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The precision of workpiece machining is critically influenced by the geometric errors in the kinematics of grind robots, which directly affect their absolute positioning accuracy. To tackle this challenge, this paper introduces a logistic-tent chaotic mapping Levenberg Marquardt algorithm designed to accurately identify and compensate for this geometric error. the approach begins with the construction of a forward kinematic model and an error model specific to the robot. Then the algorithm is adopted to identify and compensate for the geometric error. The method establishes a mapping interval around the initial candidate solutions derived from iterative applications of the Levenberg Marquardt algorithm. Within this interval, the logistic-tent chaotic mapping method generates a diverse set of candidate solutions. These candidates are evaluated based on their fitness values, with the optimal solution selected for subsequent iterations. Empirical compensation experiments have validated the proposed method's precision and effectiveness, demonstrating a 6% increase in compensation accuracy and a 47.68% improvement in efficiency compared to existing state-of-the-art approaches. This process not only minimizes the truncation error inherent in the Levenberg Marquardt algorithm but also significantly enhances solution efficiency. Moreover, simulation experiments on grind processes further validate the method's ability to significantly improve the quality of workpiece machining.

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Objective. Deep learning models, such as convolutional neural networks (CNNs), can take full dose comparison images as input and have shown promising results for error identification during treatment. Clinically, complex scenarios should be considered, with the risk of multiple anatomical and/or mechanical errors occurring simultaneously during treatment. The purpose of this study was to evaluate the capability of CNN-based error identification in this more complex scenario.Approach. For 40 lung cancer patients, clinically realistic ranges of combinations of various treatment errors within treatment plans and/or computed tomography (CT) images were simulated. Modified CT images and treatment plans were used to predict 2580 3D dose distributions, which were compared to dose distributions without errors using various gamma analysis criteria and relative dose difference as dose comparison methods. A 3D CNN capable of multilabel classification was trained to identify treatment errors at two classification levels, using dose comparison volumes as input: Level 1 (main error type, e.g. anatomical change, mechanical error) and Level 2 (error subtype, e.g. tumor regression, patient rotation). For training the CNNs, a transfer learning approach was employed. An ensemble model was also evaluated, which consisted of three separate CNNs each taking a region of interest of the dose comparison volume as input. Model performance was evaluated by calculating sample F1-scores for training and validation sets.Main results. The model had high F1-scores for Level 1 classification, but performance for Level 2 was lower, and overfitting became more apparent. Using relative dose difference instead of gamma volumes as input improved performance for Level 2 classification, whereas using an ensemble model additionally reduced overfitting. The models obtained F1-scores of 0.86 and 0.62 on an independent test set for Level 1 and Level 2, respectively.Significance. This study shows that it is possible to identify multiple errors occurring simultaneously in 3D dose verification data.

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INTRODUCTION: Many surgical procedures are prone to human error, particularly in the learning phase of skills acquisition. Task standardisation has been suggested as an approach to reducing errors, but it fails to account for the human factors associated with learning. Human reliability analysis (HRA) is a structured approach to assess human error during surgery. This study used HRA methodologies to examine skills acquisition associated with carpal tunnel decompression. METHODS: The individual steps or subtasks required to complete a carpal tunnel decompression were identified using hierarchical task analysis (HTA). The systematic human error reduction and prediction approach (SHERPA) was carried out by consensus of subject matter experts. This identified the potential human errors at each subgoal, the level of risk associated with each task and how these potential errors could be prevented. RESULTS: Carpal tunnel decompression was broken down into 46 subtasks, of which 21 (45%) were medium risk and 25 (55%) were low risk. Of the 46 subtasks, 4 (9%) were assigned high probability and 18 (39%) were assigned medium probability. High probability errors (>1/50 cases) included selecting incorrect tourniquet size, failure to infiltrate local anaesthetic in a proximal-to-distal direction and completion of the World Health Organization (WHO) surgical sign-out. Three (6%) of the subtasks were assigned high criticality, which included failure to aspirate before anaesthetic injection, whereas 21 (45%) were assigned medium criticality. Remedial strategies for each potential error were devised. CONCLUSIONS: The use of HRA techniques provides surgeons with a platform to identify critical steps that are prone to error. This approach may improve surgical training and enhance patient safety.

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In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study's contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user-system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants' mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.

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OBJECTIVES: Motivation of this study is to check the sensitivity of dosimetric tool gamma with 2D detector array combination when unexpected errors occur while transferring intensity-modulated radiation therapy treatment plans from planning system to treatment unit. METHODS: This study consists of 17 head and neck cancer patient's treatment plans. Nine types of verification plans are created for all 17 clinically approved treatment plans by consecutively deleting different segments (up to eight) one by one from each field of the plan. Decrement factor (χ) is introduced in our study which illustrated the degree of decay of gamma passing rate when intentional errors are introduced. We analyzed the data by two different methods-one without selecting the region of interest (ROI) in dose distributions and the other by selecting the region of interest. RESULTS: By linear regression, the absolute value of slopes is 0.025, 0.024 and 0.015 without ROI and 0.030, 0.027 and 0.015 with ROI for 2%/2 mm, 3%/3 mm and 5%/5 mm criteria, respectively. The higher absolute value of the fitted slope indicates the higher sensitivity of this method to identify erroneous plan in treatment unit. The threshold value for 2%/2 mm equivalent to 95% passing criteria in 3%/3 mm used in clinical practice is obtained as 83.44%. CONCLUSIONS: The 2D detector array with dosimetric tool gamma is less sensitive in detecting errors when unprecedented errors of segment deletion occur within the treatment plans.

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BACKGROUND/PURPOSE: Electronic portal imaging device (EPID) dosimetry aims to detect treatment errors, potentially leading to treatment adaptation. Clinically used threshold classification methods for detecting errors lead to loss of information (from multi-dimensional EPID data to a few numbers) and cannot be used for identifying causes of errors. Advanced classification methods, such as deep learning, can use all available information. In this study, convolutional neural networks (CNNs) were trained to detect and identify error type and magnitude of simulated treatment errors in lung cancer patients. The purpose of this simulation study is to provide a proof-of-concept of CNNs for error identification using EPID dosimetry in an in vivo scenario. MATERIALS AND METHODS: Clinically realistic ranges of anatomical changes, positioning errors and mechanical errors were simulated for lung cancer patients. Predicted portal dose images (PDIs) containing errors were compared to error-free PDIs using the widely used gamma analysis. CNNs were trained to classify errors using 2D gamma maps. Three classification levels were assessed: Level 1 (main error type, e.g., anatomical change), Level 2 (error subtype, e.g., tumor regression) and Level 3 (error magnitude, e.g., >50% tumor regression). RESULTS: CNNs showed good performance for all classification levels (training/test accuracy 99.5%/96.1%, 92.5%/86.8%, 82.0%/72.9%). For Level 3, overfitting became more apparent. CONCLUSION: This simulation study indicates that deep learning is a promising powerful tool for identifying types and magnitude of treatment errors with EPID dosimetry, providing additional information not currently available from EPID dosimetry. This is a first step towards rapid, automated models for identification of treatment errors using EPID dosimetry.

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Biological conclusions based on DNA barcoding and metabarcoding analyses can be strongly influenced by the methods utilized for data generation and curation, leading to varying levels of success in the separation of biological variation from experimental error. The 5' region of cytochrome c oxidase subunit I (COI-5P) is the most common barcode gene for animals, with conserved structure and function that allows for biologically informed error identification. Here, we present coil ( https://CRAN.R-project.org/package=coil ), an R package for the pre-processing and frameshift error assessment of COI-5P animal barcode and metabarcode sequence data. The package contains functions for placement of barcodes into a common reading frame, accurate translation of sequences to amino acids, and highlighting insertion and deletion errors. The analysis of 10 000 barcode sequences of varying quality demonstrated how coil can place barcode sequences in reading frame and distinguish sequences containing indel errors from error-free sequences with greater than 97.5% accuracy. Package limitations were tested through the analysis of COI-5P sequences from the plant and fungal kingdoms as well as the analysis of potential contaminants: nuclear mitochondrial pseudogenes and Wolbachia COI-5P sequences. Results demonstrated that coil is a strong technical error identification method but is not reliable for detecting all biological contaminants.

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International standards and practice guidelines recommend the use of delta check alerts for laboratory test result interpretation and quality control. The value of contemporary applications of simple univariate delta checks determined as an absolute change, percentage change, or rate of change to recognize specimen misidentification or other laboratory errors has not received much study. This review addresses these three modes of calculation, but in line with the majority of published work, most attention is focused on the identification of specimen misidentification errors. Investigation of delta check alerts are time-consuming and the yield of identified errors is usually small compared to the number of delta check alerts; however, measured analytes with low indices of individuality frequently perform better. While multivariate approaches to delta checks suggest improved usefulness over simple univariate delta check strategies, some of these are complex and not easily applied in contemporary laboratory information systems and middleware. Nevertheless, a simple application of delta checks may hold value in identifying clinically significant changes in several clinical situations: for acute kidney injury using changes in serum creatinine, for risk of osmotic demyelination syndrome using rapid acute changes in serum sodium levels, or for early triage of chest pain patients using high sensitivity troponin assays. A careful and highly selective approach to identifying delta check analytes, calculation modes, and thresholds before putting them into practice is warranted; then follow-up with careful monitoring of performance and balancing true positives, false negatives, and false positives among delta check alerts is needed.

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AIMS AND OBJECTIVES: To investigate hospital nurses' involvement in the identification and reporting of medication errors in Turkey. BACKGROUND: Medication safety is an international priority, and medication error identification and reporting are essential for patient safety. DESIGN: A descriptive survey design consistent with the STROBE guidelines was used. METHODS: The participants were 135 nurses employed in a university hospital in Turkey. The survey instrument included 18 sample cases and respondents identified whether errors had been made and how they should be reported. Descriptive statistics were analysed using the chi-square and Fisher's exact tests. RESULTS: The sample case of "Patient given 10 mg morphine sulphate instead of 1.0 mg of morphine sulphate" was defined as a medication error by 97% of respondents, whereas the sample case of "Omitting oral/IV antibiotics because of the need to take the patient out for X-rays for 3 hr" was defined as a medication error by only 32.1%. It was found that eight sample cases (omitting antibiotics, diluting norodol drops with saline, giving aspirin preprandially, injecting clexane before colonoscopy, giving an analgesic at the nurse's discretion, dispensing undiluted morphine, preparing dobutamine instead of dopamine and administering enteral nutrition intravenously) were assessed as errors and reported, although there were significant statistical differences between the identification and reporting of these errors. CONCLUSION: Nurses are able to identify medication errors, but are reluctant to report them. Fear of the consequences was the main reason given for not reporting medication errors. When errors are reported, it is likely to be to physicians. RELEVANCE TO CLINICAL PRACTICE: The development of a commonly agreed definition of a medication error, along with clear and robust reporting mechanisms, would be a positive step towards increasing patient safety. Staff reporting medication errors should be supported, not punished, and the information provided used to improve the system.

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We read, with great interest, the study by Baldwin and Rodriguez (2016), which described the role of the verification nurse and details the verification process in identifying errors related to chemotherapy orders. We strongly agree with their findings that a verification nurse, collaborating closely with the prescribing physician, pharmacist, and treating nurse, can better identify errors and maintain safety during chemotherapy administration.

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This study postulates that traditional human error identification techniques fail to consider motion economy principles and, accordingly, their applicability in operating theatres may be limited. This study addresses this gap in the literature with a dual aim. First, it identifies the principles of motion economy that suit the operative environment and second, it develops a new error mode taxonomy for human error identification techniques which recognises motion economy deficiencies affecting the performance of surgeons and predisposing them to errors. A total of 30 principles of motion economy were developed and categorised into five areas. A hierarchical task analysis was used to break down main tasks of a urological laparoscopic surgery (hand-assisted laparoscopic nephrectomy) to their elements and the new taxonomy was used to identify errors and their root causes resulting from violation of motion economy principles. The approach was prospectively tested in 12 observed laparoscopic surgeries performed by 5 experienced surgeons. A total of 86 errors were identified and linked to the motion economy deficiencies. Results indicate the developed methodology is promising. Our methodology allows error prevention in surgery and the developed set of motion economy principles could be useful for training surgeons on motion economy principles.

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Approximately 33% of stroke patients have difficulty performing activities of daily living, often committing errors during the planning and execution of such activities. The objective of this study was to evaluate the ability of the human error identification (HEI) technique SHERPA (Systematic Human Error Reduction and Prediction Approach) to predict errors during the performance of daily activities in stroke patients with left and right hemisphere lesions. Using SHERPA we successfully predicted 36 of the 38 observed errors, with analysis indicating that the proportion of predicted and observed errors was similar for all sub-tasks and severity levels. HEI results were used to develop compensatory cognitive strategies that clinicians could employ to reduce or prevent errors from occurring. This study provides evidence for the reliability and validity of SHERPA in the design of cognitive rehabilitation strategies in stroke populations.

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The present study brings together for the first time the techniques of hierarchical task analysis (HTA), human error identification (HEI), and business process management (BPM) to select practices that can eliminate or reduce potential errors in a surgical setting. We applied the above approaches to the improvement of the patient positioning process for lumbar spine surgery referred to as 'direct lateral interbody fusion' (DLIF). Observations were conducted to gain knowledge on current DLIF positioning practices, and an HTA was constructed. Potential errors associated with the practices specific to DLIF patient positioning were identified. Based on literature review and expert views alternative practices are proposed aimed at improving the DLIF patient positioning process. To our knowledge, this is the first attempt to use BPM in association with HEI/HTA for the purpose of improving the performance and safety of a surgical process - with promising results.

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This paper proposes a novel method for identifying carriage errors. A general mathematical model of a guideway system is developed, based on the multi-body system method. Based on the proposed model, most error sources in the guideway system can be measured. The flatness of a workpiece measured by the PGI1240 profilometer is represented by a wavelet. Cross-correlation analysis performed to identify the error source of the carriage. The error model is developed based on experimental results on the low frequency components of the signals. With the use of wavelets, the identification precision of test signals is very high.