Error Identification Research Articles

An Adaptive Multi-Population Approach for Sphericity Error Evaluation in the Manufacture of Hemispherical Shell Resonators.

The performance of a hemispherical resonant gyroscope (HRG) is directly affected by the sphericity error of the thin-walled spherical shell of the hemispherical shell resonator (HSR). In the production process of the HSRs, high-speed, high-accuracy, and high-robustness requirements are necessary for evaluating sphericity errors. We designed a sphericity error evaluation method based on the minimum zone criterion with an adaptive number of subpopulations. The method utilizes the global optimal solution and the subpopulations' optimal solution to guide the search, initializes the subpopulations through clustering, and dynamically eliminates inferior subpopulations. Simulation experiments demonstrate that the algorithm exhibits excellent evaluation accuracy when processing simulation datasets with different sphericity errors, radii, and numbers of sampling points. The uncertainty of the results reached the order of 10-9 mm. When processing up to 6000 simulation datasets, the algorithm's solution deviation from the ideal sphericity error remained around -3 × 10-9 mm. And the sphericity error evaluation was completed within 1 s on average. Additionally, comparison experiments further confirmed the evaluation accuracy of the algorithm. In the HSR sample measurement experiments, our algorithm improved the sphericity error assessment accuracy of the HSR's inner and outer contour sampling datasets by 17% and 4%, compared with the results given by the coordinate measuring machine. The experiment results demonstrated that the algorithm meets the requirements of sphericity error assessment in the manufacturing process of the HSRs and has the potential to be widely used in the future.

Biometrics in healthcare as a technological contribution to mitigate patient identification errors: A Review

Biometrics in healthcare as a technological contribution to mitigate patient identification errors: A Review

Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.

Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS. On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review. The studies included in this review allowed for the identification of three main "incident type" domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting. This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.

The Effect of Laissez-Faire Leadership Style on Academic Performance of Primary School Pupils in Selected Primary Schools in Kasese District

Education plays a crucial role in the development of society, particularly in the developing world. The purpose of this research is to assess the impact of a laissez-faire leadership style on the academic performance of primary school students in selected schools within Kasese district. The study utilized a cross-sectional survey design, employing both quantitative and qualitative methodologies. Quantitative data was processed by coding and entering it into a computer program called the Statistical Package for Social Sciences (SPSS 22.0). This data was then summarized using frequency tables, allowing for the identification of errors and necessary revisions. Among the respondents, 27 (45%) disagreed with the statement, indicating that teachers appreciate the leadership style that grants them freedom. This lack of accountability can have negative consequences. Conversely, 24 (40%) agreed that head teachers adopt a hands-off approach, empowering teachers to independently solve problems, while 60% disagreed with this statement. Additionally, 17 (28.3%) of head teachers spend the majority of their time outside the school, with their presence on campus being rather casual. This evidence suggests that head teachers do not perceive their presence as significant, failing to recognize its positive impact on academic performance. In Kasese District, the Primary Leaving Examination (PLE) results for 2018 and 2020 revealed a disappointing pass rate of only 47%. Therefore, it is crucial to examine various factors, such as the leadership styles of head teachers, in order to address this issue effectively. Keywords: laissez-faire leadership, academic performance, primaryschool, pupils, social studies.

Atlas Florae Europaeae notes, 34. Distributions and two conservation profiles of East European species of the Cytisusratisbonensis group (Fabaceae).

The Cytisusratisbonensis group (Fabaceae) includes small shrubs with attractive yellow flowers, used in ornamental cultivation. It is widely distributed in southern forest, forest steppe and steppe zones of Eastern Europe, both in the lowlands and low mountains. This group is notorious for its taxonomic complexity and difficulties in identification, which accounted for incongruent taxonomic treatments and common identification errors, and resulted in a poor understanding of the distribution areas. The increasing availability of herbarium collections and accumulation of human observations through digital resources require their critical taxonomic revision and update in order to provide reliable data for plant species mapping, conservation and analysis of distribution areas. This paper describes a distributional dataset of East European species of the Cytisusratisbonensis group, which was prepared for the pan-European grid mapping project, Atlas Florae Europaeae. The taxonomic revision includes seven species and two interspecific hybrids and is based on the critical evaluation of diagnostic characters, nomenclature and synonymy. The territorial scope of this study is Eastern Europe, but it also includes some data from Central Europe, the Caucasus and the neighbouring territories of Siberia and Kazakhstan, which are included in order to trace eastern and south-eastern limits of the species distributions and to establish reliable synonymy. We report 3699 native occurrence records included in the dataset; these records are based on major herbarium collections which are known to hold specimens from Eastern Europe (DNZ, KRA, KRAM, KW, LE, LW, LWKS, MHA, MSK, MSKH, MSKU, MW), complemented by selected specimens from other herbaria (BUNS, CSAU, H, NNSU, NS, NSK, OXF, PVB, RWBG, TUL). The herbarium collections were examined largely de visu, but partly online. Besides the herbarium specimens, recognisable photographs documenting human observations on online platforms (florafauna.by, iNaturalist, Plantarium, UkrBIN) were also examined and included. All specimen records are accompanied with textual data and georeferences, which were produced for this dataset. Point and contour distribution maps are created for each accepted species. According to the distributional data, the Cytisusratisbonensis group may be classified as temperate lowland and steppic in Eastern Europe. Among European endemic shrubs, C.cinereus (as C.paczoskii) was assessed in 2017 as Near Threatened, whereas its new IUCN protection status is proposed as Least Concern because the new data demonstrated that its distribution area is much greater than previously believed. A species restricted to the Crimea, C.wulffii, is assessed here for the first time as Vulnerable because of its restricted area and small population size (criteria D1,2).

Large Eddy Simulation Combined with System Identification Method to Analyze Non-Premixed Flame Transfer Function

ABSTRACT The heat release response (HRR) of the flame is a pivotal aspect of combustion instability investigation, commonly characterized by the Flame Transfer Function (FTF). This study employed a combination of large eddy simulation (LES) and system identification (SI) techniques to derive the FTF for a methane-air non-premixed swirling burner. The influence of memory time and excitation signals within the LES+SI was investigated as only a few studies have been conducted in this direction so far, and validated against experimental data. Variations in memory time led to notable shifts in frequency and amplitude for gain peaks and valleys, with modest effects on phase delay within the 20–140 Hz range. The number of corner frequencies increased with memory time, whereby smaller memory times flattened the gain curve and underestimated phase delay, and larger memory times intensified curve undulations, distorting finer details. By minimizing the Mean Relative Error (MRE) between LES+SI results and experimental data, the optimized memory time of 0.017 s was identified, which closely aligned with the convection time of 0.0167 s. Consequently, convection time could be referenced in a priori estimation approach for memory time in a non-premixed swirling burner. In comparing FTF obtained from different excitation signals, the Superimposed Sinusoidal Signal (SINE) excelled in the gain curve within the 20–100 Hz range, accurately capturing the 30 Hz gain peak and 80 Hz valley, while the Broadband White Noise (BBWN) excelled in the phase curve within the same range, both yielding a smaller mean relative error (MRE). However, the Discrete Random Binary Signal (DRBS) demonstrated the best consistency with experimental data within the 100–190 Hz range, and boasted an MRE of 38.01% and 28.55% respectively for the gain and phase curve within the entire frequency range. Consequently, we inferred that DRBS functioned as the excitation signal with the most minimal identification error.

Retracted: Rehmannia Radix Extract Relieves Bleomycin-Induced Pulmonary Fibrosis in Mice via Transforming Growth Factor β1 (TGF-β1).

The authors have requested retraction due to the identification of errors in the data. Reference: Xiaoming Hu, Dongzhe Zhu. Rehmannia Radix Extract Relieves Bleomycin-Induced Pulmonary Fibrosis in Mice via Transforming Growth Factor ß1 (TGF-ß1). Med Sci Monit, 2020; 26: e927240. DOI: 10.12659/MSM.927240.

Retracted: Alleviation of Inflammatory Response of Pulmonary Fibrosis in Acute Respiratory Distress Syndrome by Puerarin via Transforming Growth Factor (TGF-β1).

The authors have requested retraction due to the identification of errors in the data. Reference: Xiaoming Hu, Xiaolan Huang. Alleviation of Inflammatory Response of Pulmonary Fibrosis in Acute Respiratory Distress Syndrome by Puerarin via Transforming Growth Factor (TGF-ß1). Med Sci Monit, 2019; 25: 6523-6531. DOI: 10.12659/MSM.915570.

Adaptive Identification of the Position-independent Geometric Errors for the Rotary Axis of Five-axis Machine Tools to Directly Improve Workpiece Geometric Errors

Adaptive Identification of the Position-independent Geometric Errors for the Rotary Axis of Five-axis Machine Tools to Directly Improve Workpiece Geometric Errors

Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors

During radiologic interpretation, radiologists read patient identifiers from the metadata of medical images to recognize the patient being examined. However, it is challenging for radiologists to identify “incorrect” metadata and patient identification errors. We propose a method that uses a patient re-identification technique to link correct metadata to an image set of computed tomography images of a trunk with lost or wrongly assigned metadata. This method is based on a feature vector matching technique that uses a deep feature extractor to adapt to the cross-vendor domain contained in the scout computed tomography image dataset. To identify “incorrect” metadata, we calculated the highest similarity score between a follow-up image and a stored baseline image linked to the correct metadata. The re-identification performance tests whether the image with the highest similarity score belongs to the same patient, i.e., whether the metadata attached to the image are correct. The similarity scores between the follow-up and baseline images for the same “correct” patients were generally greater than those for “incorrect” patients. The proposed feature extractor was sufficiently robust to extract individual distinguishable features without additional training, even for unknown scout computed tomography images. Furthermore, the proposed augmentation technique further improved the re-identification performance of the subset for different vendors by incorporating changes in width magnification due to changes in patient table height during each examination. We believe that metadata checking using the proposed method would help detect the metadata with an “incorrect” patient identifier assigned due to unavoidable errors such as human error.

Online identification methods for low-order wheel polygons coupled with axle cracks

Wheel polygons pose a threat to the safety of train operations due to wheel fatigue, necessitating online identification. The online identification of wheel polygons predominantly relies on the axle box vertical nx (n-multiple integer of axle rotation frequency) signals generated by wheel polygons. The low-order vertical nx signals are easy coupling to other faults like axle cracks, leading to errors in wheel polygon identification. A signal decoupling method is crucial for removing the crack components from the vertical nx signal to obtain more accurate identification results for low-order wheel polygons. Employing principles of signal superposition for different faults, this article proposes a method to decouple the wheel polygon nx signal from the vertical nx signal by subtracting the crack-related portion. This method has been validated through an ABAQUS + SIMPACK online rigid-flexible coupling model, indicating that it significantly improves the accuracy of wheel polygon identification. The 1x amplitude of the decoupled signal decreased by 60.78%, and the 2x amplitude decreased by 55.79%.

A pre-exercise screening of helicopter underwater escape training using a modified human reliability analysis with rule-based mechanism

This study conducts a human reliability analysis to develop a pre-exercise screening tool for helicopter underwater escape training (HUET) to proactively improve training facilities and to contribute attendees' performance. In this context, a modified human error assessment and reduction technique (HEART) was adapted. The technique investigates error producing conditions (EPC) and general task type (GTT) variables considering attendees profile, training infrastructure conditions, and 16 sub-steps of HUET exercises. Since the identification of EPC and GTT is still a key limitation of traditional HEART, this study extends a basic rule-based mechanism based on if-then-else comparison matrix structure to enhance accuracy in practice. The case study underscores the feasibility of individual, provider, and procedural interventions, enhancing training success and practical application of skills in real incidents. The application of a rule-based selection algorithm for GTT determination enhances objectivity, contributing to a more controlled identification and minimisation of errors. The case study, tailored specifically for practical training, delineates the feasibility of individual-based, training provider-based, and procedural-based interventions. Additionally, mitigating factors like ‘emotional stress’ and ‘unfamiliarity’ through advanced safety measures can boost attendees' self-confidence, leading to improved training success and practical skill application in real-world scenarios.

Flight calls and trills of Evening Grosbeaks can be used to map movements and ranges of call types 1 and 2

Evening Grosbeak (Coccothraustes vespertinus) is a species of North American Fringillid finch thought to be one of the fastest declining songbirds across North America. It has been divided into five groups, potentially distinct lineages, based in part on structure of their flight calls. The primary flight calls of each type exhibit structural variation that has not been described and the degree to which that variation might lead to identification errors has not been quantified. We describe the variation in call structure of type 1 Evening Grosbeaks recorded at a spring migratory stopover site (Corvallis, Oregon) and nearby areas in the Pacific Northwest, USA. We recorded grosbeaks weekly from April through early June 2023. We reviewed more than 10,000 recorded call notes to characterize the variety of calls and their configurations. We found a high diversity of call notes including at least 11 recurring readily identifiable variants of the primary flight calls, all of which were attributed to individuals thought to be type 1 birds. Geographically, the nearest neighbors of type 1 Evening Grosbeaks are type 2s, which have uncommonly been recorded in our study area but whose breeding range appears to overlap that of type 1 in southern Oregon. We quantified recordings of type 2 flight calls and compared them with type 1 flight calls, finding that linear discriminant function analyses correctly identified >95% of recordings to type. Inclusion of a metric of asymmetry in call shape improved correct classification to 98.5%. We also found that the other dominant calls given by both types, buzzy trills, could be identified correctly to type with a high level of confidence. The sufficiently different characteristics of flight calls and trills indicate that types 1 and 2 are identifiable spectrographically in most cases, providing confidence that the geographic distribution and migratory movements of call-type populations, despite being essentially identical in plumage, can be documented effectively by characteristics of call notes.

The impact of different data handling strategies in exploratory and confirmatory factor analysis of diary measures: an evaluation using simulated and real-world asthma nighttime symptoms diary data

ABSTRACT Background Daily diaries are an important modality for patient-reported outcome assessment. They typically comprise multiple questions, so understanding their underlying structure is key to appropriate analysis and interpretation. Structural evaluation of such measures poses challenges due to the high volume of repeated measurements. Potential strategies include selecting a single day, averaging item-level observations over time, or using all data while accounting for its multilevel structure. Method The above strategies were evaluated in a simulated dataset via exploratory and confirmatory factor modelling by comparing their impact on various estimates (i.e., inter-item correlations, factor loadings, model fit). Each strategy was additionally explored using real-world data from an observational study (the Asthma Nighttime Symptoms Diary). Results Both single day and item average strategies resulted in biased factor loadings. The former displayed lower overall bias (single day: 0.064; item average: 0.121) and mean square error (single day: 0.007; item average: 0.016) but greater frequency of incorrect factor number identification compared with the latter (single day: 46.4%; item average: 0%). Increased estimated inter-item correlations were apparent in the item-average method. Non-trivial between- and within-person variance highlighted the utility of a multilevel approach. However, convergence issues and Heywood cases were more common under the multilevel approach (90.2% and 100.0%, respectively). Conclusions Our findings suggest that a multilevel approach can enhance our insight when evaluating the structural properties of daily diary data; however, implementation challenges still remain. Our work offers guidance on the impact of data handling decisions in diary assessment.

Imperfect detection and misidentification affect inferences from data informing water operation decisions

AbstractObjectiveManagers can modify river flow regimes using fish monitoring data to minimize impacts from water management infrastructure. For example, operation of the gate‐controlled Delta Cross Channel (DCC) in California can negatively affect the endangered Sacramento River winter‐run Chinook Salmon Oncorhynchus tshawytscha. Although guidelines have been developed for DCC operations by using real‐time juvenile fish sampling count data, there is uncertainty about how environmental conditions influence fish occupancy and the extent to which those relationships are affected by sampling and identification error.MethodsWe evaluated the effect of environmental conditions, imperfect detection, and misidentification error on salmon occupancy by analyzing data using hierarchical multistate occupancy models. A total of 14,147 trawl tows and beach seine hauls were conducted on 1058 sampling days between October and December from 1996 to 2019. During these surveys, 2803 juvenile winter‐run Chinook Salmon were identified, and approximately 29% of the sampling days had at least one winter‐run juvenile detected.ResultThe probability of misidentifying an individual juvenile winter‐run Chinook Salmon in the field was estimated to be 0.056 based on fish identification examinations and genetic sampling. Occupancy varied considerably and was related to flow characteristics, water clarity, weather, time of year, and whether occupancy was detected during the previous sampling day. However, these relationships and their significance changed considerably when accounting for imperfect detection and the probability of misidentifying individual juvenile salmon. Detection was <0.3 under average sampling conditions during a single sample and was influenced by flow, water clarity, site, and volume sampled.ConclusionOur modeling results indicate that DCC gate closure decisions could occur on fewer days when imperfect detection and misidentification error are not accounted for. These findings demonstrate the need to account for identification and detection error while using monitoring data to assess factors influencing fish occupancy and inform future management decisions.

Taxonomic revision of the native Magnolia (Magnoliaceae) species of Brazil.

The genus Magnolia (Magnoliaceae) has a wide and disjunct geographic distribution ranging from Eastern and South Asia to Malaysia, extending across the Neartics and reaching into the Neotropics. Regarding its infrageneric classification, the genus is divided into three subgenera: Yulania, Gynopodium and Magnolia, the latter including the section Talauma in which the native Brazilian taxa are classified. The species of Magnoliasect.Talauma can be recognized by two parallel longitudinal scars on the petiole formed by the shedding of the stipules, in addition to a woody syncarp that breaks into irregular plates at dehiscence. Currently, in Brazil, species recognition is not clear on national platforms that are widely used by the Brazilian botanical community (e.g. Flora do Brasil), with only two native Magnolia species being accepted: M.amazonica and M.ovata. The lack of knowledge about the species and their respective characteristics has resulted in many identification errors in Brazilian herbaria, which contributes to the lack of knowledge about their current conservation status. We conducted a complete taxonomic revision based on extensive fieldwork, a herbarium survey, along with literature study. Based on this, we propose to recognize three previously described species, supporting the acceptance of five native Magnolias occurring in Brazil, namely: M.amazonica, M.brasiliensis, M.irwiniana, M.ovata and M.sellowiana. However, we follow the Flora do Brasil in maintaining M.paranaensis as a synonym of M.ovata. Additionally, we designate a lectotype for M.sellowiana. We present morphological descriptions and the geographic distribution for each species, in addition to an identification key to all of these plus the two introduced ornamental species from Asia and North America, illustrations, photographs, ecological data, updated conservation status and taxonomic notes.

Visual identification of wave load on a four-cylinder array with incomplete waterline information

This study utilized computer vision technology to identify the wave force acting on a four-cylinder array arranged in a square configuration, particularly when confronted with a visual obstruction. In the absence of comprehensive waterline data, a theoretical derivation based on linear wave theory was formulated to ascertain the wave force. Experimental trials were executed in a wave flume to authenticate the identification of wave force under both regular and irregular wave conditions. The simultaneous capture of images from four cameras was employed, with the Canny operator utilized for extracting the dynamic waterline encompassing the array of four cylinders. This affirms the efficacy of the proposed method in identifying wave force within visual obstructions of waterlines. Utilizing a single-camera perspective that encompassed 35% of the waterline surrounding the cylindrical array, the identification errors of the standard value were found to be less than 10%. The reduction of identification error can be achieved by enhancing the completeness of waterline information.

Efficient Identification of Error-in-Variables Switched Systems Using a Riemannian Embedding

This paper considers the problem of error in variables identification for switched affine models. Since it is well known that this problem is generically NP hard, several relaxations have been proposed in the literature. However, while these approaches work well for low dimensional systems with few subsystems, they scale poorly with both the number of subsystems and their memory. To address this difficulty, we propose a computationally efficient alternative, based on embedding the data in the manifold of positive semidefinite matrices, and using a manifold metric there to perform the identification. Our main result shows that, under dwell-time assumptions, the proposed algorithm is convergent, in the sense that it is guaranteed to identify the system for suitably low noise. In scenarios with larger noise levels, we provide experimental results showing that the proposed method outperforms existing ones. The paper concludes by illustrating these results with academic examples and a non-trivial application: action video segmentation.

Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Abstract A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

Quality of locally designed surveys in a quality improvement collaborative: review of survey validity and identification of common errors

ObjectiveSurveys are a commonly used tool in quality improvement (QI) projects, but little is known about the standards to which they are designed and applied. We aimed to investigate the...