often than OPD due to incorrect phlebotomy techniques. This accentuates the need for proper hands-on phlebotomy training sessions for novice nurses following their recruitment, as their competency will be instrumental in bringing down the errors in pre-analytical phase.

Large-scale observational study of AI-based patient and surgical material verification system in ophthalmology: real-world evaluation in 37 529 cases

BackgroundSurgical errors in ophthalmology can have devastating consequences. We developed an artificial intelligence (AI)-based surgical safety system to prevent errors in patient identification, surgical laterality and intraocular lens (IOL) selection....