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Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as a nascent approach. To address this, we design the LG-SWC-R3 model based on an attention mechanism to leverage its powerful learning capabilities. To enhance efficiency, we propose a simple yet effective encoder–decoder architecture (PVP-Transformer-ED) designed on the principle of eliminating redundant spatial information from images. This architecture involves masking a high proportion of soil images and predicting the original image from the unmasked area to aid the PVP-Transformer-ED in understanding the spatial information correlation of the soil image. Subsequently, we fine-tune the SWC recognition model on the pre-trained encoder of the PVP-Transformer-ED. Extensive experimental results demonstrate the excellent performance of our designed model (R2 = 0.950, RMSE = 1.351%, MAPE = 0.081, MAE = 1.369%), surpassing traditional models. Although this method involves processing only a small fraction of original image pixels (approximately 25%), which may impact model performance, it significantly reduces training time while maintaining model error within an acceptable range. Our study provides valuable references and insights for the popularization and application of image-based SWC recognition methods. [ABSTRACT FROM AUTHOR]

The creation of lightscapes in colleges and universities are of great significance in enhancing the perception of the campus environment, improving physical and mental health, and shaping humanistic connotations. This research aims to examine lightscape perceptions and impacts of different campuses. At Chongqing University A and B Campuses, lightscape walking experiments, subjective questionnaires, objective luminance measurements, and HDR picture processing were used to examine lightscape perception and factors. The relevance and differences in the perception of circadian lightscapes in the two campuses were analyzed using SPSS software. The study found: (1) natural lightscapes such as sky light, cloud shadow, and lake water reflection were the most popular during the daytime, while artificial lightscapes, such as decorative lighting of buildings, were positively evaluated at night; (2) the frequency of visits by a crowd directly impacts the ambiance of the environmental area; (3) males showed strong emotional awareness and social interaction skills in daytime, leading to increased social activity and stronger emotional responses, but no differences in nighttime; (4) optimal nighttime luminance enhances the overall perception satisfaction of the illumination; (5) the amount and arrangement of outdoor space, vegetation, minor landscape design, and service facilities all affect the perception of circadian lightscapes. In conclusion, design concepts and proposals of landscapes were suggested to optimize college and university lightscapes. [ABSTRACT FROM AUTHOR]

Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km2 nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming. [ABSTRACT FROM AUTHOR]

Gravity surveys constitute an important method for investigating the Earth's interior based on density contrasts related to Earth material differentials. Because lithology depends on the environment and the period of formation, there are generally clear boundaries between rocks with different lithologies. Inversions with convex functions for approximating the L 0 norm are used to detect boundaries in reconstructed models. Optimizations can easily be found because of the convex transformations; however, the volume of the reconstructed model depends on the weighting parameter and the density constraint rather than the model sparsity. To determine and adapt the modelling size, a novel non-convex framework for gravity inversion is proposed. The proposed optimization aims to directly reduce the L 0 norm of the density matrix. An improved iterative hard thresholding algorithm is developed to linearly reduce the L 0 penalty during the inner iteration. Accordingly, it is possible to determine the modelling scale during the iteration and achieve an expected scale for the reconstructed model. Both simple and complex model experiments demonstrate that the proposed method efficiently reconstructs models. In addition, granites formed during the Yanshanian and Indosinian periods in the Nanling region, China, are reconstructed according to the modelling size evaluated in agreement with the magnetotelluric profile and density statistics of rock samples. The known ores occur at the contact zones between the sedimentary rocks and the reconstructed Yanshanian granites. The ore-forming bodies, periods, and processes are identified, providing guidance for further deep resource exploration in the study area. [ABSTRACT FROM AUTHOR]

Background: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. Methods: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. Results: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. Conclusions: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing. [ABSTRACT FROM AUTHOR]

Heat flow is a geothermal parameter for indicating the heat source distribution and evaluating geothermal reservoirs. Only 1,230 heat flow points are distributed unevenly in China, mainly concentrated in high‐temperature geothermal and southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making evaluating the geothermal genetic mechanism difficult. Sparse data interpolation using deep learning methods is highly accurate and widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flows and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance‐based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m2, the average is 67.1 mW/m2, and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low‐velocity sedimentary layer. Some high‐temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional‐scale heat flow, providing reliable heat source information for evaluating geothermal resources. Plain Language Summary: Geothermal has received extensive global attention as a new renewable energy resource. Geothermal heat flow (GHF) is crucial among various indicators for assessing potential geothermal fields, with over 20,000 measurement points worldwide. However, due to the expensive cost of drilling measurements, the spatial distribution of heat flow points could be more balanced, posing challenges to geothermal exploration and investigation. Traditional heat flow prediction methods rely on interpolation based on sparse measurement points to estimate the regional heat flow distribution. However, spatial variations in the measurement points often affect the prediction results, leading to lower accuracy. Therefore, this study proposes a deep learning approach to establish the complex relationship between heat flow and geological/geophysical features, aiming to estimate the heat flow distribution in the target area. The proposed method has been successfully applied to heat flow prediction in the Songliao Basin, accompanied by an uncertainty analysis technique that provides confidence intervals for the prediction results. These findings offer direct evidence for the geothermal formation mechanisms and thermal evolution laws in the Songliao Basin. Key Points: We propose a deep neural network (DNN) method for predicting terrestrial heat flowCorrelation and Distance‐based Generalized Sensitivity Analysis (DGSA) methods are used to evaluate the uncertainty of DNN predicted heat flow resultsThe prediction of terrestrial heat flow in the Songliao Basin provides direct evidence for deep heat source channels and geothermal genesis mechanisms [ABSTRACT FROM AUTHOR]

Coastline changes in estuarine areas can result from a combination of natural processes such as erosion, sedimentation, and sea-level rise, as well as human activities, including urbanization and infrastructure development. These changes have the potential to affect the local environment, including submarine groundwater discharge, wetlands, and navigation routes. The Yalu River Estuary (YRE), situated on the border between China and North Korea, has been experiencing significant changes in its coastline over recent years. This study aims to investigate the coastline dynamics in the YRE from 2000 to 2020. The study employs Landsat 5/7/8 satellite data and proposes a modified Normalized Difference Water Index (NDWI) to accurately delineate the coast boundary, particularly in areas with extensive tidal flats like the YRE. The research findings indicate that from 2010 to 2020, significant changes occurred in the YRE shoreline, with erosion being the dominant trend. Human activities and alternations in hydrological conditions are important factors affecting the YRE coastline changes, contributing to the formation of distinctive spatiotemporal patterns. An extreme flooding event in the year 2010 also altered the inner estuarine coastline in the YRE, indicating the impact of strong natural drivers. Findings from this study provide a comprehensive understanding of the evolving coastal environment, considering natural and anthropogenic drivers, and highlight the importance of continuous monitoring in a region of ecological and geopolitical significance. [ABSTRACT FROM AUTHOR]

With the deepening internationalization of China's economy and society, English has gradually become a common language for Chinese people to connect with the world, and plays an increasingly important role in daily life. At the same time, Chinese people's mastery of English is gradually improving. Grasping the foundation of this period can effectively improve students' English level, and it will play a very important role in further learning English or job hunting in the future. Therefore, how to improve the interest of college school students in English learning and improve their ability and level of English learning is particularly important. Virtual community refers to a group of people who communicate and discuss through the Internet and are willing to share some knowledge and information in the exchange, and gradually develop into a social network aggregation. This paper designs an English learning platform for college school students based on virtual community and image processing technology. The platform is developed with SSM framework. After the development, the platform is tested for college school students in several schools in Xi'an. The experimental results show that the system can effectively stimulate students' interest in learning English, and then improve students' vocabulary and academic performance, which provides an effective reference for the study of students' learning methods. [ABSTRACT FROM AUTHOR]

Accurate near-surface velocity models are necessary for land seismic imaging. First-arrival traveltime tomography (FTT) routinely used for estimating near-surface velocity models may fail in geological complex areas. Supervised deep learning (SDL) is capable of building accurate velocity models, based on tens of thousands of velocity model-shot gathers training pairs. It takes lots of time and memory space, which may be unaffordable for practical applications. We propose integrating the FTT and SDL to build near-surface velocity models. During the neural network training, the FTT-inverted models rather than the original seismic data are used as the network inputs and corresponding true models are the outputs. The FTT-inverted and true models are the same physical quantities and with the same dimensions. Their relationship is less non-linear than that between shot gathers and true models. Thus, the neural network of the proposed method can be trained well using only a small number of training samples, dramatically reducing the time and memory costs. Numerical tests demonstrate the feasibility and effectiveness of the proposed method. We applied the proposed method to a land data set obtained in mountainous areas in the west of China and obtained satisfactory near-surface velocity models and stacking images. [ABSTRACT FROM AUTHOR]

Using deep learning in remote sensing interpretation could reduce a lot of human and material costs. Semantic segmentation is the main method for this task. It can automatically outline the objects and it has recently achieved great success in remote sensing images. However, in the appliance of remote sensing interpretation, the accuracy of contour largely determines the evaluation of remote sensing interpretation. Though the current loss functions reflect the segmentation performance, they could not guide the model to optimize itself toward a more precise contour. This paper proposed an exactly defined contour loss (CL) for remote sensing interpretation with Residual Attention U-Net (RA U-Net) as the main framework. The RA U-Net uses the residual attention module as the skip connection layer. It enhances the judgment of U-Net. In CL, image processing methods are used to extract the contours of the foreground. And elements-sum and elements-subtract operations are used to transfer the contour information to a matrix of the same size as label images. Then, these matrices would be the weights for CE. By assigning different weights for different elements in different regions, this function will guide the model to reach a balance between accurate segmentation results and precise contours. The experiment on open datasets shows a good performance. The proposed model was also trained on the Construction Disturbance Dataset collected from Jiang Xi Province, China. The dataset was labeled manually. The evaluation enhanced a lot on the Construction Disturbance Dataset and the IoU on two datasets increased 1 % to 2 % when using CL as the loss function. This paper also compared the proposed method with other state-of-the-art methods and the results showed extensive effectiveness. [ABSTRACT FROM AUTHOR]

To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- and distance-related attenuation and scattering when light propagates in water, we propose a method based on an attention mechanism and adversarial autoencoder for enhancing underwater images. Firstly, the pixel and channel attention mechanisms are utilized to extract rich discriminative image information from multiple color spaces. Secondly, the above image information and the original image reverse medium transmittance map are feature-fused by a feature fusion module to enhance the network response to the image quality degradation region. Finally, the encoder learning is guided by the adversarial mechanism of the adversarial autoencoder, and the hidden space of the autoencoder is continuously approached to the hidden space of the pre-trained model. The results of the experimental images acquired from the Beihai Bay area of China on the HYSY-163 platform show that the average value of the Natural Image Quality Evaluator is reduced by 27.8%, the average value of the Underwater Color Image Quality Evaluation is improved by 28.8%, and the average values of the Structural Similarity and Peak Signal-to-Noise Ratio are improved by 35.7% and 42.8%, respectively, compared with the unprocessed real underwater images, and the enhanced underwater images have improved clarity and more realistic colors. In summary, our network can effectively improve the visibility of underwater target objects, especially the quality of images of submarine pipelines and marine organisms, and is expected to be applied in the future with underwater robots for pile legs of offshore wellhead platforms and large ship bottom sea life cleaning. [ABSTRACT FROM AUTHOR]

Efficient detection of pepper clusters can be crucial to realize the automated harvesting in the development of pepper industry. However, the existing detection of pepper cluster cannot fully meet the large-scale in recent years, due to the low efficiency, generalization, and real-time algorithm. In this study, an improved YOLOv5s model was proposed to accurately detect the pepper clusters in real time. 400 images were collected from the Xuande Pepper Industrial Park, Qinglong County, Guizhou Province, Qianxinan Buyi and Miao Autonomous Prefecture, Guizhou Province, China. 148 images were also downloaded from the Internet, and then screened to enhance data diversity. The collected 548 images were then labeled using the LabelImg tool. In addition, the data enhancement was performed on each image and labeled bounding box. Two new images were randomly generated using random rotation and brightness adjustment. As such, a total of 1644 images were obtained as the dataset. Firstly, the EfficientViT network was optimized using the lightweight convolutional neural network (CNN) MobileNetV3 Block Convolution (MBConv) module and the ReLU with lightweight self-attention mechanism. CSPDarkNet53 network was used to replace the backbone of YOLOv5s. The global receptive field of model was enhanced to extract the important features. The better performance was suitable for the targets with the different scales, shapes, and background information. Secondly, Optimal Transport Assignment (OTA) was selected as the label assignment strategy. The optimal label assignment plan was obtained from the global perspective to achieve the effective label assignment. The robustness and accuracy of the model were improved for the complex scenes and target distribution. The WIoU loss function was also used as the bounding box localization of the detector, instead of the CIoU one. At the same time, the high-quality anchor frames were achieved to generate the benefit gradients for the better overall performance of the detector. A common experiment of target detection was performed on a homemade dataset, in order to verify the effectiveness of the improved model. The experimental results show that the improved model was achieved in a mean accuracy (mAP) of 97.3%, a parameter count of 5.9 M, and a detection speed of 131.6 frames per second (FPS). Specifically, the mAP and detection speed of the improved YOLOv5s model increased by 1.9 percentage points, and 14.5%, respectively, whereas, the number of parameters decreased by 15.7%, compared with the original. In addition, the mAP of the improved YOLOv5s model was 8, 16.9, 8.6, and 1.5 percentage points higher than that of EffcientDet-D1, SSD512, RetineNet-R50, and YOLOXs, respectively. The better performance of the improved YOLOv5s was achieved in terms of detection accuracy, detection speed, and number of model parameters. Therefore, the improved YOLOv5s model can be expected to accurately and rapidly detect the pepper clusters. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the capabilities of the inexpensive theodolite created by the authors, compared with modern commercial instruments, equipped with a Leica TPS1203 robotic total station with built-in GoPro Hero6 camera image sensors. This theodolite was tested in the laboratory where simulated dynamic displacements were determined. The results of the experimental tests showed that the displacement errors and differences between the simulated displacements of the test machine and those detected by the theodolite were in the range of Δ = − 0. 1 5 mm to +0.13 mm depending on the simulated amplitude. The theodolite was further used in the field for static and dynamic tests of the Wanzhou Railway Bridge, China. Determination of the dynamic motions of the bridge and the results of calculating the natural frequencies from the measurement data are presented. During loading tests of the bridge, the frequencies were also determined by accelerometers and these data were used as a reference to assess the accuracy of the theodolite and its suitability for dynamic tests. An original algorithm was developed in the MATLAB software environment to process the recorded videos, i.e. image processing to determine displacements and natural frequencies. It is demonstrated that the position of the theodolite at a distance of 28.5 m from the bridge provides the correspondence of one pixel of 0.333 mm, which provides a high level of accuracy when determining the dynamic vertical displacements of a moving target. It is established that the proposed theodolite will cost less than 5 000 euros. The results obtained are relevant for use in civil engineering for monitoring the amplitude vibrations of structures. [ABSTRACT FROM AUTHOR]

Overlapping occlusion has seriously limited the yield estimation of bagged grapes in recent years, due to the ever-increasing grape volume after bagging and the large surface area of grape leaves. The unstable speed of manual video shooting can also lead to the loss of bagging grape targets. In this study, a yield estimation was proposed for the bagged grape using self-correcting Non-Maximum Suppression (NMS)-ByteTrack. Firstly, the bagged grapes were detected in the video using object detection (YOLOv5s). The NMS operation was also post-positioned to the tracking stage to retain the fruit detection boxes that filtered under the occlusion. Specifically, the detection boxes of bagged grapes were detected by object detection (YOLOv5s), whereas, the prediction boxes of bagged grapes were predicted to calculate the intersection over union using the Kalman filter. If the intersection over the union was less than the given threshold, the detection box was filtered to reserve the detection boxes closer to the predicted position. Then, the final detection box was obtained using the NMS operation. Secondly, the camera motion compensation and improved Kalman filter algorithm were added to automatically correct the position of the fruit prediction boxes and track them using ByteTrack. Specifically, the camera motion compensation was used to first extract the background key points in the bagged grape pictures of the previous frame and the current frame except for the tracking target. The sparse optical flow was used to match the extracted background in the key points of the bagged grapes. Then the affine transformation matrix of background motion was calculated by the RANSAC algorithm, and the Kalman filter was utilized to predict the bagged grape prediction boxes of the current frame. Finally, the affine transformation matrix was obtained to convert the prediction boxes in the coordinate system from the previous to the current frame, in order to realize the position information correction for the prediction boxes of the current frame. In addition, the improved Kalman filter algorithm can be used to change the aspect ratio in the state vector, and then to express the position of the bagged grape tracking boxes in the Kalman filter directly as the width, in order to more accurately estimate the position of the bagged grape tracking boxes. As such, a line-counting strategy was proposed to automatically count the bagged grapes. The strategy was also to set the counting line in the middle of the video, and also retain the idle frames without the bagged grapes in the first few seconds of the video. Furthermore, the bagged grapes with the same ID were only counted once to avoid repeated collision line counting. The successful collision between the center point of the tracking box of bagged grapes and the counting line was achieved in the automatic counting of bagged grapes. The dataset was collected from the Paidengte Agricultural Science and Technology Demonstration Park, Bishan District, Chongqing of China. The mobile phone cameras of Redmi K40 and OPPO Reno6pro+ were selected to capture the pictures of the same bagged grape at 8:00, 12:00, and 18:00 from different angles, such as frontal, sideways, and overhead shots. The total shooting time was about 6 h, the shooting height was about 1.5 m from the ground, and the shooting route was line by line. A total of 500 images and six videos were obtained for the bagged grapes. Among them, 500 images were expanded to 2000 images after saturation, brightness enhancement, brightness reduction, and mirror operations. Then, 2000 images were randomly divided into the training and validation sets in the ratio of 8:2, where six valid videos were used as the test set. Relevant experiments were conducted using this dataset. The experimental results showed that the yield estimation achieved a significant improvement in the tracking performance of bagged grapes using self-correcting NMS-ByteTrack. The multi-object tracking accuracy and precision, as well as the identification F1-score, were 64.6%, 82.4% and 80.8%, respectively, which increased by 1.7, 1.0 and 4.1 percentage points, respectively, compared with the ByteTrack. The number of ID switch was reduced by 3 times. Then, the average counting accuracy reached 82.8% in terms of counting performance, compared with manual counting. In addition, a comparison was also made with the five tracking methods. A better tracking performance was achieved, compared with the rest. The applicability of this estimation was also verified in tracking and counting bagged grapes. Therefore, the yield estimation can be expected to effectively promote the tracking and counting of bagged grapes in real scenarios using self-correcting NMS-ByteTrack. More accurate yield estimation of bagged grapes was also achieved in this field. [ABSTRACT FROM AUTHOR]

Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. [ABSTRACT FROM AUTHOR]

Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement. [ABSTRACT FROM AUTHOR]

As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat's yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m² (SNpM²) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM² and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a costeffective approach. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security 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.)

The ecological environment in the karst region of southwest China is fragile, and vegetation degradation is considered the main cause of this phenomenon. Vegetation growth is affected by preferential flow due to a large number of fissures and macropores in the soil. Therefore, the preferential flow and preferential paths in local typical vegetation need to be studied. The distribution and developmental characteristics of preferential flow and preferential paths in three typical local stands were obtained by conducting field staining tracer experiments and using image processing techniques. The results showed that there was preferential flow movement in the soil moisture of the local understorey and that there was a high degree of preferential flow development and high variability. The degree of preferential flow in several typical forests can be expressed, from highest to lowest, in the following order: secondary forests, Cupressus duclouxiana Hickel (Hickel., 1814) forest, and Pinus massoniana (Lamb., 1803) forest. The preferential paths in the soil were mainly distributed above 15 cm, and the number of preferential paths with a small influence radius was much greater than that with a large influence radius. The preferential paths with an influence radius of 2.5~5 mm accounted for the largest proportion. The total number of preferential paths in the P. massoniana forest was small. The preferential paths in the secondary and C. duclouxiana Hickel forests were similar in number, but the secondary forests had more preferential paths with a large influence radius. This study investigated the characteristics of preferential flow and preferential paths in the typical forests of the karst region in southwest China, and it can provide data and research directions for the study of hydrological processes in local forest vegetation. [ABSTRACT FROM AUTHOR]

In 1982, a water-soluble aminoplastic resin embedding method for electron microscopy (EM) was developed to study lipids. It involves a silylation technique for processing cured resins by converting them from hydrophilic to hydrophobic status, facilitating ultra-thin sectioning. The technique offers better preservation of lipovitellin material in frog (Rana amurensis) yolk platelets, and it shows membrane ultrastructure better than traditional epoxy embedding methods. An early (1982) pseudo-colored TEM image processing technique applied to the micrographs obtained by EM of silylated aminoplastic thin sections revealed the lipovitellin molecules, in relation to the biosynthesis of new membranes in the first cleavage of the egg, with remarkable clarity and explains the lipovitellin molecules in relation to the biosynthesis of new membranes. Because the original article was written in Chinese and published in China before its open policy, many scientists may not be aware of the publication. Thus, in this short review, the essential components of the procedure are presented in English to help guide investigators who might be interested in the use of water-soluble embedding methods for lipid research. [ABSTRACT FROM AUTHOR]

In the operation of railway vehicles, the quality of bogies directly affects the operation quality and driving safety. Wheel set is one of the most important components in bogie, so the maintenance of wheel set is very important. For a long time, the detection of train wheel sets in China is still in the stage of manual measurement with backward technology and low efficiency. A new automatic detection method of wheel flange tread based on fuzzy neural network image processing technology is proposed in this paper. This method can accurately detect the defects of wheel flange tread. It collects the original image of the tested wheel set through the digital camera, inputs it into the computer, through certain calculation and processing, and compares it with the model established based on fuzzy neural network, so as to detect the defects of wheel flange and tread. First, the research status of wheel tread defect detection is summarized. Second, the basic principles of digital image technology are studied, the image processing models are confirmed, and the image processing method based on fuzzy neural network is established. Finally, eight wheel set treads are selected to carry out defect detection, and the analysis results show that the proposed method can obtain the better inspection precision. [ABSTRACT FROM AUTHOR]

Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing. [ABSTRACT FROM AUTHOR]

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Corn is one of the main food crops in China, and its area ranks in the top three in the world. However, the corn leaf disease has seriously affected the yield and quality of corn. To quickly and accurately identify corn leaf diseases, taking timely and effective treatment to reduce the loss of corn yield. We proposed identifying corn leaf diseases using the Mobilenetv3 (CD-Mobilenetv3) model. Based on the Mobilenetv3 model, we replaced the model's cross-entropy loss function with a bias loss function to improve accuracy. Replaced the model's squeeze and excitation (SE) module with the efficient channel attention (ECA) module to reduce parameters. Introduced the cross-layer connections between Mobile modules to utilize features synthetically. Then we Introduced the dilated convolutions in the model to increase the receptive field. We integrated a hybrid open-source corn leaf disease dataset (CLDD). The test results on CLDD showed the accuracy reached 98.23%, the precision reached 98.26%, the recall reached 98.26%, and the F1 score reached 98.26%. The test results are improved compared to the classic deep learning (DL) models ResNet50, ResNet101, ShuffleNet_x2, VGG16, SqueezeNet, InceptionNetv3, etc. The loss value was 0.0285, and the parameters were lower than most contrasting models. The experimental results verified the validity of the CD-Mobilenetv3 model in the identification of corn leaf diseases. It provides adequate technical support for the timely control of corn leaf diseases. [ABSTRACT FROM AUTHOR]

Straw mulching has been a common measure for black land conservation in the northeastern plains of China. It is a great significance of an effective solution to reduce soil erosion for environmental protection and sustainable development in modern agriculture. However, it is difficult to calculate the coverage rate at one time, due to the wide area of crop cultivation. Therefore, it is a high demand for the rapid acquisition of image information on a large area of farmland, in order to improve the efficiency and accuracy of straw mulching. Fortunately, aerial drone images and videos can be widely used in various fields, such as agricultural monitoring, weather forecasting, and geographic mapping. Among them, the application of remote sensing technology focuses mainly on the crops and soil in agriculture. The spectral patterns of the ground can be utilized to monitor the crops and soil in the field. Specifically, the spectral characteristics of the ground are used to monitor the growth of crops, crop quality, crop pests, and diseases. The high-resolution images can be collected by unmanned aerial vehicle (UAV) aerial photography. The panoramic stitching can be selected for rapid and accurate access to the large-area farmland image information. Therefore, it is of great practical significance to agricultural intelligence, particularly for the high efficiency and accuracy of straw cover monitoring of conservation tillage. In this study, a fast-stitching method was proposed for the aerial panoramic images of the fields using the optimized scale-invariant feature transform (SIFT) algorithm. Firstly, the high-resolution image was down-sampled to effectively detect the overlapping regions of the image. The gradient normalization-based feature descriptors were then used to match the feature points. The false matches were removed by the progressive sample consistency algorithm, in order to accurately calculate the stitching conversion model. Finally, the multi-resolution fusion algorithm was used for the high-quality stitched image using the best stitching line. The experimental results show that the improved algorithm reduced the number of feature points by 97% and 90%, the running time by 94% and 69%, and the average matching efficiency by 65.17% in the image alignment stage, respectively, compared with the traditional SIFT and Speeded Up Robust Feature (SURF) algorithm, which was about 4 times that of SIFT algorithm, and 9 times that of SURF algorithm, respectively. Compared with the as-projective-as-possible (APAP), the shape-preserving half-projective (SPHP), and the adaptive as-natural-as-possible (AANAP) algorithms, the new algorithm significantly improved the information entropy, the average gradient, and image contrast of the stitched image. More importantly, the stitched image was of higher quality with more than a 90% reduction in the running time, particularly with better overall performance. The systematic evaluation demonstrated that the layered stitching method significantly improved the clarity and fusion effect of the panoramic stitched images, compared with the traditional. Moreover, there was no misalignment and ghosting that appeared in the traditional. Specifically, the information entropy and average gradient of the panoramic images were slightly improved to increase the contrast by about 20%, indicating a higher stitching efficiency than before. The overall stitching time was also shortened by about 90% or more. The research structure can also provide a scientific reference to monitor the conservation of tillage with straw returning. [ABSTRACT FROM AUTHOR]

Unconventional shale reservoirs and typical fine-grained rocks exhibit complicated, oriented features at various scales. Due to the complex geometry, combination and arrangement of grains, as well as the substantial heterogeneity of shale, it is challenging to analyze the oriented structures of shale accurately. In this study, we propose a model that combines both multifractal and structural entropy theory to determine the oriented structures of shale. First, we perform FE–SEM experiments to specify the microstructural characteristics of shale. Next, the shape, size and orientation parameters of the grains and pores are identified via image processing. Then fractal dimensions of grain flatness, grain alignment and pore orientation are calculated and substituted into the structural entropy equation to obtain the structure-oriented entropy model. Lastly, the proposed model is applied to study the orientation characteristic of the Yan-Chang #7 Shale Formation in Ordos Basin, China. A total of 1470 SEM images of 20 shale samples is analyzed to calculate the structure-oriented entropy (SOE) of Yan-Chang #7 Shale, whose values range from 0.78 to 0.96. The grains exhibit directional arrangement (SOE ≥ 0.85) but are randomly distributed (SOE < 0.85). Calculations of samples with different compositions show that clay and organic matters are two major governing factors for the directivity of shale. The grain alignment pattern diagram analyses reveal three types of orientation structures: fusiform, spider-like and eggette-like. The proposed model can quantitatively evaluate the oriented structure of shale, which helps better understand the intrinsic characteristics of shale and thereby assists the successful exploitation of shale resources. [ABSTRACT FROM AUTHOR]

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world's major scientific and technological powers to develop. Although China's satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world's advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoImageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN's recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments. [ABSTRACT FROM AUTHOR]

This study investigated the visual harmony of an urban stream considering changes to the ratio of water to greenery on the riverbed. The Baxi stream, a third-order stream in Yongan City, Fujian Province, China was selected as the study site. The stream reach is disturbed by several hydraulic structures, such as restricted water flow by a vertical revetment and water level regulation by submerged dams. Images of the river were captured, and image processing was performed to change the proportion of water and greenery, and the proportions of various landscape elements in the image were calculated. Based on the statistical analysis of survey results, cognitive indicators (vividness and naturalness) associated with harmony and preference, and the relationship between harmony or preference and landscape elements, were established. Landscape elements included ratios of visible water (WR), visible greenery (GR), visible buildings, and visible infrastructure. The results demonstrated that visual preference, P, is positively correlated with harmony, H, vividness, V, and naturalness, N. In particular, H is almost consistent to P. The proportion of visible water and greenery had a significant impact on the H and P of the stream landscape. When the ratio of WR to GR was approximately 0.8, H was optimal, and the public's P was high. These results can be used to improve and enhance the visual landscape quality of this stream reach. The methodology proposed in this study could provide other study areas with a reference for how to obtain the best visual harmony or achieve public acceptance by changing the amount of visible water and/or greenery. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which directly reflect the stress of the lining, which is very important for the study of tunnel diseases. In view of the current detection status and detection requirements, this article has carried out research work on a vehicle-mounted tunnel lining crack detection system based on image processing. Due to the grayscale difference between the cracks on the lining surface and the lining background, these differences lead to significant crack edge features and relatively stable detection. Therefore, this article designs an intelligent edge algorithm system for cracks on the lining surface to detect the edges of the image, extract the edges of cracks, and remove useless interference information in the lining background. The experiment proves that the paired sample t-test can find that after the experiment is over, the P value of different edge detection operators for global threshold segmentation is less than 0.05, which has a significant difference. The Canny, Deriche, and Lanser filters are relatively strong, and the extracted crack edge noise is relatively small. Finally, the parameter values of the crack image are calculated, and the calculated values of the crack parameters provide a scientific and reliable basis for tunnel safety evaluation. [ABSTRACT FROM AUTHOR]

Apples are produced in the large quantities each year, particularly for as the largest economic fruit in China. It is highly required for the rapid picking within the harvesting period. Therefore, the automatic apple picking is essential to the apple harvesting in intensive farming. An accurate and rapid identification of fruit can be fundamental for the automatic picking. However, some environmental factors surrounding the fruit can pose a great interference in the fruit identification under the natural, complex, and variable backgrounds, such as the light intensity, occlusion, and overlap of the fruit. In this study, an apple recognition was proposed using two-level segmentation and region-marked Hough transform. Experimental results show that the robust and practical performance was achieved for the apple recognition under different illumination, branch and leaf occlusion, as well as the fruit overlap. Specific steps were as follows. Firstly, the front camera (NikonD90) of the information acquisition robot was used to capture from 600-800 mm away from the fruit tree under the conditions of nature natural light and backlight, respectively. The brightness adaptive correction algorithm was then used to correct the brightness of apple images with the uneven distribution of surface brightness, in order to enhance the image details. The Cr component images of YCbCr color space were extracted as the preprocessing samples to combine with the feature of the color of the apple. Secondly, the improved Otsu algorithm was utilized to obtain the binary image of the apple target for the initial segmentation, in order to accurately extract the contour of the target fruit under different growth states (mainly including single and double fruits with the overlap and occlusion). A morphological open-close reconstruction filter was also introduced to the Otsu algorithm to remove the background noise. The traversal range of the gray level was reduced to shorten the complexity and running time of the algorithm for the high segmentation rate. Thirdly, the watershed algorithm was combined to perform the secondary segmentation of the segmented fruit region using distance transformation. The conglutinated and overlapping apples were separated to effectively extract the apple target contour. Finally, the gradient Hough circle transformation was selected to identify the number of apples. But the algorithm traveled through the whole image for the computational complexity, time time-consuming, and easy to produce the false identification. Therefore, the minimum circumscribed rectangle outside the contour was set as the effective area for the gradient Hough circle transform in the effective area, particularly for the recognition speed and accuracy. The experimental results show that: 1) The improved Otsu algorithm was achieved in the higher segmentation accuracy of fruit targets, especially with the less segmentation time. The improved algorithm was also filled the tiny holes in the apple to suppress the noise in the background branches and leaves, further to more clearly segment the target region more than before. The average segmentation time was 1.458 s, which was 0.643 and 1.060 s shorter than the Otsu and K-means algorithm before the improvement. In the adhesion of partial apple regions in the binary images, a watershed segmentation with the distance transformation was used for the quadratic segmentation to effectively separate the sticky apples for the full apple target boundary. 2) The gradient Hough circle transform was used to recognize the 200 apple images under different lighting conditions, where the recognition accuracy was 90.75% in the nature natural light and 89.79% in the backlight, which were improved by 15.03 and 16.41 percentage points, respectively, compared with the traditional. The average recognition time was 0.665 and 0.693 s, which was 0.664 and 0.643 s shorter than before. Therefore, the proposed algorithm can meet the requirements of apple-picking robots, in terms of recognition speed and accuracy. The findings can provide a strong reference for the fast recognition of spherical fruits, such as apples. [ABSTRACT FROM AUTHOR]

Dance sport is a competition of athletes for qualities such as speed, strength, endurance, flexibility, and coordination, requiring a high level of overall physical fitness. As China's sports fever continues to heat up, especially among students, the active nature of young people can lead to overenthusiasm for sports, dance, and other sports, which can lead to various physical discomforts during sports. Especially in the current sports habits of students, there is a great hidden danger of sports injuries. It is mainly due to excessive exercise, irregular exercise methods, and incomplete sports protective equipment. On this basis, this study proposes a model to further assess the health status of individuals and groups from the rate of energy consumption during exercise by building a real-time motion detection system. The development of sports health assessment systems, including the application of imaging techniques, is also analyzed. The experimental results in this article show that when the error threshold is set to 10%, the accuracy of the model is 85%, and the obtained results are ideal. [ABSTRACT FROM AUTHOR]

GF-3 is the first C-band multipolarization synthetic aperture radar (SAR) satellite in China. The SAR system is equipped with an active phased array antenna to electronically generate various antenna beams. The accuracy of SAR antenna patterns is of main importance for precise SAR image processing. The traditional method for antenna pattern measurement is the near-field method but it is unable to satisfy the demand for fast measuring in-orbit. In order to measure the SAR antenna pattern quickly with high accuracy, we propose the internal calibration method. The internal calibration uses calibration signals which are routed along the nominal signal path in SAR systems, thus monitoring the gain and phase variations for each T/R channel is possible. This paper will focus on this method, including its principle, operation steps, and results, compared to the near-field method. The internal calibration method provides a valuable solution for SAR antenna pattern measurement. [ABSTRACT FROM AUTHOR]

In the cold season, wine aids in maintaining body temperature and is advised for military officers. This paper proposes a study on the multimotor drive control method of the upper-retort-robot based on machine vision for wine brewing automation to meet the demand of military areas located in cold regions, as wine is recommended to keep soldiers' body temperatures normal in China's extremely cold regions. Based on machine vision, the target is converted into an image signal by an image pickup device and is sent to the image processing system. Pixel distribution, brightness, color, and other data are transformed to digital signals, and target attributes are retrieved to control the field equipment's operations. The Monte-Carlo approach is used to generate joint variables at random within each joint's fluctuation range. The positive aspects of kinematics model are utilized and the working space of the upper-retort-robot is calculated using multimotor drive control method. The multimotor drive compensates the harmonic ripple torque and establishes the fault-tolerant automatic control of the system to maintain quality of the liquor. The results of the experiments reveal that the robot arm can reach any place within the barrel's set range. To control the quality of the liquor, the robot will function in an automatic manner. The robot's transmission performance is capable of meeting the requirements for automated liquor quality control during the production of wine from grapes. The results show that the suggested multimotor drive control (MMDCM) approach is robust and viable in terms of robot transmission performance and dexterity. [ABSTRACT FROM AUTHOR]

Digitization has been one of the significant directions in the rural land governance reform in the future. However, it is still lacking in the logical concepts and framework for the digital governance of rural land. In this study, a three-dimensional system framework of “demand-baseline-function” was established using the perspective of the transaction cost. An emphasis was also put on the construction of the digital system for land acquisition and relocation. Firstly, the basic, core and conflict needs of the participants were clarified ranging from the government, land users, and the original land stakeholder. Secondly, the bottom line was defined as the legal, spatial, and regulatory governance. Thirdly, the digital system was classified to design various digital realities. The future functional modules were selected to entirely reduce the transaction costs, thereby integrating the multiple sharing systems for the overall operational efficiency. In addition, Zhejiang Province of China was selected as the experimental area for the system design. The reason was that Zhejiang Province has been the representative region in the digital reform of land governance in recent years. Nevertheless, there was a high transaction cost in the process of land acquisition and relocation. It is also a high demand to effectively build a digital system during this time. As such, the actual system was designed to combine with the improved the digital framework. The result showed that the “demand-baseline-function” framework effectively guided the development and design of the “intelligent land acquisition and demolition” digital system in the actual application situation. The transaction costs were also reduced to fill the logical blank for the previous system of land digital governance. The demand analysis demonstrated that the improved system provided the port to fully meet the basic and core needs of the government, land users, collectives, and farmers. The information asymmetry, discourse game, and conflict transmission effectively reduced the transaction cost of the game between the subjects. The bottom line was clarified for the implementation boundary of the law and space of the land requisition and relocation process. The whole supervision system of land requisition and relocation was constructed to stabilize the land requisition and relocation environment. The functional analysis revealed that the improved system coordinated the actual and upgrade function of the “intelligent land acquisition and demolition” digital system. The logical framework effectively reduced the negotiation and decision cost, while the implementation and supervision cost, as well as the management and sunk costs in the land acquisition and demolition. Therefore, the “demand baseline function” framework can provide a strong reference for the transaction cost in the digital reform of land governance. Anyway, the underlying construction logic of the framework can also be applicable to the other areas without the digital construction guidance, providing for the most general transaction cost in the rural land governance. In addition, the logical framework of the system can also provide a strong reference for the digital transformation of rural land governance in the future, in order to improve the digital level of land space and the modernization of governance capacity. [ABSTRACT FROM AUTHOR]

A shale lithofacies scheme is commonly used to characterize source rock reservoirs of the Lower Cambrian Niutitang Formation. However, this classification ignores that individual components such as quartz may have different origins, potentially affecting reservoir quality. The main objective of this article is, therefore, to present a refined scheme for lithofacies and an image processing workflow for the detection of quartz types in the Niutitang Formation shales from the Jiumen outcrop in the Guizhou Province (Upper Yangtze Basin, SW China). In order to do so, a combination of bulk density, optical and scanning electron microscopy and image analysis was used. The shale lithology was macroscopically classified into seven major categories and nineteen subcategories. Subsequently, the shales were investigated at the microscopic level, mainly focusing on quartz types and microstructural variations. Afterwards, the workflow to calculate the weight per unit volume (1 cm3) of the quartz types was presented, i.e., firstly, by calculating the weight of mineral matter by subtraction of the measured weight of organic matter from the bulk shale; secondly, by calculating the weight of total quartz in bulk shale from the weight of mineral matter and its proportion calculated from X-ray diffraction data; thirdly, by calculating the weight of detrital quartz and non-detrital quartz with energy dispersive X-ray mapping, image processing and quartz density; finally, by calculating the weight of clay-sized quartz by subtracting of the weight of detrital and non-detrital quartz from the weight of the total quartz. The bulk quartz content was found to be dominated by clay-sized quartz, which may mainly control the mesopore volume available for gas storage and, hence, the shale gas reservoir development. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, an increasing number of Chinese researchers have joined the ranks of the world’s top scientists. Some international organizations have observed this phenomenon and ranked the world’s top Chinese researchers. However, investigation of highly cited interdisciplinary research (IDR) scholars is insufficient, although IDR tends to have a greater social impact. Looking at the top 2% of the world’s Chinese scholars, this study analyzes the structural attributes of IDR by those top scholars in detail using network analysis, cluster analysis, block modeling, and quadratic assignment procedure analysis. The results show that the proportion of highly cited scholars in technical categories is higher than in social categories. The fields of artificial intelligence and image processing, oncology and carcinogenesis, plus neurology and neurosurgery serve as bridges across disciplines, with materials, energy, and artificial intelligence and image processing having higher eigenvector centrality. The field of social sciences has the widest range of IDR activities, but cooperation within this field is low. Forty-two of the world’s first-class universities are in China, and of the world’s top 2% scholars who come from China, 46.3% work for these institutions. The research themes of highly cited academics from World First-Class universities in China are most similar to the themes of scholars from universities in China with first-class academic disciplines. There are differences between non-university and university scholars in terms of research topics. It is suggested that the government can promote a triple-helix effect (public institute, industrial enterprise, and research school) so that organizations of different natures can produce synergistic effects. [ABSTRACT FROM AUTHOR]

t: Stalk set-row planting has been one of the conservation tillage systems to minimize the frequency or intensity of tillage operations in northeast China. The agricultural management approach can integrate the tillage and planting operations to cover at least 30% of the soil surface with crop residues. A narrow row zone can be sown to clear out from the stalk-covered surface in the wide leisure zone. However, the pesticide liquid cannot reach the stalk strip surface, particularly when weeding the crop during the seedling stage under stalk mulching in the traditional full-coverage spraying mode. In this study, a precision spraying device was designed to promote the utilization rate of pesticide liquid using image recognition. Firstly, a digital camera was mounted in front of the plant protection machine to real-time capture the stalk set-row planting images in fields. A Digital Signal Processor (DSP) was used as the host computer to pre process the images. Then, an optimized multi-threshold segmentation was performed on the images of stalk and seedling strip parts. A series of morphological operations were utilized to correct the dividing edges, where an external moment was selected to frame and separate the seedling strip areas of the field. As such, the position of the seedling strip center was evaluated to mark out the line of the seedling strip. Secondly, an STM32 microcontroller was used as the lower computer. Specifically, the controller drove the stepper motor slide to move the nozzle on the top of the seedling strip for spraying, according to the line coordinates of seedling strip positioning. At the same time, a multi-subdivision drive was used to improve the operation precision of the stepping motor. Nevertheless, some unfavorable conditions occurred in the horizontal and vertical kinds of airflow during spraying, whether the droplets drifted into the stalk rows, or the uneven spraying was caused by the air. Therefore, the drift rate of the droplet was calculated under different velocities of airflow using Fluent fluid simulation software. After that, the fuzzy controller was set up, with the horizontal and vertical airflow speed as the input, and the spray volume as the output. The spray volume was changed to adjust the opening degree of the electric valve for a better uniform settlement of the chemical liquid. Finally, the precision spraying device on a platform was tested to verify the simulation in the field. The test results showed that the accuracy of the self-developed device was about 88.3% for the positioning line of the seedling strip. A WFS-II integrated test bench was used to evaluate the spray performance. The spraying of a single nozzle was measured on the rows to save the liquid by about 64.8% more than before. The qualified rate of spraying on the rows was about 85.8% than before. The variable spraying test was conducted to maintain the liquid coverage at 28.5%-33.8% in the variable spraying mode. The field trials demonstrated that the precision spraying saved about the pesticide liquid of 52.84% in the stalk set-row planting mode, compared with the traditional full-coverage one. Furthermore, the uniformity level of liquid deposition was also improved significantly. Consequently, this device can be expected for the plant protection machinery in the precision application of seedling strips during the stalk set-row planting. The finding can provide a strong reference for the precision spraying to improve the liquid utilization rate and environment, particularly for the technical support to promote the conservation tillage. [ABSTRACT FROM AUTHOR]

The product design major was formally established in 2011 and has been an important part of Chinese art education since then. At the same time, it has laid a solid foundation for the development of China's higher education. Therefore, the product design major occupies an important position in China's higher education and plays an irreplaceable role in cultivating industrial design talents for the country. With the continuous development and progress of national education, the teaching of product design is also constantly innovating, in order to better improve the teaching effect of product design and cultivate more product design professionals for the country. The purpose of this paper is to study the teaching of product design major based on intelligent network teaching, establish an intelligent network teaching system, and carry out intelligent network teaching experiment of product design major based on this system. The experiment concluded that the overall satisfaction of the students majoring in product design on the intelligent network teaching reached 82%, and the intelligent network teaching mode also increased the outstanding performance rate of the students majoring in product design by 30%. [ABSTRACT FROM AUTHOR]

One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (Bemisia tabaci) and thrips (Frankliniella occidentalis) are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one. To alleviate this situation, this study proposed an automated detection approach to meet the need for continuous monitoring of pests in greenhouse conditions. Candidate targets were firstly located using a spectral residual model and then different color features were extracted. Ultimately, Whitefly and thrips were identified using a support vector machine classifier with an accuracy of 93.9 and 89.9%, a true positive rate of 93.1 and 80.1%, and a false positive rate of 9.9 and 12.3%, respectively. Identification performance was further tested via comparison between manual and automatic counting with a coefficient of determination, R 2, of 0.9785 and 0.9582. The results show that the proposed method can provide a comparable performance with previous handcrafted feature-based methods, furthermore, it does not require the support of high-performance hardware compare with deep learning-based method. This study demonstrates the potential of developing a vision-based identification system to facilitate rapid gathering of information pertaining to numbers of small-sized pests in greenhouse agriculture and make a reliable estimation of overall population density. [ABSTRACT FROM AUTHOR]

Different satellite images have different positioning accuracy. For example, stereo satellite images have higher positioning accuracy than resource survey satellite images. In addition, for a large number of non stereo satellite images, due to the inability to build a strong triangulation model, it is impossible to carry out block adjustment alone to improve the image positioning accuracy. High precision and high resolution Orthophoto Images are the basis of resource investigation and monitoring and basic geographic information updating. For example, China's third national land survey, national geographic situation monitoring and other national projects require that the survey base map must reach the accuracy of 1:10000 scale, that is, the mean square plane error of points is less than 5m. For most satellite images, a certain number of ground control points need to be deployed to achieve this accuracy. Due to the difficulty of obtaining high-precision ground control points and DEM data in difficult areas, high-precision mapping has always been an unsolved problem, such as Western China. In addition, due to the limited coverage of a single satellite image, to realize the complete coverage of an image in a large area requires the joint application of multiple satellite images. In this paper, the high-precision collaborative geometric processing model and technical method of high-resolution multi-source remote sensing satellite images are proposed. The high-precision collaborative geometric processing of more than ten kinds of high-resolution domestic satellite images is completed by integrating multi-source observation data. An automatic construction method of large-scale block adjustment model of remote sensing images from domestic satellites based on multivariate generalized control network is proposed, including key technologies such as automatic optimization of optimal tie points under different modes, automatic matching of multi-node parallelization tie points, multi-level gross error elimination and so on, which realizes the automatic and stable construction of aerial triangulation model. The test shows that the positioning accuracy of satellite images is better than the accuracy requirements of 1:10000 scale without ground control points, which solves the problem of geometric positioning of 1:10000 scale accuracy in areas where it is difficult to obtain ground control points in the field of Western China. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Litchi has been one of the characteristic fruits in Guangdong Province of South China. Traditionally, the litchi can be inspected manually in the orchard, due to its susceptibility to the weather, diseases, and insect pests. The number of fruits in the litchi tree can be counted to determine the subsequent agricultural operations, such as pouring nutrient solution, or the removal of insect pests. Nowadays, an unmanned aerial vehicle (UAV) low-altitude remote sensing has been a promising way to observe the litchi, particularly for safety, efficiency, cost-saving, and easy operation. But the litchi images taken by the UAV are characterized by the smaller target size and fewer features. In this study, an improved Single Shot MultiBox Detector (SSD) model was proposed to detect the small litchi fruits using multiple feature enhancement and feature fusion (MFEFF). Firstly, 160 litchi images with a resolution of 4000×3000 pixels were collected in the orchard using the UAV. A sliding window of 512 pixels was also applied to the original litchi images, according to the target pixels between 20×20 and 30×30 pixels. As such, 3 590 images of 1 024×1 024 pixels were captured to boost the feature information of litchi, such as the size. Secondly, the data enhancement was implemented to improve the robustness of the model using some operations, including the image flip, color space transformation. Thirdly, the last two convolution layers of Vgg16 were deleted to reduce the unnecessary computation. The Receptive Field Block (RFB) was used on the Conv8 and Conv9 layers, and the feature map of Conv3_3 layer was added for the feature extraction, where much more features of the litchi were expanded the receptive field for the detailed information. The Efficient Spatial Pyramid (ESP) Block was also applied on the enhancement of the shallow features in the maps. Finally, the improved Path Aggregation Network (IPANet) was used for the multi-scale features fusion at the Conv3_3, Conv4_3, and fc7 layers. A Squeeze and Extension (SE) module was also introduced in the first two feature layers, further to improve the detection accuracy. The channel attention network was also used the global information of litchi images to selectively enhance the weight of the litchi channel, but to efficiently suppress the useless feature information, such as green leaves against the background. At the same time, the size and quantity of anchors were adjusted to match the size of the small target litchi. A training detection was carried out to verify the model for the total of 3 590 labeled images, where 3 to 114 litchi targets were set in each image. Among them, 2 907 images were distributed as the training set, 324 as the validation set, and 359 as the testing set. The precision indexes were the mean Average Precision (mAP), recall, precision, and F1-Score (F1). The results showed that the RFB module was significantly improved the detection, compared with the original, where the mAP, Recall (R), Precision (P), and F1 increased by 2.61, 0.48, 1.49, and 1 percentage points, respectively. The IPANet detection was better than that of the feature pyramid network (FPN), where the mAP value increased by 0.44 percentage points. The SE module was better than the Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA), indicating the best score of the three modules. The ESP detection was superior to the atrous spatial pyramid pooling (ASPP), in which the mAP, R, and P increased by 2.51, 0.38, and 1.13 percentage points, respectively. Consequently, the MFEFF-SSD had improved the mean average precision by 30.62, 14.58, 44.46, and 15.93 percentage points, respectively, compared with the SSD, Yolov4-tiny, Faster-RCNN, and CenterNet models. Anyway, the MFEFF-SSD model can be widely expected to more accurately and effectively detect the litchi images taken by UAV. This finding can also provide a strong reference for the detection of small target fruits. [ABSTRACT FROM AUTHOR]

The maximum reflectance of vitrinite or graphite microcrystals in Yongan Coalfield, Fujian Province, China, ranges from 5.33% to 10.21%, comprising meta-anthracite, semi-graphite, and graphite. The morphological characteristics of aromatic fringes during different evolution stages were quantitatively analyzed using high-resolution transmission electron microscopy and image processing techniques. The results show that a nonlinear evolution of aromatic fringe morphology in the process of coal graphitization is manifested in four aspects, involving the rapid connection and growth, enhancement of ordering alignment, increase of stacking and development of straightening. In meta-anthracite samples, 3 × 3 aromatic rings representing short aromatic fringes are dominated, a majority of aromatic fringes were curved, disjoint or separate, and show a turbostratic layers, with poorly aligned. The spacing between adjacent aromatic fringes exceeded the range of graphitized carbon, and structural defects, such as nanopores were generally visible. In semi-graphite samples, in addition to 3 × 3 aromatic rings, the proportion of >8 × 8 aromatic rings represented longer aromatic fringes increasing significantly, along with ordering alignment and straightening. Stacking with two more layers increased and the spacing between adjacent aromatic fringes decreased to the range of graphitized carbon, and the structural defects decreased considerably. In graphite sample, the proportion of >8 × 8 aromatic rings increase significantly, the length, stacking, and ordering alignment of aromatic fringes increased abruptly, the spacing between adjacent aromatic fringes remained within the range of natural graphite, and a rapid graphite-like structure with flat aromatic fringes formed. A stage evolution of aromatic fringe morphology, whereas remaining almost constant of mainly organic elements, which imply the evolution of aromatic fringes in meta-anthracite originated from ring condensation dominated by late coalification, graphitization starts at semi-graphite stage and enhanced during graphite stage. The making up, originating from linkup and pileup, considered as a special mechanism for the graphitization of aromatic fringes. [Display omitted] • A nonlinear evolution of length, direction, stacking and curvature of aromatic fringes. • Coal graphitization starts in semi-graphite and show an abruptly change. • Making up of BSUs is the main mechanism of coal graphitization. [ABSTRACT FROM AUTHOR]

The current era is an information age, and society is turning to the information age. The image processing technology is also widely used in various fields, and the technology of sports action recognition based on image processing technology can also be said to be appropriate. This article uses a spatial visual feature analysis algorithm to implement it. To implement this algorithm, a series of work such as image collection, feature extraction, and action recognition must be completed first and then implemented through texture functions and other related functions. This algorithm can be used to complete the image-based sports action recognition technology at the minimum time cost. This algorithm can help sportsmen better complete training and standardize movements to a certain extent. As for the development of China's current sports industry structure, it is also steadily improving. The people's love for sports is getting stronger and stronger, which also makes the development of China's sports industry still benefit a lot. [ABSTRACT FROM AUTHOR]

Lameness is an abnormal gait or stance of an animal, indicating a more severe disorder of the locomotion system. The disturbance in the gait and body has posed a great threat to the welfare, health, and production of dairy cows in the herds. An early observation is typically performed on the sudden change in gait during day-to-day dairy practices. However, the current detection of lameness was widely used in the 2D cow video, particularly susceptible to light and environmental inferences. In this study, a novel approach was proposed to extract the movement features of the lameness cows using thermal infrared video under deep learning and image processing, in order to improve the detection accuracy and robustness against the complex illumination and environmental disturbances. Taking the multiparous lactating Holstein cows as the study objects, the datasets were collected at the Dingyuan farm in Hebei Province of China in September 2020. The gait features of lameness were related to the head, hoof, back, shoulder, and hip of cows. The performances of different target detection were then evaluated on the dataset of locomotion features from the thermal infrared and 2D images of cows walking. The results showed that the average accuracies of deep learning for the detection of cows locomotion features were 90.84%, 86.68%, and 79.68%, respectively, in the thermal infrared, daylight, and daylight images, whereas, those in the traditional image processing were 74.26%, 54.31%, and 48.95%, respectively. The test results demonstrated that the target detection of deep learning and image processing performed better on the thermal infrared images, compared with the 2D images. The thermal infrared images had effectively reduced the influences of external factors, such as the light and background. In addition, a lameness detection test was conducted on the walking cows using the thermal infrared video and 2D video, according to the arched curvature. It was found that the detection accuracies of cow lameness in the thermal infrared and 2D video were 90.0% and 83.3%, respectively, where the values of Macro-F1 were 0.90 and 0.83, respectively. Consequently, a better performance was achieved for the application of thermal infrared cameras in cow lameness detection using computer vision. This finding can provide a new potential strategy for the timely diagnosis and prevention of abnormal gait in cows in the dairy farming industry. [ABSTRACT FROM AUTHOR]

The slique is one of the most important organs to store the energy matter in an oilrape plant, a typical oil crop in China. It is necessary to measure the seed number per silique of oilrape for the breeding traits, even for the estimation of rape yields. But the current manual counting cannot fully meet the requirement of modern breeding, due to the time-consuming, labor-intensive, and less accuracy for the seed number per silique of oilrape, particularly by manually breaking the pod of rape. In this study, Image recognition of the silique length was proposed to count the seed number per silique of oilrape. Three varieties of oilrape were also used as the experiment materials. The siliques were first selected from the upper, middle and lower parts of rape plants. After that, a digital calliper was utilized to measure the length of silique pod without considering the fruit stalk and beak part. A correlation model was then established for the length of silique and seed number per silique. The images of siliques were also captured by a scanner in the tiled state. The image preprocessing included the gray scale, binarization, hole filling, and the removal of small areas. Meanwhile, the fruit stalk and fruit beak of silique were removed from the images of siliques using the opening operation of mathematical morphology. After that, the images were segmented to the sub-images of siliques with the single connected domain. Each sub-image of siliques was then thinned to a line with one pixel. The endpoint and intersection point of siliques images were finally obtained using hit-or-miss transformation. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) was also used to remove the redundant intersection points. The endpoints of silique images were classified and paired to recognize the single silique. As such, the number of image pixels was obtained for the length of a single silique. The actual length of silique was calculated by a relative formula between the single pixel and actual length. Correspondingly, the seed number per silique was finally calculated from the images using the correlation model between the actual length of silique and seed number per silique. The determined coefficient for the correlation model between the length of silique and seed number per silique in the three varieties were 0.891, 0.881, 0.887, respectively, indicating a better performance of the estimation on the seed number per silique using the length of the silique. Furthermore, the average recognition accuracy was 97.25% for the length of silique, and the average prediction accuracy of seed number per silique was 83.87% using image processing. Consequently, image processing can be widely expected to realize the automatic recognition and counting of the seed number per silique for higher efficiency of rape production in modern agriculture. [ABSTRACT FROM AUTHOR]

• CNNs and SVMs outperform in detecting and measuring road surface damages (RSD). • Smartphones, cameras, and accelerometers are frequent in RSD detection. • Server receiving images and backed by ML tools are potential in detecting RSD. • Sensor-based studies are less effective in detecting road surface cracks. • Engaging road users' community is an emerging technique to grab images for RSD detection. This review critically analyzes available tools and techniques for road damage assessment and monitoring and identifies further research areas. After the review, several are found for image and sensor data collection, such as smartphones, cameras, accelerometers, and Unnamed Aerial Vehicles (UAVs). Of these, smartphones are the most frequently used among the data processing tools, including Machine Learning (ML) algorithms, Convolution Neural Networks (CNNs), and Support Vector Machines (SVMs): they outperform in detecting and measuring various forms of road surface damage, including potholes, bumps, ruts, and patches. It is also found that the study of crack identification is comparatively lacking. Moreover, some studies attempt to capitalize on crowdsourcing or road users in data collection connected to a server while driving over road networks. However, physical observation and smartphone-based image posting to the server backed by appropriate ML tools for automatic and dynamic road damage detection are yet to be demonstrated or developed and are, therefore, of further research potential. Finally, from a national perspective, India and China are ahead of other countries. Thus, future research can focus on image processing-based automatic road performance monitoring in any country with a transportation system that is highly dependent on paved roads. [ABSTRACT FROM AUTHOR]

With the gradual improvement of the quality of life, taste, and ecological and environmental awareness of urban residents in China, the environmental landscape of residential areas has gradually become a hot spot. At present, the level of the residential environmental landscape has become a necessary means for real estate developers to publicize products and improve economic benefits. Although many residential areas have invested a high cost in constructing environmental landscapes, there are always some deficiencies and defects in the design and implementation of environmental landscapes in residential areas due to various reasons. Therefore, to ameliorate the low efficiency and high cost of manual processing of landscape images, a Fully Convolutional Network (FCN) model based on the traditional Convolutional Neural Network (CNN) is designed for semantic segmentation of landscape images to deal with the excessive amount of landscape elements in landscape image processing. The deconvolution method is utilized to realize pixel-level semantic segmentation. Besides, the image preprocessing method enhances the data to prevent overfitting from commonly occurring in FCN. Moreover, the model two-stage training method ameliorates long training time and complex convergence in deep learning. Finally, three upsampling network structures, i.e., FCN-32s, FCN-16s, and FCN-8s, are selected for a comparative experiment to determine the most suitable network. The experimental results demonstrate that the FCN-8s upsampling network structure is the most prominent; it attains a pixel accuracy of more than 90%, an average accuracy of 88%, and an average Image Understanding of 75%. The three values are the highest among the three upsampling structures, indicating that the FCN-8s can realize accurate landscape image processing. Besides, the recognition accuracy of FCN for landscape elements reaches 90%, 25% higher than that of CNN. This method is effective and accurate in classifying landscape elements, improves the classification accuracy intelligently, and significantly reduces the cost of landscape element classification, which is feasible. [ABSTRACT FROM AUTHOR]

Since the founding of the People's Republic of China, the advantages of logistics are neglected, and the scale operation and the welfare in the industry are difficult to achieve due to the influence of the economic system and social environment. Therefore, a new intelligent logistics distribution management system based on machine vision and visual sensor image processing technology is constructed to respond to the shortcomings of the traditional system, including slow efficiency, huge cost, complex data, and low degree of informatization. Through the analysis and research on the visual sensor image processing technology and the order processing, receipt management, distribution management, scheduling management, and return management that affect logistics distribution, a simulation experiment is used to verify that the visual sensor image processing technology is rigorous, intelligent, and efficient and has high precision. The intelligent logistics distribution management system can effectively solve the problems existing in the traditional logistics distribution management. The experimental results show that the visual sensor image processing technology can collect and analyze the target image and effectively track and monitor it in the logistics distribution process. The average distribution precision of the intelligent logistics distribution management system reaches more than 99.5%, which is greatly improved compared with 90% of the traditional logistics distribution. And it can greatly improve the distribution efficiency, which increases by about 26.5%. The study realizes the information management of the logistics system and automatically completes all the work according to the designed program, so that the real-time dynamic distribution can be transmitted to the urban logistics distribution at any time. [ABSTRACT FROM AUTHOR]

BACKGROUND: Currently, forensic anthropology, ergonomics and footprint studies have all found that there is a significant correlation between foot information (such as foot length and foot width) and body height and mass. However, there is no enough quantitative research on barefoot morphological characteristics contact area (BMCCA) as well as human body height and mass. OBJECTIVE: To analyze the correlation between BMCCA and body mass and height, and to innovate the theoretical systems of forensic anthropology and ergonomics, thereby providing reference for individual recognition by barefoot footprint. METHODS: Samples were collected using traditional plane footprint ink printing method, and digital software MATLAB was then used for image processing. A total of 100 healthy young men with a height distribution between 165-190 cm were randomly selected as subjects. Body height and mass were recorded. BMCCA was obtained under normal walking for statistical analysis. The implementation of the study complied with the Declaration of Helsinki and the relevant ethical requirements of Criminal Investigation Police University of China. RESULTS AND CONCLUSION: For healthy young men with normal arch of the foot (excluding interrupted arch of the foot and expanded arch of the foot), there is a significant positive correlation between BMCCA and height and mass (P < 0.01). The relationship between BMCCA and human body height can be applied for height analysis in forensic anthropology, ergonomics, and footprint. Compared with body mass, body height has a more significant impact on BMCCA and is more directly related. [ABSTRACT FROM AUTHOR]