Co-occurrence Matrix Method Research Articles

Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model’s R2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status.

Application of a Photothermal Microscope To Study the Process of Cutaneous Lesion Formation of Malignant Melanoma.

In this study, we applied a photothermal microscope to study the process of malignant melanoma formation. We analyzed benign papilloma tumors, their metastatic carcinomas, and metastatic melanoma on the preparation of mouse skin using a gray-level co-occurrence matrix (GLCM) method. Based on the analysis of nine GLCM parameters investigated, the characteristics during the degenerative and metastatic processes are clarified by the investigation. The determination of characteristic parameters corresponding to three processes before, during, and after the degeneration indicated that this investigation enables the detection of the malignant transformation and related processes.

Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning

AbstractSign language serves as the primary means of communication utilized by individuals with hearing and speech disabilities. However, the comprehension of sign language by those without disabilities poses a significant challenge, resulting in a notable disparity in communication across society. Despite the utilization of numerous effective Machine learning techniques, there remains a minor compromise between accuracy rate and computing time when it comes to sign language recognition. A novel sign language recognition system is presented in this paper with an exceptionally accurate and expeditious, which is developed upon the recently devised metaheuristic WAR Strategy optimization algorithm. Following the preprocessing, both of spatial and temporal features has been extracted using the Linear Discriminant Analysis (LDA) and Gray-level cooccurrence matrix (GLCM) methods. Afterward, the WAR Strategy optimization algorithm has been adopted in two procedures, first in optimizing the extracted set of features, and second to fine-tune the hyperparameters of six standard machine learning models in order to achieve precise and efficient sign language recognition. The proposed system was assessed on sign language datasets of different languages (American, Arabic, and Malaysian) containing numerous variations. The proposed system attained a recognition accuracy ranging from 93.11% to 100% by employing multiple optimized machine learning classifiers and training time of 0.038–10.48 s. As demonstrated by the experimental outcomes, the proposed system is exceptionally efficient regarding time, complexity, generalization, and accuracy.

Implementation of laser-light backscattering imaging for authentication of the geographic origin of Indonesia region citrus

Citrus fruit (Citrus nobilis Lour.) from the Indonesian region is reported to have high economic value due to attractive nutritional, nutraceutical, and sensory attributes. However, authenticating the geographic origins is challenging because of adulteration and similarity in visual appearance. Therefore, this study aimed to develop an effective method based on laser-light backscattering imaging (LLBI) for authentication of the geographic origin of the region citrus. A total of 200 citrus samples were collected from Medan, Malang, Jember, and Banyuwangi regions, which were the four main citrus-producing areas in Indonesia. Approximately three different laser wavelengths, namely 450, 532, and 648 nm were beamed to produce the backscattering image. Furthermore, a combination of the gray-level co-occurrence matrix (GLCM) method and support vector machine (SVM) algorithm was applied to extract texture features and build a classification model, respectively. In this context, three kernel functions, such as linear, radial basis function, and polynomial, were compared in authenticating the geographic origin of citrus. The results showed that the proposed technique achieved 96.667 % accuracy and 3.333 % apparent error for authentication of the geographic origin. The proposed LLBI technique applied a laser wavelength of 450 nm and a polynomial kernel function as the best combination to produce reliable predictive power. This study held valuable implications for advancing sensing technology devices to authenticate geographic origin, specifically citrus fruit.

Classification of Lung Disease in X-Ray Images Using Gray Level Co-Occurrence Matrix Method and Convolutional Neural Network

The lungs are a very important part of the human body, as they serve as a place for oxygen exchange. They have a very complex task and are susceptible to damage from the polluted air we breathe every day, which can lead to various diseases. Lung disease is a very common health problem that can be found in everyone, but there are still many people who do not pay attention to their lung health, making them vulnerable to lung disease. One of the methods used to detect lung disorders is by examining images obtained from X-rays. Image processing is one of the techniques that can also be used for lung disease identification and is most commonly used in medical images. Therefore, the purpose of this research is to implement image processing to determine the accuracy of lung disease identification using deep learning algorithms and the application of feature extraction. In this research, there are two experiments conducted consisting of the application of the classification method, namely Convolutional Neural Network and Gray Level Co-Occurrence Matrix feature extraction with CNN. The results show that the CNN model gets a precision of 0.92, recall of 0.92, f1-score of 0.92, and average accuracy of 0.92. The combination of the GLCM method with CNN produces a precision of 0.87, recall of 0.87, f1-score of 0.87, and average accuracy of 0.87. The results of this study indicate that the use of CNN in the lung disease classification model based on X-ray images is superior to the GLCM-CNN method.

Classification of Malignancy of Lung Cancer Using Backpropagation Algorithm on CT-Scan Images

In this study, we investigate the classification of lung cancer CT scan images based on malignancy level using a backpropagation artificial neural network (ANN). Lung cancer is a deadly disease characterized by the growth of abnormal lung cells. The proposed method involves preprocessing to enhance image quality, followed by feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method with angle variations of 0°, 45°, 90°, 135°, and d=1. The extracted features include energy, contrast, correlation, and homogeneity. The energy value range in malignant cancer is 0.27 to 0.81, while in benign cancer it is 0.26 to 0.73. The contrast in benign cancer ranges from 1.38 to 11.87, while in malignant cancer it is 1.47 to 13.67. The image correlation for malignant cancer is between 0.63 to 0.94, while for benign cancer it is 0.69 to 0.96. Homogeneity in malignant cancer has a value range between 0.67 to 0.91, while in benign cancer it ranges from 0.70 to 0.92. The classification of lung cancer malignancy is restricted to benign and malignant levels using a network architecture of [4 10 2], maximum iteration of 100000, and learning rate of 0.001. The accuracy of the testing data from the ANN is between 90% and 100%. These results demonstrate the effectiveness of the GLCM method and backpropagation algorithm in accurately classifying the malignancy level of lung cancer, which could aid in the early detection and treatment of the disease.

Experimental and Numerical Study of Computer Vision-Based Real-Time Monitoring of Polymeric Particle Mixing Process in Rotary Drum.

In the drum mixing of particulate polymers, segregation may occur. By measuring the mixing status in real time, it is possible to implement corrective measures to prevent separation and improve the efficiency of the process. This study aims to develop and validate a real-time vision system designed to monitor the mixing process of polymeric particles in a rotary drum mixer, employing a novel centroid-based model for determining the mixing index. The proposed centroid-based model is capable of addressing the radial particle segregation issue without the need for extra image-processing procedures like image subdivision or pixel randomization. This innovative approach greatly improves computational efficiency by processing over 68 image frames per second. The new processing method is 2.8 times faster than the gray-level co-occurrence matrix method and 21.6 times faster than the Lacey index approach. This significantly improves real-time monitoring capabilities and enables real-time image processing using only affordable single-board computers and webcams. The proposed vision-based system for monitoring rotary drum mixing has undergone validation via cross-validation using discrete element method simulations, ensuring its accuracy and reliability.

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics

Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.

Detection and classification of invisible weld defects by magneto-optical imaging under alternating magnetic field excitation

Aiming at the difficult to detect invisible weld defects, a magneto-optical (MO) imaging non-destructive testing (NDT) system excited by an alternating magnetic field is proposed for feature extraction and detection classification of invisible weld defects. The relationship between the MO image features and the magnetic field intensity is analyzed based on the Faraday MO effect. A magnetic dipole model is proposed to study the magnetic field distribution on weld defect. A three-dimensional finite element model of invisible weld defects (with a width of 0.01 mm) is established, and the distribution of leakage magnetic field for different types of invisible defects is analyzed. In order to detect different invisible weld defects under the excitation of alternating magnetic field, MO imaging NDT experiments are carried out. The effectiveness of the finite element model is verified by the experiment. The gray value of the MO image can match the corresponding leakage magnetic field intensity. The Tamura method and gray-level co-occurrence matrix (GLCM) method are used to extract the texture features of natural invisible weld defect’s MO image, and these texture features of MO images are used as input data for the defect classification model established using back propagation (BP) neural network. Experimental results show that the classification accuracy of the GLCM + Tamura-BP model is higher than that of the GLCM-BP model, and its overall classification accuracy reaches 91.1%, indicating that this model can effectively and accurately classify natural invisible weld defects.

Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture

Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound

Objective. Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line. Approach. The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images. Main results. We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P < 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios. Significance. The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

An Intelligent Computer Aided Diagnosis System for Classification of Ovarian Masses using Machine Learning Approach

Ovarian cancer, a difficult and often asymptomatic malignancy, remains a substantial global health concern in women. An ovary is a female reproductive organ, which lies on each side of the uterus and used to store eggs. Computer-aided diagnosis (CAD) is an approach that involves using computer algorithms and machine learning techniques to assist medical professionals in diagnosing ovarian malignancies, benign tumors or Poly-cystic ovaries (PCOS). The need for models that can effectively predict benign ovarian tumors and ovarian cancer has led to the use of machine learning techniques. Our research objective is to propose a machine learning-based system for accurate and early ovarian mass detection utilizing novel annotated ovarian masses. We have used an actual patient database whose input features were extracted from 187 transvaginal ultrasound images from database. The input image is preprocessed using the Block Matching 3D filter. The process involves employing binary and watershed segmentation techniques, followed by the integration of Gabor, Gray-Level Co-Occurrence Matrix (GLCM), Tamura, and edge feature extraction methods. K-Nearest Neighbors (KNN) and Random Forest (RF) are two classifiers used for classification. Based on our results, we are able to demonstrate that binary segmentation with RF classifiers is more accurate (above 86%) than KNN classifiers (under 84%).

Automatic crimping state classification in X-ray images of crimped tension clamp

ABSTRACT The crimping states of the tension clamp can typically be recognised by using X-ray radiography. However, it is challenging to process X-ray images directly for inspection, especially when the crimping state and position of the groove are unclear. To address this problem, this paper firstly adopted the Beta greyscale stretching algorithm to enhance the X-ray images by increasing the contrast and reducing the noise. Subsequently, the Canny algorithm was implemented to locate and segment the groove region. Finally, the Grey-Level Co-occurrence Matrix (GLCM) method was applied to extract the texture features of the groove region, and identify the groove crimping state using support vector machine (SVM) classifier. Three types of the crimping states were utilised as test objects for classification. The results of the experiments demonstrated that the Beta enhancement algorithm, GLCM features used in this paper can effectively improve the accuracy rate of SVM classifier up to 99.51%. This study applies image processing technology towards the crimping states classification of tension clamp with accurate detection results enabling early-stage detection of abnormal crimping states leading to timely repairs.

Prediction of milling surface roughness based on DGLCM and neural network

With the manufacturing industry’s increasingly automated and intelligent development, traditional workpiece roughness measurement methods can no longer meet the requirements for short time, high efficiency, and online measurement. Therefore, this article proposes a roughness evaluation model for vertical milling workpiece roughness measurement and improves upon the traditional Gray Level Co-occurrence Matrix (GLCM) method to achieve high accuracy and robustness in measuring milling surface roughness. First, the milled surface is illuminated by a coaxial light source, and an image is captured using an industrial camera and a telecentric lens. Then, four features of the image are extracted using the Direction Measure-based Gray Level Co-occurrence Matrix (DGLCM) to construct a feature descriptor. Finally, Finally, create an RBF network to predict surface roughness. Through experimental comparison with traditional GLCM feature extraction methods, our proposed feature extraction method has a prediction error of only 2.01%, which is superior to traditional methods.

Razvoj modela mašinskog učenja zasnovanog na algoritmu slučajnih šuma za detekciju promena u tkivu jetre nakon izlaganja česticama oksida gvožđa

Introduction/Aim: The aim of our study was to create a machine learning model, specifically a random forest model, which uses textural data from liver micrographs to differentiate between normal hepatic tissue and damaged tissue exposed to iron oxide nanoparticles. Material and Methods: Regions of interest in micrographs of hepatic tissue, obtained from mice treated with iron oxide nanoparticles and controls, were analyzed using the gray-level co-occurrence matrix (GLCM) method. The resulting GLCM features were employed as input data for the training and testing of the random forest model using the "Scikit-learn" library in the Python programming language. Additionally, a conventional decision tree model was developed, based on the classification and regression tree (CART) algorithm. Results: The random forest model outperformed the alternative CART decision tree approach in terms of classification accuracy, correctly predicting the class for 73.67% of the instances in the validation ROI dataset. The area under the receiver operating characteristic curve was 0.81, indicating relatively good discriminatory power. The F1 score for the model was 0.74, showcasing fairly good precision and recall, though not perfect. Conclusion: The data obtained from this study may be utilized for further development of artificial intelligence computation systems to identify physiological and pathophysiological changes in hepatic tissue. The results also serve as a starting point for additional research on the automation of histopathological analysis of liver tissue exposed to external toxic agents.

Deteksi Mata Katarak Berdasarkan Tekstur Gray Gray Level Co-Ocurrence Matrix Dengan Metode Self Organizing Map

Cataracts are a common eye problem in Indonesia. Untreated cataracts are the main cause of blindness and the most dominant vision impairment in Indonesia among people over 50 years old, with a proportion reaching 77.7%. Regular eye examinations are necessary to prevent cataracts, but there are obstacles due to the availability of equipment and the cost of eye examinations. Therefore, an efficient and effective cataract detection system is needed. This study aims to detect cataracts in the eyes by utilizing a dataset of eyes from the internet. This research uses RGB and HSV feature extraction combined with the GLCM extraction method. The Gray Level Co-occurrence Matrix (GLCM) method describes the spatial relationship between pixel intensities in an image. With the GLCM matrix, texture information can be extracted from eye images. The Self Organizing Map (SOM) method performs learning based on feature extraction obtained from previously labeled eye images. Based on the test results, this study successfully detects cataract and normal eyes. With the greatest accuracy achieved in the comparison of 80% training data and 20% test data with dimension size = [2 2] and maximum iterations of 100, the highest accuracy of 90% was obtained.

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.

The number of diabetic retinopathy (DR) patients is increasing every year, and this causes a public health problem. Therefore, regular diagnosis of diabetes patients is necessary to avoid the progression of DR stages to advanced stages that lead to blindness. Manual diagnosis requires effort and expertise and is prone to errors and differing expert diagnoses. Therefore, artificial intelligence techniques help doctors make a proper diagnosis and resolve different opinions. This study developed three approaches, each with two systems, for early diagnosis of DR disease progression. All colour fundus images have been subjected to image enhancement and increasing contrast ROI through filters. All features extracted by the DenseNet-121 and AlexNet (Dense-121 and Alex) were fed to the Principal Component Analysis (PCA) method to select important features and reduce their dimensions. The first approach is to DR image analysis for early prediction of DR disease progression by Artificial Neural Network (ANN) with selected, low-dimensional features of Dense-121 and Alex models. The second approach is to DR image analysis for early prediction of DR disease progression is by integrating important and low-dimensional features of Dense-121 and Alex models before and after PCA. The third approach is to DR image analysis for early prediction of DR disease progression by ANN with the radiomic features. The radiomic features are a combination of the features of the CNN models (Dense-121 and Alex) separately with the handcrafted features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP), Fuzzy colour histogram (FCH), and Gray Level Co-occurrence Matrix (GLCM) methods. With the radiomic features of the Alex model and the handcrafted features, ANN reached a sensitivity of 97.92%, an AUC of 99.56%, an accuracy of 99.1%, a specificity of 99.4% and a precision of 99.06%.

Identification of Condition of Corn Plant Based on Leaf Image Features Using Gray Level Co-Occurrence Matrix and Backpropagation Neural Network

This study aims to identify the condition of corn plants based on imagery leaf using the gray level co-occurrence matrix (GLCM) method and artificial neural network (ANN) backpropagation. The GLCM method is used for extracting features from image leaf corn, whereas ANN backpropagation is used for classification condition plant corn based on features. The classification was done using a dataset of corn leaves with four conditions: healthy, leaf spot, blight, and leaf rust. Next, the leaf features are extracted using method GLCM and training on model ANN backpropagation to classify conditions of corn plants. After training on the model, the next step is model evaluation using the confusion matrix method. The research results show that the technique can produce accuracy, which is tall enough to identify condition corn plants, with an accuracy of 95%. This indicates that the use of GLCM and ANN backpropagation can be a good alternative in determining the condition of corn plants. This research provides benefits in facilitating the identification of the state of corn plants quickly and accurately.

Texture features-based lightweight passive multi-state crowd counting algorithm

Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting.

Quantitative Assessment of Hyperpigmentation Changes in Human Skin after Microneedle Mesotherapy Using the Gray-Level Co-Occurrence Matrix (GLCM) Method.

The aim of the study was to quantitatively assess the effectiveness of microneedle mesotherapy in reducing skin discoloration. The results were analyzed using the gray-level co-occurrence matrix (GLCM) method. The skin of the forearm (7 × 7 cm) of 12 women aged 29 to 68 was examined. Microneedle mesotherapy was performed using a dermapen with a preparation containing 12% ascorbic acid. Each of the volunteers underwent a series of four microneedle mesotherapy treatments. The effectiveness of the treatment was quantified using the methods of image analysis and processing. A series of clinical images were taken in cross-polarized light before and after a series of cosmetic procedures. Then, the treated areas were analyzed by determining the parameters of the gray-level co-occurrence matrix (GLCM) algorithm: contrast and homogeneity. During image pre-processing, the volunteers' clinical images were separated into red (R), green (G) and blue (B) channels. The photos taken after the procedure show an increase in skin brightness compared to the photos taken before the procedure. The average increase in skin brightness after the treatment was 10.6%, the average decrease in GLCM contrast was 10.7%, and the average homogeneity increased by 14.5%. Based on the analysis, the greatest differences in the GLCM contrast were observed during tests performed in the B channel of the RGB scale. With a decrease in GLCM contrast, an increase in postoperative homogeneity of 0.1 was noted, which is 14.5%.