Error Evaluation Research Articles

Evaluation of errors related to surgical pathology specimens of different hospital departments with a patient safety approach: a case study in Iran

BackgroundMost surgical specimen errors occur in the pre-analysis stage, which can be prevented. This study aims to identify errors related to surgical pathology specimens in one of the most comprehensive healthcare centers in Northeast Iran.MethodsThe present study is descriptive and analytical research conducted cross-sectionally in 2021 at Ghaem healthcare center in the Mashhad University of Medical Sciences on the basis of a census sampling. We used a standard checklist to collect information. Professors and pathologists evaluated the validity and reliability of the checklist using Cronbach’s alpha calculation method of 0.89. We analyzed the results using statistical indices, SPSS 21 software, and the chi-square test.ResultsOut of 5617 pathology specimens studied, we detected 646 errors. The highest number of errors is the mismatch of the specimen with the label (219 cases; 3.9%) and the non-compliance of the patient’s profile in the specimen sent with the label (129 cases; 2.3%), and the lowest errors are the inappropriate volume of the fixator(24 cases; 0.4%), and they accounted for insufficient sample size (25 cases; 0.4%). Based on Fisher’s exact test results, there was a significant difference between the proportion of errors in different departments and months.ConclusionConsidering the frequency of labeling errors in the stage before the analysis in the pathology department, the use of barcode imprinted in specimen containers, the removal of the paper request for pathology, the use of radio frequency chip technology, the use of the rechecking system and improving communication in different departments can be effective in reducing these errors.

Regularised Semi-parametric Composite Likelihood Intensity Modelling of a Swedish Spatial Ambulance Call Point Pattern

Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefteå during 2014–2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people’s movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.

Approximation of the image of the L p ball under Hilbert-Schmidt integral operator

Abstract In this article, an approximation of the image of the closed ball of the space L p {L}_{p} ( p > 1 p\gt 1 ) centered at the origin with radius r r under Hilbert-Schmidt integral operator F ( ⋅ ) : L p → L q F\left(\cdot ):{L}_{p}\to {L}_{q} , 1 p + 1 q = 1 \frac{1}{p}+\frac{1}{q}=1 is considered. An error evaluation for the given approximation is obtained.

Abnormality Detection Method for Wind Turbine Bearings Based on CNN-LSTM

Wind turbine energy generators operate in a variety of environments and often under harsh operational conditions, which can result in the mechanical failure of wind turbines. In order to ensure the efficient operation of wind turbines, the detection of any abnormality in the mechanics is particularly important. In this paper, a method for detecting abnormalities in the bearings of wind turbine energy generators, based on the cascade deep learning model, is proposed. First, data on the mechanics of wind turbine generators were collected, and the correlation between the data was studied in order to select the parameters related to the bearing temperature. Then, the logical relationship between the observation parameters and the target parameters was established based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network, and the difference between the predicted temperature and the actual temperature was assessed using the root mean square error evaluation model. Finally, a numerical example was used to verify the operational data from a wind farm unit in northwest China. The results show that the CNN-LSTM model proposed in this paper can detect abnormalities earlier in the state of the main bearing than the LSTM model, and the CNN-LSTM model can detect abnormalities in the main bearing that the LSTM network cannot find.

Evaluation of Errors Encountered in Photogrammetric Studies on Lower Extremities

Objective: The aim of our study is to reveal the errors that can be encountered during the shooting of photogrammetric studies on the lower extremities. Methods: We revealed the necessary tools that used during photogrammetry measurements of the lower limb. Also, the errors have been encountered of our two previous studies performed on photogrammetry of lower limbs. The technical errors or incorrect positioning of 20 from 220 volunteers were encountered. Results: The identified errors of 20 volunteers’ photographs related to lower limb were about the inadequate quality image, calibration, poor lightining, positioning error of trunk or parts of lower limb and clothes that cover the anatomical points affected the measurements. Conclusion: Photogrammetry is an important and useful tool for evaluation, diagnosis, treatment, and efficacy monitoring. In anatomy, it is frequently used as a time-saving method in terms of measurement and evaluation in the laboratory, which can be applied and repeated for research. For this reason, errors that occur during the lower extremity have been reported and we think that it will be useful for studies on this part of the body and can be a guide.

FEAR OF LOSS: THE SANTIAGUEROS SCHOOL'S CYNIC MENTAL CONTROL METHODS

The findings of a study conducted on weightlifters between the ages of 15 and 16 at the EIDE "Captain Orestes Acosta" in Santiago de Cuba are compiled in this paper. From the application of methods and techniques to training and competitions, such as observations, semi-structured interviews with athletes and coaches, the Inventory of Psychological Performance, the Attitude Test for Competition, the Inventory of Evaluation of Error in Performance, and others; It was discovered that these athletes failed to successfully complete the lifts, resulting in a high level of tension, insecurity, and nervousness, among other negative psychological manifestations that harmed the athlete's performance and were connected to irrational or maladaptive beliefs and thoughts. An action plan was put into place with the help of the psychologist, coaches, athletes, and family members to help solve this problem. It was an open, adaptable, active, practical, and experiential group. The cognitive restructuring of dysfunctional ideas and thoughts that prevent adequate sports performance demonstrated its effectiveness.

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.

The Importance of Type II Error in Hypothesis Testing

Statistical tests of significance theoretically deal with two mutually exclusive hypotheses: the null and the alternative. However, at least in biomedical assays, only the null hypothesis is taken into account through type I error evaluation. But, basing these tests solely on type I error has two drawbacks: first, the probability limits (5%, 1% and 0.1%) arbitrarily set to the significance levels have no scientific justification. Second, acceptation of the null hypothesis is just a matter of chance, as it is mainly conditioned by the sample size due to its direct effect on the power of the test. In this sense, while the alternative hypothesis should be accepted due to its higher likelihood, the inference based on type I error alone would lead erroneously to accepting the null one. A numerical example illustrates how considering type I error alone, a same difference was declared non-significant first but turned out to significant thereafter when the sample size was increased. Therefore, the same null hypothesis was initially accepted and rejected afterwards. However when type II error was included in the test, the same decision was adopted no matter what the sample size was. This was possible through a reformulation of the alternative hypothesis. On the other hand, type II error may, in many cases have more far-reaching consequences than type I, and then should never be ignored, especially in assays dealing with human health, food, toxicity, etc.

Seasonal peak load prediction of underground gas storage using a novel two-stage model combining improved complete ensemble empirical mode decomposition and long short-term memory with a sparrow search algorithm

Accurate seasonal peak load (SPL) prediction of underground gas storage (UGS) is of great significance for enterprises to formulate scheduling plans. In this work, a novel two-stage model is proposed to predict it. First, improved complete ensemble empirical mode decomposition (ICEEMDAN) is used to decompose the seasonal peak load into several intrinsic mode function. Second, the highest frequency components are smoothed by using Gaussian smoothing (GS), and second highest frequency component is decomposed into several components by applying wavelet transform (WT). Third, long short-term memory (LSTM) optimized by improved sparrow search algorithm (ISSA) is utilized to predict them, and the prediction results of these components are recombined to obtain the final prediction results. Finally, the historical data of Wen 23 in the Zhongyuan gas storage group is taken as a case, and three key issues are discussed. The results: (1) Utilizing ICEEMDAN, GS, WT, and ISSA can effectively improve the prediction performance of the LSTM; (2) The prediction performance of one-step is better than that of multi-step; (3) The comprehensive error and stability evaluation index is reasonable. The following conclusion is reached: the established prediction model can be used as a reference for the development of the UGS scheduling platform.

A machine learning approach to identifying suicide risk among text-based crisis counseling encounters.

With the increasing utilization of text-based suicide crisis counseling, new means of identifying at risk clients must be explored. Natural language processing (NLP) holds promise for evaluating the content of crisis counseling; here we use a data-driven approach to evaluate NLP methods in identifying client suicide risk. De-identified crisis counseling data from a regional text-based crisis encounter and mobile tipline application were used to evaluate two modeling approaches in classifying client suicide risk levels. A manual evaluation of model errors and system behavior was conducted. The neural model outperformed a term frequency-inverse document frequency (tf-idf) model in the false-negative rate. While 75% of the neural model's false negative encounters had some discussion of suicidality, 62.5% saw a resolution of the client's initial concerns. Similarly, the neural model detected signals of suicidality in 60.6% of false-positive encounters. The neural model demonstrated greater sensitivity in the detection of client suicide risk. A manual assessment of errors and model performance reflected these same findings, detecting higher levels of risk in many of the false-positive encounters and lower levels of risk in many of the false negatives. NLP-based models can detect the suicide risk of text-based crisis encounters from the encounter's content.

A perceptual evaluation of numerical errors in acoustic FEM simulation for sound quality applications

Sound perception is a key aspect in many industrial applications where the acoustic comfort of the final user is relevant. In the case of automotive industry, an important effort is put into prediction of sound quality, which in turn necessitates detailed description of the acoustic problem. The complexity of boundaries and constraints in the typical engineering problems requires the use of numerical methods for this description, and the Finite Elements Method (FEM) is one of the most spread. Numerical methods in general and FEM in particular, can be rather accurate if the simulated model is fine enough. However, this usually means a high computational load, with an associate long computation time and (or) very high hardware requirements. These costs need to be diminished to make future design process faster and more efficient. Several ways to address this problem have been proposed and successfully used, usually based on the idea of making more efficient the mathematical formulation and solution of the simulation. Another option is to consider limits of human hearing system to reduce the accuracy requirements of FE meshes. The basic idea is that, since humans are not able to detect very small differences between sounds, a simulation can have a certain degree of numerical error without affecting the perception. Despite the concept being so simple, its development is rather complex since it requires two complicated fields (numerical analysis and sound perception) to merge into one unique solution. Outcome cannot be forecast in a simple manner, and introductory studies are required to form a solid base on which future research can be built. This paper analyses the sensitivities of human perception with respect to some numerical parameters typical of a FE model for exterior acoustic problems. The concept of adaptive mesh is taken into account and the Perfectly Matched Layer (PML) is used to ensure the Sommerfeld radiation condition, thus introducing a set of parameters to be analyzed. Several sounds are obtained by convolving a source signal with several simulated Transfer Functions (TF). These TFs represent the same acoustic path but are obtained through FE models with different parameters. Psychoacoustic metrics are used to check how the changes in FE models affect the sound properties. Lastly, jury tests are performed to assess how these differences modify human perception.

Structural optimisation of transportation equipment using an adaptive approximation model

Considering the high computational cost of the optimisation process of complex transportation equipment, an efficient structural optimisation method for transportation equipment based on the multi-criteria sample updating and management strategy of the approximation model is proposed. First, the approximation models of the objective function and constraints are established based on the radial basis function, and the intergeneration projection genetic algorithm is applied to find the optimal solution. According to error evaluation of the optimal solution, second, the function fluctuation index is used to adopt the local sample points. Meanwhile, the global sample points are solved by the inherited Latin hypercube design to ensure the distribution uniformity of samples in the whole design space. Furthermore, the weighted Euclidean distance criterion is applied to evaluate the rationality between the samples. Then, the qualified samples are added to the sample space to update the approximation models of the transportation equipment. Based on the above multi-criteria sample updating and management strategy, more accurate optimisation results could be obtained. Finally, the effectiveness of the method and its applicability in practical application of transportation equipment are investigated by one numerical test and one engineering example.

Combination Forecasting Model of R&D Intensity in Anhui Province Based on IGOWA Operator

Taking the R&D intensity data of Anhui Province from 2006 to 2020 as the sample, the grey prediction model, ARIMA model and Holt Winters non seasonal model were selected to fit and predict the R&D intensity of Anhui Province, and then the IGOWA operator variable weight coefficient combination prediction model based on the minimum criterion of the sum of squares of errors was constructed, and the combination prediction model corresponding to four special values of the operator parameters was taken, Establish an error evaluation index system to illustrate the effectiveness of the model, and analyze the sensitivity of the parameters. Through model evaluation, it can be found that the prediction effect of the combined prediction model in the sample period is significantly better than the three single prediction models, greatly improving the prediction accuracy. Finally, the combined forecasting model is used to predict the R&D intensity of Anhui Province from 2021-2025. The prediction results show that in 2021-2025, the R&D intensity of Anhui Province will continue to rise, and the growth rate is increasing year by year. The scientific research and innovation ability will continue to develop steadily.

Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China

Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation.

Theoretical error formation and evaluation of acoustic source localization for cluster-based techniques

In this paper, the formation of theoretical error is presented to investigate the acoustic source localization (ASL) error that can be expected from traditional L-shaped, cross-shaped, square-shaped, and modified square-shaped sensor cluster arrangements. The response surface model based on the optimal Latin hypercube design is developed to theoretically study the effects of sensor placement parameters on the error evaluation index of root mean squared relative error (RMSRE) for the four techniques. The ASL results from the four techniques with the optimal placement parameters are analyzed theoretically. The relevant experiments are conducted for verifying the above theoretical research. The results show that the theoretical error, formed by the difference between the true and the predicted wave propagation directions is related to arrangement of sensors. The results also show that the sensor spacing and the cluster spacing are the two parameters that affect the ASL error most. Between these two parameters the sensor spacing has the stronger influence. The RMSRE increases with an increasing sensor spacing and a decreasing cluster spacing. Meanwhile, the interaction effect of placement parameters should be also emphasized, especially that between the sensor spacing and the cluster spacing for the L-shaped sensor cluster-based technique. Among the four cluster-based techniques, the newly modified square-shaped sensor cluster-based technique shows the smallest RMSRE and not the largest number of sensors. This research on error generation and analysis will provide guidance for the optimal sensor arrangements in cluster-based techniques.

Digital tools for agrobiological and phonological accounting of the Anapa ampelographic collection

Relevance. Digitalization in the monitoring system of vine plantations of agrobiological indicators is the most important resource for the development of viticulture. Digital tools for collecting, analyzing and visualizing data can improve the efficiency and accuracy of scientific research in the field of viticulture. The purpose of the work. Digitalization of methods for the rapid collection of scientific data on the state of grape plantations in real time based on mobile digital spatial positioning devices.Methods. Agrobiological and phenological accounting with recording data in the observation log on paper and entering data into spreadsheets from the keyboard and voice. Evaluation of errors and timing of transferring data from paper to a computer database when manually writing data to a log with recoding into a CSV-text format for the MySQL DBMS. AppSheet technology was used as a platform for collaborative work of mobile devices with Internet server databases.Results. The automated system for collecting, recording and processing data on agrobiological and phenological indicators of grape varieties is designed for route and field studies of the Anapa ampelographic collection during field data collection on grape bushes. It is used in agronomic and phenological accounting by specialists of research organizations. The main functions of the program are data registration using a mobile digital device in the field and their transfer to a cloud database on the Internet. The results of the calculation of agrobiological and phenological indicators of grapes are transmitted in real time to digital electronic devices connected to the Internet. The program records the date and time of the field observation, geographic coordinates, agrobiological and phenological indicators of the survey, and a photo of the bush.

Clinical Evaluation Of a 0.05 D-step Binocular Wavefront Optometer in Young Adults in China

ABSTRACT Clinical relevance Myopia has become a public health priority as its prevalence increases worldwide, and in clinical practice, the precise evaluation of refraction errors is necessary. Background This study aimed to compare objective and subjective refraction measured by a binocular wavefront optometer (BWFOM) in adults with conventional objective and subjective refractions measured by an optometrist. Methods This cross-sectional study included 119 eyes of 119 participants (34 men and 85 women; mean age:27.5 ± 6.3 years). Refractive errors were measured using BWFOM and conventional methods, with and without cycloplegia. The mean outcome measures were spherical power, cylindrical power, and spherical equivalence (SE). The agreement test was assessed using a two-tailed paired t-test and Bland – Altman plots. Results Under noncycloplegic conditions, there were no significant differences in the objective SE between BWFOM and Nidek. Significant differences in subjective SE were observed between BWFOM and conventional subjective refraction (−5.79 ± 1.86 vs −5.65 ± 1.75 D, P < 0.01). Under cycloplegic conditions, the mean objective SE was significantly different between BWFOM and Nidek (−5.70 ± 1.76 vs −5.50 ± 1.83 D, P < 0.001); the mean subjective SE was significantly different between BWFOM and conventional subjective refractions (−5.52 ± 1.77 vs −5.62 ± 1.79 D, P < 0.001). The Bland – Altman plots revealed mean percentages of 95.38% and 95.17% for the points within the limits of agreement between BWFOM and conventional measurements and those between noncycloplegic and cycloplegic refractions, respectively. Conclusion The BWFOM is a new device that measures both objective and subjective refraction. It is more convenient and faster to obtain a proper prescription at a 0.05-D interval. The subjective refraction results of the BWFOM and the conventional subjective refraction were in good agreement.

The physiology of intraoperative error: using electrokardiograms to understand operator performance during robot-assisted surgery simulations

BackgroundNo platform for objective, synchronous and on-line evaluation of both intraoperative error and surgeon physiology yet exists. Electrokardiogram (EKG) metrics have been associated with cognitive and affective features that are known to impact surgical performance but have not yet been analyzed in conjunction with real-time error signals using objective, real-time methods.MethodsEKGs and operating console point-of-views (POVs) for fifteen general surgery residents and five non-medically trained participants were captured during three simulated robotic-assisted surgery (RAS) procedures. Time and frequency-domain EKG statistics were extracted from recorded EKGs. Intraoperative errors were detected from operating console POV videos. EKG statistics were synchronized with intraoperative error signals.ResultsRelative to personalized baselines, IBI, SDNN and RMSSD decreased 0.15% (S.E. 3.603e−04; P = 3.25e−05), 3.08% (S.E. 1.603e−03; P < 2e−16) and 1.19% (S.E. 2.631e−03; P = 5.66e−06), respectively, during error. Relative LF RMS power decreased 1.44% (S.E. 2.337e−03; P = 8.38e−10), and relative HF RMS power increased 5.51% (S.E. 1.945e−03; P < 2e−16).ConclusionsUse of a novel, on-line biometric and operating room data capture and analysis platform enabled detection of distinct operator physiological changes during intraoperative errors. Monitoring operator EKG metrics during surgery may help improve patient outcomes through real-time assessments of intraoperative surgical proficiency and perceived difficulty as well as inform personalized surgical skills development.Graphical abstract

An evaluation methodology for the circumferential position error of the V-shaped apex of the double-helical gear.

Aiming to achieve an accurate evaluation of the circumferential position error of the V-shaped apex of double-helical gear, the definition of the V-shaped apex of double-helical gear and the evaluation method of the circumferential position error is studied based on the geometric characteristics of double-helical gear and the definition of shape error. First, the definition of the V-shaped apex of double-helical gear based on the helix and its circumferential position error is presented based on the (American Gear Manufacturers Association) AGMA940-A09 standard. Second, based on the basic parameters, the tooth profile characteristics, and the tooth flank forming principle of double-helical gear, the mathematical model of double-helical gear in a Cartesian space coordinate system is established, and the auxiliary tooth flank and auxiliary helix are constructed to generate some auxiliary measurement points. Finally, the auxiliary measurement points are fitted using the least-squares principle to calculate the position of the V-shaped apex of the double-helical gear in the actual meshing condition and its circumferential position error. The simulation and experimental results show that the simulation results verify the feasibility of the method, and the experimental results (the circumferential position error of the V-shaped apex is 0.0187mm) are consistent with the results of the literature [Bohui etal., Metrol. Meas. Technol. 36, 33 (2016)]. This method can effectively realize the accurate evaluation of the double-helical gear V-shaped apex position error, providing some guidance for the design and manufacture of double-helical gear.

Surface matching error evaluation system design based on QT

Based on the analysis of existing assembly simulation positioning error evaluation methods, a visualization evaluation system of surface matching error was developed in this paper. The software can read the point cloud data measured by the CMM. And then, the software can fit the data to the surface and then visualize the surface. At the same time, the software can automatically match different parts in the database, display the best matching solution, and evaluate the matching error.