Error Distribution Research Articles

Prevalence of refractive errors in school-aged and preadolescent children in Colombia.

To describe the distribution of refractive errors by age and sex among schoolchildren in Soacha, Colombia. This was an observational cross-sectional study conducted in five urban public schools in the municipality of Soacha. A total of 1161 school-aged and pre-adolescent children, aged 5-12y were examined during the school year 2021-2022. Examinations included visual acuity and static refraction. Spherical equivalent (SE) was analysed as follows: myopia SE≤-0.50 D and uncorrected visual acuity of 20/25 or worse; high myopia SE≤-6.00 D; hyperopia SE≥+1.00 D (≥7y) or SE≥+2.00 D (5-6y); significant hyperopia SE≥+3.00 D. Astigmatism was defined as a cylinder in at least one eye ≥1.00 D (≥7y) or ≥1.75 D (5-6y). If at least one eye was ametropic, children were classified according to the refractive error found. Of the 1139 schoolchildren included, 50.6% were male, 58.8% were aged between 5 and 9y, and 12.1% were already using optical correction. The most common refractive error was astigmatism (31.1%), followed by myopia (20.8%) and hyperopia (13.1%). There was no significant relationship between refractive error and sex. There was a significant increase in astigmatism (P<0.001) and myopia (P<0.0001) with age. Astigmatism is the most common refractive error in children in an urban area of Colombia. Emmetropia decreased and myopia increased with age.

Amplified DNA heterogeneity assessment with Oxford Nanopore sequencing applied to cell free expression templates.

In this work, Oxford Nanopore sequencing is tested as an accessible method for quantifying heterogeneity of amplified DNA. This method enables rapid quantification of deletions, insertions, and substitutions, the probability of each mutation error, and their locations in the replicated sequences. Amplification techniques tested were conventional polymerase chain reaction (PCR) with varying levels of polymerase fidelity (OneTaq, Phusion, and Q5) as well as rolling circle amplification (RCA) with Phi29 polymerase. Plasmid amplification using bacteria was also assessed. By analyzing the distribution of errors in a large set of sequences for each sample, we examined the heterogeneity and mode of errors in each sample. This analysis revealed that Q5 and Phusion polymerases exhibited the lowest error rates observed in the amplified DNA. As a secondary validation, we analyzed the emission spectra of sfGFP fluorescent proteins synthesized with amplified DNA using cell free expression. Error-prone polymerase chain reactions confirmed the dependency of reporter protein emission spectra peak broadness to DNA error rates. The presented nanopore sequencing methods serve as a roadmap to quantify the accuracy of other gene amplification techniques, as they are discovered, enabling more homogenous cell-free expression of desired proteins.

Angle Expansion Estimation and Correction Based on the Lindeberg–Feller Central Limit Theorem Under Multi-Pulse Integration

The radar monopulse angle measurement can obtain a target’s angle information within a single pulse, meaning that factors such as target motion and amplitude fluctuations, which vary over time, do not affect the angle measurement accuracy. However, in practical applications, when a target’s signal-to-noise ratio (SNR) is low, the single pulse signal is severely affected by noise, leading to a significant deterioration in angle measurement accuracy. Therefore, it is usually necessary to coherently integrate multiple pulses before estimating the angle. This paper constructs an angle expansion model for a multi-pulse angle measurement under coherent integration. The analysis reveals that even under noise-free conditions, after coherently integrating multiple pulses, the coupling of target amplitude fluctuations and motion state can still cause significant errors in the angle measurement. Subsequently, this paper conducts a detailed analysis of the impact of the amplitude fluctuations and target maneuvers on the random angle measurement error. It also derives approximate probability density functions of angle measurement errors under various fluctuation and motion scenarios based on the Lindeberg–Feller central limit theorem. In addition, based on the angle expansion model and the random error distribution, this paper proposes an angle correction algorithm based on multi-pulse integration and long-term estimation. Numerical experiments and radar data in the field verify the impact of target characteristics on the angle measurement under multi-pulse integration and the effectiveness of the angle correction algorithm.

Dorsal cortex line is more reliable than transepicondylar axis for rotation in revision total knee arthroplasty with severe bone loss.

The transepicondylar axis is a well-established reference for the determination of femoral component rotation in total knee arthroplasty (TKA). However, when severe bone loss is present in the femoral condyles, rotational alignment can be more complicated. There is a lack of validated landmarks in the supracondylar region of the distal femur. Therefore, the aim of this study was to analyze the correlation between the surgical transepicondylar axis (sTEA) and the suggested dorsal cortex line (DCL) in the coronal plane and the inter- and intraobserver reliability of its CT scan measurement. A total of 75 randomly selected CT scans were measured by three experienced surgeons independently. The DCL was defined in the coronal plane as a tangent to the dorsal femoral cortex located 75 mm above the joint line in the frontal plane. The difference between sTEA and DCL was calculated. Descriptive statistics and angulation correlations were generated for the sTEA and DCL, as well as for the distribution of measurement error for intra- and inter-rater reliability. The external rotation of the DCL to the sTEA was a mean of 9.47° (SD 3.06°), and a median of 9.2° (IQR 7.45° to 11.60°), with a minimum value of 1.7° and maximum of 16.3°. The measurements of the DCL demonstrated very good to excellent test-retest and inter-rater reliability coefficients (intraclass correlation coefficient 0.80 to 0.99). This study reveals a correlation between the sTEA and the DCL. Overall, 10° of external rotation of the dorsal femoral cortical bone to the sTEA may serve as a reliable landmark for initial position of the femoral component. Surgeons should be aware that there are outliers in this study in up to 17% of the measurements, which potentially could result in deviations of femoral component rotation.

An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperature exerted the most significant influence at 100%, while sample dimensions had a minimal impact at 17.9%. In addition, the mathematical model closest to the proposed was the Bazli model, because the latter depends on two variables (thickness and temperature). The ANN accurately predicted the residual tensile strength of GFRP at elevated temperatures, achieving a correlation coefficient of 97.3% and a determination coefficient of 94.3%.

Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Digital-driven in-situ monitoring for thermally-induced volumetric errors of CNC machine tools

In order to realize the in-situ monitoring of thermally-induced volumetric errors of CNC machine tools, a digital twin system is designed and developed through combined programming of C#, ANSYS, and MATLAB. The thermal error mapping models are constructed through superposing the axis motion-induced thermal errors and the spindle rotation-induced thermal deformations. The models of axis motion-induced thermal errors are established based on the orthogonal polynomial tabular modeling method, and real-timely corrected through correction models which are constructed based on the predicted and measured errors. The spindle rotation-induced thermal deformations are obtained via digital twin for thermal characteristics of the spindle system. The error distributions are generated by MATLAB through pre-creating the DLL file. Experimental results show that the prediction accuracy of digital twin-driven in-situ monitoring for thermally-induced volumetric errors is greater than 95 %, which effectively improvs the prediction accuracy of thermally-induced volumetric errors of CNC machine tools and provides a basis for thermal error compensation.

A novel hybrid deep learning model for real-time monitoring of water pollution using sensor data

This study developed a hybrid model for predicting dissolved oxygen (DO) using real-time sensor data for thirteen parameters. This novel hybrid model integrated one-dimensional convolutional neural networks (CNN) and long short-term memory (LSTM) to improve the accuracy of prediction for DO in water. The hybrid CNN-LSTM model predicted DO concentration in water using soft sensor data. The primary input parameters to the model were temperature, pH, specific conductivity, salinity, density, chlorophyll, and blue-green algae. The model used 38,681 water quality data for training and testing the hybrid deep learning network. The training procedure for the model was successful. The training and test losses were both nearly zero and within a similar range. With a coefficient of determination (R2) of 0.94 and a mean squared error (MSE) of 0.12, the hybrid model indicated higher performance compared to the classical models. The normal distribution of residual errors confirmed the reliability of the DO predictions by the hybrid CNN-LSTM model. Feature importance analysis indicated pH as the most significant predictor and temperature as the second important predictor. The feature importance scores based on extreme gradient boosting (XGBoost) for the pH and temperature were 0.76 and 0.12, respectively. This study indicated that the hybrid model can outperform the classical machine learning models in the real-time prediction of DO concentration.

Unpaid insurance claims: Language and communication gaps

Language and communication play pivotal roles in the insurance industry, impacting claims processing, customer satisfaction, and operational efficiency. This study investigates the dynamics of errors within insurance claims across six distinct languages—African, Afrikaans, English, French, German, and Asian languages. The purpose is to identify language-specific error patterns and their implications for customer-centric methods. This study utilises a dataset of insurance claims spanning multiple languages. The analysis encompasses the probability theories approach to unravel error distributions and their associations. The study reveals that linguistic diversity significantly impacts the insurance industry, emphasising the need for language-centric approaches to error reduction This highlights the need for customised communication strategies and enlightening initiatives to address the unique challenges faced by this linguistic demographic especially the complex legal jargon that is not easily understood by all. In conclusion, this paper advises insurance companies to navigate the complex terrain of multilingual interactions. It enriches the existing body of knowledge surrounding insurance claim management, offering a roadmap to navigate the intricacies of language diversity within the industry. By addressing the linguistic-specific challenges identified in this study, insurance providers can enhance their services, reduce errors, and ultimately foster improved customer experiences.

Network traffic anomaly detection model based on feature grouping and multi‐autoencoders integration

AbstractThis paper presents a network traffic anomaly detection model based on feature grouping and multiple autoencoders (multi‐AEs) integration. This model comprises four modules: feature grouping module, feature learning module, AUC and optimal threshold calculation module, and anomaly detection application module. In the feature grouping module, multiple group features are constructed by selecting the different features according to their attributes and variances. In the feature learning module, the group features of normal traffic are learned based on multi‐AEs. In the AUC and optimal threshold calculation module, the AUC of each AE is calculated according to the ROC curve of the verification data, and the optimal thresholds for each AE are determined using the Youden index. In the anomaly detection application module, the AEs that participated in fusion are selected and their weights are calculated by analysing AUC value, and the scores of unknown traffic in each AE are evaluated considering both the reconstruction error distribution and the optimal threshold. Finally, the anomaly detection result can be obtained by the fusion of these multiple scores. Through validation on the UNSW‐NB15 and CICIDS2017 datasets, the accuracy of the proposed model is improved by 12.04% and 10.52%, respectively, compared to the baseline model.

Impacts of Offline Nonlinear Bias Correction Schemes Using the Machine Learning Technology on the All‐Sky Assimilation of Cloud‐Affected Infrared Radiances

AbstractBias correction (BC) of the cloud‐affected infrared (IR) radiances is one of the most difficult challenges in the all‐sky data assimilation. This study introduces an offline nonlinear bias correction model based on the machine learning (ML) technology of Random Forest to enhance the impacts of Fengyun‐4A Advanced Geostationary Radiation Imager (AGRI) all‐sky radiance data assimilation. The effects of the developed model were comprehensively evaluated through sensitivity experiments based on the NoBC, BC and modified BC schemes for two super typhoon cases. Among them, the modified BC scheme is designed to extract the features of cloud‐affected systematic biases, which are more prevalent in the all‐sky IR radiance assimilation. Results showed that the modified BC scheme outperforms other schemes in terms of removing the cloud‐impacted systematic bias while retaining the useful meteorological signal. Whereas, those biases were improperly corrected by the original BC scheme when the inputs of a grid point were handled by the ML model site by site without the feature extraction, leading to a non‐Gaussian error distribution. Assimilating those better‐corrected IR radiances in the modified BC experiments would lead to a greater improvement in the analysis of the humidity and cloud ice. Based on the improved initial condition, the positive effects of the modified BC scheme are also evident in the forecasts of atmospheric variables and typhoon systems.

The Distribution and Frequency of Grammatical Errors in Second Language Writing: A Case Study of Chinese High School Students

As Error analysis in second language writing remains controversial, this study aims to investigate errors with high frequency in L2 writing of high school students in China and reveal the relationship between writing tasks and error types. By analyzing compositions in high school English exams, the study will discover the distribution pattern frequencies of different errors and make comparisons of students at different levels. The findings indicate that verb tense errors are the most frequent but decrease as students progress in grade level, while syntactical errors become more prevalent in more complex tasks, such as continuation writing. This study highlights the persistent challenges students face in constructing grammatically accurate sentences in flexible writing tasks. These insights provide empirical evidence for targeted grammar instruction, emphasizing verb usage and sentence structure complexity to improve overall writing proficiency.

Deep learning model using continuous skin temperature data predicts labor onset

BackgroundChanges in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be linked to hormonal status. Finally, we developed a deep learning model using temperature patterning to provide a daily forecast of time to labor onset.MethodsWe evaluated patterns in continuous skin temperature data in 91 (n = 54 spontaneous labors) pregnant women using a wearable smart ring. In a subset of 28 pregnancies, we examined daily steroid hormone samples leading up to labor to analyze relationships among hormones and body temperature trajectory. Finally, we applied an autoencoder long short-term memory (AE-LSTM) deep learning model to provide a novel daily estimation of days until labor onset.ResultsFeatures of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 37 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The input to the pipeline was 5-min skin temperature data from a gestational age of 240 days until the day of labor onset. During cross-validation AE-LSTM average error (true – predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.ConclusionContinuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Linear and neural network models for predicting N-glycosylation in Chinese Hamster Ovary cells based on B4GALT levels

Glycosylation is an essential modification to proteins that has positive effects, such as improving the half-life of antibodies, and negative effects, such as promoting cancers. Despite the importance of glycosylation, data-driven models to predict quantitative N-glycan distributions have been lacking. This article constructs linear and neural network models to predict the distribution of glycans on N-glycosylation sites. The models are trained on data containing normalized B4GALT1–B4GALT4 levels in Chinese Hamster Ovary cells. The ANN models achieve a median prediction error of 1.59% on an independent test set, an error 9-fold smaller than for previously published models using the same data, and a narrow error distribution. We also discuss issues with other models in the literature and the advantages of this work’s model over other data-driven models. We openly provide all of the software used, allowing other researchers to reproduce the work and reuse or improve the code in future endeavors.

Modeling the distribution and progression of motor ability among children with cerebral palsy: An analysis of three reference centile sets

BackgroundReference centiles describing gross motor function in children with cerebral palsy (CP) are used in clinical and research settings to guide treatments and evaluate interventions. However, it is unknown how existing references generalize to populations in novel settings. AimsThe aim of this study is to evaluate the cross-sectional and longitudinal performance of three reference centiles to describe the motor function of children with CP aged 2–12 years at a large urban US pediatric hospital through a retrospective observational study. Methodsand Procedures: We assessed cross-sectional performance by ranking our clinical population by quartile distributions described by the references. We assessed longitudinal performance by analyzing the distribution of prediction errors and correlations between predictions and observed scores. Outcomes and ResultsFor cross-sectional distribution, the reference centiles by Hanna more closely described our population than those by Duran. For longitudinal progression, prediction error was less than 6 GMFM points for most records at 24-, 12-, and 6-month time scales for all three sets of reference centiles, but higher at a 48-month time scale. Prediction errors increased at younger ages and higher motor ability. Conclusions and implicationsDespite differences in cross-sectional performance, all three reference centiles achieved similar longitudinal performance and are sufficient for most clinical and research uses. Caution should be used when applying these curves to locations with different standards of care.

Learning high-accuracy error decoding for quantum processors

Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical quantum systems1. Quantum error-correction codes2 present a way to reach this goal by encoding logical information redundantly into many physical qubits. A key challenge in implementing such codes is accurately decoding noisy syndrome information extracted from redundancy checks to obtain the correct encoded logical information. Here we develop a recurrent, transformer-based neural network that learns to decode the surface code, the leading quantum error-correction code3. Our decoder outperforms other state-of-the-art decoders on real-world data from Google’s Sycamore quantum processor for distance-3 and distance-5 surface codes4. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk and leakage, utilizing soft readouts and leakage information. After training on approximate synthetic data, the decoder adapts to the more complex, but unknown, underlying error distribution by training on a limited budget of experimental samples. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.

The Epistemic Policies of Anti-Ageing Medicines in the European Union.

Anti-ageing medicines are products intended to extend lifespan and healthspan in humans that have a good potential use in public health policies. In the European Union, their development, production and consumption are dependent on regulatory science performed by the European Medicines Agency and its associated epistemic policies. They impose, among other things, an unfavourable burden of proof, a strict standard of proof and meta-methodological constrictions related to some theoretical issues. This results in a distribution of errors that tends to reduce false positives while increasing false negatives, leading to a set of social consequences that are generally accepted when the focus is placed on conventional medicines. However, when the same epistemic policies are applied to anti-ageing medicines, the distribution of errors is imbalanced, and undesirable outcomes like research discouragement and waiting time extensions appear. Three possible strategies that policymakers could implement to unblock the situation are presented for future reflection: the consideration of ageing as a disease, the application of methodological asymmetry and the use of biomarkers during clinical research.

Radar False Target Recognition Method Based on Reconstruction Error Distribution Characteristics of Frequency Response From Multi-CPIs

Radar False Target Recognition Method Based on Reconstruction Error Distribution Characteristics of Frequency Response From Multi-CPIs

Enhancing Metalinguistic Awareness Through Microlearning: A Comparative Analysis of Catalan Acquisition Between Multilingual French and English Learners

Knowing that previously learned languages may influence and support language learning among multilingual learners, equipping educators with diverse tools and resources empowers them to make more informed decisions in facilitating language learning. This holds particular significance in the case of minority languages like Catalan, where resource availability tends to be more restricted. This paper presents the findings of a project aimed at identifying the specific needs of multilingual B1 learners of Catalan. Using a comprehensive five-way classification system, the study compares the written productions of multilingual learners from different linguistic backgrounds (English and French L1 learners of Catalan). Analyses revealed a similar pattern regarding the nature of the errors, with incorrect word selections at the lexical-semantic, syntactic, and orthographic levels being the most frequent. Both groups differ significantly in terms of the distribution of errors; however, they did not differ when the analyses were conducted by error types. In both cases, the most frequent error was the lexical-semantic misselections, with false analogies and incomplete applications of rules explaining the errors. These findings led to the creation of just-in-time microlearning capsules tailored to address these issues. The microlearning capsules were designed to enhance students' metalinguistic awareness. Students' and teachers' perceptions of the tool were also assessed, and both groups predominantly viewed interlinguistic information as beneficial to language learning. This project highlights how microlearning, tailored to frequent errors, can bolster multilingual learners' acquisition and provides promising tools for language teaching.

An Indicator Based on Spatial Coordinate Information for Assessing the Capability for Dynamic Machining Performance of Five-Axis Flank Milling.

As a spatial coordinate sensor, the touch-trigger on-machine probe is a key equipment in manufacturing that ensures machining quality, and it has played an important role in five-axis flank milling. However, in flank milling, the utilization of the deviation as a conventional indicator for quality assessment of the machining performance is incomprehensive without considering the characteristics of the machining method. In this paper, the error mutual moment is introduced as an indicator to assess the capability for dynamic machining performance of the machine tool in flank milling based on the spatial coordinate information of the touch-trigger on-machine probe considering the characteristic of the error distribution of the flank milling. Experiments are carried out to validate the advantages of the error mutual moment to assess the capability for dynamic machining performance compared with the deviation. Results show that the error mutual moment shows more significant discrepancies than the deviation in assessing the capability for dynamic machining performance of flank milling. The error mutual moment has the potential to be applied as a quality assessment sensor.