Types Of Errors Research Articles

Pre-trained models for linking process in data washing machine

Entity Resolution (ER) has been investigated for decades in various domains as a fundamental task in data integration and data quality. The emerging volume of heterogeneously structured data and even unstructured data challenges traditional ER methods. This research mainly focuses on the Data Washing Machine (DWM). The DWM was developed in the NSF DART Data Life Cycle and Curation research theme, which helps to detect and correct certain types of data quality errors automatically. It also performs unsupervised entity resolution to identify duplicate records. However, it uses traditional methods that are driven by algorithmic pattern rules such as Levenshtein Edit Distances and Matrix comparators. The goal of this research is to assess the replacement of rule-based methods with machine learning and deep learning methods to improve the effectiveness of the processes using 18 sample datasets. The DWM has different processes to improve data quality, and we are currently focusing on working with the scoring and linking processes. To integrate the machine model into the DWM, different pre-trained models were tested to find the one that helps to produce accurate vectors that can be used to calculate the similarity between the records. After trying different pre-trained models, distilroberta was chosen to get the embeddings, and cosine similarity metrics were later used to get the similarity scores, which helped us assess the machine learning model into DWM and gave us closer results to what the scoring matrix is giving. The model performed well and gave closer results overall, and the reason can be that it helped to pick up the important features and helped at the entity matching process.

Dos and don'ts for conducting mediation analysis: A commentary with practical tips to avoid common problems.

Purpose: Mediation analyses allow for exploration of causal mechanisms that explain how a predictor is related to an outcome. Tests of mediation are fundamental to addressing some of the most consequential questions in rehabilitation science. In recent decades, the development of easy-to-use analytical tools has made conducting statistical tests of mediation more accessible to researchers. Unfortunately, there are persistent problems in the conceptual underpinning of many tests of mediation. Even in cases where the statistical analyses are correctly run, problems with the underlying rationale for the mediational analysis will render the results inconsequential, in the best case, or misleading, in the worst case. Method: In this commentary, I summarize the uses of mediation analysis and through a series of six main types of errors provide practical, plain language guidance ("Dos and Don'ts") for conducting a conceptually robust mediation analysis. Results: The "Dos and Don'ts" laid out in this commentary highlight that there are persistent issues with lack of understanding of mediation, confusion about the differences between moderation, mediation, and covariates, lack of strong theoretical justification for mediation, and lack of attention to methodological issues (e.g., measurement) in many mediation analyses. Conclusions: Promoting the use of mediation analysis in rehabilitation research will advance theory and effective practice in our field. Researchers undertaking mediation analysis are encouraged to prioritize developing a strong theoretical framework that justifies use of mediation analysis, ensuring study methodology supports and enables tests of mediation, as well emphasizing a strong statistical approach to conducting the test of mediation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Speech sound errors in adolescents with congenital hearing loss

Children with hearing impairment (HI) often have vowel and consonant errors. Speech-language pathologists need this information to correct articulation errors. Despite the prevalence of research on speech sound error patterns in hearing aid users, there is a lack of literature in the adolescent group. The present study aimed to investigate the common speech sound errors in HI adolescents from the Malayalam-speaking community who have severe to profound hearing loss and are digital hearing aid users. A total of 15 monolingual Malayalam-speaking children between the ages of 9 and 16 years were recruited who had been diagnosed with speech sound disorders secondary to congenital hearing impairment. The minimum language age for inclusion in the study was over three years. Articulatory error analysis was performed for all consonants and vowels of the Malayalam language, and frequently produced vowel and consonant errors were included in the SODA and PMV analyses. The consonants frequently produced speech sound errors and no vowel errors were found. The most commonly produced consonant errors were fricatives, trills, laterals, velars, and alveolars. Substitution and omission errors were the most common type of error. Place and manner errors were more common than other types of errors. The results of this study may help parents and rehabilitation professionals to understand the common errors that persist even after intensive intervention. It may also be a useful guide for AAC therapists and speech-language pathologists in setting appropriate speech production goals for adolescents with HI.

Universal quantum processors in spin systems via robust local pulse sequences

We propose a protocol to realize quantum simulation and computation in spin systems with long-range interactions. Our approach relies on the local addressing of single spins with external fields parametrized by Walsh functions. This enables a mapping from a class of target Hamiltonians, defined by the graph structure of their interactions, to pulse sequences. We then obtain a recipe to implement arbitrary two-body Hamiltonians and universal quantum circuits. Performance guarantees are provided in terms of bounds on Trotter errors and total number of pulses. Additionally, Walsh pulse sequences are shown to be robust against various types of pulse errors, in contrast to previous hybrid digital-analog schemes of quantum computation. We demonstrate and numerically benchmark our protocol with examples from the dynamics of spin models, quantum error correction and quantum optimization algorithms.

Prevalence of symptomatic dry eye and influencing factors among Chinese adolescents: A cross-sectional study.

Comprehensive research on the impact of various types of refractive errors (RE) and anisometropia on dry eye disease is still lacking. This study aimed to estimate the prevalence rates and potential lifestyle factors related to symptomatic dry eye (SDE) among adolescents in eastern China. A cross-sectional study was performed in 2023, and a stratified cluster sampling technique was used among adolescents in Nantong, China. Demographic information, including sex, age and BMI, were collected. All participants underwent optometric tests, while Ocular Surface Disease Index (OSDI) and self-designed questionnaires were administered. Both univariate and multivariate logistic regression analyses were used to assess associations between SDE and related parameters, and various types of RE and anisometropia were also included in the study. A total of 1,518 participants were enrolled in the study, and the overall prevalence of SDE was 20.3% among adolescents in Nantong, China. Multiple logistic regression analyses showed that high myopia (aOR = 3.42, 95% CI = 1.60-3.36, p = 0.025), frequent use of eye drops (aOR = 2.31, 95% CI = 1.60-3.36, p<0.001), a history of allergic conjunctivitis (aOR = 1.93, 95% CI = 1.09-3.34, p = 0.025), and frequent blinking (aOR = 3.23, 95% CI = 2.31-4.53, p<0.001) were identified as risk factors for SDE. Conversely, male gender (aOR: 0.76, 95% CI: 0.58-0.99, p = 0.043), increased sleep time (6-7 h: aOR = 0.64, 95% CI = 0.46-0.89, p = 0.009; 7-8 h: aOR = 0.64, 95% CI = 0.43-0.95, p = 0.026; >8 h: aOR = 0.43, 95% CI = 0.23-0.82, p = 0.010), and timely intervention when vision decline occurred were protective factors against SDE (aOR = 0.61, 95% CI = 0.43-0.85, p = 0.004). High myopia was found to be independently associated with an increased risk of SDE., while hyperopia, astigmatism, and anisometropia were not independent risk factors for SDE. The identified risk and protective factors may help provide valuable insights for future research and interventions aimed at improving ocular health in adolescents.

Order-Preserving Pattern Matching with Partition

Order-preserving pattern matching, which considers the relative orders of strings, can be applied to time-series data analysis. To perform a more meaningful analysis of time-series data, approximate criteria for the order-isomorphism are necessary, considering diverse types of errors. In this paper, we introduce a novel approximation criterion for the order-isomorphism, called the partitioned order-isomorphism. We then propose an efficient O(n+sort(m))-time algorithm for the order-preserving pattern matching problem considering the criterion of partition. A comparative experiment demonstrates that the proposed algorithm is more effective than the exact order-preserving pattern matching algorithm.

Optimization design of assembly sequence for the proton exchange membrane fuel cell stacks with dimensional errors

Proton exchange membrane fuel cell (PEMFC) stacks are assembled by numerous bipolar plates (BPPs) and membrane electrode assemblies (MEAs). Component manufacturing errors are inevitably caused during the production process and brought into the stack, affecting the mechanical consistency of every cell. Aiming to improve the uniformity of the assembled stack effectively, this paper establishes a novel assembly deviation model based on the asymptotic homogenization (AH) method and proposes an assembly sequence optimization approach for the stack with dimensional errors using the genetic algorithm. The results reveal that dimensional errors significantly affect the equivalent mechanical properties of the stack. Reasonable matching of components with different types of dimensional errors is an effective method to avoid the cumulative negative impacts on the mechanical behavior of the stack. Statistically, the optimization of the assembly sequence can improve contact pressure uniformity in a 10-cell stack by more than 9.00%, while reducing the maximum contact pressure by 15.55%. This methodology is also applicable to improving the assembly uniformity of other stacked structures.

Atmospheric Neutron Single Event Effects Research of Multiple Convolutional Neural Networks based on 28 nm and 16 nm SoC

Abstract The single event effects (SEEs) evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network (CNN) models (Yolov3, MNIST, and ResNet50) in the atmospheric neutron irradiation spectrometer (ANIS) at the China Spallation Neutron Source (CSNS). The Yolov3 and MNIST models were implemented on the XILINX 28 nm system-on-chip (SoC). Meanwhile, the Yolov3 and ResNet50 models were deployed on the XILINX 16 nm FinFET UltraScale+ MPSoC. The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects, including chip type, network architecture, deployment methods, inference time, datasets, and the position of the anchor boxes. The various types of SEE soft errors, SEE cross-sections, and their distribution were analyzed to explore the radiation sensitivities and rules of 28 nm and 16 nm SoC. The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability, long-lifespan domestic artificial intelligence chips.

Artificial Intelligence-Assisted Daily Quality Control System for the Histologic Diagnosis of Gastrointestinal Endoscopic Biopsies: A 1-Year Experience.

Seegene Medical Foundation, one of the major clinical laboratories in South Korea, developed SeeDP, an artificial intelligence (AI)-based postanalytic daily quality control (QC) system that reassesses all gastrointestinal (GI) endoscopic biopsy (EB) slides for incorrect diagnoses. To review the operational records and clinical impact of SeeDP since its launch in March 2022. Operational records of SeeDP were retrieved for the period of March 1, 2022, to February 28, 2023. Among cases scanned during 40 working days (March 10, 2022, to May 4, 2022), all discordant cases encountered by 2 pathologists were reviewed. Cases of SeeDP-assisted revised diagnoses were collected and compared with cases recognized using conventional methods. Occasional scanner failures and various types of aberrant errors compromised QC coverage, resulting in the scanning of only 67.7% (572 254 of 844 906) of all EB slides submitted and 0.8% of the scanned slides being further excluded from the AI analysis. The AI predictions differed from the pathologists' diagnoses in 42 760 of the 557 672 gastrointestinal EB slides (7.7%) successfully assessed by the AI models; however, a detailed review of discordant slides revealed that true misdiagnosis accounted for only 5.5% (25 of 454) of the disagreements. Compared with conventional error recognition methods, SeeDP detected more misdiagnoses (7 versus 14) within a significantly shorter time (average, 3.6 versus 38.7 days; P < .001), including 1 signet ring cell carcinoma initially diagnosed as gastritis. AI-based daily QC systems are plausible solutions to guarantee high-quality pathologic diagnosis by enabling rapid detection and correction of misdiagnosis.

Investigation on meshing characteristics of spiral bevel gear under wear fault evolution with assembly errors

Spiral bevel gears have the advantage of transmitting high torque and power comparing to helical gear and straight bevel gear. However, they are susceptible to wear due to factors such as high loads, poor lubrication, and surface imperfections resulting from machining. In this study, we propose an autonomous model for predicting tooth wear and analyze the meshing characteristics of spiral bevel gear (SBG) pairs under different assembly errors. Concurrently, we establish a finite element model to validate the accuracy of our meshing characteristic analyses. We also compare the distribution patterns of wear depth on the tooth surface with existing literature. Our investigation delves into meshing characteristics, encompassing transmission errors and Time-varying mesh stiffness (TVMS), considering wear evolution under assembly errors. The results demonstrate that the fluctuations in TVMS remain consistent across all four types of assembly errors examined. Gear tooth wear leads to an increase in gear backlash and fluctuations in unloaded transmission error. The meshing stiffness fluctuation of the SBG pair enlarges under wear evolution. Assembly errors result in shifts in the initial contact line and contact pattern, subsequently altering the distribution of wear depth. Different forms of assembly errors may cause a similar effect on the distribution of wear depth and meshing characteristics.

Error Analysis on the Written English of Students in Grade 8 at Orr's Hill Vivekananda College, Trincomalee, Sri Lanka

This study attempts to identify and analyze “The errors in the written sentences produced by the students in grade 8 of Orr’s Hill Vivekananda School, Trincomalee”. The study begins with a systematic review of the concepts and theories concerning Error Analysis, which sets the platform for the theoretical base. Subsequently, the Error Analysis focuses on finding the syntactical errors under the selected grammatical categories while searching for the possible reasons causing errors. Eventually, the researcher suggests some of the complications involved in language learning and language teaching process. Understanding grammatical rules and applying them in sentences without making mistakes is what the teachers expect from the students in the school. However, it does not happen in the way it is expected. In other words, making grammatical errors can be assumed to be part and parcel of the language learning process. There are many linguistic aspects governing this learning process. Therefore, this research examines the type of errors the students make and why they make such errors in their writing. This dissertation is an eye-opener for teachers, with as they should understand the root causes of errors while attempting to find treatment for them. It is paramount that with teachers approach error corrections based on the student’s linguistic competence and their affective factors embedded with different socio-ethnic backgrounds. This error analysis has been undertaken with to with raise awareness among the teaching staff and help students produce better writing with fewer or no grammatical mistakes.

The basic statistical concepts and their interrelationships in diagnostic research.

Many medical postgraduate students exhibit a lack of clarity in their understanding of relevant statistical concepts during the conduct of diagnostic studies. This article, grounded in research practice, delves into the role of understanding statistical concepts in diagnostic research. It includes an exploration of sensitivity, specificity, types of statistical errors, and their interrelationships, as well as a discussion on statistical power-an often-overlooked but crucial concept in research. The article elucidates these important concepts with specific examples and illustrations, and addresses an issue of inconsistency related to the receiver operating characteristic curve in research practice. By drawing analogies between basic concepts in diagnostic tests and concepts in statistics, this article helps to enhance researchers' abilities in designing and interpreting clinical diagnostic studies, thereby improving the quality of clinical diagnostic research.

ChatGPT as a prospective undergraduate and medical school student.

This article reports the results of an experiment conducted with ChatGPT to see how its performance compares to human performance on tests that require specific knowledge and skills, such as university admission tests. We chose a general undergraduate admission test and two tests for admission to biomedical programs: the Scholastic Assessment Test (SAT), the Cambridge BioMedical Admission Test (BMAT), and the Italian Medical School Admission Test (IMSAT). In particular, we looked closely at the difference in performance between ChatGPT-4 and its predecessor, ChatGPT-3.5, to assess its evolution. The performance of ChatGPT-4 showed a significant improvement over ChatGPT-3.5 and, compared to real students, was on average within the top 10% in the SAT test, while the score in the IMSAT test granted admission to the two highest ranked Italian medical schools. In addition to the performance analysis, we provide a qualitative analysis of incorrect answers and a classification of three different types of logical and computational errors made by ChatGPT-4, which reveal important weaknesses of the model. This provides insight into the skills needed to use these models effectively despite their weaknesses, and also suggests possible applications of our analysis in the field of education.

The manuscript diagrams of Theodosios’ Spherics

This paper is a study of the visual characteristics of the medieval manuscript diagrams found in Theodosios’ Spherics. It provides a new taxonomy of eight types of diagrams found in the manuscript sources, and examines the way in which geometric information is encoded in the manuscript diagrams using a number of visual encodings. It then argues that these diagrams can be used to address ongoing research debates about whether the striking visual characteristics of medieval diagrams in mathematical texts are the result of deliberate choices by the premodern authors, the result of accidents of transmission, or both. Along the way, the paper identifies a further type of transmission error that is prevalent in the medieval transmission of mathematical diagrams.

Exploration of Lexical Collocations Employed in EFL Students’ Essay Writing

This study investigates the use of different types of lexical collocations and common errors made related to these collocations in essay writing. The participants included EFL students who wrote an essay as a class writing test in 60 minutes. A 5-level Likert close-ended survey questionnaire was designed to collect quantitative data regarding students’ perceptions of the categories of lexical collocations they have used and the difficulties they have encountered in using these. To get qualitative data for the study, students’ essays were then analyzed to identify errors related to different categories of lexical collocations. The results revealed that adjective-noun collocations were the most frequently used by the participants while lexical collocations related to adverbs were used least frequently. Errors related to the morpheme -s such as the omission of morpheme -s in plural nouns and overuse of morpheme -s in singular or uncountable nouns were most commonly made by students, followed by the wrong choice of component words and non-existent lexical collocations. Pedagogical implications are then discussed to assist students in enhancing collocational use in essay writing.

Sequence-to-Sequence Models and Their Evaluation for Spoken Language Normalization of Slovenian

Sequence-to-sequence models have been applied to many challenging problems, including those in text and speech technologies. Normalization is one of them. It refers to transforming non-standard language forms into their standard counterparts. Non-standard language forms come from different written and spoken sources. This paper deals with one such source, namely speech from the less-resourced highly inflected Slovenian language. The paper explores speech corpora recently collected in public and private environments. We analyze the efficiencies of three sequence-to-sequence models for automatic normalization from literal transcriptions to standard forms. Experiments were performed using words, subwords, and characters as basic units for normalization. In the article, we demonstrate that the superiority of the approach is linked to the choice of the basic modeling unit. Statistical models prefer words, while neural network-based models prefer characters. The experimental results show that the best results are obtained with neural architectures based on characters. Long short-term memory and transformer architectures gave comparable results. We also present a novel analysis tool, which we use for in-depth error analysis of results obtained by character-based models. This analysis showed that systems with similar overall results can differ in the performance for different types of errors. Errors obtained with the transformer architecture are easier to correct in the post-editing process. This is an important insight, as creating speech corpora is a time-consuming and costly process. The analysis tool also incorporates two statistical significance tests: approximate randomization and bootstrap resampling. Both statistical tests confirm the improved results of neural network-based models compared to statistical ones.

Add-on perceptual learning on refractive amblyopia in children.

To evaluate the visual outcomes of standard amblyopic treatment add-on training via perceptual learning in refractive amblyopic children and to identify the risk factors for treatment failure. Retrospective charts were reviewed in children with refractive amblyopia who received standard treatment and add-on Cambridge Visual Stimulator (CAM) training. The add-on CAM group that was enrolled had worn full-corrected glasses for at least 2mo before training. A control group received only the standard treatment. Treatment success was defined as best-corrected visual acuity (BCVA) ≥20/25. The age, sex, initial BCVA, refractive errors, sessions and duration of training, and final BCVA were recorded. A total of 209 children (129 children in add-on CAM group and 80 children in control group) were enrolled. Seventy-six percent of unilateral and 87% of bilateral amblyopic children achieved treatment success. In children with unilateral or bilateral moderate amblyopia, the duration to reach BCVA ≥20/25 was significantly shorter in add-on CAM group than in control group. Poor initial BCVA (P<0.001) and high astigmatism (P=0.007) were risk factors for treatment failure after add-on CAM training. Age, sex, and types of refractive error were not associated with treatment success. Add-on CAM training is an effective strategy for visual improvement and can shorten the treatment course when the effect of standard treatment is limited in amblyopic children.

Compliance and Determinants of Spectacle Wear Among Moroccan Adults Residing Beni-Mellal Khénifra Region.

Ensuring a high level of adherence to wearing spectacles is essential to preserve eye health and achieve optimal vision correction. Comprehending the factors influencing adherence to wearing spectacles can inform strategies to improve eye care outcomes. This study aimed to assess the prevalence and factors influencing adherence to wearing spectacles among Moroccan adults residing the Beni-Mellal Khenifra region. A cross-sectional interview survey was conducted involving 389 adult spectacle wearers. Participants were recruited through a multilevel random sampling technique and interviewed using a structured questionnaire. This questionnaire collected data on demographics, spectacle prescription, usage patterns, and barriers to compliance. Compliance was assessed based on self-reported frequency and duration of spectacle wear according to the prescription. Possible factors influencing the wearing of spectacles were investigated using both univariate and multivariate regression models. This study revealed that 57.6% of participants adhered to the recommended use of spectacles. Factors associated with compliance were higher education level, longer duration of spectacle use, awareness about the importance of spectacle wear, absence of a family history, specific refractive error types, and increased severity of refractive errors. Participants reported various reasons for not adhering to the recommended use of spectacles, with the most common being forgetfulness, perceiving improved vision without spectacle, discomfort while wearing spectacle, difficulty adapting to spectacle, and loss of spectacle. Given the high prevalence of non-compliance to spectacle wear among adults in the Beni-Mellal Khenifra region, it is evident that additional efforts are required to improve understanding and education regarding the advantages of consistent spectacle usage. Targeted educational and awareness initiatives have the potential to substantially enhance adherence rates and consequently improve visual health outcomes in the region.

Type-migrating C-to-Rust translation using a large language model

Rust, a modern system programming language, introduces new types that prevent memory bugs and data races. This makes translating legacy system programs from C to Rust a promising approach to enhance their reliability. Since manual code translation is time-consuming, it is desirable to automate the translation. To yield satisfactory results, the translator should have the ability to perform type migration, i.e., removing C types and introducing Rust types in the code. In this work, we aim to automatically port an entire C program to Rust by translating each C function to a Rust function with a signature containing proper Rust types through type migration. This goal is challenging because (1) type migration cannot be achieved through syntactic mappings between type names, and (2) after type migration, function bodies should be correctly restructured based on the precise understanding of the functions’ behavior. To address these difficulties, we leverage large language models (LLMs), which possess knowledge of program semantics and programming idioms. However, naïvely instructing LLMs to translate each function produces unsatisfactory Rust code, containing unmigrated or improperly migrated types and a huge number of type errors. To resolve these issues, we propose three techniques: (1) generating candidate signatures, (2) providing translated callees’ signatures to LLMs, and (3) iteratively fixing type errors using compiler feedback. Our evaluation shows that the proposed approach yields a 63.5% increase in migrated types and a 71.5% decrease in type errors compared to the baseline (the naïve LLM-based translation) with modest performance overhead.

Error analysis based on a tunable wave plate polarization interferometric imaging spectrometer

Interference imaging spectroscopy combines modern imaging technology with spectral technology, holding significant importance for object imaging and spectral detection. This article introduces the principle of an adjustable wave plate polarization interferometric imaging spectrometer. The example design specifications are set for an observation wavelength range of 450–780 nm and a maximum resolution of 2 nm at 450 nm, with a 0.5 in detector as the base for calculating the specific dimensions of the Soleil–Babinet compensator. An investigation was conducted on the issues of nonuniform sampling, as well as three types of mechanical errors: flatness, wedge angle tolerance, and optical axis orientation accuracy. Emphasis was placed on discussing the impact of these errors on the instrument’s optical path difference and spectral reconstruction accuracy. This research provides theoretical guidance for the design and engineering of this miniaturized imaging spectrometer.