Error Reduction Research Articles

Ensuring Patient Safety in Pediatric Dental Care

This review aims to examine safety concerns in pediatric dental care and underscore the need for comprehensive patient safety initiatives within the Korean Academy of Pediatric Dentistry. Drawing insights from the prevailing patient safety policies of the American Academy of Pediatric Dentistry, case reports, and systematic reviews, this review elucidates issues such as dental fires during sedation, ocular complications from local anesthesia, and surgical emphysema. This review highlights the significance of safety toolkits encompassing infection control, medical error reduction, dental unit waterline infection, and nitrous oxide safety in pediatric dental settings, underscoring the need to foster a safety culture. Furthermore, this study explores the curriculum for pediatric dentistry residency programs, emphasizing concepts such as high-reliability organizations and mortality and morbidity conferences. The study suggests the need for initiatives to enhance patient safety, including establishing safety committees, expanding reporting systems, policy development, and supporting research related to patient safety. In conclusion, this study underlines key messages, emphasizing the utmost priority of patient safety, acknowledging the inevitability of human error, promoting effective communication, and cultivating a patient safety culture. These principles are vital for advancing patient safety in pediatric dental care and improving outcomes among pediatric patients.

Emle-engine: A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics Simulations.

We present in this work the emle-engine package (https://github.com/chemle/emle-engine)─the implementation of a new machine learning embedding scheme for hybrid machine learning potential/molecular-mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of the electronic density and induction with a handful of tunable parameters derived from in vacuo properties of the subsystem to be embedded. This scheme is completely independent of the in vacuo potential and requires only the positions of the atoms of the machine learning subsystem and the positions and partial charges of the molecular mechanics environment. These characteristics allow emle-engine to be employed in existing QM/MM software. We demonstrate that the implemented electrostatic machine learning embedding scheme (named EMLE) is stable in enhanced sampling molecular dynamics simulations. Through the calculation of free energy surfaces of alanine dipeptide in water with two different ML options for the in vacuo potential and three embedding models, we test the performance of EMLE. When compared to the reference DFT/MM surface, the EMLE embedding is clearly superior to the MM one based on fixed partial charges. The configurational dependence of the electronic density and the inclusion of the induction energy introduced by the EMLE model leads to a systematic reduction in the average error of the free energy surface when compared to MM embedding. By enabling the usage of EMLE embedding in practical ML/MM simulations, emle-engine will make it possible to accurately model systems and processes that feature significant variations in the charge distribution of the ML subsystem and/or the interacting environment.

Site index curves using the generalized algebraic difference approach – Gada in planted forests of Pinus taeda L. in the midwest of Santa Catarina, Brazil

One of the most widely used tools for assessing the productive potential of planted Pinus taeda L. forests is to generate site index curves. Site index is a measure of the productive potential of forests and is influenced by various factors such as soil, water, light, among others, representing the relationship between the dominant height of trees and age at a specific location. This relationship produces a curve and can be used for site classification, consequently estimating the productivity of the forest stand. The objective of this study was to evaluate the performance of dynamic equations, the Algebraic Difference Approach (ADA), and its generalization (GADA). The data were obtained from permanent plots of Continuous Forest Inventory (CFI), collected in forest stands, through a sequence of annual remeasurements from 5 to 17 years of age. Dominant height was estimated as a function of age using nonlinear equations, and subsequently, the ADA and GADA methods were applied using the Chapman-Richards basic equation. The results of the GADA method provided a substantial reduction in the root mean square error (RMSE), less than half when compared to the traditional guide curve method, and an increase from 0.95 to 0.99 in R2adj. Therefore, the GADA methodology proved to be suitable and efficient for site index classification, with noticeable flexibility and reliability on its estimates.

Improving Minority Class Recall through a Novel Cluster-Based Oversampling Technique

In this study, we propose an approach to address the pressing issue of false negative errors by enhancing minority class recall within imbalanced data sets commonly encountered in machine learning applications. Through the utilization of a cluster-based oversampling technique in conjunction with an information entropy evaluation, our approach effectively targets areas of ambiguity inherent in the data set. An extensive evaluation across a diverse range of real-world data sets characterized by inter-cluster complexity demonstrates the superior performance of our method compared to that of existing oversampling techniques. Particularly noteworthy is its significant improvement within the Delinquency Telecom data set, where it achieves a remarkable increase of up to 30.54 percent in minority class recall compared to the original data set. This notable reduction in false negative errors underscores the importance of our methodology in accurately identifying and classifying instances from underrepresented classes, thereby enhancing model performance in imbalanced data scenarios.

Improving short-term forecasting of solar power generation by using an EEMD-BiGRU model: A comparative study based on seven standalone models and six hybrid models

ABSTRACT Accurate and timely forecasting is critical for grid-connected solar power safety and stability, achieved through machine learning (ML) for both common and real-time applications. To mitigate the impact of nonstationarity and volatility in solar power generation, we employed empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), and complete EEMD with adaptive noise (CEEMDAN) to decompose time series into frequency components, reducing fluctuations and noise. A combination of four decomposition methods (EMD, EEMD, VMD, and CEEMDAN) and two ML models, bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (BiLSTM) were utilized to construct six hybrid forecasting models (EMD-BiLSTM, EMD-BiGRU, EEMD-BiLSTM, EEMD-BiGRU, VMD-BiGRU, and CEEMDAN-BiGRU), which were validated on a dataset from a 20 MW solar power station in Hebei, compared to the seven standalone ML models, backpropagation neural networks (BPNN), support vector machines (SVM), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), BiLSTM, and BiGRU, these six hybrid models demonstrated enhanced forecast accuracy. Of these, EEMD-BiGRU, VMD-BiGRU, and CEEMDAN-BiGRU significantly reduced prediction errors, with percentage reductions in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) ranging from 44.18 ~ 49.43%, 43.67 ~ 48.59%, and 44.64% ~53.53%, respectively. The EEMD-BiGRU model outperformed all hybrid models, achieving an RMSE of 0.7662, MAE of 0.3990, MAPE of 7.982%, and R2 of 0.9865. The findings of this study can provide insights and support for applying a hybrid model based on decomposition methods for the short-term forecasting of solar power generation.

A hybrid artificial intelligence approach for modeling the carbonation depth of sustainable concrete containing fly ash

One of the major challenges in the civil engineering sector is the durability of reinforced concrete structures against carbonation during the physico-chemical process of interaction of hydrated cementitious composites with carbon dioxide. This aggressive process causes carbon penetration into the reinforcement part, which affects the behavior of the structure during its lifetime due to corrosion risk. A countermeasure is using alternative cementitious materials to improve concrete texture and resist increased carbonation depth (CD). Considering that the CD test requires a long time and a skilled technician, this study strives to provide an alternative approach by moving from traditional laboratory-based methods towards artificial intelligence (AI) techniques for modeling the CD of sustainable concrete containing fly ash (CCFA). Despite the development of single AI models so far, it is undeniable that utilizing metaheuristic optimization techniques in the form of hybrid models can improve their performance. To this end, a new hybrid model from the integration of biogeography-based optimization (BBO) technique with artificial neural network (ANN) is developed for the first time to estimate the CD of CCFA. The error distribution results revealed that 59% of the ANN predictions had errors within the range of (− 1 mm, 1 mm], while the corresponding percentage for the ANN-BBO predictions was 70%, indicating an 11% reduction in the prediction errors by the proposed hybrid model. Furthermore, A10-index highlighted a performance improvement of 78% for the hybrid model, which met the closeness of the predicted values to the observed ones, so that the value of this index for models of ANN and ANN-BBO was 0.5019 and 0.8947, respectively. Analyzing the cross-validation confirmed the reliability and generalizability of the developed model. Also, the three most influential variables in estimating the CD were exposure time (27%), carbon dioxide concentration (22%), and water/binder (18%), respectively. Finally, the superiority of the ANN-BBO model was verified by comparing it with previous studies’ models.

Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts.

Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly disordered chemical spaces, such as LDRs, where the performance of such MLIPs has not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five atom-centered MLIPs, including the artificial neural network potentials developed by the atomic energy network (AENET), the Gaussian approximation potential (GAP), the spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and the moment tensor potential (MTP), in modeling a 11-component LDR chemical space. Specifically, we generate a DFT-calculated data set of 10,842 configurations of disordered LiTMO2 and TMO2 compositions, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. To provide a point-of-comparison on the performance of atom-centered MLIPs, we also trained the neural equivariant interatomic potential (NequIP) on a subset of our data. Importantly, we find AENET to be the best potential in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train among the MLIPs considered at low number of epochs (300), the training time increases significantly as epochs increase (3300), with a corresponding reduction in training errors (∼60%). Note that AENET and GAP tend to overfit in small data sets, with the extent of overfitting reducing with larger data sets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO2 frameworks, compared to DFT (∼10% error on average). Our study should pave the way both for discovering novel disordered rocksalt electrodes and for modeling other configurationally complex systems, such as high-entropy ceramics and alloys.

Advancing Accuracy in Sea Level Estimation with GNSS-R: A Fusion of LSTM-DNN-Based Deep Learning and SNR Residual Sequences

The global navigation satellite system reflectometry (GNSS-R) technique has shown promise in retrieving sea levels using signal-to-noise ratio (SNR) data. However, its accuracy and performance are often limited compared to conventional tide gauges, particularly due to constraints in satellite elevation angles. To address these limitations, we propose a methodology integrating Long Short-Term Memory Deep Neural Networks (LSTM-DNN) models, utilising SNR residual sequences as key feature inputs. Our study focuses on the SC02 station, examining elevation angles ranging from 5° to 10°, 5° to 15°, and 5° to 20°. Results reveal notable reductions in root mean square errors (RMSE) of 2.855%, 17.519%, and 15.756%, respectively, showcasing improvements in accuracy across varying elevation angles. Of particular significance is the enhancement in precision observed at higher elevation angles. This underscores the valuable contribution of our approach to nearshore sea level wave height retrieval, promising advancements in the GNSS-R technique.

Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis

There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.

Versatile Simplistic Correction of T-Higrow Sensors for Improved Soil Moisture Measurement Accuracy

Abstract The use of soil moisture sensors for irrigation can help reduce water and energy consumption and risks of groundwater contamination, which are essential aspects for pursuing sustainable development goals. However, increased adoption of this technology is limited by calibration requirements, technical complexities, and sensor costs. In this work, a simplified method for reducing the measurement error of a recently released low-cost soil sensor (T-Higrow) is presented. The method only requires measurements of a dry sample from the target soil, which are inputted into a simple correction formula to reduce the measurement error at higher moisture levels. The requirements of the proposed method are simple enough for most labs or extension services. This method was compared to the commonly used linear, polynomial, and logarithmic regression models based on repeated bench-scale experiments within 0-35% moisture range in silt and sandy loam soils and in silica sand. Uncorrected sensor readings correlated well with soil moisture (r: 0.94-0.98), but with significant overestimation (25-60% error). The simplified correction method showed comparable error reduction to regression models across all soil types. All methods reduced error down to 2-10% (0.02-0.1 cm3/cm3) and maintained high correlations (r >0.94), except for logarithmic regression which reduced correlation by around 3%. Variability amongst sensor measurements was generally low (Standard Deviation: 0.01-0.03) particularly at moisture ranges below 20%, this was also the case for sensor-to-sensor variability (Standard Deviation: 0.01-0.03). Sensor evaluation and calibration works are needed to increase the accessibility to this technology for improved water and energy conservation.

Instability Compensation of Recording Interferometer in Phase-Sensitive OTDR

In the paper, a new method of phase measurement error suppression in a phase-sensitive optical time domain reflectometer is proposed and experimentally proved. The main causes of phase measurement errors are identified and considered, such as the influence of the recording interferometer instabilities and laser wavelength instability, which can cause inaccuracies in phase unwrapping. The use of a Mach–Zender interferometer made by 3 × 3 fiber couplers is proposed and tested to provide insensitivity to the recording interferometer and laser source instabilities. It is shown that using all three available photodetectors of the interferometer, instead of just one pair, achieves significantly better accuracy in the phase unwrapping. A novel compensation scheme for accurate phase measurements in a phase-sensitive optical time domain reflectometer is proposed, and a comparison of the measurement signals with or without such compensation is shown and discussed. The proposed method, using three photodetectors, allows for very good compensation of the phase measurement errors arising from common-mode noise from the interferometer and laser source, providing a significant improvement in signal detection. In addition, the method allows the tracking of slow temperature changes in the monitored fiber/object, which is not obtainable when using a simple low-pass filter for phase unwrapping error reduction, as is customary in several systems of this kind.

Enhanced accuracy in 3D structured illumination microscopy through binary encoding with accelerated speed using sampling Moiré

Structured Illumination Microscopy (SIM) is widely recognized as a precise and stable technique for three-dimensional inspection. However, efficiently achieving precise results still poses a challenge to be addressed. This research paper presents significant advancements aimed at enhancing the precision and efficiency of the SIM system. We leverage the defocus sensitivity of binary fringes, which yields more accurate height mapping, reducing the error by an outstanding 5% to 20%. Additionally, we introduce a novel technique called sampling Moiré (SM) for modulation decoding from a single shot, resulting in a reduction of camera exposure time by (N−1)/N compared to the N-step phase shift technique and a minimum 20% reduction in error compared to the Fourier transform (FT). These advancements elevate the system's precision and increase efficiency, making SIM an even more powerful tool for high-quality three-dimensional (3D)inspections.

Using satellite data assimilation techniques to combine infrasound observations and a full ray-tracing model to constrain stratospheric variables

Abstract Infrasound waves generated at the Earth’s surface can reach high altitudes before returning to the surface to be recorded by microbarometer array stations. These waves carry information about the propagation medium, in particular, temperature and winds in the atmosphere. It is only recently that studies on the assimilation of such data into atmospheric models have been published. Intending to advance this line of research, we here use the Modulated Ensemble Transform Kalman Filter (METKF) –commonly used in satellite data assimilation– to assimilate infrasound-related observations in order to update a column of three vertically varying variables: temperature and horizontal wind speeds. This includes stratospheric and mesospheric heights, which are otherwise poorly observed. The numerical experiments on synthetic data but with realistic reanalysis product atmospheric specifications (following the Observing System Simulation Experiment paradigm) reveal that a large ensemble is capable of reducing errors, especially for the wind speeds in stratospheric heights close to 30 – 60 km. While using a small ensemble leads to incorrect analysis increments and large estimation errors, the METKF ameliorates this problem and even achieves error reduction from the prior to the posterior mean estimator.

STFEformer: Spatial–Temporal Fusion Embedding Transformer for Traffic Flow Prediction

In the realm of Intelligent Transportation Systems (ITSs), traffic flow prediction is crucial for multiple applications. The primary challenge in traffic flow prediction lies in the handling and modeling of the intricate spatial–temporal correlations inherent in transport data. In recent years, many studies have focused on developing various Spatial–Temporal Graph Neural Networks (STGNNs), and researchers have also begun to explore the application of transformers to capture spatial–temporal correlations in traffic data. However, GNN-based methods mainly focus on modeling spatial correlations statically, which significantly limits their capacity to discover dynamic and long-range spatial patterns. Transformer-based methods have not sufficiently extracted the comprehensive representation of traffic data features. To explore dynamic spatial dependencies and comprehensively characterize traffic data, the Spatial–Temporal Fusion Embedding Transformer (STFEformer) is proposed for traffic flow prediction. Specifically, we propose a fusion embedding layer to capture and fuse both native information and spatial–temporal features, aiming to achieve a comprehensive representation of traffic data characteristics. Then, we introduce a spatial self-attention module designed to enhance detection of dynamic and long-range spatial correlations by focusing on interactions between similar nodes. Extensive experiments conducted on three real-world datasets demonstrate that STFEformer significantly outperforms various baseline models, notably achieving up to a 5.6% reduction in Mean Absolute Error (MAE) on the PeMS08 dataset compared to the next-best model. Furthermore, the results of ablation experiments and visualizations are employed to clarify and highlight our model’s performance. STFEformer represents a meaningful advancement in traffic flow prediction, potentially influencing future research and applications in ITSs by providing a more robust framework for managing and analyzing traffic data.

A statistical learning approach to pasture, rangeland, forage (PRF) insurance coverage selection

AbstractThis study uses statistical learning methods to identify robust coverage alternatives for the Pasture, Rangeland, Forage (PRF) insurance program. Shrinkage and ensemble learning techniques are adapted to the context of the PRF coverage selection process. The out‐of‐sample performance of the proposed methods is evaluated on 116 representative grids throughout Texas during 2018–2022. Ensemble learning methods generated more stable coverage choices compared with the other selection strategies considered. Depending on the target return, a reduction in the prediction error between 5% and 14% was observed. Furthermore, the proposed coverages can provide a broader protection than current coverage choices made by farmers.

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Tailoring data assimilation to discontinuous Galerkin models

AbstractIn recent years discontinuous Galerkin (DG) methods have received increased interest from the geophysical community. In these methods the solution in each grid cell is approximated as a linear combination of basis functions. Ensemble data assimilation (DA) aims to approximate the true state by combining model outputs with observations using error statistics estimated from an ensemble of model runs. Ensemble data assimilation in geophysical models faces several well‐documented issues. In this work we exploit the expansion of the solution in DG basis functions to address some of these issues. Specifically, it is investigated whether a DA–DG combination (a) mitigates the need for observation thinning, (b) reduces errors in the field's gradients, and (c) can be used to set up scale‐dependent localisation. Numerical experiments are carried out using stochastically generated ensembles of model states, with different noise properties, and with Legendre polynomials as basis functions. It is found that strong reduction in the analysis error is achieved by using DA–DG and that the benefit increases with increasing DG order. This is especially the case when small scales dominate the background error. The DA improvement in the first derivative is, on the other hand, marginal. We think this to be a counter‐effect of the power of DG to fit the observations closely, which can deteriorate the estimates of the derivatives. Applying optimal localisation to the different polynomial orders, thus exploiting their different spatial length, is beneficial: it results in a covariance matrix closer to the true covariance than the matrix obtained using traditional optimal localisation in state space.

Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia

The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau’s Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF), and Poor Man’s Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall–runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included the following: CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts; NSE, PCC, MAE, KGE, PBias, and RMSE; and three categorical metrics, CSI, FAR, and POD, for deterministic forecasts. The skill scores, CRPS, relative CRPS, CRPSS, and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE, and RMSE as well as for the categorical metrics. Forecast performance also progressively decreased with higher streamflow volumes. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country—they were generally better in the high-runoff-generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia, and the mid-west of Western Australia. We did not find any association between the model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resource management.

A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

Effect of in-hospital training on medical errors made by teams during neonatal resuscitation.

Neonatal resuscitation is one of the most critical and risky events that requires a high level of individual skill and team performance. To evaluate the effect of training of resuscitation teams on the frequency and type of medical errors (ME) that result from neonatal resuscitation. A prospective observational study was performed using a checklist to detect ME related to neonatal resuscitation. The rate of ME was 24.82%. There was a significant reduction in the percentage of errors from 17.28% in pre- pre-training phase to 7.54% in post post-training phase. Near miss MEs (98.77%) were significantly higher than adverse events. The active errors were significantly higher than latent errors, P <0.001, and decreased from 39.19% during pre-training to 19.64% in the post-training phase. The commission ME s were significantly higher than the omission, P <0.001. The latent errors percentage was 41.17% of the total errors and were not significantly reduced after training. Training sessions reduced ME that occurred during resuscitation, however careful distinguishing and recognizing the type of MEs is important to plan for further reduction of errors. Special attention to latent errors is imperative as it needs a specific approach rather than just training.