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Novel bimetallic-doped-MCM-41(Mobil Composition of Matter No. 41) (Zn/Ni-MCM-41 (ZNM)) and (Zn/Ag-MCM-41 (ZAM)) catalysts were synthesized and characterized for their structural, textural, morphological, and optical properties. XRD analysis confirmed metal incorporation into the MCM-41 framework, while N2 adsorption-desorption isotherms indicated a decrease in specific surface area (1210 in pure MCM-41 to 722.86 and 700.36 m2/g for ZNM and ZAM, respectively) due to partial pore filling. TEM images verified this finding. Boosted absorption extending into the visible light region was detected in the metal incorporated (ZNM and ZAM) samples with additional band gaps, related to transitions in Zn2+, Ag+ and Ni2+ ions. Photoluminescence studies revealed efficient charge carrier separation in ZNM and ZAM. Both catalysts exhibited superior tetracycline (TC) removal from aqueous solution with efficiency (95.59% and 95.30% within one hour for ZNM and ZAM, respectively) with pronouncing visible light photocatalytic capability compared to pure MCM-41. The degradation process followed pseudo-first-order kinetics. The enhanced photocatalytic activity of ZNM and ZAM is attributed to the synergistic effects of metal incorporation, increased light absorption, and efficient charge carrier dynamics. Additionally, a possible photocatalytic mechanism for degradation of TC over ZNM and ZAM has been proposed and involvement of superoxide radicals (O2•−) and holes (h+) as reactive species is elucidated by radical trapping experiments. A distinct pH-dependent trend was observed in TC degradation efficiency using the ZAM photocatalyst. The efficiency gradually increased with increasing pH until reaching a maximum at pH 7, followed by a decline at higher pH values. These results demonstrate the potential of ZNM and ZAM as promising materials for removal of tetracycline antibiotic from water. [ABSTRACT FROM AUTHOR]

Chlorophyll is crucial for pear tree growth and fruit quality. In order to integrate the unmanned aerial vehicle (UAV) multispectral vegetation indices and textural features to realize the estimation of the SPAD value of pear leaves, this study used the UAV multispectral remote sensing images and ground measurements to extract the vegetation indices and textural features, and analyze their correlation with the SPAD value of leaves during the fruit expansion period of the pear tree. Finally, four machine learning methods, namely XGBoost, random forest (RF), back-propagation neural network (BPNN), and optimized integration algorithm (OIA), were used to construct inversion models of the SPAD value of pear trees, with different feature inputs based on vegetation indices, textural features, and their combinations, respectively. Moreover, the differences among these models were compared. The results showed the following: (1) both vegetation indices and textural features were significantly correlated with SPAD values, which were important indicators for estimating the SPAD values of pear leaves; (2) combining vegetation indices and textural features significantly improved the accuracy of SPAD value estimation compared with a single feature type; (3) the four machine learning algorithms demonstrated good predictive ability, and the OIA model outperformed the single model, with the model based on the OIA inversion model combining vegetation indices and textural features having the best accuracy, with R 2 values of 0.931 and 0.877 for the training and validation sets, respectively. This study demonstrated the efficacy of integrating multiple models and features to accurately invert SPAD values, which, in turn, supported the refined management of pear orchards. [ABSTRACT FROM AUTHOR]

Production of probiotic (PRO) cereal based products is an important strategy to increase consumption amounts of these beneficial microorganisms in our daily diet worldwide. In the present study, textural features, sensory properties and viability of an adjunct PRO culture were determined in supplemented cupcake with fermented black rice (FBR) and coated with malt. Based on the obtained results, survival of the PRO yeast in malt edible coating reached to 106 CFU/g after 6 days of storage in cupcake samples supplemented with FBR. In addition, crumb hardness (1529.77 g) and gumminess (962.14) of the produced cupcake supplemented with FBR were significantly ( P<0.05) higher than those of the control sample. Meanwhile, proper porosity and overall acceptability were observed for the aforementioned FBR added cupcake. Accordingly, application of a potential PRO yeast via malt coating as a proper carrier can maintain viability of this adjunct culture during shelf-life period of the product in the recommended dose for PRO products. Considering the health promoting potentials of PRO and consumed amounts of cupcake, it is a good choice to consume a high dose of PRO in our today's diet style. [ABSTRACT FROM AUTHOR]

The study presented in this paper is devoted to the development of technology for detecting osteoporotic changes in vertebral bodies according to computed tomography data based on the analysis of groups of textural features. This technology will be of interest to radiologists when assessing the structure of vertebral bodies in patients with osteoporosis, and especially in patients with malignant neoplasms, in which the disease proceeds with a decrease in bone mineral density. Automation of the analysis process will significantly reduce the time spent by a radiologist on the evaluation of vertebral bodies, including when evaluating the effectiveness of osteoporosis treatment. During the development process, the most informative group of signs was identified, which allows achieving high accuracy in detecting signs of osteoporotic changes in the vertebral bodies in 94.5%. [ABSTRACT FROM AUTHOR]

Copyright of Acta Prataculturae Sinica is the property of Acta Prataculturae Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indigenous sourdough (SD) yeasts have promising potential applications as starter culture in processing of the baked goods. In the present study, techno-functional capabilities of the predominant yeasts isolated from barley SD were characterized to select a proper starter culture for type IV SD. Accordingly, techno-functional characteristics of the produced barley bread including textural features using a texture analyzer, sensory properties through evaluation of the panelist's scores, mold-free shelf-life using a challenge test, and their antioxidant capacity based on the DPPH free radical scavenging activity were compared to each other. According to the results, crumb hardness of the barley bread produced with BSY 3 isolate was significantly (P

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson's correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB. [ABSTRACT FROM AUTHOR]

Plant diseases can significantly reduce crop yield and product quality. Visual inspections of plants by human observers for disease identification are time-consuming, costly, and prone to error. Advances in artificial intelligence (AI) have created opportunities for the rapid diagnosis and non-destructive classification of plant pathogens. Several machine vision techniques have been developed to identify and classify plant diseases automatically based on the morphology of specific symptoms. The use of deep learning models has achieved acceptable disease classification results, but they require large datasets for training, which can be labor-intensive, time-consuming, and computationally costly This problem can be solved, to a point, by using data augmentation techniques and generative AI in order to increase the size of the datasets. Furthermore, a combination of deep feature extraction and classification by machine learning was used for accurate disease detection and classification. In some cases, traditional base classifiers trained with small datasets including basic shape, color, and texture features can be feasible for the efficient identification of plant diseases. The performance of such classifiers depends primarily on the features extracted from images; therefore, feature extraction plays a vital role in identifying diseases. Feature engineering, a process to identify the most relevant variables from raw data in order to develop an efficient predictive model, is explored in this paper.

Objective: Our objective is to develop a robust emotion recognition system based on facial expressions, with a particular emphasis on two key regions: the eyes and the mouth. This paper presents a comprehensive analysis of emotion recognition achieved through the examination of various facial regions. Facial expressions serve as invaluable indicators of human emotions, with the eyes and mouth being particularly expressive areas. By focusing on these regions, we aim to accurately capture the nuances of emotional states. Methodology: The algorithm we devised not only detects facial features but also autonomously isolates the eyes and mouth regions. To enhance classification accuracy, we utilized various feature extraction and selection techniques. Subsequently, we assessed the performance of multiple classifiers, including Support Vector Machine (SVM), Logistic Regression, Bayesian Regression, and Decision Trees, to identify the most effective approach. Results: Our experimental methodology involved employing various classification techniques toassess performance across different models. Among these, SVM exhibited exceptional performance, boasting an impressive accuracy rate of 99.2 %. This outstanding result surpassed the performance of all other methods examined in our study. Through meticulous examination and experimentation, we explore the effectiveness of different facial regions in conveying emotions. Our analysis encompasses two datasets and evaluation methodologies to ensure a comprehensive understanding of emotion recognition capabilities. Conclusions: Our investigation presents compelling evidence that analyzing the eye region using a Support Vector Machine (SVM) along with textural, HoG, and LBP features achieves an outstanding accuracy rate of 99.2 %. This remarkable finding underscores the significant potential of prioritizing the eyes alone for precise emotion recognition. In doing so, it challenges the conventional approach of including the entire facial area for analysis.

In the context of food science and engineering, the in vitro chewing effect on food bolus formation is a critical area of research that explores the mechanical and textural properties of ingested materials. This article presents a pioneering approach to assess the in vitro chewing impact on food bolus formation using the gray level co-occurrence matrix (GLCM) image analysis technique. As technological advancements lead to the development of mastication robots, the need for evaluating in vitro chewed food bolus has grown. To address this challenge, a case study is conducted. The study's objectives encompass utilizing GLCM to determine the in vitro chewing cycle phase, analyzing texture features, and investigating chewing trajectory differences for beef and plant-based burger patties. Applying GLCM as a methodology, the research quantitatively analyzes textural features of food bolus formations under controlled in vitro chewing conditions. The outcomes reveal distinct differences between beef and plant-based samples through GLCM parameters. Significantly, the study identifies a consistent trend across various scenarios, indicating an increase in energy and homogeneity and a decrease in dissimilarity with an increasing number of in vitro chewing cycles. This investigation offers valuable insights into the dynamic relationship between chewing cycles and textural features in the oral processing of beef and plant-based burger patties.

This paper focuses on the automated extraction of textural features from segmented Sentinel-1A Synthetic Aperture Radar (SAR) imagery, which was captured on 25th August 2017. Textural properties play a pivotal role in discerning regions of interest in satellite imagery, essential for precise classification in diverse applications, including remote sensing, medical image analysis, biometric analysis, and document image analysis. To achieve this, the Local Adaptive Threshold (LAT) technique was employed for segmenting the foreground and background areas of the pre-processed vertical transmit-vertical receive (VV) polarized Sentinel-1A SAR data. The approach relies on second-order statistics, specifically co-occurrence measures, which evaluate the relationships between pairs of pixels within their local neighbourhood's in the input image. Four fundamental second-order statistical measures, namely correlation, energy, homogeneity, and contrast, were computed using the Grey Level Co-occurrence Matrix (GLCM). The GLCM was generated by quantizing the foreground region of the segmented grey-scale image into different levels, including 8, 16, and 64 grey levels, considering two inter-pixel distances, d=1 and d=2, and adopting an omni-directional orientation (θ = 00, 450, 900, 1350).The results underscore the effectiveness of the GLCM-based approach in computing second-order statistical texture measures from the segmented SAR image. Notably, the findings highlight that quantizing the image to Ng=8 with d=1 and considering omni-directional statistical measures (θ = 00, 450, 900, 1350) significantly enhances performance. [ABSTRACT FROM AUTHOR]

Our study evaluated the prognostic value of the metabolic parameters and textural features in pretreatment 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) of primary lesions for pediatric patients with neuroblastoma. In total, 107 pediatric patients with neuroblastoma who underwent pretreatment 18F-FDG PET/CT were retrospectively included and analyzed. All patients were diagnosed by pathology, and baseline characteristics and clinical data were collected. The four metabolic parameters and 43 textural features of 18F-FDG PET/CT of the primary lesions were measured. The prognostic significance of metabolic parameters and other clinical variables was assessed using Cox proportional hazards regression models. Differences in progression-free survival (PFS) and overall survival (OS) in relation to parameters were examined using the Kaplan–Meier method. During a median follow-up period of 34.3 months, 45 patients (42.1%) experienced tumor recurrence or progression, and 21 patients (19.6%) died of cancer. In univariate Cox regression analysis, age, location of disease, International Neuroblastoma Risk Group Staging System (INRGSS) stage M, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), four positron emission tomography (PET) metabolic parameters, and 33 textural features were significant predictors of PFS. In multivariate analysis, INRGSS stage M (hazard ratio [HR] = 19.940, 95% confidence interval [CI] = 2.733–145.491, P = 0.003), skewness (＞0.173; PET first-order features; HR = 2.938, 95% CI = 1.389–6.215, P = 0.005), coarseness (＞0.003; neighborhood gray-tone difference matrix; HR = 0.253, 95% CI = 0.132–0.484, P ＜ 0.001), and variance (＞103.837; CT first-order gray histogram parameters; HR = 2.810, 95% CI = 1.160–6.807, P = 0.022) were independent predictors of PFS. In univariate Cox regression analysis, gender, INRGSS stage M, MYCN amplification, NSE, LDH, two PET metabolic parameters, and five textural features were significant predictors of OS. In multivariate analysis, INRGSS stage M (HR = 7.704, 95% CI = 1.031–57.576, P = 0.047), MYCN amplification (HR = 3.011, 95% CI = 1.164–7.786, P = 0.023), and metabolic tumor volume (＞138.788; HR = 3.930, 95% CI = 1.317–11.727, P = 0.014) were independent predictors of OS. The metabolic parameters and textural features in pretreatment 18F-FDG PET/CT of primary lesions are predictive of survival in pediatric patients with neuroblastoma. [ABSTRACT FROM AUTHOR]

Due to severe deterioration and writing style differences, keyword spotting from historical handwritten documents remains challenging. This paper uses query-by-example (QBE) and a segmentation-based technique to investigate keyword spotting in historical documents. To match the image of the query to those in a reference database, features extracted from word images by a set of textural features such as Local Directional Number Pattern (LDNP), Complete Local Binary Patterns (CLBP), and Completed Robust Local Binary Pattern (CRLBP) are employed. The process of classifying data involves minimizing a similarity criterion that is derived from the distance between two feature vectors. High performance is achieved by a series of evaluations utilizing various combinations of distance measurements, and these are compared with the approaches used in the ICFHR 2014 word spotting competition. [ABSTRACT FROM AUTHOR]

To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model's accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R2) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm−2, and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index. [ABSTRACT FROM AUTHOR]

Copyright of Tecnura is the property of Tecnura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Subsidence seriously affects the structural stability and safety of pavements and foundation soils. In heavy-loaded pavements, there is a risk of floor sinking and further construction collapse; hence, there is a need to develop efficient methodologies to detect subsidence earlier. This work proposes the use of ground penetrating radar (GPR) as a solution to non-invasively inspect the subsoil. Furthermore, as the interpretation of the GPR data is arguably subjective and highly dependent on who interprets it, different imaging techniques are herein exploited to improve the interpretability and detection of subsidence and settlement phenomena. The approach was applied to a heavily loaded reinforced concrete pavement servicing a manufacturing facility. Amplitude- and texture-based imaging methods were used to detect subsidence. The interpretation of such imaging was validated with additional geotechnical studies, which show that the proposed methods provide reliable results with good agreement between techniques. [ABSTRACT FROM AUTHOR]

Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models. [ABSTRACT FROM AUTHOR]

Yellow peaches are soft, and they bruise easily; the bruised areas of them are prone to breed bacteria and molds, so the consumption and the safety of related products of yellow peaches are affected by the difference in the storage time of light bruises in them. In order to accurately distinguish of the storage time of light bruises in yellow peaches, the spectra of the sample bruised region were combined with texture features extracted based on gray‐level co‐occurrence matrix (GLCM), and the deep learning algorithm was used for modeling. A total of 80 samples were prepared in the experiment, and the hyperspectral images of them were acquired at four time periods (2, 8, 24, and 48 h), and the reflection spectral data as well as the texture features of the bruised samples were extracted from the hyperspectral images. First, the random forest (RF) and extreme gradient boosting (XGBoost) models were built based on spectral, texture, and spectral features combined with texture features (Feature Fusion 1), respectively, and the best model discrimination was the RF model under Feature Fusion 1, with an overall accuracy of 98.33%. In order to remove the redundant information of spectrum, the UVE and CARS algorithms were used to screen the normalized spectral feature data, and then, the texture features were combined again (Feature Fusion 2), and the RF and XGBoost models were built. The results show that the optimal model for distinguishing the storage time of yellow peaches after bruising is the RF model under Feature Fusion 2 (CARS), with an overall accuracy of 98.33%. In summary, this study shows that spectral features combined with texture features can be used to effectively improve the model's discrimination of storage time after bruising of yellow peaches, and it also provides a certain theoretical basis for hyperspectral imaging technology to discriminate storage time after bruising of fruits. In this paper, the spectral information and texture feature information in hyperspectral images were fused, and random forest and eXtreme Gradient Boosting models were built. The uninformed variable elimination and competitive adaptive reweighted sampling algorithms were used to improve the accuracy of the model to discriminate the storage time of yellow peaches after light bruising. [ABSTRACT FROM AUTHOR]

Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high‐resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low‐resolution multi‐view means of production owned from either the hologram's high‐resolution hologram's elemental images captured via integral imaging. Next, the feature extraction process includes entropy, geometrical features, and textural features. The entropy‐based normalization is used to optimize the features. Finally, it attains the classification process of the proposed ENDNN classifies the breast cancer images into normal or abnormal. The experimental outcomes demonstrate that our presented technique overtakes the traditional techniques. [ABSTRACT FROM AUTHOR]

Identifying the writer of a handwritten document has remained an interesting pattern classification problem for document examiners, forensic experts, and paleographers. While mature identification systems have been developed for handwriting in contemporary documents, the problem remains challenging from the viewpoint of historical manuscripts. Design and development of expert systems that can identify the writer of a questioned manuscript or retrieve samples belonging to a given writer can greatly help the paleographers in their practices. In this context, the current study exploits the textural information in handwriting to characterize writer from historical documents. More specifically, we employ oBIF(oriented Basic Image Features) and hinge features and introduce a novel moment-based matching method to compare the feature vectors extracted from writing samples. Classification is based on minimization of a similarity criterion using the proposed moment distance. A comprehensive series of experiments using the International Conference on Document Analysis and Recognition 2017 historical writer identification dataset reported promising results and validated the ideas put forward in this study.

AbstractThe process of synthesizing multiple images into a seamless panoramic image is referred to as remote sensing image stitching. Existing studies focus less on the influence of topography on the appearance and texture of images and the perturbation of image spectra by topographic changes. This paper presents a remote sensing image stitching method that considers the impact of topography and geomorphology. First, the feature matching was optimized using the Euclidean distance similarity of texture features and the nearest neighbor distance ratio of feature points in remote sensing images as constraints. Then, the Delaunay triangle mesh of feature points in the image overlapping region was constructed, the geometric features of Delaunay triangles were used to optimize the triangle matching and reduce the matching redundancy, and the affine transformation matrix was solved based on the comprehensive consideration of the geometric features of Delaunay triangles and the texture features of the remote sensing images. Finally, the weighted fusion algorithm was applied to stitch and fuse the images. Three image datasets were selected for the experiments, one in which there were large terrain undulations in the imaging regions, one in which the main body of the imaging regions was water, and one in which the overall terrain of the imaging regions had relatively gentle slopes but obscuring features were also present. The results showed that the average correct rates of method feature matching were 89.78%, 94.99%, and 96.17%, which were the best for each algorithm, and the average feature matching times were 4.13, 8.27 and 7.19 s. These times are much lower than those obtained with the APAP and AANAP algorithms and basically the same as those achieved with the SPHP and SURF algorithms. In terms of visual effect, the AG, [Formula: see text] and [Formula: see text] indices of the proposed method were all significantly improved compared to the APAP, SPHP, AANAP, and SURF algorithms, the advantages were most obvious when dealing with datasets with large topographic relief in the imaging regions, with the maximum improvement of the AG, [Formula: see text] and [Formula: see text] indices were 85.61%, 95.68%, and 93.12%, respectively. Therefore, it is possible to conclude that the proposed method is more suitable for remote sensing image stitching and fusion of different topographic and geomorphological conditions.

The conventional audio feature extraction methods employed in the audio analysis are categorized into time-domain and frequency-domain. Recently, a new audio feature extraction approach using time-frequency texture image is developed and utilized for different applications. In this approach, the input audio signal is first converted into a time-frequency image, and then textural features are extracted from the visual representation. The distinctive two-dimensional time-frequency visualization textural descriptors can produce better features for improved audio detection and classification. In this article, a comprehensive review of state-of-the-art techniques used for audio detection and classification is presented. The generalized architecture of time-frequency texture feature extraction approaches in audio classification algorithms is presented first. Based on a review of over 70 papers, the key contributions in the area of time-frequency representations of various researchers are highlighted in addition to the textural features. This survey also compares and analyzes the existing experimental algorithms proposed for various audio classification tasks. Finally, the critical challenges and limitations with different visual representations are highlighted, along with potential future research directions. [ABSTRACT FROM AUTHOR]

Simple Summary: This article aims to investigate the nutritional behavior of dairy cattle, aiming to comprehend their dietary requirements and eating habits. In this regard, an effort has been made to scrutinize dietary patterns by analyzing sound recordings captured from the cows' jaws during the chewing process. The paper outlines the methodology for developing various models to discern nutritional patterns in dairy cattle, employing six well-known classifiers. Understanding nutritional behavior and dietary patterns in dairy cattle is crucial for livestock managers and animal welfare. By comprehending the dietary requirements and eating habits of dairy cattle, managers can ensure that the cows are receiving the appropriate nutrients to maintain their health and productivity. This information can also help managers identify any potential health issues or deficiencies in the cows' diets, allowing for early intervention and prevention of further health problems. Additionally, understanding the nutritional behavior of dairy cattle can lead to more efficient feeding practices, reducing waste and costs associated with overfeeding or underfeeding. Ultimately, establishing an appropriate pattern for evaluating the nutrition of dairy cattle can serve as a valuable guide for livestock managers to ensure the well-being and welfare of the cows while also improving the overall productivity and profitability of the dairy farm. This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices. [ABSTRACT FROM AUTHOR]

Virtual reality haptic rendering has high requirements for image texture feature extraction.However, a single texture extraction algorithm cannot accurately describe the characteristics of image texture due to the complex and irregular texture factors.Therefore, a texture material classification approach based on GLCM (Gray-Level Co-occurrence Matrix) and Tamura fusion features is proposed.Additionally, we optimize the GLCM and propose the T-GLCM operator, thus improve the rotation invariance of GLCM pair and reduce a lot of redundant information.In this approach, the Tamura texture features are used to quantify the image, and the feature regions are quantified and then cascaded into a set of feature vectors.The texture features of T-GLCM are fused, and the texture materials are classified by Support Vector Machine (SVM).The experimental results show that the proposed approach outperforms traditional texture feature extraction algorithms in classification accuracy and robustness. [ABSTRACT FROM AUTHOR]

Selected antifungal lactic acid bacteria (LAB) isolated from mature spontaneous quinoa sourdough was used as potential starter culture to produce loaf wheat bread containing controlled fermented quinoa (CFQ) supplemented with red lentil (RL) flour. Phylogenetic evolutionary tree led to the identification of Enterococcus hirae as the selected LAB isolate. Furthermore, there was no significant difference (P > 0.05) between bread containing CFQ and control in terms of hardness. The highest loaf specific volume and overall acceptability were also observed in control sample and wheat bread containing CFQ + RL, respectively. Meanwhile, the rate of surface fungal growth on wheat bread enriched with CFQ was significantly lower than the other samples. In accordance with a non-linear multivariable model, positive and negative correlations were observed between porosity and specific volume (+ 0.79), and also specific volume and crumb hardness (− 0.70), respectively. Accordingly, CFQ can be used as bio-preservative to produce clean-label supplemented wheat bread. [ABSTRACT FROM AUTHOR]

The susceptibility of grains in sediment to the liquefaction process causes the development of deformation structures. Some sediments undergo liquefaction, others do not. There is a group of sediments especially prone to liquefaction, which was proven during laboratory experiments. However, the field results are often slightly different from those obtained experimentally because of many unpredictable factors influencing the course of the liquefaction process. For this reason, we tested 144 samples of unconsolidated Quaternary‐age sediments, collected from eight study sites in Germany, Lithuania and Latvia, which have been liquefied. We also present some new dating results. These samples were divided into two groups of soft‐sediment deformation structures: concave up (e.g., injection structures) and concave down (e.g., load casts, pseudonodules). The granulometry of all deformation types was statistically evaluated, which allowed identifying textural differences between sediment contained in concave up and concave down structures. We suggest that the mobilization of silt fraction is responsible for the further deforming process. We also confirm that the maximum content of clay in sediment prone to liquefaction cannot exceed 14%, but only with a significant content of coarser fractions (silt and sand). Moreover, we identified two separate zones of the specific grain size in which only concave down structures or only concave up structures develop as an effect of liquefaction, and the third "transitional zone" where all forms occur. The "transitional zone" is separated from the concave up structures and concave down structures zones by two "gap zones" in which no liquefied sediments were observed. Plain Language Summary: Liquefaction is a process of temporary loss of shear strength of water‐saturated sediments, during which the solid‐state sediment behaves like a plastic mass or a viscous solid. It can be triggered in natural conditions by numerous factors, including waving, rapid sedimentation, earthquakes and the fall of meteorites. Sandy silt and silty sand are commonly known as the most liquefaction‐prone sediments, but the specific granulometric features are still insufficiently characterized. We analyzed 144 samples of unconsolidated Quaternary‐age sediments deposited in lacustrine, shallow marine and fluvial environments, which were liquefied. We divided all liquefaction‐induced soft‐sediment deformation structures into active and passive concave up (e.g., injection structures) and active and passive concave down (e.g., load structures). As a result of statistical tests, provided for each group separately, we observed that the silt content is the main factor for sediment deformation processes initiating the further development of all deformation structures. On the basis of grain‐size composition of liquefied sediments, we also identified two separate zones for which only concave down structures or only concave up structures develop as an effect of liquefaction, and the third "transitional zone" where all forms occur. These zones are separated by two "gap zones," where no liquefied sediments were observed. Key Points: Liquefaction‐induced soft‐sediment deformation structures were divided into active and passive concave up structures and active and passive concave down structuresContent of silt is the main factor for deformation processes initiating the further development of all deformation structuresConcave up structures zone, concave down structures zone and transitional zone were distinguished on the basis of grain‐size composition of liquefied sediments [ABSTRACT FROM AUTHOR]

This paper explores the integration of eco-friendly materials in clothing design to maintain high-quality and aesthetically pleasing interior clothing design. Texture, as a fundamental element in images, reveals surface characteristics and aids in visual interpretation. The paper introduces an enhanced DCNN-based algorithm for extracting texture features from clothing materials. The algorithm addresses the limitation of moment invariants by combining them with boundary direction characteristics, enhancing its ability to capture shape and spatial distribution information across the entire image. Experimental results validate the algorithm’s effectiveness in image analysis and the extraction of apparel material texture features. Applying environmental design principles to the apparel design domain, this study offers a novel perspective on sustainable clothing design. Overall, the paper emphasizes the importance of using eco-friendly materials in clothing design for a better future.

Objective: The purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NECT) scanning images. Materials and methods: The computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA). Results: The selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA. Conclusion: Machine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively. [ABSTRACT FROM AUTHOR]

Dozens of textural features have been published, but their realistic validation for efficient recognition applications still needs to be discovered. Textural features are derived using various approaches. We present a benchmark that can be used to evaluate these features and categorize them based on their information efficiency. We propose how the features can be benchmarked and explain different ways of measuring their properties and performance. Most textural feature-extracting algorithms are only based on information extraction from monospectral images (gray-level). Apart from native multispectral algorithms, we generalize some of these originally monospectral features for hyperspectral textures in our illustrating examples. [ABSTRACT FROM AUTHOR]

Crop residue cover is vital for reducing soil erosion and improving soil fertility, which is an important way of conserving tillage to protect the black soil in Northeast China. How much the crop residue covers on cropland is of significance for black soil protection. Landsat-8 and Sentinel-2 images were used to estimate corn residue coverage (CRC) in Northeast China in this study. The estimation model of CRC was established for improving CRC estimation accuracy by the optimal combination of spectral indices and textural features, based on soil texture zoning, using the random forest regression method. Our results revealed that (1) the optimization C5 of spectral indices and textural features improves the CRC estimation accuracy after harvesting and before sowing with determination coefficients (R2) of 0.78 and 0.73, respectively; (2) the random forest improves the CRC estimation accuracy after harvesting and before sowing with R2 of 0.81 and 0.77, respectively; (3) considering the spatial heterogeneity of the soil background and the usage of soil texture zoning models increase the accuracy of CRC estimation after harvesting and before sowing with R2 of 0.84 and 0.81, respectively. In general, the CRC estimation accuracy after harvesting was better than that before sowing. The results revealed that the corn residue coverage in most of the study area was 0.3 to 0.6 and was mainly distributed in the Songnen Plain. By the estimated corn residue coverage results, the implementation of conservation tillage practices is identified, which is vital for protecting the black soil in Northeast China. [ABSTRACT FROM AUTHOR]

Objectives: To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. Materials and Methods: Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan–Meier estimators were performed. Results: The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively. Conclusion: This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients. [ABSTRACT FROM AUTHOR]

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth's surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture. [ABSTRACT FROM AUTHOR]

To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model’s accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R2) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm−2, and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index.

• Reliable cognitive workload estimation with EDA signals is developed. • Novel textural features for EDA based on SPWVD and GLCM are proposed. • The role of cascade forward neural network has been explored for CWE. • This study brings insight from local TF changes into CWE using textural analysis. • It is possible to use only contrast feature of EDA for CWE with high performance. Automatic, cost-effective, and reliable cognitive workload estimation (CWE) is one of the important issues in diagnosis and treatment of neurocognitive diseases, cognitive performance improvement and error preventive strategies. To address this issue, this paper has proposed a novel and robust CWE method by detecting the time–frequency (TF) changes of electrodermal activities (EDA). Firstly, the local and global properties of the time-variant characteristics of EDA have been presented using Smooth Pseudo Wigner-Ville distribution with enhanced TF resolution. Then, the transient changes in TF images of EDA signals have been quantified using a set of textural features based on Gray Level Co-occurrence Matrix descriptor (GLCM). Several static and dynamic classifiers, such as support vector machine, K- k-nearest neighbor, cascade forward neural network, and recurrent neural network have been explored. A real EDA data experiment recorded during arithmetic task with different workload levels have been used to evaluate the performance of the proposed method. The obtained results have confirmed that it can achieve a high estimation performance of 97.71% using contrast feature for discrimination of three workload levels. Further analysis has also suggested that the model is robust to GLCM parameters and classifiers and can provide a better tradeoff between computational complexity and high performance using minimum number of textural features in comparison with previous studies. [ABSTRACT FROM AUTHOR]

Objective: To evaluate whether textural features obtained from F-18 FDG PET/CT offer clinical value that can predict the outcome of patients with locally advanced cervical cancer (LACC) receiving concurrent chemoradiotherapy (CCRT). Methods: We reviewed the records of 68 patients with stage IIB–IVA LACC who underwent PET/CT before CCRT. Conventional metabolic parameters, shape indices, and textural features of the primary tumor were measured on PET/CT. A Cox regression model was used to examine the effects of variables on overall survival (OS) and progression-free survival (PFS). Results: The patients included in this study were classified into two groups based on median value of PET/CT parameters. The high group of GLNU derived from GLRLM is only independent prognostic factor for PFS (HR 7.142; 95% CI 1.656–30.802; p = 0.008) and OS (HR 9,780; 95% CI 1.222–78.286; p = 0.031). In addition, GLNU derived from GLRLM (AUC 0.846, 95% CI 0.738–0.923) was the best predictor for recurrence among clinical prognostic factors and PET/CT parameters. Conclusion: Our results demonstrated that high GLNU from GLRLM on pretreatment F-18 FDG PET/CT images, were significant prognostic factors for recurrence and death in patients with LACC receiving CCRT. [ABSTRACT FROM AUTHOR]

The process of synthesizing multiple images into a seamless panoramic image is referred to as remote sensing image stitching. Existing studies focus less on the influence of topography on the appearance and texture of images and the perturbation of image spectra by topographic changes. This paper presents a remote sensing image stitching method that considers the impact of topography and geomorphology. First, the feature matching was optimized using the Euclidean distance similarity of texture features and the nearest neighbor distance ratio of feature points in remote sensing images as constraints. Then, the Delaunay triangle mesh of feature points in the image overlapping region was constructed, the geometric features of Delaunay triangles were used to optimize the triangle matching and reduce the matching redundancy, and the affine transformation matrix was solved based on the comprehensive consideration of the geometric features of Delaunay triangles and the texture features of the remote sensing images. Finally, the weighted fusion algorithm was applied to stitch and fuse the images. Three image datasets were selected for the experiments, one in which there were large terrain undulations in the imaging regions, one in which the main body of the imaging regions was water, and one in which the overall terrain of the imaging regions had relatively gentle slopes but obscuring features were also present. The results showed that the average correct rates of method feature matching were 89.78%, 94.99%, and 96.17%, which were the best for each algorithm, and the average feature matching times were 4.13, 8.27 and 7.19 s. These times are much lower than those obtained with the APAP and AANAP algorithms and basically the same as those achieved with the SPHP and SURF algorithms. In terms of visual effect, the AG, D Tenengrad , and D Laplacian indices of the proposed method were all significantly improved compared to the APAP, SPHP, AANAP, and SURF algorithms, the advantages were most obvious when dealing with datasets with large topographic relief in the imaging regions, with the maximum improvement of the AG, D Tenengrad , and D Laplacian indices were 85.61%, 95.68%, and 93.12%, respectively. Therefore, it is possible to conclude that the proposed method is more suitable for remote sensing image stitching and fusion of different topographic and geomorphological conditions. [ABSTRACT FROM AUTHOR]

Sein Ta lone mango of different maturity level images has been obtained using reflectance and fluorescence imaging systems. It has been found that fluorescence images show interesting patterns in correlation with the accumulation of bluish fluorescence compounds in the lenticel spots on the mango surface. Color and textural features of both reflectance and fluorescence images have been evaluated to develop a ripeness prediction model. The results show that combining color features of reflectance image and textural features could increase the R2 of the Partial Least Square Regression (PLSR) model up to 0.97 for Brix prediction and 0.99 for pH prediction with Root Mean Square Error (RMSE) of 0.5 for both. These results show the potential of the combined reflectance-fluorescence imaging system for mango ripeness assessment.

Weeds pose a major challenge in achieving high yield production in corn. The use of herbicides although effective can be expensive and their excessive use poses ecological concerns and herbicide resistance. Precise identification of weeds using Machine Learning (ML) models significantly reduces the use of herbicides. In this study, we provide a brief overview of the important ML methods used for identifying weeds in corn i.e., classification and object detection. The various metrics that are used for the evaluation of the performance of ML methods are also discussed. In the end, we identify some important research gaps which warrant future investigation. Most ML methods for the identification of weeds use digital images as input data, however, in some cases, hyperspectral data were used. Most of the current studies employ support vector machines and neural networks for the identification of weeds. Classification accuracy and F1 score are the two most frequently used accuracy metrics to evaluate the performance of ML models used. Future research on the identification of weeds may focus on improving the data volume using data augmentation, transfer learning to benefit from existing models, and interpretability of neural networks to avoid overfitting and make models more transparent.