Error Process Research Articles

Related Keyframe Optimization Gaussian–Simultaneous Localization and Mapping: A 3D Gaussian Splatting-Based Simultaneous Localization and Mapping with Related Keyframe Optimization

Simultaneous localization and mapping (SLAM) is the basis for intelligent robots to explore the world. As a promising method for 3D reconstruction, 3D Gaussian splatting (3DGS) integrated with SLAM systems has shown significant potential. However, due to environmental uncertainties, errors in the tracking process with 3D Gaussians can negatively impact SLAM systems. This paper introduces a novel dense RGB-D SLAM system based on 3DGS that refines Gaussians through sub-Gaussians in the camera coordinate system. Additionally, we propose an algorithm to select keyframes closely related to the current frame, optimizing the scene map and pose of the current keyframe. This approach effectively enhances both the tracking and mapping performance. Experiments on high-quality synthetic scenes (Replica dataset) and low-quality real-world scenes (TUM-RGBD and ScanNet datasets) demonstrate that our system achieves competitive performance in tracking and mapping.

Cytoplasmic mRNA decay and quality control machineries in eukaryotes.

mRNA degradation pathways have key regulatory roles in gene expression. The intrinsic stability of mRNAs in the cytoplasm of eukaryotic cells varies widely in a gene- and isoform-dependent manner and can be regulated by cellular cues, such as kinase signalling, to control mRNA levels and spatiotemporal dynamics of gene expression. Moreover, specialized quality control pathways exist to rid cells of non-functional mRNAs produced by errors in mRNA processing or mRNA damage that negatively impact translation. Recent advances in structural, single-molecule and genome-wide methods have provided new insights into the central machineries that carry out mRNA turnover, the mechanisms by which mRNAs are targeted for degradation and the general principles that govern mRNA stability at a global level. This improved understanding of mRNA degradation in the cytoplasm of eukaryotic cells is finding practical applications in the design of therapeutic mRNAs.

Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms

Abstract. Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. Retrieving consistent high-resolution ADT and SST information from space is challenging, due to instrument limitations, sampling constraints, and degradations introduced by the interpolation algorithms used to obtain gap-free (L4) analyses. To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. Building upon recent findings, we conduct an Observing System Simulation Experiment (OSSE) relying on Copernicus numerical model outputs (with respective temporal and spatial resolutions of 1 d and 1/24°), and we present a strategy for further refinements. Previous OSSEs combined low-resolution L4 satellite equivalent ADTs with high-resolution “perfectly known” SSTs to derive high-resolution sea surface dynamical features. Here, we introduce realistic SST L4 processing errors and modify the network to concurrently predict high-resolution SST and ADT from synthetic, satellite equivalent L4 products. This modification allows us to evaluate the potential enhancement in the ADT and SST mapping while integrating dynamical constraints through tailored, physics-informed loss functions. The neural networks are thus trained using OSSE data and subsequently applied to the Copernicus Marine Service satellite-derived ADTs and SSTs, allowing us to reconstruct super-resolved ADTs and geostrophic currents at the same spatiotemporal resolution of the model outputs employed for the OSSE. A 12-year-long time series of super-resolved geostrophic currents (2008–2019) is thus presented and validated against in situ-measured currents from drogued drifting buoys and via spectral analyses. This study suggests that CNNs are beneficial for improving standard altimetry mapping: they generally sharpen the ADT gradients, with consequent correction of the surface currents direction and intensities with respect to the altimeter-derived products. Our investigation is focused on the Mediterranean Sea, quite a challenging region due to its small Rossby deformation radius (around 10 km).

Trueness and precision of facial scan and virtual patient representation workflow.

To investigate the feasibility and accuracy (trueness and precision) of facial scanning and virtual patient representation (VPR). One participant was recruited and informed consent was obtained. VPR was performed 30 times with a custom fabricated intraoral scan body (ISB). Thirteen adhesive markers were added to the face as an extraoral scan body (ESB). Two facial scans were obtained for each VPR using an infra-red laser accessory sensor (Structure sensor; Occipital Inc) mounted on a computer tablet (iPad Pro; Apple Inc), including one with seated ISB and one without ISB. Two maxillary intraoral scans were obtained using an intraoral scanner (Omnicam; Dentsply Sirona) with and without the ISB. All files were imported to a dental software program (exocad; exocad GmbH) and VPR was obtained by aligning the facial and IOS scans using the ISB and ESB as common elements for alignment. Five fiducial face landmarks, four intraoral dental landmarks, and six perioral landmarks were selected for measurements. A total of 32 linear measurements, including 14 face-face (for facial scan accuracy) and 18 face-dental (for VPR accuracy) representing total face, lower face, and perioral regions, were performed directly using a digital caliper (FINO Digital Caliper; FINO GmbH) and virtually on the VPR. Trueness was evaluated by mean absolute difference (MAD) between the virtual and direct measurements, and the standard deviation represented Precision. Statistical analyses were performed with a statistical software package (IBM SPSS Statistics v25; IBM Corp), with α = 0.05. Data were analyzed for normality with Shapiro-Wilk test, and 1-sample t- (or Wilcoxon signed rank test), technical error of measurement (TEM), and relative error magnitude (REM). The facial scan had 2.04, 1.66, 0.8 trueness, and 1.05, 0.92, 0.91 precision for total, lower face, and perioral regions. VPR had higher MAD (lower trueness) than facial scan, including 3.32, 2.40, 1.21 trueness and 2.2, 1.47, 1.2 precision for total, lower face, and perioral regions. Both TEM and REM were lowest for the perioral region and increased with increasing measurement distance. Error in face scanning increased with increased distance and intricate details. VPR accuracy was lower than face scan accuracy because of added errors in the alignment process. The investigated VPR workflow might be feasible for treatment planning and smile design. However, it would be unreliable for more demanding prostheses manufacturing purposes.

Neurobiological fingerprints of negative symptoms in schizophrenia identified by connectome-based modeling.

As a central component of schizophrenia psychopathology, negative symptoms result in detrimental effects on long-term functional prognosis. However, the neurobiological mechanism underlying negative symptoms remains poorly understood, which limits the development of novel treatment interventions. This study aimed to identify the specific neural fingerprints of negative symptoms in schizophrenia. Based on resting-state functional connectivity data obtained in a large sample (n = 132) of first-episode drug-naïve schizophrenia patients (DN-FES), connectome-based predictive modeling (CPM) with cross-validation was applied to identify functional networks that predict the severity of negative symptoms. The generalizability of identified networks was then validated in an independent sample of n = 40 DN-FES. A connectivity pattern significantly driving the prediction of negative symptoms (ρ = 0.28, MSE = 81.04, P = 0.012) was identified within and between networks implicated in motivation (medial frontal, subcortical, sensorimotor), cognition (default mode, frontoparietal, medial frontal) and error processing (medial frontal and cerebellum). The identified networks also predicted negative symptoms in the independent validation sample (ρ = 0.37, P = 0.018). Importantly, the predictive model was symptom-specific and robust considering the potential effects of demographic characteristics and validation strategies. Our study discovers and validates a comprehensive network model as the unique neural substrates of negative symptoms in schizophrenia, which provides a novel and comprehensive perspective to the development of target treatment strategies for negative symptoms.

Reduced Theta Inter-Trial Phase Coherence in Error Processing: A Marker of Neural Dysfunction in Attention Deficit Hyperactivity Disorder.

Cognitive control deficits and increased intra-subject variability have been well established as core characteristics of attention deficit hyperactivity disorder (ADHD), and there is a growing interest in their expression at the neural level. We aimed to study neural variability in ADHD, as reflected in theta inter-trial phase coherence (ITC) during error processing, a process that involves cognitive control. We examined both traditional event-related potential (ERP) measures of error processing (i.e., error-related negativity [ERN] and error-positivity [Pe]) and theta ITC within a prospective longitudinal study of children at familial risk for ADHD. The participants were 63 male adolescents who were followed since birth. At the age of 17 years old, they performed the stop-signal task (SST) while an electroencephalogram (EEG) recording was continuously carried out. The EEG data from the trials in which the subjects failed to inhibit their response were used to calculate three different neurophysiological measures (i.e., ERN, Pe, and theta ITC). Consistent with our hypotheses, theta ITC during error processing predicted ADHD symptomatology above and beyond the traditional ERP measures. Moreover, we found that ADHD symptoms throughout childhood were uniquely associated with theta ITC, beyond ADHD symptomatology during adolescence. Overall, our findings strengthen the view of increased neural variability (as reflected by theta ITC) as a neurophysiological characteristic of a core neural dysfunction in ADHD.

Designing a Revenue Forecast Scheme for Analog Ensemble Method Applied Solar PV Forecasting in South Korea

Power companies have found that solar irradiance forecasting is a reliable method for anticipating and preparing for the intermittent nature of renewable energy sources (RESs). However, when the percentage of RESs in the energy mix rises, there is a negative correlation between forecasting accuracy and process error, which could impact not only the grid but also the whole RES’s economic sustainability. In order to tackle this problem, this paper examines the economic implications of employing the Analog Ensemble (AnEn) forecasting model within Korea’s solar energy sector. The levelized cost of energy (LCOE) model and the revenue prediction model are applied to assess the efficacy of the AnEn. The proposed revenue scheme scheme was initiated on a 1 MW photovoltaic (PV) ground‐type power plant deployed in the mainland of South Korea. Various methods have been examined to assess the financial benefit of utilizing the AnEn, such as forecast verification and LCOE and revenue forecast scheme. Key findings reveal that the AnEn model consistently outperforms traditional models in terms of accuracy, particularly during critical solar peak months. This improved accuracy translates into more reliable predictions of solar irradiance, essential for minimizing overestimations and supporting realistic financial planning. Furthermore, the paper explores the economic incentives implemented by the Korean government to encourage precision in energy forecasting, including a structured incentive scheme tied to forecast accuracy.

Is there a role for nutritional advice during breastfeeding for infant colic relief? A mixed-method study.

This study aimed to explore the extent and impact of maternal dietary change for colic relief in a cohort of breastfeeding women. A mixed-method non-sequential approach was devised, including a web-based survey (n=66) and three semi-structured interviews. Most women (70%) changed their diet while breastfeeding a baby with colic and perceived a positive impact on their babies (63%). The choice of foods eliminated was individual, based on a process of trial and error and on the perceived benefit to the baby. A sub-group of colicky babies, those with less intense colic symptoms, benefited significantly from the removal of cruciferous vegetables (p=0.01) and were found to be 'less windy' (p=0.10, ES0,20), a result trending towards statistical significance. Women felt unsupported while making changes to their diet. When dietary change brought relief to the baby, it turned into a coping tool for women facilitating extended breastfeeding. Maternal dietary change can play a positive role in providing relief to breastfed babies with colic. Novel findings from this study revealed that different baby subtypes got relief from the elimination of different foods. This underscores the potential of personalised nutritional advice for colic relief in breastfed babies.

Parametric Modal Regression with Autocorrelated Error Process

Parametric Modal Regression with Autocorrelated Error Process

Routines and the Performance Piece in Don DeLillo’s The Body Artist

ABSTRACT By anatomising Laruen's performance piece — Body Time in DeLillo's The Body Artist, this essay rereads her piece not merely as a response to personal loss, but rather, evolving contemplation on an uncertain existence of which death is only an episode. Staging three socially invisible bodies and their respective daily routines, Lauren goes beyond the private realm of personal loss to address in a broader context the lure of mechanical repetition bred by a routine-dictated life and to spotlight the universality of irregularities. As manifested by its excruciating monthslong gestation period, the three bodies featured in the piece are inextricably interweaved to unveil different ways to approach routines and irregularities. Body Time thus foregrounds a grieving artist's trial and error process with routines and her ambitious artistic venture to approach an uncertain existence contingent on irregularities.

AI-Enhanced Healthcare: Integrating ChatGPT-4 in ePROs for Improved Oncology Care and Decision-Making: A Pilot Evaluation.

Since 2023, ChatGPT-4 has been impactful across several sectors including healthcare, where it aids in medical information analysis and education. Electronic patient-reported outcomes (ePROs) play a crucial role in monitoring cancer patients' post-treatment symptoms, enabling early interventions. However, managing the voluminous ePRO data presents significant challenges. This study assesses the feasibility of utilizing ChatGPT-4 for analyzing side effect data from ePROs. Thirty cancer patients were consecutively collected via a web-based ePRO platform, reporting side effects over 4 weeks. ChatGPT-4, simulating oncologists, dietitians, and nurses, analyzed this data and offered improvement suggestions, which were then reviewed by professionals in those fields. Two oncologists, two dieticians, and two nurses evaluated the AI's performance across roles with 540 reviews. ChatGPT-4 excelled in data accuracy and completeness and was noted for its empathy and support, enhancing communication and reducing caregiver stress. It was potentially effective as a dietician. This study offers preliminary insights into the feasibility of integrating AI tools like ChatGPT-4 into ePRO cancer care, highlighting its potential to reduce healthcare provider workload. Key directions for future research include enhancing AI's capabilities in cancer care knowledge validation, emotional support, improving doctor-patient communication, increasing patient health literacy, and minimizing errors in AI-driven clinical processes. As technology advances, AI holds promise for playing a more significant role in ePRO cancer care and supporting shared decision-making between clinicians and patients.

Neural markers of error processing relate to task performance, but not to substance-related risks and problems and externalizing problems in adolescence and emerging adulthood.

Detecting errors and adapting behavior accordingly constitutes an integral aspect of cognition. Previous studies have linked neural correlates of error processing (e.g., error-related negativity (ERN) and error-related positivity (Pe)) to task performance and broader behavioral constructs, but few studies examined how these associations manifest in adolescence. In this study, we examined neural error processing markers and their behavioral associations in an adolescent/emerging adult sample (N = 143, Mage = 18.0 years, range 11-25 years), employing a stop-signal task. Linear regressions were conducted using bootstrap resampling to explore associations between ERN/Pe peak amplitudes and latencies, stop accuracy, stop-signal reaction time (SSRT), and post-error slowing, as well as self-reported substance-related risks and problems and externalizing problems. After adjusting for age and sex, smaller frontocentral Pe amplitude and later Pe latency were associated with longer SSRT, and later Pe latency was associated with lower stop accuracy. This might indicate that the Pe, which is thought to reflect conscious error processing, reflects task performance on a response inhibition task better than the ERN, which reflects subconscious error processing. After correcting for multiple testing, there were no associations between ERN/Pe parameters and substance-related or externalizing problems, and no age interactions for these associations were detected.

In chunks we trust . . . the problem of gender assignment in foreign language learning of French

Aims and Objectives: The study explores the challenges foreign language learners face in gender assignment and agreement within nominal, verbal, or adjective phrases, specifically in comparison to native speakers and bilingual children. It aims to identify where learners struggle with gender assignment and to what extent these difficulties can be explained by their multilingual background. Additionally, it investigates whether integrating models of Universal Grammar and Construction Grammar can provide a more comprehensive understanding of the gender assignment process. Research Questions: The article adresses the following research questions: What are the specific challenges foreign language learners face in gender assignment within nominal, verbal, and adjective phrases, and to what extent can these challenges be explained by their multilingual background? Furthermore, how can insights from Universal Grammar and Construction Grammar be combined to develop a comprehensive explanatory model for these difficulties? Methodology/Approach: The research is based on a corpus of authentic learner productions written by learners of French as a foreign language (L3) with German as the learners’ first language (L1). Data analysis is focusing on processing and production errors related to gender assignment and agreement. Data and Analysis: Learner errors in gender assignment are analysed with regard to their frequency and patterns, considering the learners’ multilingual backgrounds, especially possible transfer effects of their L1. Findings/Conclusions: The findings suggest that learners face significant challenges in extracting and applying morphosyntactic rules for gender assignment. Errors are often linked to difficulties in processing gender noun-constructions as multiword units. Combining insights from Universal Grammar and Construction Grammar indicates that these constructions should be treated as holistic units, which may facilitate their acquisition and processing. Originality: The study bridges two prominent linguistic models—Universal Grammar and Construction Grammar—to offer a novel explanatory framework for understanding gender assignment in foreign language learners. This dual-model approach highlights the potential of treating gender noun-constructions as multiword units in language acquisition. Significance/Implications: The results have important implications for foreign language teaching, particularly in designing input manipulation strategies. Emphasizing the role of multiword units in language processing could enhance learners’ ability to filter morphosyntactic rules from input and improve their gender assignment accuracy.

Delusional Unreality and Predictive Processing.

Phenomenological psychopathologists have recently highlighted how people with delusions experience multiple realities (delusional and non-delusional) and have suggested this double bookkeeping cannot be explained via predictive processing. Here, we present data from Kamin blocking and extinction learning that show how predictive processing might, in principle, explain a pervasive sense of dual reality. This cross-sectional study involved three participant groups: patients with schizophrenia (SZ; n=42), healthy participants with elevated esoteric beliefs (EEB; clairaudient psychics; n=31), and heathy controls (with neither illness nor significant delusional ideation, n=62). We examined belief formation using a Kamin blocking causal learning task with extinction, and delusions with the 40-item Peters Delusion Inventory, specifically the unreality item: "Do things around you ever feel unreal, as though it was all part of an experiment?" as a proxy for unreality experiences and beliefs. A clinician also assessed symptoms with a structured clinical interview. Some people with schizophrenia did not report a sense of unreality, and some people with EEB (but no psychotic illness) reported unreality experiences. No HC endorsed them (despite endorsing other delusion-like beliefs). Unreality experiences in clinical delusions and non-clinical delusion-like beliefs were associated with different types of aberrant prediction error processing. These data suggest how predictive processing might explain the sense of unreality. They indicate that different prediction error dysfunctions are associated with delusions with different contents. In this case we have used predictive processing to address a salient issue raised by phenomenological colleagues, namely the impact of psychosis on experiences of and beliefs about reality.

Error monitoring and response inhibition in adolescents with bipolar disorders: An ERP study.

Cognitive control develops throughout adolescence, a high-risk period for bipolar disorders (BD) onset. Despite neurobehavioral abnormalities in adults with BD, there is minimal research investigating deviations in cognitive control in adolescents with BD. Cognitive control involves numerous processes. Identifying the specific neural abnormalities in adolescent BD could provide precise targets for novel interventions that improve illness outcomes. The present study administered a Go/No-Go (GNG) task to 98 adolescents (44 BD; 54 controls) to activate response inhibition and error processes and recorded EEG for event-related potentials (ERPs) analysis. Stimulus-locked N2 and P3 (response inhibition) and response-locked error-related negativity (ERN; early error detection) and error positivity (Pe; conscious error detection) were analyzed. Adolescents with BD had attenuated Pe mean amplitudes following failed inhibition trials. There were no group differences in other ERP amplitudes, including N2, P3, and ERN. The pattern of findings implicates conscious error detection impairment in adolescents with BD, without support for deficits in more automatic, earlier error detection. Impaired conscious error detection in adolescents with BD may be an early expression of BD pathophysiology and a possible intervention target for cognitive rehabilitation. Further studies are needed to examine Pe in BD across the lifetime.

Layanan Administrasi Kesiswaan di SMP Negeri 55 Palembang

This study aims to analyze student administration services at SMP Negeri 55 Palembang, covering service processes, as well as supporting and inhibiting factors. The research employs a descriptive qualitative method with interviews, observations, and documentation. Informants include key and supporting participants, totaling 20 individuals. Key informants consist of the head of administration, administrative staff, and students. Supporting informants include vice principals, teachers, and parents. The data analysis techniques used include data reduction, data presentation, data verification, and conclusion drawing. Data validity was tested using source triangulation, technique triangulation, and time triangulation. The findings indicate that student administration services have been running effectively, supported by a digital system that minimizes errors in the administrative process. Administrative staff exhibit reliability, active responsiveness, and a high level of empathy in assisting students, parents, and teachers requiring help

Raman scattering impairments caused by 50G-PON introduction and mitigation techniques

We explore the impact of stimulated Raman scattering (SRS) in the context of multiple PON generations sharing the same optical distribution network. The 50G-PON, being the last specified ITU-T PON, imposes the use of high launch power to meet strong optical path loss requirements. We show that the XGS-PON upstream could be the first victim of the introduction of 50G-PON since its wavelength spacing maximizes Raman scattering. We exploit a model to describe both in simulation and experiment the SRS-induced depletion on XGS-PON. Assuming that 50G-PON will be deployed on the current passive infrastructure, we also exploit field data to precisely predict the amount of terminations exceeding the requirements. The results show that up to 1.8 dB can be lost on XGS-PON upstream signals, 1.3 dB on the 50G-PON upstream, and 0.5 dB on the G-PON upstream, over a distance of 20 km of fiber. Up to 3% of terminations may be affected, according to our field data exploitation. Improving the receivers’ sensitivity through either better photodiodes, signal processing, or forward error correction could relax the high launch power constraints, and thus the SRS. Deploying an optical splitter at the central office could also limit the power actually launched into the fiber while meeting high split ratios. Finally, decreasing the 50G-PON downstream wavelength seems a possible option to mitigate the Raman scattering.

Enhanced Ulcer Detection Using GAN-Augmented Image Processing and Convolutional Neural Networks

Ulcers, especially chronic wounds and pressure sores, create significant healthcare challenges and need early detection for effective treatment. Due to the persistent challenges posed by human error in manual detection and classification processes, there is a need for automated methodologies that can offer robust and reliable solutions. To enhance the accuracy and generalization of the Convolutional Neural Network (CNN), a Generative Adversarial Network (GAN) based image augmentation technique is used in this study to classify the Kvasir dataset. With an initial 1512 ulcer images selected from the dataset, after stable 70 epochs, 70000 images were generated using GAN for 2 classes: Ulcer or Non-Ulcer. To remove the noise from an enhanced batch of images and classify the set of newly generated datasets, GAN-based CNN (G-CNN) was employed to get 99.00% training and 96.04% validation accuracy. The study was compared with conventional CNN which achieved a training accuracy of 98.5% and validation accuracy of 94.34%. A significant improvement has been observed in f1-scores of 0.94 and 0.97 for CNN and the proposed algorithm, respectively. The study is replicable across datasets with a limited number of images, thereby facilitating heightened accuracy levels.

Some Aspects of Interactive Simulation of Software Development with Integration of Ontological Models

Modern simulation technologies are of great importance, especially in those areas where errors in real processes can lead to significant costs and risks caused by the consequences of errors. The purpose of the article is to research of the interactive simulators in the field of software development with the integration of ontological models. The research methods are the main methodological approaches and technological tools for the development of interactive simulators in the field of software development with the integration of ontological models. Such methods, in particular, are: system and comparative analyzes – to identify the features of the creation and further use of interactive simulators in the field of software development with the integration of ontological models; the method of expert assessments, which involves the analysis of literary sources and information resources, conducting interviews and surveys of experts, the processes of development and testing of scalable and high-performance interactive simulators in the field of software development with the integration of ontological models. The novelty of the conducted research is the research of the possibility of integrating ontological models directly into the process of developing software simulated in interactive environments. The conclusion of the research carried out in the article is that the work was: the research was conducted on the use of simulation technologies in various subject areas; the role of interactive simulators is determined, which help the participants practice various scenarios in a virtual environment; development of critical thinking; improving decision-making skills; it is determined that interactive simulators are an important learning tool in the field of software development, where the integration of ontological models allows automating part of the processes and ensuring the standardization of procedures. Difficulties in implementing simulation training were identified: high initial costs and technical difficulties. For the successful integration of simulations, it is necessary to: provide a clear definition of educational goals (in particular, for the training of IT specialists); adapt simulations to the specific needs of the organization; integrate simulations into the general training program of IT specialists. It was determined that: ontological models play a key role in the improvement of interactive simulators, helping to structure and formalize knowledge in various subject areas; the use of ontologies contributes to the process of integration of various software components, thereby ensuring flexibility and scalability of systems.

Identification and evaluation of human errors in the care of pregnant mothers using the extensive CREAM technique

Background Care of pregnant mothers is one of the most important services provided in the healthcare system. Objective This study aims to identify and evaluate human errors in the care process of pregnant mothers using the extensive Cognitive Reliability and Error Analysis Method (CREAM). Methods In this study, tasks and activities related to the care of pregnant mothers in the health center and hospital were first analyzed using the HTA (hierarchical task analysis). Then, using extensive CREAM, human errors in tasks related to the care of pregnant mothers in the health center and hospital were identified and evaluated. Results In general, 34 main tasks and 106 subtasks were identified in the process of caring for pregnant Mothers. Thirty seven activities are related to midwifery associates or experts, and 19 activities are related to gynecologists. The highest Cognitive Failure Probability total (CFPt) was related to the duties of measures related to the infant, preparing the pregnant mother for transfer to the operating room, routine obstetric measures, taking delivery, measures related to resuscitation, and measures related to the control of the condition after delivery. Conclusions The process of caring for pregnant mothers is very vital and prone to errors due to different levels of service. The presence of experienced and capable personnel can largely prevent the occurrence of human errors. Increasing the number of personnel can be effective in reducing workload and preventing errors.