Test Error Research Articles

Molecular flow model of gas transmission rate test device with auxiliary chamber for high barrier film

The oxygen barrier test of plastic film is an important step in the sealing and preservation process of food and drugs, and the system leakage is pivotal factor of the test device that affecting the efficiency and accuracy. In order to reduce the impact of system leakage on the oxygen barrier detection of plastic film, a test device with an auxiliary seal on the outer layer is designed by using series dual sealing structure technology. Based on the molecular flow states hypothesis, according to the conservation of gas throughput and the invariance of flow resistance, a mathematical model and simulation platform for permeability test of flexible packaging materials with high barrier with outer auxiliary sealing chamber and forward leakage are established. By using Laplace transform method, the analytical solutions and corresponding characteristics of the pressure and leakage rate changes in the inner and outer sealing chamber are discussed, and the equivalent test time expression of the forward leakage test device is derived. The simulation and measured results show that the equivalent test time of the test device with the outer auxiliary sealing chamber is much longer than 24 h, and the test results of the pressure change of the inner sealing chamber are the same as the ideal leak-free system. Compared with the single sealing structure test device, the test error of the same high barrier film permeability test is negligible. By using the test device with an auxiliary sealing chamber, the oxygen barrier property can be accurately measured without any leak measurement and correction.

CORRELATION BETWEEN MUSCULOSKELETAL PAIN AND COGNITION AMONG UNIVERSITY LEVEL STUDENTS USING THE RUFF FIGURAL FLUENCY TEST.

BACKGROUND: Hand grip strength and function are important for day- to-day tasks. Although chronic pain has long been known to affect cognitive outcomes, new research published in the last decade has focused on the multi-dimensional impacts of pain on multiple cognitive areas; OBJECTIVE: To study cognitive impairment and musculoskeletal pain in university level students and to evaluate the relationship between musculoskeletal pain and cognition in university level students; METHODOLOGY: Study design is cross sectional and observational with sample size87 at MGM SCHOOL OF PHYSIOTHERAPY With age 18 to 27 years having any musculoskeletal pain; RESULTS: The statistical significance of the participants' pain in days with the p value is 0.001 (p<0.001). The amount of errors made by participants with a p value of 0.001 (i.e., p<0.001), the correlation coefficient r-value between the length of the pain episode and the length of the rough figural fluency test was recorded as -0.068, which is statistically insignificant at the 5% level with a non-linear link with a non-linear link, the correlation coefficient r-value for pain duration and ruff figural fluency test errors was observed as -0.11, which is statistically insignificant at the 5% level.CONCLUSION: This study concluded that there is a significant relation between pain and cognition and has a positive correlation between pain and cognition.

Осознание языковой нормы студентами-логопедами как фактор готовности к работе в инклюзивной образовательной среде

<p>The work is aimed at studying the formation of language reflection and the peculiarities of awareness of the language norm by speech therapy students as an indicator of their readiness to work with children with disabilities. The research materials obtained from a sample of first-year students of the Faculty of Defectology of the Moscow State University (N=450; EG=100) are presented. The study was conducted in 2 stages, including the analysis of video recordings of samples of spontaneous speech and reading texts of varying complexity. The results obtained show the insufficient formation of language reflection and the difficulty of understanding the language norm by future speech therapists. The influence of the complexity of the text on the number and prevalence of errors in the reading test has been determined (with a significance of p<0,001 there are differences in the groups). Purposeful attention to their own speech and the speech of others, recording self-observations helped to increase the number of students who completed the tasks without errors (from 15% to 40%). At the same time, with a significance of p<0,001, it was revealed that students are better at detecting errors during self-control than testing others. The study proposes ways of improving the language reflection of the students for their better formation for working in an inclusive environment.</p>

SLOVAK AS A FOREIGN LANGUAGE IN TERMS OF THE INTEGRATION PROCESSES IN THE SLOVAK REPUBLIC

The article represents a methodological starting point for research on teaching Slovak as a foreign language in the Slovak Republic. After the Slovak Republic joined the eurozone, it was necessary to start understanding Slovak as the state language – as a compulsory subject for Slovaks, but also to start teaching it to different groups of foreigners moving to Slovakia. This need has become even more pressing since February 2022, when the war conflict in Ukraine forced thousands of Ukrainians to leave their homes and seek refuge in neighbouring countries, including Slovakia. This raised the question of how to deal with Slovak as a foreign language within the educational system of the Slovak Republic. Legislation (a law on the Residence of Foreigners in Slovakia) is also currently being prepared that will require foreigners asking for living in Slovakia to take a standardized test/exam at an accredited educational institution. However, since very few research studies on this topic have been conducted, this scholarly article lays foundations and poses methodological questions within five key thematic areas (i.e., learners’ motivation, intermediary language, textbooks, testing reference levels and error analysis, particular case studies) when directing a follow-up series of research studies and to justify why these issues are relevant. Therefore, the article explains the starting points that are anchored in previous scientific works in the field. Furthermore, the elaborated theoretical foundations and proposed research studies that will be carried out by university teachers and educational researchers are aimed at deepening scientific knowledge not only in the context of Slovak as a foreign language, but also of other minor foreign languages (e.g. Polish, Czech, Croatian, etc.).

Opto-mechanical-thermal integration design of the primary optical system for a tri-band aviation camera

This paper presents a tri-band aviation camera that integrates short-wave infrared (SWIR, 0.9–1.7 μm), mid-wave infrared (MWIR, 3.7–4.8 μm) and visible (VIS, 0.486–0.656 μm) bands into a primary optical system and three subsystems, to enhance optical remote sensing capabilities. The focus is on opto-mechanical-thermal integration to effectively manage thermal stress and minimise deformation. Finite Element Analysis is employed to extract Zernike coefficients for deformation analysis, facilitating a comprehensive assessment of the camera’s performance across a temperature range of −40 °C to 60 °C. An innovative flexible support system is integrated to maintain the optimal surface figure of the primary mirror, further reducing thermal effects. Extensive empirical testing, including resolution and wavefront error detection, has validated the system’s robustness under various thermal conditions.

Quantifying thermoplastic mask quality for precision radiotherapy in head and neck cancer: A 3D stress test and 6D-axis error analysis.

Patients with head and neck cancers often require radiotherapy, where immobilization devices like thermoplastic masks ensure precise radiation delivery by minimizing movement. However, the quality of these masks lacks standard reference data. This study aimed to establish institutional acceptance criteria for thermoplastic mask quality and quantify their effectiveness using a 3 dimensional stress test and verified the setup errors using daily megavoltage computed tomography (MVCT). Between April and June 2022, 30 patients underwent radiotherapy with thermoplastic masks. Four key facial points (forehead, bilateral cheekbones, and chin) were tested for supporting force. Mean forces ranged from 3.97 N to 8.8 N. MVCT was used to assess 6 dimensional-axis errors, with mean translational errors (x, y, z) of 0.32 mm, -1.09 mm, and 2.24 mm, respectively, and rotational errors (yaw, pitch, roll) of -0.12°, 0.22°, and 0.35°, respectively. The results demonstrated that the thermoplastic masks provided precise immobilization, minimizing setup errors in 6 dimensions. Our findings offer a quantifiable method to ensure high-quality immobilization during radiotherapy for patients with head and neck cancers.

Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning.

Deep learning has transformed computer vision, natural language processing, and speech recognition. However, two critical questions remain obscure: 1) why do deep neural networks (DNNs) generalize better than shallow networks and 2) does it always hold that a deeper network leads to better performance? In this article, we first show that the expected generalization error of neural networks (NNs) can be upper bounded by the mutual information between the learned features in the last hidden layer and the parameters of the output layer. This bound further implies that as the number of layers increases in the network, the expected generalization error will decrease under mild conditions. Layers with strict information loss, such as the convolutional or pooling layers, reduce the generalization error for the whole network; this answers the first question. However, algorithms with zero expected generalization error do not imply a small test error. This is because the expected training error is large when the information for fitting the data is lost as the number of layers increases. This suggests that the claim "the deeper the better" is conditioned on a small training error. Finally, we show that deep learning satisfies a weak notion of stability and provides some generalization error bounds for noisy stochastic gradient decent (SGD) and binary classification in DNNs.

3-D full-field reconstruction of chemically reacting flow towards high-dimension conditions through machine learning

Monitoring chemically reacting flow is crucial for optimizing and controlling the conversion processes in various chemical engineering scenarios. These processes are often influenced by multiple factors, creating a high-dimensional condition space that imposes intensive computational cost to computational fluid dynamics (CFD) methods. We present a machine learning (ML) framework to reconstruct the 3-D scalar field preserving the accuracy and resolution of CFD modeling. We demonstrate the framework’s effectiveness through a case study on biomass combustion in an industrial-scale grate furnace, which exemplifies the challenges of modeling chemically reacting flow in complex geometric domains influenced non-linearly by multiple parameters. The framework implements three core principles: (1) a Reactor-Structure-Resembled Network (RSRNet) reflecting the operational logics of the reactor, (2) an incremental learning strategy based on random Latin Hypercube Sampling, and (3) the incorporation of self-extracted high-level features from data as soft constraints in the loss function. Ground truth data were calculated through a CFD model, with all cases sampled from a 11-variable condition space. Using only 240 randomly sampled cases (around 1 % total cases in the discretized condition space), RSRNet precisely replicated 1-D and 2-D scalar distributions through incremental training. Further reconstruction of 3-D temperature field only used 40 3-D CFD cases, resulting in the training and testing errors decreased to 7.94 × 10-4 and 1.55 × 10-3, respectively, approaching the level of the 2-D reconstruction, with the average error in temperature field predictions not exceeding 50 K. The generalization ability was validated using a separate test dataset with continuously changing excess air ratio and capacity ratio in wide ranges, including some extreme values, and the model successfully captured complex phenomena under unseen conditions.

ADAPTATION OF TESTING PROCEDURES AND STABILIZATION OF RESULTS IN PRESCHOOL RHYTHMIC GYMNASTS

The aim of this study was to familiarize preschool rhythmic gymnasts with testing procedures and to determine the number of familiarization attempts needed to stabilize the test results. Thirty-six female rhythmic gymnasts aged 5.14±0.79 years (body height: 119.44±5.98 cm; body mass: 21.46±3.35 kg) volunteered for the study. All gymnasts were free of injury, trained regularly (two days per week, approximately 60 minutes per session), and participated in the individual B level competitions. The main findings indicated a statistically significant difference between the variables of spine and leg flexibility (p<0.00), standing long jump (p<0.00), balance (p<0.00), and shoulder flexion and extension (p<0.00). According to the results obtained in this research, familiarization can significantly improve scores in motor tests for all variables (spine and leg flexibility, balance, standing long jump, and shoulder flexion and extension) in preschool rhythmic gymnasts. These findings suggest several possibilities for further research, such as investigating the long-term effects of familiarization on motor skill development, the specific mechanisms through which familiarization enhances performance, and its impact across different age groups and skill levels in rhythmic gymnastics. Rhythmic gymnastics trainers and other coaches of aesthetic sports should adapt and introduce participants to test protocols to avoid errors in testing.

Neural fields for rapid aircraft aerodynamics simulations

This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for different flow conditions, handle non-parametric 3D geometric variations, and generalize to unseen shapes at test time. The coordinate-based formulation naturally leads to robustness with respect to discretization, allowing an excellent trade-off between computational cost (memory footprint and training time) and accuracy. The method is demonstrated on two industrially relevant applications: a RANS dataset of the two-dimensional compressible flow over a transonic airfoil and a dataset of the surface pressure distribution over 3D wings, including shape, inflow condition, and control surface deflection variations. On the considered test cases, our approach achieves a more than three times lower test error and significantly improves generalization error on unseen geometries compared to state-of-the-art Graph Neural Network architectures. Remarkably, the method can drastically accelerate high fidelity numerical solver, achieving a five order of magnitude speedup on the RANS transonic airfoil dataset.

A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters

Surface roughness is crucial for the functional and aesthetic properties of mechanical components and must be carefully controlled during machining. However, predicting it under varying machining parameters is challenging due to limited experimental data and fluctuating factors like tool wear and vibration. This study develops a deep transfer learning model that incorporates the correlation alignment method and tool wear to enhance model generalization and reduce data acquisition costs. It utilizes multi-sensor data and the ResNet18 with a convolutional block attention module (CBAM-ResNet) to extract features with improved generalization and accuracy for monitoring milled surface roughness under varying conditions. The performance of the model is evaluated from different perspectives. First, the proposed model achieves high accuracy with fewer than 500 experimental samples from the target domain by using the CORAL module in the CBAM-ResNet model. This demonstrates the model's strong generalization capability by minimizing second-order statistical discrepancies between different datasets. Second, ablation experiments reveal a significant reduction in test error when incorporating CORAL and tool wear, highlighting their contributions to improved model generalization. Integrating tool wear information significantly reduces test errors across various transfer conditions, as it reflects changes in cutting force, vibration, and built-up edge formation. Third, comparisons with existing deep transfer models further emphasize the advantages of the proposed approach in improving model generalization. In summary, the proposed surface roughness model, which incorporates tool wear and multi-sensor signal features as inputs and employs feature transfer and CBAM-ResNet, demonstrates superior generalization and accuracy across various machining parameters.

Some Improvements of the Bootstrap over the Delta Method Probability Errors for Whittle Estimators

The purpose of this paper is to compare the coverage probability errors of the parametric bootstrap with that of the delta method for the covariance parameters of a regression model with auto-regressive fractionally integrated moving average (ARFIMA) errors. We consider the coverage probability errors of both confidence intervals (CIs) and tests based on the the plug-in Whittle maximum likelihood (PWML) estimators. We first show that, under some sets of conditions on the regression coefficients, the spectral density function, and the parameter values, the bounds on the coverage probability errors of the two-sided delta method and parametric bootstrap confidence intervals on the plug-in Whittle likelihood estimator or the covariance parameter are shown to be $O(n^{-1})$ and $o(n^{-3/2}\ln{n})$, respectively, where n is the sample size. Next, we show that those of the one-sided parametric bootstrap confidence intervals are shown to be $O(n^{-1/2})$ and $o(n^{-1}\ln{n})$, respectively. These results show that for both one-sided and two-sided confidence intervals and tests, the bootstrap provides a significant improvement over that of the delta method.

High-dimensional asymptotics of denoising autoencoders*

Abstract We address the problem of denoising data from a Gaussian mixture using a two-layer non-linear autoencoder with tied weights and a skip connection. We consider the high-dimensional limit where the number of training samples and the input dimension jointly tend to infinity while the number of hidden units remains bounded. We provide closed-form expressions for the denoising mean-squared test error. Building on this result, we quantitatively characterize the advantage of the considered architecture over the autoencoder without the skip connection that relates closely to principal component analysis. We further show that our results accurately capture the learning curves on a range of real data sets.

Deterministic equivalent and error universality of deep random features learning*

Abstract This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied random features model to deeper architectures. First, we prove Gaussian universality of the test error in a ridge regression setting where the learner and target networks share the same intermediate layers, and provide a sharp asymptotic formula for it. Establishing this result requires proving a deterministic equivalent for traces of the deep random features sample covariance matrices which can be of independent interest. Second, we conjecture the asymptotic Gaussian universality of the test error in the more general setting of arbitrary convex losses and generic learner/target architectures. We provide extensive numerical evidence for this conjecture. In light of our results, we investigate the interplay between architecture design and implicit regularization.

A novel dynamic structural adaptive multivariable grey model and its application in China's solar energy generation forecasting

Accurately predicting China's solar energy generation is crucial for energy planning and environmental protection. However, the nonlinearity and complexity of the data pose challenges to prediction. This article proposes a dynamic structural adaptive multivariate grey model to overcome these challenges. The model constructs a dynamic nonlinear correction function with an uncertain structure that can adaptively adjust to the data characteristics. Additionally, the Gaussian function is utilized to derive the time response function, overcoming the mechanism defects present in traditional multivariate grey models. The whale optimization algorithm is applied to optimize the background value of the model, improving accuracy. The model is compatible with 14 classical models via parameter changes and has strong compatibility. The results show that the fitting error and test error of the model are 0.23 % and 1.95 % respectively, which is significantly better than the other 11 methods, indicative of strong robustness and accuracy. The forecast indicates that China's solar energy generation will experience a consistent upward trajectory from 2023 to 2030, with an average annual growth rate of 12.11 %. This study significantly contributes to the advancement of the theory of multivariate grey dynamic prediction and provides essential data support for strategic planning within China's solar power industry.

A seamless Phase I/II platform design with a time-to-event efficacy endpoint for potential COVID-19 therapies.

In the search for effective treatments for COVID-19, the initial emphasis has been on re-purposed treatments. To maximize the chances of finding successful treatments, novel treatments that have been developed for this disease in particular, are needed. In this article, we describe and evaluate the statistical design of the AGILE platform, an adaptive randomized seamless Phase I/II trial platform that seeks to quickly establish a safe range of doses and investigates treatments for potential efficacy. The bespoke Bayesian design (i) utilizes randomization during dose-finding, (ii) shares control arm information across the platform, and (iii) uses a time-to-event endpoint with a formal testing structure and error control for evaluation of potential efficacy. Both single-agent and combination treatments are considered. We find that the design can identify potential treatments that are safe and efficacious reliably with small to moderate sample sizes.

Developing guidelines for a reproducible method for low-frequency impact measurements in buildings

The impact noise performance in buildings is evaluated using the measured radiated sound pressure level when impacted by a standard tapping machine. Recent work has shown that there can be 4 - 5 dB variation in the sound pressure levels at different measurement locations in the room. This can lead to a high reproducibility error for field tests. In this work, the authors have developed a novel reciprocity-based measurement method that shows approximately 1 - 1 .25 dB standard deviation in low-frequencies. Additionally, this method provides output/input ratio-based measurements, which can be useful in design predictions. The method was developed using a simulation model and verified using testing in three structures.

Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study.

In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection. The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models' performance with conventional delta check methods. Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models. This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing.

Predictive Modeling and Drug Repurposing for Type-II Diabetes.

Diabetes mellitus (DM) is a global health concern, and dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target. The study used three machine learning and deep learning models to predict potential DPP-4 inhibitors using a curated data set of 6,750 compounds. The models included support vector machine (SVM), random forest (RF), naive Bayes (NB), and multitask deep neural network (MTDNN). The MTDNN model demonstrated strong predictive performance, achieving 98.62% train accuracy and 98.42% test accuracy for predicting DPP-4 inhibitors and a correlation coefficient of 0.979 for training and 0.977 for the test data set, with low training and test errors while predicting corresponding IC50 values. The MTDNN model predicted potential inhibitors using an external data set of FDA-approved drugs, identifying 100 compounds. Among these, five compounds stood out with promising molecular docking and dynamic profiles, suggesting their potential as repurposed drugs for targeting DPP-4 and offering hope for the future of diabetes treatment.

A-209 Preanalytical Phase Errors Constitute the Vast Majority of Errors in Clinical Laboratory Testing

Abstract Background Laboratory errors can occur during the preanalytical, analytic, or post-analytic phase of testing and may pose a considerable threat to patient safety. Studies have estimated that pre-analytical error encapsulates ∼45-68% of laboratory errors and occurs in up to 1.52% of specimens. However, limitations of previously published studies include the use of manual error detection, reliance on manual transcription, small study sizes, and exclusion of common errors. Herein, we aim to identify the types and frequency of errors occurring in each phase of clinical laboratory testing using a large retrospective dataset across a broad range of errors detected in clinical laboratories. Methods This study was performed in the Barnes-Jewish Hospital core laboratory which provides testing for a 1,400-bed tertiary care hospital and included all tests performed for clinical chemistry, special chemistry, toxicology, automated hematology, blood gas testing, coagulation, non-infectious serology, infectious disease serology, urinalysis, and send out testing between January 2022 and May 2023. Errors were compiled from two separate sources. The first source of errors was captured from the laboratory information system (LIS, Cerner). These errors were flagged manually by laboratory staff in real time. The second source of error was a daily report manually curated by a trained medial laboratory scientist as part of routine, laboratory operating procedures and standard workflow. Results There were ∼40,000,000 individual results from ∼11,000,000 specimens during the study period and 87,317 errors observed (∼0.8% of specimens). Of these errors, specimens that were hemolyzed sufficiently to impact result but still reportable were the majority (41,047, 47.2%). When excluding minimal hemolysis, there were 46,270 errors documented. Among these, 44,847 (97.0%) were in the preanalytical phase, 451 (1.0%) were in the analytical phase, and 972 (2.0%) occurred in the postanalytical phase. Subcategorization of preanalytical errors revealed that most of the errors were related to hemolyzed samples requiring results to be suppressed (n=19,701), quantity not sufficient samples (n=8,068), clotted specimens (n=5,840), collection errors (e.g., wrong tube type, n=5,780), not on ice (n=1,369) and IV fluid contamination (n=1,122). In the analytical phase, the causes of error were interfering substances (n=279), QC out of range (n=65), instrument problems (n=58) and reagent issues (n=49). Among the postanalytical errors, 581 were comment errors, 216 were results entry errors, 131 were comments errors with potential impact on patient care, and 44 were dilution errors. Conclusions This study demonstrates that the vast majority of lab errors (∼97.0%) occurring during the preanalytical phase. The high incidence of preanalytical errors, especially errors related to sample integrity implies a need for enhanced tools and training to minimizing errors from specimen collections, maintenance of sample stability, transportation, and processing.