High Accuracy Value Research Articles

Melanoma Skin Cancer Identification with Explainability Utilizing Mask Guided Technique

Melanoma is a highly prevalent and lethal form of skin cancer, which has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the identification of diseases using medical imaging. The paper introduces a computational model for classifying melanoma skin cancer images using convolutional neural networks (CNNs) and vision transformers (ViT) with the HAM10000 dataset. Both approaches utilize mask-guided techniques, employing a specialized U2-Net segmentation module to generate masks. The CNN-based approach utilizes ResNet50, VGG16, and Xception with transfer learning. The training process is enhanced using a Bayesian hyperparameter tuner. Moreover, this study applies gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ to generate heatmaps to explain the classification models. These visual heatmaps elucidate the contribution of each input region to the classification outcome. The CNN-based model approach achieved the highest accuracy at 98.37% in the Xception model with a sensitivity and specificity of 95.92% and 99.01%, respectively. The ViT-based model approach achieved high values for accuracy, sensitivity, and specificity, such as 92.79%, 91.09%, and 93.54%, respectively. Furthermore, the performance of the model was assessed through intersection over union (IOU) and other qualitative evaluations. Finally, we developed the proposed model as a web application that can be used as a support tool for medical practitioners in real-time. The system usability study score of 86.87% is reported, which shows the usefulness of the proposed solution.

Ağız İçi Tarayıcıların Farklı Restorasyon Tasarımlarının Tarama Doğruluğuna Etkisi

Objective: The aim of this in vitro study was to evaluate the effect of intraoral scanner used in different preparation geometries on trueness and precision.
 Materials and Methods: Three different dental models of inlay, onlay and full crown preparation designs were used in the study. Reference scans were performed with an industrial-grade scanner (ATOS Core 80, GOM), while each model was scanned 10 times with the following intraoral scanners, respectively: Trios 4 (3Shape), Trios 3 (3Shape), Primescan (Dentsplay Sirona), Omnicam (Dentsplay) Sirona), Planmeca Emerald (Planmeca), Medit i700 (Medit Corp). All scan data were converted to STL file and saved. Then, the data were recorded in the 3D image processing software (Geomagic Control X 2020.1) and the accuracy and precision values were calculated. Shapiro Wilk, two-way ANOVA and post hoc Tukey tests were performed for statistical analysis.
 Results: Both the geometry of the preparation and the intraoral scanner used were found to be effective in the accuracy analysis. The highest accuracy values were obtained in the full crown design and with the Medit i700. The precision values of the full crown preparation were found to be higher in all intraoral scanners except Trios 4, but there was no significant difference between the scanners.
 Conclusion: It should be considered that the accuracy and sensitivity of the preparations with complex geometries may decrease depending on the intraoral scanner used.

Implementasi Algoritma Graf dan Algoritma Genetika pada Peringkasan Single Document

In today's technological advancements, finding information is easier and faster. But not a little information that is not true or commonly referred to as hoaxes. Therefore, information must be obtained from several sources to ensure the accuracy of the information. Automatic Text Summarization System is a system used for text based document summarization. This system can help find the core of a news document, so it does not require much time to read. Researchers use Graph Algorithms and Genetic Algorithms in system development. From the test results obtained by the accuracy of the system produced by the system with manual numbers have a cosine similarity value of 71.21%. This can prove that the system built can be used by users because the results of tests carried out get high accuracy values.

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

Convolutional Neural Network Activation Function Performance on Image Recognition of The Batak Script

Deep Learning is a sub-set of Machine learning, Deep Learning is widely used to solve problems in various fields. One of the popular deep learning architectures is The Convolutional Neural Network (CNN), CNN has a layer that transforms feature extraction automatically so it is widely used in image recognition. However, CNN's performance using the tanh function is still relatively low, therefore it is necessary to select the right activation function to improve accuracy performance. This study analyzes the use of the activation function in image recognition of the Batak script. The result of this study is that the CNN model using the ReLU and eLU functions produces the highest accuracy compared to the CNN model using the tanh function. The CNN model using eLU produces the best accuracy performance in the training process, which is 99.71% with an error value of 0.0108. Meanwhile, in the testing process, the highest accuracy value is generated by the CNN Model using the ReLU function with an accuracy of 94.11%, an error value of 0.3282, a precision value of 0.9411, a recall of 0.9411, and an f1-score of 0.9416.

Improving the quality of pulmonary nodules segmentation using the new proposed U-Net neural network

Diagnosing lung cancer is difficult due to the complexity of the nature of nodules. CT scan imaging is the most common imaging to diagnosis of lung cancer. Detection of nodules from these images is a challenge for radiologists and doctors. In recent years, neural networks have been developed for automatic, faster and more accurate diagnosis of diseases from medical images. In the present study, a new improved U-Net neural network is introduced for the automatic detection and segmentation of pulmonary nodules. The evaluation of this model has been done on LIDC-IDRI database. Our results have high values of recall, specificity and accuracy. The highest Recall value is 97.97 and is related to Juxtra-vascular. Specificity and accuracy for non-solid, partially solid and tiny has a value of 96.99.

UPLC METHOD DETERMINATION AND QUANTIFICATION OF BELUMOSUDIL AND ITS IMPURITIES IN THE HUMAN PLASMA SAMPLES

This current analysis demonstrates the development of the related substances (RS) method for separating and quantifying BSL and its related substances from plasma using the UPLC technique. The solid phase extraction (SPE) method was employed to extract the belumosudil (BSL) and related impurities from plasma. This SPE method has been shown to have high recovery values for BSL and its related impurities. The method employed a column from Waters, specifically the X-Bridge BEH C18 150x4.6mm, 3.5µm, with a mobile phase flow rate (0.8 mL/min) and a UV-wavelength (250 nm). In this method, BSL and its impurities show good peak shapes and high resolutions. This developed method shows high sensitivity, accuracy, and precision values. While performing the stability studies, the drug was stable and it showed less than 4.53% degradation. Six different concentration solutions (BSL (25-250µg/mL) and impurities (3-18µg/mL)) were prepared for constructing linearity plots. The obtained linearity plot shows the accepted correlation coefficient (R2) value (≥0.999). The Limit of detection (LOD) and Limit of quantification (LOQ) were determined using the signal-to-noise (S/N) ratio method. This UPLC method was validated according to ICH guidelines.

Risk Factors and Predictive Nomogram for Carbapenem-Resistant Klebsiella pneumoniae in Children in a Grade 3 First-Class General Hospital in Central China.

This study determined risk factors for Carbapenem-resistant Klebsiella pneumoniae (CRKP)in children admitted to a grade 3 first-class general hospital and developed an individualized line graph predictive model. The clinical data of 185 children infected with Klebsiella pneumoniae from January 2015 to December 2019 were analyzed retrospectively. Patients were grouped according to carbapenem resistance: CRKP group (50 cases) and CSKP (carbapenem-sensitive Klebsiella pneumoniae) group (135 cases). Risk factors for CRKP in children were screened by logistic regression analysis. The predictive model was established using R software and validated using the Bootstrap method. Age (odds ratio [OR]=0.104, 95% confidence interval [CI]: 0.026-0.408), intensive care unit admission (OR =2.829, 95% CI: 1.138-7.030), mechanical ventilation (OR =7.510, 95% CI: 3.140-17.961), surgery history (OR =5.005, 95% CI: 1.507-16.618) and glucocorticoid (OR =0.235, 95% CI: 0.099-0.557) were independent risk factors for CRKP in children (P < 0.05), The total risk score of each factor was 362.5, and the risk rate was 0.1-0.9. In receiver-operating characteristic curve analysis, the area under the curve of CRKP predicted by the total risk score was 0.872 (95% CI=0.844-0.901; P < 0.001). The correction curve indicated that the consistency between the observed value and the predicted value was good. This study successfully established a model based on the risk factors, with high accuracy and good predictive value for CRKP in children. Hospitals should take necessary preventive measures against the risk factors for drug-resistant bacteria, such as optimizing the configuration of ICU space, timely isolation of infected children, and adequate disinfection of ICU equipment. Which may reduce CRKP infection rate.

Predictive value of blood coagulation and routine blood indices for rebleeding after endoscopic treatment in hepatitis B-related cirrhotic patients with esophagogastric fundal varices: a logistic regression model analysis.

To evaluate the predictive value of blood coagulation and routine blood indices for rebleeding after endoscopic treatment of ruptured esophagogastric fundal varices (EGVB) in cirrhotic patients with hepatitis B infection. This retrospective analysis included 248 patients with hepatitis B-related cirrhosis and EGVB who received initial endoscopic treatment from October 2019 to March 2022 and were followed up for 12 months. Patients were divided into rebleeding and non-rebleeding groups. Laboratory indices were analyzed, and univariate and multivariate analyses identified predictors of rebleeding. The efficacy of a logistic regression model was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), and a risk factor nomogram was constructed for assessing the predictive efficiency of those risk factors. Univariate analysis showed significant differences in portal vein diameters and lower Child-Pugh scores in the rebleeding group in contrast to those in the non-rebleeding group. Key laboratory markers such as platelet count (PLT), albumin (ALB), alanine aminotransferase (ALT), lymphocytes (LYM), and prognostic nutritional index (PNI) were lower, while prothrombin time (PT) and lactate levels (LN) were higher in the rebleeding group than those in the non-rebleeding group. Multivariate analysis identified portal vein diameter, PLT, ALT, PT, LYM, and PNI as significant predictors of rebleeding. The logistic model demonstrated high accuracy (AUC=0.986) and clinical value, validated by ROC curves, calibration curves (C-index =0.986), and DCA results. A risk factor predictive nomogram was successfully constructed. This study developed a logistic regression model with a nomogram for predicting EGVB-related rebleeding in patients with hepatitis B-related cirrhosis, achieving an AUC of 0.986, indicating high accuracy and significant clinical relevance.

Nomogram Model for Cardiac Surgery-Associated Acute Kidney Injury Based on Clinical Characteristics Combined with Plasma suPAR.

Analyze risk factors for cardiac surgery-associated acute kidney injury (CSA-AKI) in adults and establish a nomogram model for CSA-AKI based on plasma soluble urokinase-type plasminogen activator receptor (suPAR) and clinical characteristics. In a study of 170 patients undergoing cardiac surgery with cardiopulmonary bypass, enzyme-linked immunosorbent assay (ELISA) measured plasma suPAR levels. Multivariable logistic regression analysis identified risk factors associated with CSA-AKI. Subsequently, the CSA-AKI nomogram model was developed using R software. Predictive performance was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). Internal validation was performed through the Bootstrap method with 1000 repeated samples. Additionally, decision curve analysis (DCA) assessed the clinical applicability of the model. Multivariable logistic regression analysis revealed that being male, age ≥ 50 years, operation time ≥ 290 minutes, postoperative plasma suPAR at 2 hours, and preoperative left ventricular ejection fraction (LVEF) were independent risk factors for CSA-AKI. Employing these variables as predictive factors, a nomogram model was constructed, an ROC curve was generated, and the AUC was computed as 0.817 (95% CI 0.726-0.907). The calibration curve indicated the accuracy of the model, and the results of DCA demonstrated that the model could benefit the majority of patients. Being male, age ≥ 50 years, operation time ≥ 290 minutes, low preoperative LVEF, and elevated plasma suPAR at 2 hours are independent risk factors for CSA-AKI. The nomogram model established based on these risk factors has high accuracy and clinical value, serving as a predictive tool for assessing the risk of CSA-AKI.

Research on the Innovation and Effectiveness of the Teaching Mode of Civics and Political Science Courses Supported by Mobile Computing Platforms

Abstract In today’s world, with the rapid development of information technology and scientific education, data technology has triggered important changes in ideological and political education in colleges and universities, effectively promoting the transformation of educational concepts, ways of thinking, educational methods, and practice modes. Based on the research background under big data, this study investigates the traditional knowledge-tracking model, introduces the attention mechanism and the characteristics of ideological and political learners on the basis of the traditional model, and proposes an improved knowledge-tracking model. At the same time, a mobile teaching platform was constructed, then the improved knowledge tracking model was tested, and the effect of the knowledge platform application on the relevant dataset was explored. The results show that using the optimized knowledge state tracking model, the teacher’s teaching time can be adjusted according to the students’ state, and there is a good effect on the prediction of students’ grades, and the average grade of the students is only 2-6 points of error from the predicted grades. In terms of model application accuracy, the average value of low accuracy is 6.92%, the average value of medium accuracy is 12.9%, and the average value of high accuracy is 79.79%, so the model assesses students’ knowledge status with high accuracy. Finally, the optimized model was used to analyze the learning effect of the students and to conduct a survey on the effect of using satisfaction, and the survey concluded that the students were more satisfied with the model.

Accuracy of Kampala Trauma Score as a Predictor in Assessing the Prognostic Value of Multiple Trauma

Determining a trauma scoring system appropriate for certain conditions will help in predicting mortality and morbidity, and can determine the need for treatment in patients. Kampala Trauma Score (KTS) is a trauma scoring system that uses a decent number of assessment variables and can be used in all general groups. This study was a retrospective prognostic test conducted in Dr. Hasan Sadikin Hospital Bandung, Indonesia, from December 2021 to July 2022. Data obtained was processed using Microsoft Excel and SPSS spreadsheets. The respondents were 66 people with multiple trauma. Results showed that this study was dominated by patients with blunt trauma (n=63, 95.45%). The mean KTS score in this study was 13.08±1.03. In 49 (74.24%) patients who survived, the mean KTS score was 13.41±0.89 and in 17 (25.76%) patients who died, the mean KTS score was 12.12±0.79. The greatest mortality accuracy was at the cutoff point of the KTS score &gt;12 (81.82% accuracy; 70.59% sensitivity; 85.71% specificity). The accuracy value of the KTS score is in line with the ROC where the optimal KTS score is &gt;12 (85.77%). In conclusion, the Kampala trauma score can be used as a predictor in assessing the prognostic value of multiple trauma patients with the obtained cut-point value as it has high accuracy, sensitivity, and specificity values.

Arabic Character Recognition Using CNN LeNet-5

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.

Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.

Sentiment classification of coral reef 101 content using decision tree algorithm through CRISP-DM

This research aims to classify public sentiment regarding the content of "Coral Reef 101," published by National Geographic. The methodology employed is the Cross-Industry Standard Process for Data Mining (CRISP-DM), encompassing stages such as business understanding, data understanding, modeling, evaluation, and deployment. The Decision Tree algorithm is utilized in conjunction with the SMOTE operator. This comprehensive approach enables the systematic analysis of public sentiment towards coral reef content, facilitating a deeper understanding of public perception and attitudes. The results of this study indicate that the DT algorithm with SMOTE demonstrates an accuracy of 87.51% +/- 4.28% (micro average: 87.50%), a precision of 80.35% +/- 5.10% (micro average: 80.00%) (positive class: Positive), recall of 100.00% +/- 0.00% (micro average: 100.00%) (positive class: Positive), f-measure of 89.02% +/- 3.22% (micro average: 88.89%) (positive class: Positive), and an AUC of 0.875 +/- 0.044 (micro average: 0.875) (positive class: Positive). These metrics demonstrate the effectiveness of the DT algorithm with SMOTE in accurately classifying public sentiment towards coral reef-related content, particularly in correctly identifying positive sentiment instances. The high accuracy, precision, recall, f-measure, and AUC values underscore the robustness and reliability of the model in sentiment analysis tasks.

Mapping Pinus spp. Forestry and Land Cover Classes Using High-resolution PlanetScope Satellite Data: Experimenting Images from Different Seasons and Machine Learning Methods

The Remote Sensing and machine learning techniques are cost-effective ways of mapping land use and cover, especially forestry areas. This is essential for the management and planning of such resources. The purpose of this study was to identify which classifier (Random Forest or Support Vector Machine) reach the best accuracy in land use and cover classification and determine which is the best season of year for Pinus spp. forest mapping. PlanetScope multispectral image was used with 3.7 m of spatial resolution, collected over the coastal region of Rio Grande do Sul state. The input variables for the classifiers were the four spectral bands: RGB and NIR, and the NDVI vegetation index. In both classifiers, high accuracy values were obtained, as well as for all seasons of the year. The Random Forest classifier obtained better results in the spring and summer seasons, while in the autumn and winter seasons there was no significant difference between the classifiers for the classification of Pinus spp. forests. The results reached an adequate precision to be used for the management and monitoring of the land use and cover in the municipality of São José do Norte, RS.

Testing the Reliability of Maximum Entropy Method for Mapping Gully Erosion Susceptibility in a Stream Catchment of Calabria Region (South Italy)

Gully erosion poses severe problems for land degradation in several areas worldwide. This study aims to evaluate the accuracy and robustness of the maximum entropy (MaxEnt) method for assessing gully erosion susceptibility. We selected the catchment of the Mesima stream as the test site, which is situated in the southwest sector of the Calabria region (South Italy). An inventory map of gully erosion was realised and 12 predisposing factors, such as lithology, soil texture, soil bulk density, land use, drainage network, slope gradient, aspect, length–slope (LS), plan curvature, stream power index (SPI), topographic position index (TPI), and topographic wetness index (TWI), were selected to implement the dataset in the MaxEnt method. The accuracy and uncertainty of the method were tested by 10-fold cross-validation based on accuracy, kappa coefficient, and receiver operating characteristic curve (ROC) and related area under curve (AUC). The dataset was randomly divided into 10 equal-sized groups (folds). Nine folds (90% of the selected dataset) were used to train the model. Instead, the remaining fold (10% of the dataset) was used for testing the model. This process was repeated 10 times (equal to the number of the folds) and each fold was used only once as the validation data. The average of 10 repeated processes was performed to generate the susceptibility map. In addition, this procedure allowed the reliability of the susceptibility map to be assessed, in terms of variables, importance and role of predisposing factors selected, prediction ability, and accuracy in the assessed probabilities for each pixel of the map. In addition to exploiting the 10-fold cross-validation, the mean value and standard deviation for the probability estimates of each pixel were computed and reported in the susceptibility and uncertainty map. The results showed that the MaxEnt method has high values of accuracy (&gt;0.90), of the kappa coefficient (&gt;0.80), and AUC (&gt;0.92). Furthermore, the achieved findings showed that the capacity of the method used for mapping gully erosion susceptibility is quite robust when the training and testing sets are changed through the 10-fold cross-validation technique.

TDCAU-Net: retinal vessel segmentation using transformer dilated convolutional attention-based U-Net method

Retinal vessel segmentation plays a vital role in the medical field, facilitating the identification of numerous chronic conditions based on retinal vessel images. These conditions include diabetic retinopathy, hypertensive retinopathy, glaucoma, and others. Although the U-Net model has shown promising results in retinal vessel segmentation, it tends to struggle with fine branching and dense vessel segmentation. To further enhance the precision of retinal vessel segmentation, we propose a novel approach called transformer dilated convolution attention U-Net (TDCAU-Net), which builds upon the U-Net architecture with improved Transformer-based dilated convolution attention mechanisms. The proposed model retains the three-layer architecture of the U-Net network. The Transformer component enables the learning of contextual information for each pixel in the image, while the dilated convolution attention prevents information loss. The algorithm efficiently addresses several challenges to optimize blood vessel detection. The process starts with five-step preprocessing of the images, followed by chunking them into segments. Subsequently, the retinal images are fed into the modified U-Net network introduced in this paper for segmentation. The study employs eye fundus images from the DRIVE and CHASEDB1 databases for both training and testing purposes. Evaluation metrics are utilized to compare the algorithm’s results with state-of-the-art methods. The experimental analysis on both databases demonstrates that the algorithm achieves high values of sensitivity, specificity, accuracy, and AUC. Specifically, for the first database, the achieved values are 0.8187, 0.9756, 0.9556, and 0.9795, respectively. For the second database, the corresponding values are 0.8243, 0.9836, 0.9738, and 0.9878, respectively. These results demonstrate that the proposed approach outperforms state-of-the-art methods, achieving higher performance on both datasets. The TDCAU-Net model presented in this study exhibits substantial capabilities in accurately segmenting fine branching and dense vessels. The segmentation performance of the network surpasses that of the U-Net algorithm and several mainstream methods.

Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches

Recent clinical studies have emphasized the importance of understanding the morphology and mechanics of the ciliary muscle. The ciliary muscle plays a vital role in various functions related to the anterior segment of the eye, including the regulation of intraocular pressure and the maintenance of the shape of the crystalline lens. To advance research in this area, we propose a fully automated methodology for the segmentation and biomarker measurement of the ciliary muscle in two different scan depths (6mm and 16mm), which are commonly used by clinicians to analyze biomarkers. Our methodology aims to provide repeatable, and immediate results through an exhaustive analysis of different network architectures, encoders, and transfer learning strategies. We also extracted a comprehensive set of relevant biomarkers, including parameters that provide essential information about its behavior during the accommodation process, overall dimensions, and biomechanical properties. These biomarkers can help clinicians and researchers in the diagnoses and monitor of different ocular diseases such as glaucoma, myopia, and presbyopia and develop new therapeutic strategies, potentially leading to more effective treatments and improved patient outcomes. Our methodology achieved accurate qualitative and quantitative results, with high accuracy values of 0.9665±0.1280 and 0.9772±0.0873 for the best combinations for 6mm and 16mm, respectively. Our proposed system provides a valuable and automatic tool for clinicians and researchers in the segmentation and analysis of the ciliary muscle in AS-OCT images.

Calculation method of external fault short-circuit current for variable-speed pumped storage units based on time-domain dynamic circuit

The calculation method of the external fault short-circuit current of large units with clear mechanisms and practicality is of great value in engineering practice. Due to the special AC excitation structure and control mode, the external fault short-circuit characteristics of variable-speed pumped storage units (VSPSUs) are very different from those of conventional units. Therefore, this paper proposes a calculation method for external fault short-circuit current based on a time-domain dynamic circuit. According to the excitation and control characteristics of VSPSUs under external fault, the VSPSU under external fault is divided into two cases: continuous excitation and jumper action. Based on space vector analysis, the time-domain dynamic circuit models are established, respectively. On this basis, the analytical formula of the external fault short-circuit current of VSPSUs is derived by combining the transition boundary conditions of the two cases and the dynamic characteristics of the stator flux linkage. Finally, the comparison with PSCAD/EMTDC simulation results shows that the proposed VSPSUs’ external fault short-circuit current calculation method has high accuracy and practical value, which can meet the needs of primary equipment selection and relay protection of variable-speed pumped storage power plants.