Error Identification Research Articles

"Covering provider": an effort to streamline clinical communication chaos.

This report describes a root cause analysis of incorrect provider assignments and a standardized workflow developed to improve the clarity and accuracy of provider assignments. A multidisciplinary working group involving housestaff was assembled. Key drivers were identified using value stream mapping and fishbone analysis. A report was developed to allow for the analysis of correct provider assignments. A standardized workflow was created and piloted with a single service line. Pre- and post-pilot surveys were administered to nursing staff and participating housestaff on the unit. Four key drivers were identified. A standardized workflow was created with an exclusive treatment team role in Epic held by a single provider at any given time, with a corresponding patient list column displaying provider information for each patient. Pre- and post-survey responses report decreased confusion, decreased provider identification errors, and increased user satisfaction among RNs and residents with sustained uptake over time. This work demonstrates structured root cause analysis, notably engaging housestaff, to develop a standardized workflow for an understudied and growing problem. The development of tools and strategies to address the widespread burdens resulting from clinical communication failures is needed.

Analyzing Translation Errors of Trainee Translators: A Case Study on Tourism Promotional Material

The present study aims to scrutinize the trainee translators’ errors in their translations of tourism promotional material, a brochure, from Turkish into English. Thus, this study is designed as a descriptive qualitative research and corroborated with quantitative analysis, presenting a thorough analysis of the translated texts based on the American Translation Association (ATA) translation error taxonomy. The framework of this study employs Corder’s taxonomy of phases for error analysis, namely, selection of a text, identification of errors, classification of errors, and explanation of possible reasons for those errors. The data discussed in this paper derive from the translated texts of fifteen trainee translators i.e., third- and fourth-year T&I students at a private university enrolled in the Cultural Aspects of Translation and Interpretation course. The results revealed a statistical difference between the main error categories. According to the findings, trainee translators made most errors in the form of the language, i.e. target language mechanics, followed by errors in meaning and errors in writing quality. In acting as a “cultural mediator” in tourism text translations, trainees have also been found to be not so effective as they are supposed to be. With the results unfolded, it is hoped to benefit to both translation professors and trainees themselves.

Identification and Compensation of the Flexure-Induced Motion Error with a Voice Coil Motor

Identification and Compensation of the Flexure-Induced Motion Error with a Voice Coil Motor

Piston Error Automatic Correction for Segmented Mirrors via Deep Reinforcement Learning.

The segmented mirror co-phase error identification technique based on supervised learning methods has the advantages of simple application conditions, no dependence on custom sensors, a fast calculation speed, and low computing power requirements compared with other methods. However, it is often difficult to obtain a high accuracy in practical application situations with this method because of the difference between the training model and the actual model. The reinforcement learning algorithm does not need to model the real system when operating the system. However, it still retains the advantages of supervised learning. Thus, in this paper, we placed a mask on the pupil plane of the segmented telescope optical system. Moreover, based on the wide spectrum, point spread function, and modulation transfer function of the optical system and deep reinforcement learning-without modeling the optical system-a large-range and high-precision piston error automatic co-phase method with multiple-submirror parallelization was proposed. Finally, we carried out relevant simulation experiments, and the results indicate that the method is effective.

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.

Differentiation of petro-sourced plastic microfilaments from organic microfilaments by SEM-EDX in environmental samples

Quantifying microplastics (MPs) in marine environments is challenging due to the complexities of differentiation from other materials. This study aims to distinguish petro-sourced plastic microfilaments from organic ones in environmental samples using scanning electron microscope coupled with energy dispersive X-ray (SEM-EDX) analysis. Therefore, 38 particles resembling MPs (PRMPs) from sediments and organisms in Madagascar were analyzed. MPs identification thresholds were determined from SEM-EDX analysis of macroplastics and seaweed, the primary sources of microfilaments. SEM-EDX results of the 38 PRMPs were compared with identification/misidentification made by five MPs observation experts. Results indicated that a carbon/oxygen (C/O) ratio >1 distinguished macroplastics and MPs from other sources (seaweed and organic microfilaments). SEM-EDX analysis identified 81.58% of PRMPs as MPs. The average error made by one observer was 36.32%. These findings highlight the need for a method to assess identification error in MPs quantification and recommend SEM-EDX analysis of microfilaments to gauge observer error.

GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness.

Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder's ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering.

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

BackgroundChemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary. MethodsFirstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution. Significant findingsThis paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.

Field cabbage detection and positioning system based on improved YOLOv8n

BackgroundPesticide efficacy directly affects crop yield and quality, making targeted spraying a more environmentally friendly and effective method of pesticide application. Common targeted cabbage spraying methods often involve object detection networks. However, complex natural and lighting conditions pose challenges in the accurate detection and positioning of cabbage.ResultsIn this study, a cabbage detection algorithm based on the YOLOv8n neural network (YOLOv8-cabbage) combined with a positioning system constructed using a Realsense depth camera is proposed. Initially, four of the currently available high-performance object detection models were compared, and YOLOv8n was selected as the transfer learning model for field cabbage detection. Data augmentation and expansion methods were applied to extensively train the model, a large kernel convolution method was proposed to improve the bottleneck section, the Swin transformer module was combined with the convolutional neural network (CNN) to expand the perceptual field of feature extraction and improve edge detection effectiveness, and a nonlocal attention mechanism was added to enhance feature extraction. Ablation experiments were conducted on the same dataset under the same experimental conditions, and the improved model increased the mean average precision (mAP) from 88.8% to 93.9%. Subsequently, depth maps and colour maps were aligned pixelwise to obtain the three-dimensional coordinates of the cabbages via coordinate system conversion. The positioning error of the three-dimensional coordinate cabbage identification and positioning system was (11.2 mm, 10.225 mm, 25.3 mm), which meets the usage requirements.ConclusionsWe have achieved accurate cabbage positioning. The object detection system proposed here can detect cabbage in real time in complex field environments, providing technical support for targeted spraying applications and positioning.

A novel method to enhance the accuracy of parameter identification in elasto-geometrical calibration for industrial robots

Elasto-geometrical calibration is crucial for enhancing the absolute accuracy of robots in machining operations through the identification and compensation of parameter errors. However, the presence of inconsistent measurement units and improper selection of measuring poses can result in the ill-conditioned identification matrix (ICIM) issue, consequently impacting the accuracy of parameter identification. This paper introduces a novel method to tackle this challenge. Initially, an elasto-geometrical error model is developed based on the orientation-independent measurements (OIM), efficiently reducing the impact of mismatched positions and orientations on the ICIM problem. Subsequently, a PSO-SFFS algorithm is proposed to optimize the measurement configurations and minimize the influence of measurement noise. Furthermore, the incorporation of screw theory and the consideration of parallelogram mechanisms enhance the precision and comprehensiveness of the error model. Subsequent to the development of the error model, calibration comparison experiments are conducted on an industrial robot. Both simulation and experimental results validate the effectiveness of the proposed method in improving parameter identification accuracy.

Online early warning method for motorized-spindle degradation without failure data

Early warning for the performance degradation of motorized-spindle can effectively prevent machine tool failure and improve machining accuracy and production efficiency. This study proposes an online motorized-spindle early warning method without failure data. First, vibration data are collected and then processed by a Butterworth band-stop filter algorithm. Second, the idle condition of motorized-spindle can be identified online based on the frequency match strategy. Subsequently, an early warning method is proposed to obtain an appropriate first degradation time (FDT). Finally, experimental results show that the idle condition identification accuracy is 99.16% and FDT identification error is 0.61%, which indicates that the proposed method can obtain accurate early warning results of motorized-spindle without failure data.

Photogrammetry-Based Method for Measuring Volume Changes of Soil Specimens During Critical Phases of Triaxial Testing

Triaxial tests has been routinely used to measure the stress–strain relationship for geomaterials. During triaxial testing, many sources of errors that cannot be completely avoided but are often ignored or approximated using empirical equations. This paper presents a systematic investigation of soil volume change, volume strain nonuniformity, and cross-sectional calculation along the specimen during triaxial testing using a photogrammetry-based method. Consolidated drained triaxial tests were performed in which two parallel measurements were taken: (1) relative volume using the conventional triaxial testing and (2) absolute volume using the photogrammetry-based method. The difference in the observed volume and void ratio measurements between the two methods highlighted the importance of absolute soil volume over the relative volume method. The results revealed the effect of soil volume change in the interpretation of triaxial testing findings. Various preparation steps and procedures during triaxial testing have influenced the initial measured volume and thus caused deviation of the subsequent associated measurements. This method would present a quality control approach for different aspects of triaxial testing to improve the test simulations to accurately measure the soil behavior. The proposed method is important for more refined simulations and identification of setup-induced errors. Additional applications of the current research would allow correct determination of the stress path followed during triaxial testing, the critical-state soil mechanics parameters, and the stress–strain relationship, including deformation and strength parameters.

Study on the Identification of Execution Errors, Causes, and Specific Corrective Kinetic and Prophylactic Means in Free Throws in Basketball

The identification of execution errors in real-time correction through an innovative specific device to free throw refinement, and through corrective kinetic and prophylactic exercises adapted to basketball game. The study included 92 subjects structured into sports categories: U14, U16, U18, and by gender: female (46 subjects) and male (46 subjects). Subjects were observed to identify the incidence of execution errors in free throws, their causes and correction methods during the study's testing. The study results highlight the decrease in the number of execution errors between the final and initial testing due to the implementation of innovative technology specific to free throw refinement and the application of specific corrective kinetic and prophylactic exercises. For all junior and gender categories of the study, the number of errors decreased in the final testing compared to the initial one, highlighting the importance of identifying errors and causes in real-time and correcting them through specific means.

Revolutionizing the Application of Automatic Inspection System for Industrial Parts Using AI Machine Vision Technology

This paper addresses the challenges of large data management, prolonged operation times, and low detectionefficiency encountered in automatic detection systems. To overcome these issues, we propose a novel research and application method utilizing AI machine vision technology. The methodology employs the Pulse-Coupled Neural Network (PCNN) algorithm for analyzing machine control points, enhancing system reliability and detection efficiency. Furthermore, a three-stage sliding table mechanism is implemented to facilitate seamless operation restarts. Notably, our approach significantly reduces the time required for key operations, such as feeding, imaging, decision-making, on-site inspection, and re-inspection, all within 5 seconds and 1 meter distance. It supports high-precision dynamic identification, detection, and correction of errors during high-speed movement, thereby enhancing overall system performance. The experimental results demonstrate exceptional accuracy, particularly in detecting small parts measuring 28.87 mm in length and 12.36 mm in width, achieving an impressive precision of 0.04 mm. Additionally, our system boasts meticulous hardware selection, robust software stability, and high-performance capabilities, culminating in improved detection efficiency and accuracy. This research not only contributes novel ideas and results but also holds significant commercial value in industrial applications. Overall, our proposed methodology represents a noteworthy advancement in automatic inspection systems, offering superior performance and reliability compared to existing approaches.

Multivariate anomaly detection models enhance identification of errors in routine clinical chemistry testing.

Conventional autoverification rules evaluate analytes independently, potentially missing unusual patterns of results indicative of errors such as serum contamination by collection tube additives. This study assessed whether multivariate anomaly detection algorithms could enhance the detection of such errors. Multivariate Gaussian, k-nearest neighbours (KNN) distance, and one-class support vector machine (SVM) anomaly detection models, along with conventional limit checks, were developed using a training dataset of 127,451 electrolyte, urea, and creatinine (EUC) results, with a 5 % flagging rate targeted for all approaches. The models were compared with limit checks for their ability to detect atypical EUC results from samples spiked with additives from collection tubes: EDTA, fluoride, sodium citrate, or acid citrate dextrose (n=200 per contaminant). The study additionally assessed the ability of the models to identify 127,449 single-analyte errors, a potential weakness of multivariate models. The KNN distance and SVM models outperformed limit checks for detecting all contaminants (p-values <0.05). The multivariate Gaussian model did not surpass limit checks for detecting EDTA contamination but was superior for detecting the other additives. All models surpassed limit checks for identifying single-analyte errors, with the KNN distance model demonstrating the highest overall sensitivity. Multivariate anomaly detection models, particularly the KNN distance model, were superior to the conventional approach for detecting serum contamination and single-analyte errors. Developing multivariate approaches to autoverification is warranted to optimise error detection and improve patient safety.

Frequency of Refractive Error in Patients with Squint

Objective: To determine the frequency of refractive errors in patients with squint at a tertiary care Hospital. Methodology: A cross-sectional study was conducted at the Department of Ophthalmology, Liaquat University Eye Hospital in Hyderabad from May 2020 to October 2020. A total of 128 patients either exotropia or esotropia were included in this study. Auto refraction and Hirschberg reflex test was done. A refractive error of ?+2 was taken as hypermetropia and ? -2 was taken as Myopia. This information was entered in the proforma and analyzed using SPSS version 20. Results: The average age of the patients was 6.26±3.09 years. There were 73(5.03%) male and 55(42.97%) female. Hypermetropia was observed in 47.66% (61/128) cases while myopia in 11.72% (15/128) cases. Furthermore, the frequency of hypermetropia was found statistically insignificant according to age, gender, types of squint and Hirschberg reflex p-value (&gt;0.05). Frequency of Myopia was found statistically insignificant across age groups, gender-wise and across different angles of the Hirschberg reflex-value (&gt;0.05), while it was statically significant according to types of squint, (p-0.033). Conclusion: Study revealed that the hypermetropia emerges as the most prevalent refractive error among the children assessed. It underscores the necessity for all these children to undergo cycloplegic refraction, with glasses being identified as the primary treatment approach. Mass screening initiatives are essential aimed at facilitating early identification and treatment of refractive errors.

Evaluation of RPLC stationary phases for tocopherol and tocotrienol positional isomer separation: Method development and profiling

Reversed-phase separation of tocopherols (Ts) and tocotrienols (Ts) using C18 stationary phases results in the coelution of β and γ positional isomers, leading to identification errors. This study investigates the potential of alternative stationary phase chemistries to effectively resolve tocochromanols, specifically focusing on the critical pair of β and γ positional isomers. Initial screening of seven different stationary phases (C18, C18-PFP, C30, PFP, 5PYE, πNAP, and RP-Amide) was conducted. Linear solvent strength (LSS) studies were performed to assess the impact of the organic modifier (methanol) and temperature on the chromatographic performance parameters. Five columns were found to be suitable for the tocochromanol separation and two different chromatographical conditions per column were proposed. Elution order of tocochromanols was unique for 5PYE, πNAP and C30 columns in comparison to RP-Amide and PFP. Method development for the quantitative analysis of four tocopherol and four tocotrienol homologues was performed. The optimised method employed the RP-Amide (150 × 4.6 mm, 2.6 μm dp) superficially porous particle column, mobile phase of methanol:water of 92:8, v/v, with a flow rate of 1.0 mL/min, column oven temperature of 40 °C and fluorescence detection (λex 295 nm, λem 330 nm). The analysis run time was 10.5 min with 13.6 MPa back pressure. The method was validated and the obtained LOQs were found to be 1.30-3.13 μg/mL. The method developed was successfully applied for the determination of tocochromanols in twenty samples with unique tocochromanol profiles. Principal component analysis illustrated three distinct groups based on the tocochromanol profile.

Lean approach applied to the blood gas system in point-of-care testing (POCT): The IRCCS San Gerardo dei Tintori experience

ABSTRACT Costs of medical care have increased worldwide, which may be due to unnecessary operational inefficiency. Different approaches exist to enhance procedural processes, and the Lean concept is used to improve quality and performance by systematically eliminating waste. In this study, Lean methodology with the four-stage Deming cycle (Plan, Do, Check, and Act) was applied to the point-of-care testing (POCT) system of the IRCCS San Gerardo dei Tintori. Starting from the evaluation of the current strategies used in the clinical laboratory, our goal was to facilitate workflow standardization, increase the efficiency, appropriateness, and quality standards in the execution of blood gas analysis in POCT, and to plan improvements, staff satisfaction, and safety. Six qualitative key process indicators, such as workflow, work area management, visual checks, reagents and supplies inventory, sample traceability, and work standardization, were used to investigate problems and identify solutions. The adoption of cutting-edge technologies, the training of personnel on the best pre-analytical practices, the standardization of cleaning procedures, the distribution of easy check workflow charts, and standardized material request documents reduced manual operations from 70 to 40% and patient identification errors of 10x. This approach created a diffuse collaborative culture and improved standardization of processes and safety.

Assessment of Accuracy of Moderate-Resolution Imaging Spectroradiometer Sea Surface Temperature at High Latitudes Using Saildrone Data

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

ANALYSIS OF FILES IN STL-FORMAT AS THE BASIS OF MODELING FOR 3D PRINTING OF BUILDING OBJECTS

Problem statement. The integration of advanced technologies in the field of software at the stages of construction becomes one of the key tasks of designers. Creating objects using 3D printing requires the use of appropriate high-tech solutions. One of these solutions consists in the analysis of the process of converting three-dimensional models into a control code for 3D printers, in particular, the analysis of files in STL format. The efficiency of manufacturing construction structures and structures by 3D printing depends primarily on this analysis. Purpose of the article. This study aims at an in-depth analysis of STL files in light of the growth of additive manufacturing and the advancement of digital technologies in the construction industry. The purpose of the work is to provide a comprehensive overview of the basic information related to the use of this format, including methods for calculating the area and volume of the STL grid. Identification and analysis of typical errors that may occur when working with files of this format and definition of key criteria for evaluating the geometric quality of the grid. Consideration of strategies and alternative approaches to overcome possible drawbacks that may arise when using the STL format. Creation of a comprehensive view of this format and provision of appropriate recommendations for further improvement of the processes of working with it. Conclusion. The study of STL files plays an important role in the development of additive manufacturing and digital technologies in the construction industry. This study aims to provide a detailed overview of the basic information related to the use of this format, including methods for calculating the area and volume of an STL mesh, which are crucial for efficient modeling and production of structures. During the research, a number of errors were identified and key criteria for assessing the geometric quality of the grid were determined. This includes correctly orienting normals, detecting and correcting overlaps, intersections, and isolated faces. For further research, it is proposed to consider alternative options for overcoming some of the shortcomings of the STL format. For example, you can consider automated methods of detecting and correcting errors in the network, developing new file formats with greater functionality and support for additional model properties. Investigating such alternatives can help improve the efficiency and accuracy of file use in construction and additive manufacturing.