Error Reduction Research Articles

Exploring Arabic Learners’ English Errors: Interlingual vs. Intralingual Analysis and Remedial Teaching Strategies

Arabic students frequently encounter errors while learning English as a second language (ESL). This study investigates prevalent interlingual and intralingual errors observed among Arabic learners of English. The authors posit that by addressing morphological, lexical, and semantic inaccuracies, English instructors can assist Arabic learners in cultivating a more comprehensive and nuanced proficiency in the English language, thereby enhancing their communicative abilities across various contexts. The instructional strategies explained in this paper offer valuable insights for ESL teachers and tertiary-level Arabic students aiming to mitigate or eliminate such errors. Additionally, the teaching recommendations presented in this paper serve as beneficial resources for experienced and novice instructors, facilitating the reduction of errors commonly encountered by Arabic students in their English language acquisition journey.

Comparison of Depth-Related Visuomotor Task Performance in Uniocular Individuals and in Binocular Controls With and Without Temporary Monocular Occlusion.

Do one-eyed (uniocular) humans use monocular depth cues differently from those with intact binocularity to perform depth-related visuomotor tasks that emulate complex activities of daily living? If so, does performance depend on the participant's age, duration of uniocularity and head movements? Forty-five uniocular cases (age range 6-37years; 2.4months-31.0years of uniocularity) and 46 age-similar binocular controls performed a task that required them to pass a hoop around an electrified wire convoluted in depth multiple times, while avoiding contact as indicated by auditory feedback. The task was performed with and without head restraint, in random order. The error rate and speed were calculated from the frequency of contact between the hoop and wire and the total task duration (adjusting for error time), respectively, all determined from video recordings of the task. Head movements were analyzed from the videos using face-tracking software. Error rate decreased with age (P < 0.001) until the late teen years while speed revealed no such trend. Across all ages, the error rate increased and speed decreased in the absence of binocularity (P < 0.001). There was no additional error reduction with duration of uniocularity (P = 0.16). Head movements provided no advantage to task performance, despite generating parallax disparities comparable to binocular viewing. Performance in a dynamic, depth-related visuomotor task is reduced in the absence of binocular viewing, independent of age-related performance level. This study finds no evidence for a prolonged experience with monocular depth cues being advantageous for such tasks over transient loss of binocularity.

A Community Ionosphere‐Thermosphere Observing System Simulation Experiment (OSSE) Tool: Geospace Dynamics Constellation Example

AbstractObserving System Simulation Experiments (OSSEs) provide an effective way to evaluate the impact of assimilating data from a specific observing system on hindcasting, nowcasting, and forecasting of environmental systems. The NSF NCAR's Data Assimilation (DA) Research Testbed/Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (DART/TIEGCM) tool, to be hosted at the NASA Community Coordinated Modeling Center, serves as a valuable and accessible community resource for quantitatively evaluating the impact of observations from both current and future ionosphere‐thermosphere (IT) observing systems. This study demonstrates the utility of DART/TIEGCM as an IT OSSE tool, using synthetic observations simulated using a currently planned NASA Geospace Dynamics Constellation (GDC) observing system design. Five sets of OSSEs are carried out to compare the effects of assimilating various combinations of prospective GDC observations (e.g., neutral temperature, neutral wind, neutral composition, atomic oxygen ion density, and ion and electron temperature) during a major geomagnetic storm period of the St Patrick's Day Storm on 17 March 2013. The maximum error reduction in neutral temperature and atomic ion oxygen density is 24.6% and 43.3% compared to the control experiment. These OSSEs indicate the benefits of coupled IT DA approaches implemented in DART/TIEGCM to maximize the impact of multi‐parameter IT observations, such as those expected from the GDC mission. Although more work is required to draw any definitive conclusion on the GDC data impact, the study provides an illustrative example of how the DART/TIEGCM community tool can be used to evaluate observational impacts of planned or existing missions for geospace research and applications.

Experience and effectiveness of the no-loft linking shape and dimension method using laser optical systems in aircraft production

The object of research is the application of the no-loft linking shapes and dimension method using laser measuring tools to reduce the labor intensity and cycle of mounting work. The study of the accuracy of the geometric parameters of cantilever wing and technological equipment at various stages of production was carried out. The problem is to create a method of using laser optical systems in aircraft production at the stage of mounting of technological equipment to minimize the impact on the accuracy of the dimensions of assembled parts of aggregates. The following results were obtained: the advantages of using the no-loft linking method in the modern production of aviation equipment were analyzed, which makes it possible to reduce the preparation cycle by 2–3 times. A study was conducted on the effective use of the Coordinate Measuring Machine (CMM) of laser tracker in the manufacture of the cantilever wing (CW) of the AN series airplane at all stages of mounting, as well as on the accuracy inspection of geometric parameters in comparison with the theoretical master geometry (MG). The practical significance of the research is that the proposed method of using laser optical systems during the installation of equipment allows to reduce to a minimum the impact on the accuracy of low rigidity frames. And also, to reduce the equipment deformation due to the mass of the parts of the assembled aggregates and temperature deformations, which allows to ensure a reduction of the mounting error to ±0.1 mm. Also, the application of this technique allows to enter the plane's coordinate system without prior leveling, to mounting and inspection the installation of the wing, fin, stabilizer, engines and landing gear on the fuselage. In general, the application of the no-loft linking shape and dimension method with using laser optical systems in aircraft production allows to reduce the labor intensity and cycle of mounting work up to 10 times.

Learning cleanroom microfabrication operations in virtual reality – An immersive and guided learning experience

This paper details the design and development of an immersive and self-guided Virtual Reality training system (iSGVRTS) for learning cleanroom microfabrication operations, with a specific emphasis on the photolithography process, within a college-level semiconductor laboratory curriculum. It presents the thorough construction of the iSGVRTS environment as well as the incorporation of integrated instructional methodologies. To assess the impact of the iSGVRTS intervention, pre-and post-tests were administered to evaluate learners' performance. The implementation of iSGVRTS yielded a notable enhancement for learners in laboratory operational proficiency, evidenced by improvements in task correct rates, reduction in procedural errors, and knowledge acquisition. Moreover, post-session interviews revealed learners’ reported increased confidence, a heightened sense of presence, manageable cognitive load, and positive feedback regarding the immersive learning experience.

Advanced taper tension profile for improved control performance in roll-to-roll winding processes

Products coated with flexible film can be stored in wound rolls after the winding process. However, if the roll product experiences excessive internal stress, it can lead to various defects in the coated layer. To alleviate the stress within the wound roll, a taper tension profile is utilized to progressively reduce tension as the winding length increases. As a substantial amount of web is wound onto the core, the moment of inertia of the wound roll increases. It has been observed that, as the winding length increases, the tension control performance of the taper tension tends to deteriorate. We propose an advanced taper tension profile to enhance tension control performance and validates the effectiveness of this profile through winding experiments. It was observed that, compared to the parabolic taper tension, maximum tension error could be reduced by approximately 16.1 % throughout the winding process. This reduction in tension error enables the preservation of the radial stress reduction effect of the taper tension, resulting in a 13.3 % decrease in the maximum radial stress compared to the parabolic taper tension. The proposed taper tension profile contributes to a more uniform quality of the coated layer, especially as the maximum winding length is extended.

Super-resolution of digital rock images with hybrid attention multi-branch neural network

High-resolution digital rock images are vital in studying microstructure and fluid flow characterization of oil and gas reservoirs. Computed tomography (CT) is currently the primary tool to acquire digital rock images because of its non-destructiveness and three-dimensional imaging capability. However, the restriction of imaging capability of CT sets a compromise between image resolution and field of view (FOV). The Hybrid Attention Multi-branch Super-Resolution Network (HAMSR) proposed in this study aims to remedy this deficiency. HAMSR employs a limited discrete distribution to capture all possible high-order information and generate reliable high-resolution images. The network is built to prioritize the recovery of pores, cracks, and textures in digital rock images by introducing channel and spatial attention mechanisms. The structure together with modules of HAMSR have been designed and optimized to yield superior performance with fewer parameters and less training time. HAMSR and several contemporary advanced models are trained on the DeepRock-SR 2D dataset which contains lots of sandstone and carbonate images. It is evident that HAMSR-generated images have minimal noise, clearer edges, and more micropores. Quantitative calculations show that compared to traditional bicubic interpolation, HAMSR acquires a 2.669 dB and 1.376 dB increase in peak signal-to-noise ratio (PSNR) respectively on the test sets (27%–38% reduction in relative error). Furthermore, the high-resolution (HR) images reconstructed by HAMSR are generally consistent with real HR images concerning statistical characteristics such as porosity, pore size distribution, shape factor distribution, and two-point correlation function. This work provides a new super-resolution model to generate high-quality digital rock images for further analysis.

African Lovegrass Segmentation with Artificial Intelligence Using UAS-Based Multispectral and Hyperspectral Imagery

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.

Fuzzy logic based thermal image processing for temperature monitoring of rotating cylindrical surfaces

When a non-contact and remote temperature measurement is of importance, several environmental factors must be considered. Of these, two are of crucial importance — the surface emissivity of the observed object and the directional emissivity in the object–sensor relationship. This paper proposes FDEM — a Fuzzy Directional Emissivity Model built with the assumption that the observed surface is cylindrically shaped. The model consists of two submodels — for vertical and horizontal directions separately. The main contribution of this paper is the formalization of a linguistic description of measurement conditions typical for an industrial environment. Additionally, dual linguistic fuzzy modifiers were used to increase the accuracy of the modeled emissivity. Finally, the proposed model achieved up to 40% reduction of RMSE error when used for correcting the temperature measurements of a rotating cylinder. Overall, our model provides a novel and reliable way for temperature readout correction on the entire surface of a drying roller, such as ones used in the papermaking industry. Furthermore, the proposed approach is not only inherently capable of compensating for the position of the camera, but also does it in an interpretable, and hence expert-verifiable, manner.

Exploring sperm cell motion dynamics: Insights from genetic algorithm-based analysis

Accurate analysis of sperm cell flagellar dynamics plays a crucial role in understanding sperm motility as flagella parameters determine cell behavior in the spatiotemporal domain. In this study, we introduce a novel approach by harnessing Genetic Algorithms (GA) to analyze sperm flagellar motion characteristics and compare the results with the traditional decomposition method based on Fourier analysis. Our analysis focuses on extracting key parameters of the equation approximating flagellar shape, including beating period time, bending amplitude, mean curvature, and wavelength. Additionally, we delve into the extraction of phase constants and initial swimming directions, vital for the comprehensive study of sperm cell pairs and bundling phenomena. One significant advantage of GA over Fourier analysis is its ability to integrate sperm cell motion data, enabling a more comprehensive analysis. In contrast, Fourier analysis neglects sperm cell motion by transitioning to a sperm-centered coordinate system (material system). In our comparative study, GA consistently outperform the Fourier analysis-based method, yielding a remarkable reduction in fitting error of up to 70% and on average by 45%. An in-depth exploration of the sperm cell motion becomes indispensable in a wide range of applications from complexities of reproductive biology and medicine, to developing soft flagellated microrobots.

Mobile Accounting Software and Financial Performance of Small-Scale Hotels in Mombasa, Kenya

This research explores the effects of mobile accounting software on the financial performance of Small and Medium Enterprises (SMEs) in Kenya, specifically focusing on the hotel industry. The utilization of accounting software, which includes various modules like trial balances, payroll, accounts receivable, and accounts payable, improves efficiency through task automation and error reduction. Mobile applications, accessible via platforms such as Google Play Store and Apple Store, further enhance financial processes for SME proprietors, providing instant access to financial data and aiding in precise record-keeping. The study emphasizes the categories of mobile accounting applications—bookkeeping, expense tracking, and invoice tracking—and their combined impact on financial performance. Empirical data from different nations, such as Indonesia and Germany, highlights the advantages of these apps in enhancing financial literacy and management capabilities among SME owners. Particularly in areas like Tudor Ward, Mombasa, Kenya, the awareness and utilization of mobile accounting applications exhibit promising outcomes in elevating financial performance significantly. The study concludes that integrating mobile accounting software is crucial for attaining heightened efficiency, transparency, and financial advancement for SMEs. As technology progresses, its role in bolstering the financial well-being and endurance of small enterprises will grow increasingly pivotal.

Sistem Pengelolaan Surat Perintah Perjalanan Dinas di DPRD Provinsi NTB

The Official Travel Order (SPPD) Management System at the Regional People's Representative Council (DPRD) of West Nusa Tenggara (NTB) Province is an important innovation in enhancing the efficiency, transparency, and accountability of official travel administration. Prior to the implementation of this system, SPPD management was carried out manually, leading to various obstacles such as slow processes, administrative errors, and lack of transparency. With the adoption of an information technology-based system, the entire process from creation, approval, to reporting of SPPD can be done electronically. The system provides features for data validation, fillable templates, and automatic notifications to expedite the approval process. Integration with the regional financial system enables more efficient and accurate budget management, while a centralized database simplifies data storage and retrieval. The implementation results show significant improvements in time efficiency, reduction of administrative errors, and enhanced transparency and accountability in the use of official travel budgets. The system also facilitates better data management and supports easier audit processes. Thus, the SPPD Management System at the DPRD of NTB Province has successfully delivered positive impacts on the performance of official travel administration, creating a more productive and responsible work environment.

Методи iнтелектуалiзацiї процесiв просторового монiторингу сцени

Introduction. The development of intelligent technologies requires the active use of advanced technologies and innovative approaches for the intellectualization of spatial scene monitoring processes. The relevance of the topic lies in the great need to improve the quality of video content production. In particular, there is interest in the automation and further intellectualization of shooting processes. The use of new methods of intellectualization leads to a reduction of permissible errors when creating a creative video project. Intellectualization of data processing processes from markers, namely the use of artificial intelligence (AI) methods, allows to obtain a controlled level of quality with minimal human involvement. Intellectualization of stage production contributes to the creation of exciting and innovative performances that captivate the audience. It allows creating new ways of interacting with the audience and providing them with unique impressions from cultural events. The purpose of the paper is to study the methods of intellectualization of data processing from markers during the use of automatic video cameras in tasks of observing stage action for video-photography. The results. The issue of the interaction of markers with cameras in three-dimensional space, which is completely identical to the built 3D model, is considered. Conclusions. The information technology of spatial monitoring of the scene can increase the efficiency and simplify the use of automatic video cameras in the tasks of monitoring the stage action for video-photo shooting. There is no one universal "best" method, as each algorithm has its own advantages and disadvantages. However, the optical flow gradient calculation method may be considered more suitable for use in stage production. The introduction of information technology for spatial scene monitoring based on the optical flow gradient calculation method will improve efficiency and simplify the use of automatic video cameras. The use of surveillance information technology will reduce the burden on the personnel who maintain and manage the filming and are involved in the work. Keywords: intellectualization of data processing processes, intelligent monitoring, automatic video camera, animation, optical flow gradient, computer vision

Spatial-temporal neural networks for catch rate standardization and fish distribution modeling

Catch-per-unit-effort (CPUE) standardization is crucial for fishery stock assessment but often presents challenges due to spatial-temporal variations in species distribution and fishing effort. In this simulation study, we propose the use of customized artificial neural networks (ANNs) for modeling the spatial-temporal variations in CPUE standardization. This is achieved by encoding prior knowledge of the dependency structure between the variables into the architecture of the ANNs. We conducted numerical experiments on simulated data to compare our customized ANNs with Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and fully connected ANNs used in previous studies. Our simulated data cover three spatial-temporal dynamics scenarios with different degrees of species distribution shift over time: (1) steady fish distribution; (2) gradual directional shift over time; (3) sudden directional shift. In predicting the standardized CPUE in this simulation study, the customized ANNs demonstrated greater accuracy compared to the commonly used fully connected ANNs with an error reduction of over 70 %, more than 80 % compared to GLMs, and more than 40 % compared to GAMs, in terms of an error metric called the scaled mean absolute relative error. Our findings suggest that customized ANNs can serve as an alternative modeling tool alongside GLMs and GAMs in fisheries modeling.

Cognitive Factors Leading to Human Error: A Major Contributing Factor for Quality Deviations in Pharmaceutical Industry

The pharmaceutical sector is one of the top research-based sectors in the world which continuously produces new medications that improve and save human lives. Patients, as well as healthcare professionals depend on pharmaceutical companies for the treatment of ailments, so it is very important to develop a good quality product following Good manufacturing practices guidelines that ensure the quality, safety, and efficacy of the product. Human cognitive errors are a major cause of quality deviations. These quality deviations result in product recalls, a ban on products by regulators where the company’s reputation will be tarnished which can result in loss of sales and revenue. Human errors can be identified and mitigated by various techniques. In this paper, we discuss the factors contributing to cognitive errors, the impact of cognitive errors on the pharmaceutical industry, the identification of the cognitive errors causing current good manufacturing practices (cGMP) deviations, and challenges in the mitigation of cognitive errors. A survey questionnaire and the form human error assessment and reduction technique (HEART) tool were also developed and discussed in the manuscript.

Applying different levels of practice variability for motor learning: More is not better.

Variable practice is a broadly used tool to improve motor learning processes. However, controversial results can be found in literature about the success of this type of practice compared to constant practice. This study explored one potential reason for this controversy: the manipulation of variable practice load applied during practice and its effects according to the initial performance level and the initial intrinsic variability of the learner. Sixty-five participants were grouped into four practice schedules to learn a serial throwing task, in which the training load of variable practice was manipulated: one constant practice group and three groups with different variable practice loads applied. After a pre-test, participants trained for 2 weeks. A post-test and three retests (96 h, 2 weeks and 1 month) were carried out after training. The participants' throwing accuracy was assessed through error parameters and their initial intrinsic motor variability was assessed by the autocorrelation coefficient of the error. The four groups improved their throwing performance. Pairwise comparisons and effect sizes showed larger error reduction in the low variability group. Different loads of variable practice seem to induce different performance improvements in a throwing task. The modulation of the variable practice load seems to be a step forward to clarify the controversy about its benefits, but it has to be guided by the individuals' features, mainly by the initial intrinsic variability of the learner.

LSIAC-MRA for nonuniform meshes and applications to mesh adaptivity

In this article we consider the extension of the (L)SIAC-MRA enhancement procedure to nonuniform meshes. We demonstrate that error reduction can be obtained on perturbed quadrilateral and Delaunay meshes, and investigate the effect of limited resolution and its impact on the procedure for various function types. We show that utilizing mesh-based localized kernel scalings, which were shown to reduce approximation errors for LSIAC filters, improve the performance of the LSIAC-MRA enhancement procedure. Lastly, we demonstrate the usefulness of enhanced approximations generated by (L)SIAC-MRA in mesh adaptivity applications, and show that SIAC reconstruction can be used in identification of regions of high error in steady-state DG approximations.

Bedside Nurse Documentation Practices: At the Patient Bedside or Not?

High-quality care requires precise and timely provider documentation. Hospitals have used technology to document patient care within both the inpatient and outpatient areas and long-term care facilities. Research has demonstrated, by revealing a reduction in medical errors, that there has been a worldwide improvement in our community health and welfare since the implementation and utilization of documenting patient care electronically. Although electronic documentation has proven to be an improvement in patient record keeping, the most efficient location in which this documentation is to occur remains a question. At the location where this project took place, only the ICU had computers within the patient rooms for documentation purposes. This project evaluated bedside nurses' opinions related to the efficiency of documentation practices compounded by the location where documentation took place. The options were at the patient's bedside, on a workstation on wheels, or at the nursing station. Surveys were provided to bedside nursing staff both before and after computers were installed in patients' rooms in surgical and medical/surgical nursing units at a Veteran Affairs Medical Center located in the Northeastern region of the United States. The results of this project inconclusively answer the question posed: "Which mode of entry do nurses feel is more efficient to document patient care, on a computer in the patient room, at the nurses' station, or on a workstation on wheels?" Innovative strategies should be explored to develop a user-friendly design for computers located within the patient rooms for patient documentation.

Near Field Communication (NFC)-Based Prepaid Smart Power Meter

An essential part of an electric energy service is an electrical power meter. Numerous customers have already expressed dissatisfaction over the electric meter's faulty readings. The development of an electrical power meter with an NFC reader is presented in this study. When an NFC card is successfully scanned by the NFC reader, the power meter is turned on and ready to deliver electricity. Before the electricity is automatically turned off, or when the credit is almost low, the display will show the alert. By experiment, PZEM 004T sensor use 3 loads, namely 60 W, 75 W and 100 W for testing current, voltage and cos phi and have error of 0 – 1.20%. In balance reduction with loads of 60 W, 75 W, and 100 W with a time gap of 5 and 10 minutes, there is an error reduction of 11.5 – 17.4%. Thus, this electricity meter with NFC reader is suitable for monitoring and controlling electricity usage.

Real-time pavement temperature prediction through ensemble machine learning

Pavement temperature is crucial for assessing asphalt pavement's performance and structural integrity due to its viscoelastic behavior. Currently, asphalt temperature prediction includes analytical, numerical, and statistical methods and relies on field investigation with intensive labor and time to hinder routine practice. Machine learning (ML) offers a promising solution for reliable and accurate real-time predictions by considering various dynamic environmental factors. This research employs four ensemble ML algorithms: eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Adaptive Boosting (AdaBoost) to predict pavement temperature at different depths. XGB consistently outperformed other ensemble models, boasting the highest average R2 value (97.92%) and lower MAE and RMSE than LGBM, RF, and AdaBoost. XGB also significantly outperformed other commonly used models (linear regression, SVR, ANN, LSTM, and GRU) with an average reduction in errors of 35.29%, 63.75%, 42.75%, and an increase in R2 by 4.39% for MAE, MSE, RMSE, and R2, respectively. The Shapley additive explanation (SHAP) was applied to interpret the underlying influencing factors and their interactions, and revealed atmospheric temperature as the most impactful feature, contributing over 40.58% among the significant features.Furthermore, the predictive performance of XGB was enhanced by incorporating Principal Component Analysis to reduce computational consumption with average reductions of 8.98%, 16.59%, 9.01%, and 7.89% in MAE, MSE, MAPE, and RMSE. XGB with PCA emerges as the most effective and efficient model for predicting pavement temperatures at various depths to facilitate timely decision-making for preventive maintenance and rehabilitation and prevent future catastrophic failures.