The development of English language connected speech perception skills: an empirical study on Chinese EFL children.

Connected speech processes (CSPs) occur randomly in everyday conversations of native speakers; however, such phonological variations can bring about challenges for non-native listeners. Looking at CSP literature, there seems to be very few studies that involved young foreign language learners. Therefore, the present study aimed to explore the development of connected speech perception skills by focusing on 201 9- to 12-year-old Chinese EFL children. It also incorporated systematic error analysis to further probe into the specific perceptual difficulties. The results indicate that: (1) Despite a significantly ascending trend for the overall growth of perception skills, no significant differences were found between 11 and 12 year olds in elision and contraction, which suggests that the developmental trend varied depending on different CSP types; (2) Although random errors decreased with age, the number of lexicon and syntax errors gradually increased, and the distribution of perceptual errors shifted from the level of words and syllables to that of phonemes; (3) The primary types of errors resulting in the perception difficulties for elision and contraction were consonant errors, grammatical errors and morphology errors. Ergo, this study enhances the understanding of connected speech perception among EFL children and provides some implications for EFL/ESL listening instructions.

An evaluation of resolution, accuracy, and precision in FT-IR spectroscopy.

Infrared spectroscopy is a foundational technique for the elucidation of chemical structures. The advancements in interferometric spectroscopy, and specifically the development of Fourier transform infrared (FT-IR) spectroscopy, are responsible for the widespread usage of IR spectrometers ranging from teaching labs to pharmaceutical quality control. FT-IR affords an excellent signal-to-noise ratio that permits sensitive sampling with quantitative accuracy and high wavenumber precision based on well documented advantages (Jacquinot, Fellgett, Connes). However, the effect of resolution and instrument-to-instrument variation on wavenumber accuracy is not well understood, with previous work grossly overestimating error. Here, a recommendation of wavenumber accuracy as a function of spectral resolution, accounting for instrument variation among leading manufacturers, is given based on an experimental study of polystyrene and acetaminophen. For peaks that are well resolved and not saturated, the position can be known within 1.1 cm-1 at a spectral resolution of 4 cm-1 or higher, and within 2.2 cm-1 at 8 cm-1 resolution. Other sources of variation are also discussed (e.g., poorly resolved peaks, peak saturation, water interference, spectral noise) to give general recommendations on when IR peak positions can be considered significantly different. Such guidelines are critical for interpreting subtle positional variations, as are often present in different crystal forms of pharmaceuticals.

Identifying and preventing human error in the sugar production process: A multi-stage approach using HTA, HEC and PHEA techniques.

This article discusses the importance of identifying and preventing human error in industrial environments, specifically in the sugar production process. The article emphasizes the importance of choosing the right technique for risk assessment studies resulting from human errors. A cross-sectional study was conducted using a multi-stage approach - Hierarchical Task Analysis (HTA), Human Error Calculator (HEC), and Predictive Human Error Analysis (PHEA) - to identify potential human errors in the sugar production process. The HTA, HEC, and PHEA techniques were employed to evaluate each stage of the process for potential human errors. The results of the HTA technique identified 35 tasks and 83 sub-tasks in 14 units of the sugar production process. According to HEC technique 4 tasks with 80 % probability of human error and 2 tasks with 50 % probability of human error had the highest calculated error probabilities. The factors of individual skill, task repetition and importance were the most important factors of human error in the present study. The analysis of PHEA worksheets showed that the number of human errors identified in the tasks with highest probability were 8 errors, of which 50 % were action errors, 25 % checking errors, 13 % selection errors, and 12 % retrieval errors. To mitigate the consequences of human error, it was recommended training courses, raising operator awareness of error consequences, and installing instructions in the sugar production process. Based on the findings, the article concludes that the HEC and PHEA techniques are applicable and effective in identifying and analyzing human errors in process and food industries.

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. OBJECTIVES: This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. MATERIALS AND METHODS: We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. RESULTS: The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. CONCLUSION: The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.

Patterns of prospective memory errors differ in persons with multiple sclerosis.

INTRODUCTION: Prospective memory (PM) deficits have been documented in multiple sclerosis (MS). This study aimed to explore the specific types of errors made by persons with MS (PwMS), including differences between PwMS and healthy controls (HC) and PwMS who do and do not have impairments in processing speed and/or verbal learning and memory. METHOD: PwMS (n = 111) and HC (n = 75) completed the Memory for Intentions Test (MIST), an objective measure of PM that has five types of errors that can be coded (PM failure, task substitution, loss of content, loss of time, and random errors). The number and types of PM errors were calculated for the overall MIST and six subscales, which break down performance by types of delay (2-Minute and 15-Minute), cue (Time and Event), and response (Verbal and Action). Impairment was defined as performing < 1.5 SD on either the Symbol Digit Modalities Test (SDMT) or Rey Auditory Verbal Learning Test (RAVLT). Bivariate analyses were used to examine group differences, with post-hoc pairwise comparisons with Bonferroni corrections. RESULTS: Nearly 93% of PwMS made at least one PM error, compared to 76% of HC (V = .24, p = .001). The most commonly made PM error by PwMS was loss of content errors (45.0%). PwMS made significantly more task substitution errors (26.4% vs. 7.6%, p < .001) and fewer loss of time errors (9.5% vs. 21.2%, p < .001) than HC. Impaired PwMS made more errors than non-impaired PwMS, specifically PM failures on time-based tasks. CONCLUSIONS: PM errors are common in PwMS, particularly when there are longer delays and time-based cues. Not only do PwMS make more errors than demographically similar HC, but they exhibit different cognitive process failures.

Sixth order compact multi-phase block-AGE iteration methods for computing 2D Helmholtz equation.

We discuss sixth order accurate 9-point compact 2- and 3-phase block alternating group explicit (block-AGE) iteration methods for computing 2D Helmholtz equation. We use Dirichlet boundary conditions and no fictitious points are involved outside the solution region for computation. The proposed 2- and 3-phase block-AGE methods require only two and three sweeps for computation and the error analysis of the suggested approximation is analyzed. We have compared the 2- and 3-phase block-AGE iteration methods with the corresponding block successive over relaxation (block-SOR) method in three experiments, in regard to number of iterations required for convergence and cpu time, where the importance of the role performed by optimal relaxation parameters of the proposed block-AGE iteration methods become evident in stipulating the convergence and precision of the calculated results. In all cases we use the tridiagonal solver and obtain the optimal relaxation parameters through computation.

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools.

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40-60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology.

Analysis and Compensation of Phase Current Measuring Error Caused by Sensing Resistor in PMSM Application.

Field Oriented Control (FOC) effectively realizes independent control of flux linkage and torque, and is widely used in application of Permanent Magnet Synchronous Motor (PMSM). However, it is necessary to detect the phase current information of the motor to realize the current closed-loop control. The phase current detection method based on a sampling resistor will cause a measurement error due to the influence of parasitic parameters of the sampling resistor, which will lead to the decrease in PMSM control performance. This paper reveals the formation mechanism of the current sampling error caused by parasitic inductance and capacitance of the sampling resistor, and further confirms that the above error will lead to the fluctuation of the electromagnetic torque output by simulation. Moreover, we propose an approach for online observation and compensation of the current sampling error based on PI-type observer to suppresses the torque pulsation of PMSM. The phase current sampling error is estimated by the proportional and integral (PI) observer, and the deviation value of current sampling is obtained by low-pass filter (LPF). The above deviation value is further injected into the phase current close-loop for error compensation. The PI observer continues to work to keep the current sampling error close to zero. The simulation platform of Matlab/Simulink (Version: R2021b) is established to verify the effectiveness of online error observation and compensation. Further experiments also prove that the proposed method can effectively improve the torque fluctuation of the PMSM and enhance its control accuracy performance of rotation speed.

Deep analysis of adsorption isotherm for rapid sorption of Acid Blue 93 and Reactive Red 195 on reactive graphene.

Graphene-based adsorbent was prepared by adopting a green synthetic route via the chemical exfoliation of graphite and low-temperature thermal activation. Prepared reactive graphene (RG) was characterized through various techniques, and its adsorption capabilities for textile dye removal were investigated for Acid Blue-93 (AB) and Reactive Red-195 (RR) under different operational conditions. The dye sorption equilibrium and mechanism were comprehensively studied using isotherm and kinetic models and compared statistically to explain the sorption behavior. Results show AB and RR adsorption by RG attains equilibrium in 60 min and 70 min, with a high sorption quantity of 397 mg g-1 and 262 mg g-1 (initial dye concentration of 100 mg L-1), respectively. The dye sorption anticipates that the high surface area (104.52 m2 gm-1) and constructed meso-macroporous features of RG facilitated the interaction between the dye molecules and graphitic skeleton. The R-P isotherm fitted the best of equilibrium data, having the least variance in residuals for both dyes (AB = 0.00031 and RR = 0.00047). The pseudo-second order model best fitted the kinetics of sorption on RG, with chemisorption being the predominant process delimiting step. The overall results promise the dye removal capability of RG to be an efficient adsorbent for azo-based dyes from textile effluents.

Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot.

Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV's flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR's end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research.

Projection-Angle-Sensor-Assisted X-ray Computed Tomography for Cylindrical Lithium-Ion Batteries.

X-ray computed tomography (XCT) has become a powerful technique for studying lithium-ion batteries, allowing non-destructive 3D imaging across multiple spatial scales. Image quality is particularly important for observing the internal structure of lithium-ion batteries. During multiple rotations, the existence of cumulative errors and random errors in the rotary table leads to errors in the projection angle, affecting the imaging quality of XCT. The accuracy of the projection angle is an important factor that directly affects imaging. However, the impact of the projection angle on XCT reconstruction imaging is difficult to quantify. Therefore, the required precision of the projection angle sensor cannot be determined explicitly. In this research, we selected a common 18650 cylindrical lithium-ion battery for experiments. By setting up an XCT scanning platform and installing an angle sensor to calibrate the projection angle, we proceeded with image reconstruction after introducing various angle errors. When comparing the results, we found that projection angle errors lead to the appearance of noise and many stripe artifacts in the image. This is particularly noticeable in the form of many irregular artifacts in the image background. The overall variation and residual projection error in detection indicators can effectively reflect the trend in image quality. This research analyzed the impact of projection angle errors on imaging and improved the quality of XCT imaging by installing angle sensors on a rotary table.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

A Novel Method for Precision Measurement and Result Optimization of Detuning Angle for KDP Crystals.

In this paper, we investigate the theory of energy distribution when divergent light undergoes harmonic conversion in KDP crystals, and based on this theory, we design and construct a precision measuring instrument for the detuning angle of (KDP) Crystals (MIDC). The device can obtain the detuning angle of the crystal by a single measurement with an average measurement error of 72.78 urad. At the same time, it also has the function of scanning the full aperture of the crystals. Using the MIDC, it is possible to quickly measure the KDP crystal at a single point and quickly scan the crystal detuning angle at full aperture. In addition, we conduct a theoretical study on the variation of detuning angle caused by gravity-influencing factors under online conditions, propose an optimization formula for the offline measurement results of detuning angle, and calculate the optimized values of detuning angle for two kinds of crystals under 45° online conditions. We finally study the error source of the MIDC device, analyze the trend of the influence of positioning errors of the crystal and optical elements on the detuning angle measurement results, and provide theoretical support for the error monitoring and correction of MIDC.

The Right-Ear Advantage in Static and Dynamic Cocktail-Party Situations.

When presenting two competing speech stimuli, one to each ear, a right-ear advantage (REA) can often be observed, reflected in better speech recognition compared to the left ear. Considering the left-hemispheric dominance for language, the REA has been explained by superior contralateral pathways (structural models) and language-induced shifts of attention to the right (attentional models). There is some evidence that the REA becomes more pronounced, as cognitive load increases. Hence, it is interesting to investigate the REA in static (constant target talker) and dynamic (target changing pseudo-randomly) cocktail-party situations, as the latter is associated with a higher cognitive load than the former. Furthermore, previous research suggests an increasing REA, when listening becomes more perceptually challenging. The present study examined the REA by using virtual acoustics to simulate static and dynamic cocktail-party situations, with three spatially separated talkers uttering concurrent matrix sentences. Sentences were presented at low sound pressure levels or processed with a noise vocoder to increase perceptual load. Sixteen young normal-hearing adults participated in the study. The REA was assessed by means of word recognition scores and a detailed error analysis. Word recognition revealed a greater REA for the dynamic than for the static situations, compatible with the view that an increase in cognitive load results in a heightened REA. Also, the REA depended on the type of perceptual load, as indicated by a higher REA associated with vocoded compared to low-level stimuli. The results of the error analysis support both structural and attentional models of the REA.

Noise-dependent bias in quantitative STEM-EMCD experiments revealed by bootstrapping.

Electron magnetic circular dichroism (EMCD) is a powerful technique for estimating element-specific magnetic moments of materials on nanoscale with the potential to reach atomic resolution in transmission electron microscopes. However, the fundamentally weak EMCD signal strength complicates quantification of magnetic moments, as this requires very high precision, especially in the denominator of the sum rules. Here, we employ a statistical resampling technique known as bootstrapping to an experimental EMCD dataset to produce an empirical estimate of the noise-dependent error distribution resulting from application of EMCD sum rules to bcc iron in a 3-beam orientation. We observe clear experimental evidence that noisy EMCD signals preferentially bias the estimation of magnetic moments, further supporting this with error distributions produced by Monte-Carlo simulations. Finally, we propose guidelines for the recognition and minimization of this bias in the estimation of magnetic moments.

The application of objective clinical human reliability analysis (OCHRA) in the assessment of basic robotic surgical skills.

BACKGROUND: Using a validated, objective, and standardised assessment tool to assess progression and competency is essential for basic robotic surgical training programmes. Objective clinical human reliability analysis (OCHRA) is an error-based assessment tool that provides in-depth analysis of individual technical errors. We conducted a feasibility study to assess the concurrent validity and reliability of OCHRA when applied to basic, generic robotic technical skills assessment. METHODS: Selected basic robotic surgical skill tasks, in virtual reality (VR) and dry lab equivalent, were performed by novice robotic surgeons during an intensive 5-day robotic surgical skills course on da Vinci® X and Xi surgical systems. For each task, we described a hierarchical task analysis. Our developed robotic surgical-specific OCHRA methodology was applied to error events in recorded videos with a standardised definition. Statistical analysis to assess concurrent validity with existing tools and inter-rater reliability were performed. RESULTS: OCHRA methodology was applied to 272 basic robotic surgical skills tasks performed by 20 novice robotic surgeons. Performance scores improved from the start of the course to the end using all three assessment tools; Global Evaluative Assessment of Robotic Skills (GEARS) [VR: t(19) = - 9.33, p < 0.001] [dry lab: t(19) = - 10.17, p < 0.001], OCHRA [VR: t(19) = 6.33, p < 0.001] [dry lab: t(19) = 10.69, p < 0.001] and automated VR [VR: t(19) = - 8.26, p < 0.001]. Correlation analysis, for OCHRA compared to GEARS and automated VR scores, shows a significant and strong inverse correlation in every VR and dry lab task; OCHRA vs GEARS [VR: mean r = - 0.78, p < 0.001] [dry lab: mean r = - 0.82, p < 0.001] and OCHRA vs automated VR [VR: mean r = - 0.77, p < 0.001]. There is very strong and significant inter-rater reliability between two independent reviewers (r = 0.926, p < 0.001). CONCLUSION: OCHRA methodology provides a detailed error analysis tool in basic robotic surgical skills with high reliability and concurrent validity with existing tools. OCHRA requires further evaluation in more advanced robotic surgical procedures.

Prediction of Response Patterns during Conditional Discrimination Training across Data Recording Methods.

The effectiveness of an intervention is tied to the degree to which a program is implemented as described and the behavior analyst's ability to individualize the program based on client-specific factors. LeBlanc et al. (2020) found that training clinicians to use enhanced data sheets, which represent both the antecedent and response, resulted in greater procedural integrity when compared to standard data sheets. Additional benefits of enhanced data collection systems include the representation of potential error patterns, which may be used to modify the intervention program. The current study compared naïve participants' accuracy in predicting a client's performance when represented on standard and enhanced data sheets. Participants consistently identified error patterns on enhanced data sheets; however, performance did not differ across data collection methods when accurate responding or unpredictable controlling relations were shown. These findings suggest that enhanced measurement may facilitate the identification of error patterns during instruction for behavior analysts.

Standardized 3D test object for multi-camera calibration during animal pose capture.

Accurate capture of animal behavior and posture requires the use of multiple cameras to reconstruct three-dimensional (3D) representations. Typically, a paper ChArUco (or checker) board works well for correcting distortion and calibrating for 3D reconstruction in stereo vision. However, measuring the error in two-dimensional (2D) is also prone to bias related to the placement of the 2D board in 3D. We proposed a procedure as a visual way of validating camera placement, and it also can provide some guidance about the positioning of cameras and potential advantages of using multiple cameras. We propose the use of a 3D printable test object for validating multi-camera surround-view calibration in small animal video capture arenas. The proposed 3D printed object has no bias to a particular dimension and is designed to minimize occlusions. The use of the calibrated test object provided an estimate of 3D reconstruction accuracy. The approach reveals that for complex specimens such as mice, some view angles will be more important for accurate capture of keypoints. Our method ensures accurate 3D camera calibration for surround image capture of laboratory mice and other specimens.

IncRna: The R Package for Optimizing lncRNA Identification Processes.

In silico identification of long noncoding RNAs (lncRNAs) is a multistage process including filtering of transcripts according to their physical characteristics (e.g., length, exon-intron structure) and determination of the coding potential of the sequence. A common issue within this process is the choice of the most suitable method of coding potential analysis for the conducted research. Selection of tools on the sole basis of their single performance may not provide the most effective choice for a specific problem. To overcome these limitations, we developed the R library lncRna, which provides functions to easily carry out the entire lncRNA identification process. For example, the package prepares the data files for coding potential analysis to perform error analysis. Moreover, the package gives the opportunity to analyze the effectiveness of various combinations of the lncRNA prediction methods to select the optimal configuration of the entire process.

Enhance Safety in Aneurysm Surgery: Strategies for Prevention of Intraoperative Vascular Complications.

Complications during surgery for intracranial aneurysms can be devastating. Notorious pitfalls include premature rupture, parent vessel occlusion, local cerebral injury and brain contusion, and incomplete neck obliteration. These unfavorable intraoperative events can result in major neurological deficits with permanent morbidity and even mortality. Herein, the author highlights the relevant surgical strategies used in his daily practice of aneurysm surgery (e.g., aneurysm clipping with adenosine-induced temporary cardiac arrest), application of which may help prevent vascular complications and enhance surgical safety through reduction of the associated risks, thus allowing improvement of postoperative outcomes. Overall, all described methods and techniques should be considered as small pieces in the complex puzzle of prevention of vascular complications during aneurysm surgery.