Error Measures Research Articles

Cinemeducation to teach patient safety: an experience in medical students

From early in medical school, students need to learn about patient safety, and how to prevent and reduce risks, errors and harm that occur to patients during health care provision. Cinemeducation is a teaching methodology that uses fragments of feature films or TV series in medical education. We hypothesized that cinemeducation could help simplify the comprehension of complex situations for students, aiding in the learning of patient safety and the understanding of human behaviors that impact it. This concept stands as an integral component within a comprehensive medical humanities approach. The study population was the 70 medical students of the second course of a six-year undergraduate program. In a 2-h class, after learning objectives were established, students watched a fragment from TV series The Resident centered on adverse events, and discussed them afterward. To measure learning achievements, we administered a 10-question multiple-choice pre and post-test and a 2-question open-essay post-test. Given the complexity of the concepts related to patient safety, we used a mixed quali-quantitative approach. An exploratory descriptive and content analysis was performed. Sixty-eight students participated and completed the questionnaires. Despite high pre-intervention scores, post-intervention scores improved (mean difference 0.779, 95% CI: 0.475–1.083, p < 0.001). The open-essay questions provided more information about the learning points, mostly related to the understanding of system error and preventive measures against adverse events. Classical teaching methods may be insufficient for learning how to deal with complex situations in hospital contexts. Our results show that cinemeducation may significantly improve learning on patient safety. Cinemeducation methodology can be effective in teaching some complex concepts in medical education. The integration of cinemeducation within medical education, through the lens of medical humanities, offers a dynamic approach to teaching patient safety. By using cinematic narratives to explore intricate medical scenarios, students engage with real-world complexities and the influence of human factor, enhancing their understanding.

Accounting for spatio-temporal and sampling dependence in survey and CPUE biomass indices: simulation and Bayesian modeling framework

Abstract Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence.

A novel joint depth sensor calibration method without fixture for mobile robots’ navigation

AbstractCommercial mobile robots are usually equipped with multiple depth sensors that can measure the point cloud information around the robot's environment. The installation process of these sensors contains assembly error and sensor measurement error, so it is necessary to calibrate each sensor to align the point cloud. In order to obtain the sensor calibration results of commercial robots under normal working conditions, this study proposes a fixture free multi depth sensor joint calibration method that can be deployed on low‐cost embedded computing units, which efficiently aligns the point clouds of each sensor. During the calibration process, the robot is placed in the center of three upright thin plates perpendicular to the ground. 2D LIDAR depicts high‐precision contours of the upright thin plates. In the calibration process of each depth sensor, the roll angle and pitch angle of the sensor point cloud are first calibrated to make it perpendicular to the ground, and then the yaw angle and position of the point cloud are calibrated to fit the high‐precision contour of the upright thin plate. The results show that this method can be deployed on low‐cost embedded computing units, with real‐time and accurate calibration results. The convergence of calibration results can be achieved through up to 5 iterations, and the average running time is less than 120 ms. This research achievement provides a reference for multi‐sensor calibration of commercial robots.

Development of fixed-point two-degree-of-freedom angular error measurement system with precision improvement function.

In the field of precision industries, measuring micro-angular errors plays a crucial role in improving the accuracy of equipment. Therefore, this study aims to develop a two-degree-of-freedom (two-DOF) angular error measurement system for simultaneously measuring the assembly angle errors of the bond head in a die bonding machine. This system is based on the laser collimation method and constructed by using components such as a fiber laser and position-sensitive detectors. It utilizes the angular drift compensation of the laser beam and averaging function to improve the measurement accuracy. In addition, the mathematical model of the proposed system was established by using skew-ray tracing. Through experiments, the measuring accuracy of ±0.25 arcsec and measuring repeatability of 0.3 arcsec could be achieved.

A novel SPM error measurement method based on peak detection of nodes centers in a 2D orthogonal lattice as standard

Abstract This paper presented a novel scanning probe microscope (SPM) error measurement method by using a 2D micro-scale orthogonal lattice standard and peak detection (PD) method of the pitch center coordinates based on cross-correlation/convolution (CC) filtering of images raster-scanned by SPMs. The geometric errors of motion and drifting rate in x-y plane are measured. Geometric errors include positional deviations E x x and E y y , straightness deviations E x y and E y x along x- and y-axis respectively, and the orthogonality deviation E o x y between the two axes. The calibration factors C x and C y were calculated based on the number of pixels scanned on the x-axis and y-axis, scanning range, average pitch of standard lattice from metrological verification certification, and average lattice pitch calculated by using PD method. Through case study, the errors of an AFM was measured using an orthogonal lattice standard with a nominal pitch of 10 μm, resulting in C x and C y values of 0.925 and 1.050, respectively, an orthogonal deviation E o x y of 0.015°, and the maximum drift rate of the x-axis and y-axis is −366.21 nm min−1 and −317.38 nm min−1, respectively. The new error measurement method of SPMs provides technical reference for the development and performance improvement of SPM instruments.

Error measures and solution artifacts of the harmonic balance method on the example of the softening Duffing oscillator

Error measures and solution artifacts of the harmonic balance method on the example of the softening Duffing oscillator

Master stability functions of networks of Izhikevich neurons.

Synchronization has attracted interest in many areas where the systems under study can be described by complex networks. Among such areas is neuroscience, where it is hypothesized that synchronization plays a role in many functions and dysfunctions of the brain. We study the linear stability of synchronized states in networks of Izhikevich neurons using master stability functions (MSFs), and to accomplish that, we exploit the formalism of saltation matrices. Such a tool allows us to calculate the Lyapunov exponents of the MSF properly since the Izhikevich model displays a discontinuity within its spikes. We consider both electrical and chemical couplings as well as global and cluster synchronized states. The MSF calculations are compared with a measure of the synchronization error for simulated networks. We give special attention to the case of electric and chemical coupling, where a riddled basin of attraction makes the synchronized solution more sensitive to perturbations.

Agreement of synchronous remote and in-person application of the Alberta Infant Motor Scale: Cohort study.

Using standardized scales to assess motor development via telemedicine can increase access for low-income populations. Our aim was to verify the agreement and feasibility between remotely and synchronously applying the Alberta Infant Motor Scale (AIMS) and the in-person format. This was a concordance study, with 77 typical infants aged 4-18 months (mean = 13 months). The AIMS was applied remote via video calls and face-to-face. We applied a questionnaire to caregivers to verify feasibility. There was a high level of agreement between the remote and in-person assessments, with intraclass correlation coefficients above 0.98 and low standard error measure values (<1 item for each posture, <2 items for the total raw score, and =5% for the normative score). The smallest detectable change was between 1.67 and 2.45 for each posture, 3 for the total raw score, and 6% for the normative score. The Bland-Altman analysis showed low bias with the mean difference close to zero (<0.80) and low error with little dispersion of the difference points around the mean. Caregivers' perspectives on the synchronous remote assessment were positive, with good quality, clear information during the assessment, and comfort with the method. The synchronous remote application of the AIMS may be an alternative for families without access to in-person services that assess motor development.

Comparisons of Self-Report With Objective Measurements Suggest Faster Responding but Little Change in Response Quality Over Time in Ecological Momentary Assessment Studies.

Response times (RTs) to ecological momentary assessment (EMA) items often decrease after repeated EMA administration, but whether this is accompanied by lower response quality requires investigation. We examined the relationship between EMA item RTs and EMA response quality. In one data set, declining response quality was operationalized as decreasing correspondence over time between subjective and objective measures of blood glucose taken at the same time. In a second EMA study data set, declining response quality was operationalized as decreasing correspondence between subjective ratings of memory test performance and objective memory test scores. We assumed that measurement error in the objective measures did not increase across time, meaning that decreasing correspondence across days within a person could be attributed to lower response quality. RTs to EMA items decreased across study days, while no decrements in the mean response quality were observed. Decreasing EMA item RTs across study days did not appear problematic overall.

Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data.

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Predictions of Malaysia Age-Specific Fertility Rates using the Lee-Carter and the Functional Data Approaches

Global fertility has been experiencing a significant decline, reaching towards the replacement ratio. This trend, coupled with increasing life expectancies, has led to the emergence of an ageing population. In this study, we delve into an analysis of fertility patterns among Malaysian women, considering both their childbearing age and ethnic groups. A comprehensive 63-year fertility dataset, from 1958 to 2020, were obtained from the Department of Statistics Malaysia. These data were fitted into the Lee-Carter model and its modified version, which is the functional data model. The models were evaluated using the out-sample forecast error measures. Results indicate that the third-order functional data model able to capture most of variation present in the actual data, consequently outperforming the Lee-Carter model in forecasting fertility rates among Chinese and Indian populations. The 20-year forecasts reveal a noteworthy shift in maternal ages of the highest births to older ages suggesting a trend towards delayed pregnancies among women. It is predicted that the Malay total fertility rates will likely fall to below the replacement level reaching 1.71 in 2040 whereas Chinese and Indian total fertility rates will substantially decrease to the lowest level in history below 1.0 which are 0.54 and 0.70 respectively. The evolution in Malaysian fertility rates is an alarming fact as, together with low mortality rates, it may impact the Malaysian population structure in future. Proactive policy measures are urgently needed to address these demographic shifts.

Data-based modelling of arrays of wave energy systems: Experimental tests, models, and validation

One of the key steps towards economic feasibility of wave energy conversion technology concerns scaling up to farms of multiple devices, in the attempt to reduce installation costs by sharing infrastructure, and a consequent drop in levelised cost of energy. Moreover, whenever wave energy systems are deployed in proximity (in so-called arrays), the exploitation of the hydrodynamic interactions between single devices is fully enabled, potentially increasing the final energy outcome. To achieve this in real (operational) time, the employed energy-maximising control strategies require control-oriented array models, able to efficiently describe the dynamics of these interconnected systems in a representative fashion. This can be, nonetheless, a difficult task when considering first principles alone, under small motion assumptions, for modelling purposes. Recognising the uncertainty associated to array numerical models obtained from the linearisation of simplified system equations around their equilibria, this paper presents models of several array configurations identified following a frequency domain approach on the basis of experimental data. Tailored tests on laboratory-scale devices have been designed and conducted in the Aalborg University (Denmark) wave tank facility, with the purpose of performing representative system identification of the wave energy systems arrays. The obtained models are validated on different representative sea states configurations, in controlled and uncontrolled motion operational conditions. The validation results are fully discussed and analysed in terms of standard error measures and time lag, while the obtained models are made freely accessible via a linked repository (named OCEAN), in the attempt to openly provide validated models for different array configurations.

Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System.

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

Validity and reliability of the modified four square step test in total HIP arthroplasty

IntroductionThe Modified Four Square Step Test (mFSST) evaluates dynamic stabilization by using tape instead of canes to prevent people from tripping over obstacles and losing their balance. There was no study investigating reliability and validity in primary total hip arthroplasty (THA). This study aimed to evaluate the test-retest reliability, concurrent validity, and minimum detectable change in the mFSST for individuals with primary THA. MethodThis study was cross-sectional in design. A total of 29 individuals aged 56.86 ± 10.14 years participated. The study used the test-retest method to determine the reliability of the mFSST and concurrent validity. The Timed Up and Go Test (TUGT) and Berg Balance Scale (BBS) were applied for concurrent validity. The minimum detectable change (MDC95) and the standard error measurement (SEM) were calculated. ResultsThe mFSST showed excellent intra-rater reliability (ICC = 0.925). The SEM and MDC95 of the mFSST were 1.56 and 4.32. The test had a strong correlation with TUGT (r = 0.881) and BBS (r = −0.718) (p < 0.001). ConclusionThe mFSST is a valid and reliable assessment tool that can be used for individuals with primary THA.

Spatial error prediction method for precision machine tools considering the influence of repeatability error

A spatial error prediction method for precision machine tools considering the influence of repeatability errors is proposed in this paper. Firstly, the reciprocal of the repeatability error is used to evaluate the measurement data confidence to distinguish the difference of useful information contained in the measurement data of different positions. Secondly, the proposed confidence index is used to weight the measured values of PDGEs at different positions, so that the data containing more useful information can play a full role in the error prediction, and the predicted error is substituted into the error model to predict the spatial error. Then, the computer simulation based on statistical analysis is designed, the PDGEs and spatial error prediction accuracy of the weighted algorithm are verified by simulation. Finally, a multi-instrument fusion spatial error measurement scheme is proposed, and the accuracy and reliability of the model are verified on a horizontal machining center prototype.

Prediction of Gold price with comparison of forecasting methods

Gold has emerged as an extra famous and very beneficial commodity in phrases of investment. Gold has been considered, as a country wide reserved commodity for many years, which leads to very integral for the economy of any country. Most people and traders believe that gold is a protected investment from uncertainty and political chaos. The rate of motion of gold helps the buyers from the centre of attention in their investments; they make use of the year by year information from Indian Gold Council. The analysis of the data was taken from 1964 to 2020. This paper's motto is to analyze and summarize different algorithms for predicting the rate of gold. The procedures utilized to fit the data were from the Time Series analysis Auto Regressive Integrated Moving Average (ARIMA) and Neural Network models; Multi-Layer Perception (MLP) and Extreme Learning Machine (ELM). The test data were utilized for the analysis, and then the outcome was exhibited with the help of error parameters. ELM is best as compared to ARIMA and MLP. The error measures are RMSE (1634.975) and MAPE (3.002). The error measurements have been represented in the tables for ARIMA and MLP. The best prediction of Gold price was given by the ELM, which is be efficient and accurate model.

Geometric Error Measurement of Rotary Axes on Five-Axis Machine Tools: A Review

Geometric Error Measurement of Rotary Axes on Five-Axis Machine Tools: A Review

Psychometric Evaluation of the Bahasa Malaysia Version of the Lymphedema Functioning, Disability, and Health Questionnaire for Upper Limb Lymphedema in Patients with Breast Cancer-Related Lymphedema.

Background: Breast cancer-related lymphedema (BCRL) is the most prevalent comorbidity that occurs following breast cancer treatments and has negative impact on the patients' quality of life (QoL). The Lymphedema Functioning, Disability, and Health Questionnaire for Upper Limb Lymphedema (Lymph-ICF-UL) is a valid and reliable instrument in assessing the QoL of patients with BCRL. However, the Bahasa Malaysia (BM) version is not available yet. This study aimed to translate the Lymph-ICF-UL into BM and to evaluate its validity and reliability. Methods and Results: A forward-backward translation was performed based on Sousa's guideline, and then, the face, content, construct validity, internal consistency, and test-retest reliability were tested. Face validity was assessed by five patients, and content validity was evaluated by six experts. Then, construct validity and internal validity were assessed in 107 patients. Finally, test-retest reliability was analyzed in 21 patients. Two items were eliminated following suggestions from the patients and experts. All patients found the scoring system and items clear and relevant. The results showed sufficient content validity index and modified kappa statistics value. Confirmatory factor analysis showed acceptable fit indices. Cronbach's alpha values ranged from 0.67 to 0.95, intraclass correlation coefficient ranged from 0.88 to 0.99, standard error measurement was 2.29-6.15, and the Bland-Altman plot showed an agreement between two test occasions. Conclusion: These results suggested that the Lymph-ICF-UL BM has good validity and reliability in evaluating the QoL of patients with BCRL in Malaysia.

Statement of Removal

A randomly fluctuated vortex-induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non-cooperative moving target. Definition 2.1, theorem 2.2 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard CRLB parameter quantifies the VIV effect. The compensation of vortex-induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight (LOS) errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 meters. It can be useful for the sub-aquatic internet of underwater things.

Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified Constitutive Relation Error framework

This article proposes a consistent and general approach to train physics-augmented neural networks with observable data to enrich and represent nonlinear history-dependent material behaviors in terms of both state equations and evolution laws. In this learning strategy consistent with thermodynamics, the constitutive model is expressed using two potentials (free energy and dissipation potential) which are represented by input-convex neural networks, thus automatically satisfying the principles of thermodynamics. The neural network is trained thanks to an unsupervised procedure that does not rely on strain–stress pairs but needs only partial strain or displacement measurements inside the structure, moreover with uncertain boundary conditions. This method is based on the minimization of the modified Constitutive Relation Error functional, and it extends previous works on this error measure for neural networks to the case of history-dependent behaviors, which requires to design a specific minimization procedure. Given that neural networks for typical structural health monitoring applications often need to be trained online, there is here a significant emphasis placed on automatically and adaptively tuning crucial hyperparameters such as learning rate or weighting between losses.The method is evaluated on elastoplastic and elastoviscoplastic test cases with synthetic data collected from optic fiber or full-field measurements. It is shown that the method can properly learn hidden behaviors, achieves high robustness to noise level, and low sensitivity to user-defined hyperparameters.