Although deep learning-based fruit detection techniques are becoming popular, they require a large number of labeled datasets to support model training. Moreover, the manual labeling process is time-consuming and labor-intensive. We previously implemented a generative adversarial network-based method to reduce labeling costs. However, it does not consider fitness among more species. Methods of selecting the most suitable source domain dataset based on the fruit datasets of the target domain remain to be investigated. Moreover, current automatic labeling technology still requires manual labeling of the source domain dataset and cannot completely eliminate manual processes. Therefore, an improved EasyDAM_V3 model was proposed in this study as an automatic labeling method for additional classes of fruit. This study proposes both an optimal source domain establishment method based on a multidimensional spatial feature model to select the most suitable source domain, and a high-volume dataset construction method based on transparent background fruit image translation by constructing a knowledge graph of orchard scene hierarchy component synthesis rules. The EasyDAM_V3 model can automatically obtain fruit label information from the dataset, thereby eliminating manual labeling. To test the proposed method, pear was used as the selected optimal source domain, followed by orange, apple, and tomato as the target domain datasets. The results showed that the average precision of annotation reached 90.94%, 89.78%, and 90.84% for the target datasets, respectively. The EasyDAM_V3 model can obtain the optimal source domain in automatic labeling tasks, thus eliminating the manual labeling process and reducing associated costs and labor. As information and communication technology becomes more prominent, traditional agriculture has gradually incorporated artificial intelligence technology, thereby extending the development direction of intelligent information construction in the fruit industry. Of note, the construction of smart orchards based on deep learning has received considerable attention. High-performance fruit detection technology can be effectively combined with agricultural machinery by extracting various features from images and structured data collected in orchards. This process is appropriate for many smart orchard tasks, such as fruit localization, fruit sorting, fruit yield prediction, and automatic fruit picking –4. ThereFurthermore, because of the currently poor generalization performance of deep learning models, independently producing new fruit datasets and training new detection models for different scenes, environments, shooting methods, and fruit species are time-consuming and labor-intensive. Meanwhile, in actual orchard fruit image collections, fruit trees present dense growth and heavily occluded fruits and branches and most fruits represent small-scale targets. These features increase the difficulty of labeling fruit datasets, thereby increasing the associated time and cost. Therefore, an automatic labeling method that has higher generalizability, stronger domain adaptation, and the ability to satisfy different categories of fruit datasets is urgently required.1.The problem of adaptability when more fruit datasets are included within a domain: The process of selecting the source domain datasets for the EasyDAM_V1 and EasyDAM_V2 methods was subjective, and the factors driving the selection of the training set were not considered. This resulted in fluctuating performance when using fruit translation models in different types of agricultural orchards. This unstable performance posed considerable challenges in terms of model construction and applying the method on the ground.2.The problem of labeling cost of the source domain: Previous EasyDAM methods still required the selection of labeled source domain fruit data or manual labeling of the source domain fruit dataset. These methods relied on at least 1 high-quality dataset of actual orchard scenes in the source domain. Although the method could achieve automatic labeling of fruits in the target domain, it still required considerable human effort to collect and produce the source domain dataset. Thus, manual processes could not be completely eliminated, and the goal of zero-cost automated label generation was not achieved.In our previous work, we proposed the EasyDAM series, which generates simulated fruit images in the target domain via a generative adversarial network (GAN). Then, the simulated image is input into the fruit detection network as a training set for learning the fruit features of the target domain to obtain the label information. EasyDAM_V1  achievedTherefore, we performed research related to image translation models and methods to reduce the cost of labeling. Our goal is to develop an objective and quantifiable method to select the optimal source domain fruit datasets that simultaneously correspond to different target domain fruits and design a dataset production method that can automatically obtain labeled data from the training set. Thus, we hope to achieve a zero-cost method for the automatic labeling of fruit datasets.To address the first problem listed above, this study investigates a method for selecting source and target domain datasets for current image conversion models, among which high-performance image translation models use GAN techniques . Most ofTo address the second problem listed above, this study explored how to reduce the manual annotation cost via deep learning. Researchers have used small-sample training sets to train deep learning models to reduce annotation costs, and this method has been widely used in various fields. Liu et al.  proposed1.In response to the suitability problem, more fruit datasets are addressed in current research on fruit image translation tasks. In this study, an extensive analysis of different species of fruit was performed. The most suitable source domain fruit dataset was selected for the target domain using a multidimensional spatial feature model. One type of source domain fruit dataset corresponded to multiple types of target domain fruit datasets, which ensured that the fruit image translation model corresponded to different target domain fruits with higher fidelity of translation generalization.2.To address the problem of manual labeling, this study constructed a knowledge graph of the synthesis rules of orchard scene hierarchy components. A dataset with label information was generated based on transparent background fruit image translation. In addition, the domain difference between them and the target domain scene was further reduced. The fruit detection algorithm based on an anchor-free detector was used for pseudo-label self-learning so that it could simultaneously address fruit targets of different scales and shapes. Thus, the final label generation accuracy was further improved.The training set production method based on synthetic dataset construction has great potential in other fields. If applied in a complex orchard scenario, this method would be able to completely eliminate the problem of labeling costs for source domain datasets based on the automatic labeling method and improve the model generalizability and label generation quality. Therefore, this paper focuses on how to avoid manual annotation while creating synthetic datasets that have high realism and strong similarity to the target domain images, which would allow deep learning models to extract more effective target features. The EasyDAM_V3 is proposed in this study, which involves automatic generation of label information of the target domain dataset with high accuracy and without any manual annotation. The features of EasyDAM_V3 are as follows.The overall proposed scheme for EasyDAM_V3 is illustrated in Fig. To ensure that the image generation model could simultaneously address multiple target domain fruit images using a single source domain fruit image, this study quantified the phenotypic characteristics of different classes of fruits. Thus, the most suitable fruit species for the source domain was selected and was called the optimal source domain fruit. The color, texture, and shape characteristics of the different categories of fruits differed, making it difficult to accurately describe these characteristics with the naked eye. To objectively describe the phenotypic characteristics of fruits, we collected images of 14 fruit datasets belonging 11 species that are widely produced, have large cultivation areas, and present certain research value. Please refer to the contents of Table As noted above, the previous EasyDAM methods required source domain dataset collection or labeling and could not achieve the automatic labeling of all fruit labels. To effectively reduce the difficulty of image acquisition while also automatically obtaining label information to reduce labor costs, this paper proposed a method to construct a large-batch dataset based on transparent background fruit image translation (see Results of EasyDAM_V3 verification). The fruit synthesis dataset was constructed by establishing a knowledge graph system with the rules for synthesizing the hierarchical components background, leaves, and fruits, thereby simulating a real target domain scene. This dataset was then input to the fruit detection model as a training set to extract the foreground and background features of the fruits in the target domain. This method could automatically obtain various types of label information  of synthetic fruit targets, thereby replacing the manual labeling step and achieving automatic labeling at zero labeling cost.The synthetic dataset of fruits with labels in the target domain was input to the anchor-free detector-based fruit detection model for initial feature extraction and training of fruit targets. The fruit detection model showed good detection performance when dealing with fruit targets of different sizes in different scenarios. The pseudo-label self-learning method  with an Two fruit datasets were used in this study: a transparent background fruit dataset and an actual orchard dataset in the target domain. The transparent background fruit dataset was used to describe the fruit phenotypic characteristics in the optimal source domain selection module and generate foreground fruit images in the simulated target domain in the image generation module. The target domain actual orchard dataset was used to provide background images for the target domain synthetic dataset construction module and evaluate the quality of label generation.To more realistically and accurately describe the phenotypic characteristics of different fruit datasets and reduce the operational difficulty of synthesizing images, this study used only the foreground fruit images as the dataset, which contains 14 common fruit datasets available on the market. The resolution was 256 × 256 to allow for feature analysis and deep learning model training. The images in the dataset were obtained from the public dataset fruit360 , web cra1.Citrus dataset: This dataset followed the images from the source domain fruit dataset part of a previous EasyDAM study . The cit2.Apple dataset: This dataset was obtained from the MineApple dataset , which c3.Tomato dataset: This dataset was derived mainly from the dataset published by Mu et al. . The datIn this paper, fruit images in actual orchard scenes were also collected and labeled as the target domain test data. This dataset mainly contains 3 types of fruit, citrus, tomato, and apple, as shown in Fig. The optimal source domain selection module for the target domain was used to describe and analyze the phenotypic characteristics of different classes of fruits. The flow of this module is shown in Fig. Quantitative descriptions of the phenotypic features of crops provide important indicator information for evaluating the crops’ characteristics. This method can help researchers in fields outside of agriculture, such as in animal husbandry—by improving the breeding efficiency and increasing yield—and in computer vision—by improving the training and feature extraction by deep learning networks. Among these indicators, the color, shape, and texture features of crops are important indicators of their appearance, which can quantify the appearance of crops in an objective, differentiated, accurate, and direct manner ,37. In tIn this study, we characterized 14 fruits in 3 dimensions, including shape, color, and texture. To ensure the universality of the method, we chose methods that were rotation invariant, translation invariant, scale scaling invariant, and consistent in describing the subdimensions. Subsequently, we formed a high-latitude feature distance matrix by calculating the feature differences between the 2 fruits, and the relative distances were used to represent fruit features.N boundary points on the boundary of the fruiting target with the starting point . The boundary could be represented as a sequence of coordinates s(k) following the inverse direction.Shape characteristics: In this paper, Fourier descriptors were used to construct the boundary profile curve of the fruit target into a 1-dimensional sequence. The Fourier coefficients were obtained by performing a discrete Fourier transform on the sequence. The coordinates of the boundary points were used to describe the shape of the boundary against the sequence coefficients. We supposed that there were s(k) was expressed as follows:To change the 2-dimensional (2D) coordinates into a 1-dimensional sequence, we introduced a complex representation method. We considered the target boundary as a complex function starting from a certain point and rotating the perimeter of 1 week counterclockwise along the boundary. The sequence of coordinates s(k) sequence is denoted by the 1-dimensional discrete Fourier transform coefficients a (u):The complex a(u) denotes the Fourier descriptor, which was used to represent the results of the frequency analysis of the boundary contour of the fruit target. Subsequently, we normalized the Fourier descriptors so that they had translation, rotation, and scale invariance and were thus unaffected by the position of the contour in the image, the angle, or the scaling of the contour. These descriptors are widely used in real-world scenarios. The normalized Fourier descriptor d(u) can be expressed as follows:where The shape difference between different fruit individuals was then calculated based on the Pearson correlation coefficient:i and j represent 2 different fruit targets. Because the energy of the shape is mostly concentrated in the low-frequency region, the high-frequency component of the Fourier transform is generally small and easily disturbed by high-frequency noise. In general, only the low-frequency component of the normalized descriptor is used to describe the shape of the fruit target (u = 16 based on experience). At this point, the Fourier descriptor has the best discrimination between different fruits, that is, the largest difference in the shape of the object.where 1.iN, where i is the number of intervals. In this study, every 20 points were divided into 1 interval according to the range of L, a, and b values.The number of pixels falling into each interval is calculated as 2.The percentage of the number of pixels in each interval is calculated as follows:Color characteristics: To extract quantitative color distribution information, this study used a method to convert the global color information of fruits from the RGB color space to the CIELAB color space, enabling the computer to recognize and process colors in a continuous color space. Then, the pixel means and variances of the L, a, and b values of each fruit were calculated and normalized. Histograms were drawn to represent the distribution of color patterns for all fruits. The color descriptors were calculated as follows:allN denotes the total number of pixel points in the foreground area of the fruit.where iMean.3.The product of the percentage of pixels in each interval and its mean value is calculated as follows:The mean value of each interval pixel point’s L, a, and b values is calculated as LAB(x) denotes the pixel mean and variance of the L, a, and b values.4.cD between the different fruit targets using the L2 distance are calculated as follows:The differences in color characteristics where iL, jL, ia, ja, ib, jand b, denote L, a, and b values of 2 different kinds of fruits i and j respectively.where N local texture blocks in each foreground fruit image as the input, as shown in Fig. Texture characteristics: Texture features do not depend on color or brightness but reflect important information about the organization of surface structures and their variation of the values of the surrounding pixels. Owing to the characteristics of foreground fruit images, including uneven illumination, severe edge contrast, and texture heterogeneity, there is a large error in directly characterizing the foreground global image. Therefore, this study used random sampling to randomly select 1.Because different distance descriptions were used to calculate the shape, color, and texture features in this study, the distance quantization numerical scales of the different features were not uniform. Therefore, it was necessary to normalize the scale of the 3 feature descriptors. It is convenient to construct the feature space afterwards. The scale normalization method is given by the following equation:For the 14 datasets of fruit samples selected in this study, 3 different feature dimensions were selected for each fruit. The relative distances between the 3D fruit appearance features were obtained using image analysis methods. Because of the large number of fruit types and phenotypic features, the dimensionality of this description method is high, and it is difficult to intuitively obtain the optimal source domain directly based on the relative distance of a single feature between the 2 fruits. Therefore, this study proposed a method for constructing a cross-species commonality description model. A multidimensional feature space was constructed based on the relative distance between 2 fruit features. The objective was to convert the relative distances between different fruit features into absolute distances in the same feature space. The phenotypic characteristics of each fruit image could be concisely and precisely described using a pair of 2D planes of position coordinates. Subsequently, the feature points in the space were divided using a clustering algorithm, and clustering was performed according to the similarity of the fruit features. Thus, the optimal source domain fruits in the different clusters were obtained. The cross-species commonality description model was constructed as follows:sx, cx, tx denote the relative distances of the shape, color, and texture, respectively, between 2 fruit images; featurescale denotes a scale-normalized scale. Because each phenotypic feature is to be scaled separately, and feature represents the shape (s), color (c), and texture (t) in sequence.2.Because the distance feature matrix can only represent the relative distances of features between fruit images, which is not sufficiently intuitive, the multidimensional scale analysis algorithm was used in this study. The algorithm uses the principle of constant relative distance between 2 points; thus, the multidimensional distance matrix is visualized in a 2D plane coordinate system for different phenotypic features of the fruit. Three different 2D coordinate systems with absolute distances of fruit features are then constructed based on these 3 phenotypic features. By extracting the position coordinates of fruit images in different coordinate systems, phenotypic features are combined using a principal component analysis dimensionality reduction algorithm, and these features are jointly represented in a 2D planar space. The reduced dimensional shape features are used as the x-axis, and the reduced dimensional color and texture features are used as the y-axis. A new multidimensional feature space with 3 fused phenotypic features is constructed, and the fruit image distribution is obtained.3.To obtain the optimal source domains of the14 fruit datasets through the constructed multidimensional feature space analysis, this study used a clustering algorithm to classify the fruit location situation in the space. Because the manual determination of the optimal number of source domains is subject to certain errors and the processing is not sufficiently objective, the density-based spatial clustering algorithm was used in this study. This algorithm is used to automatically classify and select the fruit datasets of the source domains and the number of source domains. The number of clusters is automatically determined according to the distribution of differences in fruit characteristics. The fruit datasets at the geometric center within each cluster are used as the optimal source domain fruit datasets.where Using the above method, the degree of similarity of phenotypic feature differences between different fruits can be obtained visually. Thus, the optimal source domain fruit datasets that simultaneously correspond to different target domain datasets can be selected scientifically and objectively. This information can be used as priori knowledge for the subsequent image-translation model, and it may represent a guideline to generate more realistic target domain fruits and multispecies target domain fruit images from a single source domain dataset. Thus, the stability of the fruit translation model training is improved.During the construction of synthetic orchard images, obtaining a more realistic and detailed synthetic dataset by relying entirely on the method of randomly placing background-free fruit images in the background is difficult because of the complex orchard scenes and changing environments. It also cannot help the fruit detection model extract the fruit features in the target domain better.To make the constructed synthetic dataset approximate the real target domain fruit growth scene and posture and train the fruit detection model effectively, this study analyzed the structural and regular relationships of different elements in real orchard scenes in detail. A knowledge graph system based on the synthesis rules of hierarchical components was established using the distribution rules of semantic structures in real scenes as shown in Fig. The natural semantic-based composition rules are used to indicate that the overall construction of the synthetic image will follow the natural growth of the plants in the orchard and the scene distribution of the whole orchard scene image consisting of sky-ground-tree. These rules can help the computer vision field by providing a better understanding of images from a global scale. Since the fruits and trees in the image are affected by growth changes and scene environment changes, this paper extends 2 rules based on local semantics from natural semantics, namely, growth semantic-based constituent rules and the scenario environment-based domain adaptation rules. Among them, the scenario environment-based domain adaptation rules are used to make the synthesized image closer to the real target domain scene. It is possible to standardize the synthesized image of obscured fruits as well as the synthesized image of the target domain. The growth semantic-based constituent rules are used to normalize the synthesis of the base constituent as well as the combined constituent. The rules analyze the growth direction of trees and fruits from the foreground and background parts, respectively, simulating the real fruit tree growth dynamics.To better implement the construction of synthetic datasets, a hierarchical structure relationship in the knowledge graph was constructed in this study. The 3 rules were divided more carefully according to the different hierarchical levels. The hierarchical structure relationship of the knowledge graph can be expressed as follows:N denotes a composition rule based on natural semantics; S denotes the scenario environment-based domain adaptation rules that contain 2 subrules; S1 denotes the background domain adaptation subrule; S2 denotes the light and size subrule; G denotes the growth semantic-based constituent rules that contain 3 subrules; G1 denotes the fruit growth subrule; G2 denotes the branch and leaf colonization subrules; and G3 denotes the graded shading subrules. The pseudocode implementation of the construction method for the synthetic dataset is presented in Algorithm 1. The specific description of each of these rules is as follows.where Natural semantic-based composition rules: The scenario environment and growth state of fruits in real orchards follow certain patterns. If the method of randomly placing foreground fruit images in background images is adopted directly, it does not conform to the basic composition rules of orchard scenes and cannot build a high-quality synthetic dataset. Therefore, in this study, the construction of a synthetic dataset was normalized using natural semantic-based composition rules. In addition to following the spatial distribution rules for the sky, fruits, trees, and land from top to bottom, this method restricts the growth area of fruits. The background image is preprocessed by a threshold segmentation algorithm to distinguish the land, tree, and sky components and mark the area of the tree parts. Subsequently, when placing the fruit target, it is restricted to the tree area, thus generating a more realistic fruit image. In addition, as rules are constructed from the global semantic perspective, the composition rules based on natural semantics include 2 types of rules, namely, growth semantics and scenario environment, which will be introduced in this paper.G1 in the process of forming a tree with fruit trees growing with obscured fruit and simulated the fruit growth dynamics in different kinds of fruit trees to obtain more realistic fruit distribution characteristics. Thus, the fruit tree components that conformed to the growth semantics were obtained. The branch and leaf colonization subrule G2 was designed in the process of tree formation by skeleton and leaves; therefore, the leaves were dynamically adjusted according to the growth direction of the branches and leaves. Leaves were distributed in the tree skeleton according to a certain growth pattern. In the process of forming obscured fruits from leaves and fruits, the graded obscuration subrule G3 was designed. By setting the obscuration ratio discriminant module between leaves and fruits and between fruits and fruits, the obscuration ratio of fruit targets in the image is randomly distributed in the interval .Growth semantic-based constituent rules: Fruit growth in orchards is densely distributed and often involves a large number of heavily stacked fruit targets. This type of fruit is obscured by leaves or other fruits, thus causing variations in the semantic information of fruit growth. If they cannot be processed in a reasonable manner, they will cause serious misdetection and misdetection problems. Therefore, in this study, we designed the fruit growth subrule S1 was proposed for the process of forming synthetic images of the target domain by land, sky, and trees with fruits. The background images in the real orchard of the target domain were used as the background images in the synthetic dataset. This enables the detection model to learn the feature information of the target domain foreground as well as the background. The illumination and size subrule S2 was designed to form obscured fruits with leaves. The foreground fruit images were randomized in terms of saturation, brightness, and rotation angle to increase the diversity of fruit samples.Scenario environment-based domain adaptation rules: Because of the different growth environments of fruits in different orchards, shooting angles, shooting distances, and other human collection factors are subject to change. The fruit in the actual scenario is affected by external lighting, shading, and growth factors, which can lead to large differences in their color characteristics. The transparent background fruit images used in this study were obtained by translating the fruit images from a single-source domain. The variability in the orchard scenes was not considered. Relying only on the background and leaf images of the source domain in the previous research method as components to construct the synthetic dataset leads to domain adaptation of the training model in different target domain scenes, which is not guaranteed. Therefore, in this paper, the background domain adaptation subrule We dynamically adjusted the size of the fruit to adapt to the location and angle of the camera shot according to the specific application scenario. The dataset obtained using this method was sufficient to support the training of detection models and feature extraction as well as a certain degree of domain adaptation. The accuracy of the target domain fruit label generation in different target domain scenes was significantly improved. The components used in the process of constructing the synthetic dataset have their own category information and original scale information. The location of placement and the scaled scale information can be recorded during the placement process. The synthetic dataset algorithm can automatically construct labels from the above information. Therefore, the process is automatic and does not require manual labeling. We hope that this knowledge graph system will provide a proposal and idea for constructing a dataset for orchard scenes. Because this study adopts a completely automated method to place fruit targets, the labeling information of fruit locations in the synthetic dataset can be obtained automatically. Thus, automatic fruit labeling in the target domain with zero labeling cost was achieved.In this study, a target domain dataset simulated by the CenterNet detection algorithm  based onThe training strategy used in this paper for deep learning model training is as follows. The CycleGAN translation model mainly used in the experiments of this paper is unified with the following hyperparameters during the training process: A small-batch adaptive moment estimation optimizer (Adam) with a momentum factor was used to optimize the network, where the Momentum factor Momentum value was set to 0.5. The batch size was set to 1. The learning rate was set to 0.0002 for the first 150 training rounds. The learning rate was reduced to 0 in the second 150 training rounds with linear decline. The training device we used was an NVIDIA Geforce GTX 3090, and the time spent for each model training was 3 h. The CenterNet detection model mainly used in this paper is set to have a batch size of 4 and an epoch of 100, and the rest of the hyperparameters are the same as the original model. The training device we used is NVIDIA Geforce GTX 3090, and the time spent for each model training is 1 h.Because the final task of the proposed method was to automatically generate target location labels in different scenes, we chose to use the detection performance of the detection model in target domain scenes as an evaluation index. The effectiveness of the method proposed in this study was indirectly verified by measuring whether its prediction frame was sufficiently accurate. The 3 main metrics used were precision, recall, and average precision. A higher value of the corresponding metric indicated a higher quality of label generation in the target domain scenario. Additionally, the precision and recall rates used in this study were obtained from the values at the balance point.1.We provided a multidimensional feature quantization description as well as spatial reconstruction using transparent background fruit images and selection to obtain the optimal source domain fruit .2.The optimal source domain fruits were input into the image translation model CycleGAN. The foreground fruit images of the target domain were trained and generated. We then used it to simulate the construction of synthetic datasets of citrus, apple, and tomato scenes in the target domain (Results of target domain fruit generation and synthetic dataset construction).3.The above synthetic datasets were input into the anchor-free detector-based fruit detection model for training. The self-learning method of pseudo-labeling based on threshold segmentation proposed in a previous study was used to obtain pseudo-labeling of the unlabeled target domain citrus, apple, and tomato datasets. The 3 experiments were denoted as pear2orange, pear2apple, and pear2tomato (Results of EasyDAM_V3 verification).We selected 3 representative fruits  as the target domains. The intraclass clustering center fruits were used as the source domain fruits for fruit transformation and label generation experiments. The experiments were divided into 3 main parts.1.Citrus(orange), apple_2(red), and tomato_2(green) fruits were all in the same cluster. The optimal source domain for these 3 target domains was pear_2(white). Therefore, the pear_2(white) was selected as the optimal source domain for subsequent experiments.2.For other fruit datasets, most of the results in the multidimensional feature space distribution figure were consistent with what one would expect from intuitive observations, such as the close proximity of citrus to apple, that is, the features of these 2 types of fruits were relatively similar. This also validates the effectiveness of the CycleGAN paper using the Apple to Orange (A↔O) dataset for training.3.In addition, most fruits were distributed in the lower-left region. The center of its clustering was pear_2(white), indicating that the average distance through pear_2(white) was closer to the fruits of the other 8 datasets. More realistic results could be achieved for the 8 datasets of fruits when using only the pear_2(white) dataset for image translation. The cost of the image collection task was significantly reduced, and the applicability of the automatic annotation algorithm was improved.4.The features of hami melon(green) and tomatoe_1(red) were not expected to be very similar primarily because the texture and smoothness of fruit in 2D images are difficult to describe. However, the textural features of these 2 types of fruit were similar , cucumber(green), and banana(yellow) were diverse owing to their own phenotypic characteristics. For example, cucumber and banana have straight and curved forms, while pitaya has a nonuniformly distribution of surface scales. Therefore, the fruit distribution is more discrete and constitutes a separate species. For image translation, fruits in nearby clusters, such as strawberries and green tomatoes, could be selected.Subsequently, we used the results as priori knowledge for the image translation model to guide the image translation model in selecting the most appropriate source domain target domain dataset for training and learning. In addition, we only statistically described the phenotypic characteristics of 14 fruit datasets due to labor costs, seasonal constraints, and other factors. However, we hope that the optimal source domain selection process proposed in this study can be used as a general description method for target features. This process is not limited to the 14 fruit datasets but rather can further expand the selection range for fruit, or even other objects, in the characterization task. To more intuitively introduce and demonstrate the methods in each part of this paper, we present detailed intermediate results obtained in each step, including the visualization results after the extraction of features by texture feature descriptors  image with a transparent background into citrus, apple, and tomato images in the target domain. The phenotypic features of the fruit images with a transparent background were obvious. Errors are minimized when analyzing fruit features with quantization, and fruit images with a transparent background have relatively simple information, which makes it easier for the image translation model to learn the effective features. Thus, more realistic fruit images were generated in the target domain, which is consistent with the requirements of our study.In this study, the CycleGAN image translation model was trained using 403 foreground fruit images of pears. A total of 403 images of citrus, apple, and tomato were obtained separately. To better match the fruit characteristics in an actual tomato orchard, we conducted 2 experiments on the transformed part of the tomato images to obtain both red and green tomato images. Figure Because this study used a fruit dataset with a transparent background, the target features in the images were obvious, which could significantly reduce the performance requirements of image translation models. Most mainstream image-translation models at this stage can accomplish this task well. Therefore, using our model provides a great deal of flexibility in the dataset selection for automatic annotation and can eliminate the need and dependence of image translation models on ultrahigh-performance graphics processing unit and other graphic processing devices.In this study, regions without fruits in the actual orchard dataset in the target domain were cropped and stitched. Ten leaf images were obtained as components to simulate an occlusion scene. The foreground target domain fruit images obtained from the CycleGAN output were then used to obtain 1,000 synthetic datasets each for citrus, apple, and tomato, which were used as the training set for the fruit detection model using the target domain synthetic dataset construction method. The trained models could be used as pretrained models in the subsequent pseudo-labeling self-learning method, and they were denoted as The synthetic dataset obtained by the target domain synthetic dataset construction method is compared with a real image in Fig. orangeM, appleM, tomatoM are input to the real orchard images for testing the detection accuracy.In the experimental work described in this section, we pretrained the CenterNet detection model for the 3 target domain datasets citrus, apple, and tomato using a synthetic fruit dataset based on the target domain. In addition, the label frame information is optimized using a pseudo-label adaptive threshold selection strategy. The trained models In addition, the comparison algorithm uses the automatic labeling method EasyDAM series methods in the previous study. Among them, the experimental objects of EasyDAM_V1 and EasyDAM_V2 are different. Moreover, EasyDAM_V1 is mainly oriented to the task of automatic fruit labeling with similar phenotypic features. EasyDAM_V2 is mainly for the automatic labeling task when there are certain differences in phenotypic characteristics, and this paper focuses on the selection of the optimal source domain as well as label generation experiments for fruits with similar features in the same cluster. In addition, EasyDAM_V1 achieves the SOTA results so far in the task of automatic labeling of datasets with similar phenotypic features. Therefore, in this paper, EasyDAM_V1 is used as the comparison algorithm for the orchard scene dataset (denoted as EasyDAM dataset) generated based on GAN in its methodTo verify the reasonableness of the optimal source domain selection, we used the citrus(orange) dataset as the source domain in the comparison algorithm. From Fig. orangeM, appleM, tomatoM were obtained to further improve the label generation accuracy while eliminating false detection frames. Figure In this study, the images obtained from the synthetic dataset construction method using the target domain were pretrained for the CenterNet detection model. The prediction box was noise-filtered and cyclically updated using a pseudo-label self-learning method, which showed good performance in previous research . The finTable Pear2tomato2 experiment was achieved using the proposed method in this paper, which was mainly because of the relatively higher resolution, fruit size, and pixel share of the fruit targets in the images from the tomato dataset. Moreover, the number of fruits in each image was small  as the optimal source domain using the optimal source domain selection method. Figure In summary, the proposed automatic fruit labeling method based on optimal source domain selection can significantly improve the applicability and domain adaptability of automatic labeling algorithms. It can also achieve accurate and stable performance in datasets with large scene and fruit distribution characteristic differences. In addition, we hope to provide an effective method for selecting source domain datasets based on studies related to image translation and generate more realistic target domain images while achieving a better performance in downstream tasks.


Over the next 15 months, the observed deformation was well reproduced by shear-tensile sources, which represent an aseismic reverse-type slip and the opening of a southeast-dipping fault zone at a depth of 14–16 km. We suggest that the upwelling fluid spread at a depth of ~ 16 km through an existing shallow-dipping permeable fault zone and then diffused into the fault zone, triggering a long-lasting sub-meter aseismic slip below the seismogenic depth. The aseismic slip further triggered intense earthquake swarms at the updip.Since November 30, 2020, an intense seismic swarm and transient deformation have been continuously observed in the Noto Peninsula, central Japan, which is a non-volcanic/geothermal area far from major plate boundaries. We modeled transient deformation based on a combined analysis of multiple Global Navigation Satellite System (GNSS) observation networks, including one operated by a private sector company (SoftBank Corp.), relocated earthquake hypocenters, and tectonic settings. Our analysis showed a total displacement pattern over 2 years shows horizontal inflation and uplift of up to ~ 70 mm around the source of the earthquake swarm. In the first 3 months, the opening of the shallow-dipping tensile crack had an estimated volumetric increase of ~ 1.4 × 10 Earthquake swarms are often interpreted as phenomena related to external transient stress perturbations, including slow slip events2 and magma intrusion3, or weakening of fault strength due to an increase in pore fluid pressure4. Many earthquake swarms have been observed in areas with active volcanism, hydrothermal systems, transform faults, and subduction margins5.An earthquake swarm is a sequence of earthquakes that lasts from several hours to several years without a distinct mainshock6. A major tectonic structure in the study area is an offshore active fault system along the northern coast of the Noto Peninsula7  operates a cGNSS network consisting of approximately 3300 continuous stations all over Japan. Ohta and Ohzono21 demonstrated that with appropriate data quality control, the GNSS data acquired through the SoftBank network are useful for monitoring, although the long-term stability (> 1 year) of these data has not been examined. The use of SoftBank GNSS data is expected to reveal transient deformation associated with an ongoing seismic swarm with an average spacing of less than 10 km.A key factor in understanding this mechanism is the geodetic observation of crustal deformation. The deformation of the Japanese Islands is monitored by a continuous Global Navigation Satellite System (cGNSS) network named the GNSS Earth Observation Network System (GEONET) by the Geospatial Authority of Japan (GSI)In this study, we analyzed GNSS data acquired from multiple networks operated by SoftBank, GSI, Kyoto University, and Kanazawa University to clarify the spatiotemporal evolution of the transient deformation in the Noto Peninsula. We also relocated the earthquake hypocenters of the seismic swarm to compare them with the deformation sources. Finally, we estimated the deformation sources and proposed a mechanism for the intense seismic swarm, based on the observed deformation, seismicity, and tectonic background.The seismic swarm in the Noto Peninsula exhibited a spatial distribution that revealed four distinct clusters, which we refer to as Clusters S, W, N, and NE Fig. b. This a22. This fault type is consistent with the type of active faults and stress states estimated from the focal mechanisms of pre-swarm earthquakes24 s24 Fig. . More deA time series of the corrected GNSS coordinates Fig.  clearly 27 and anthropogenic28 earthquake swarm sources. We focused on the transient deformation before the M5.4 earthquake and divided it into Periods A, B, and C based on the deformation pattern and seismicity as specified below. A key component of the period division was the horizontal displacement of BR30. The parameters of a rectangular fault in each period were estimated from the three components of displacement.Despite dense GNSS networks, the observed displacement was insufficient to resolve a source model due to limited station coverage in the peninsula and source depth. A point inflation source, a shallow-dipping tensile crack, and reverse slip of a southeast-dipping fault could roughly reproduce the observed displacement, whereas the first two sources and the last source can produce a significantly different displacement pattern in offshore regions  in the same period, converted using an empirical relation between the JMA magnitude and seismic moment34. A comparison between the seismic moment and the geodetically estimated moment for past slow slip events (SSEs) suggests that the aseismic moment often exceeds the seismic moment35. The moment ratio for the Noto swarm is in the range of the moment ratios for the past SSEs . The gradual increase in earthquakes in Clusters W, N, and NE was likely triggered by the stress increase due to the downdip slow aseismic slip. ΔCFS suggests that the stress increases up to a few MPa at the bottom of Clusters W, N, and NE in Periods B and C  suggests stress increases and decreases in the deep and shallow parts of Cluster S, respectively Fig. a,b, whic38 and L’aquila 39. Several earthquake swarms after the 2011 Mw9.0 Tohoku-oki earthquake in northeastern Japan were likely triggered by an increase in pore fluid pressure41. However, transient deformation associated with seismic swarms is not usually detected geodetically except in the vicinity of volcanoes and major faults. The Noto swarm is characterized by a large volumetric increase , which is comparable to the seismic swarms in Matsushiro38 and volcanic areas42. The fluid causing the Noto swarm upwelled from the fluid-rich region in the lower crust and likely originated from the deep dehydration of the subducting slabs of the Pacific and/or Philippine Sea plates through mantle, as suggested by geochemical studies of the helium isotope around the swarm area43 and the Pb–Sr–Nd isotope in central Japan44, and a study of regional seismic tomography45. The location of the estimated open crack in Period A coincided with the S-wave reflector induced from the reflected phases in the seismograms25. These results support our scenario that the upwelling fluid pooled from the vicinity of Cluster S at the end of November 2020 and subsequently diffused and triggered an aseismic slip in the existing fault zone.A number of examples of crustal seismic swarms related to upwelling fluids have been observed in non-volcanic areas, including Matsushiro (Japan)46. Furthermore, the stress drop  was greater than that of a typical SSE and comparable to that of a regular earthquake47. These characteristics may result from a large and long supply of upwelling fluid.Although we modeled the observed deformation using a single shear tensile source in each period, the deformation likely represents a macroscopic view of numerous small shear tensile cracks in the fault zone. It is unrealistic to assume a single ten-square-kilometer fault accommodating both opening and shear slip, as a fault with overpressurized pore fluid pressure would slip rapidly to release shear stress. The characteristics of aseismic slip in the Noto Peninsula are not entirely typical for a SSE. For example, the duration  relative to a moment is much longer than that predicted from the empirical scaling law of slow earthquakes49. Past earthquakes and active faults 54 for pairs of events. We utilized the differential travel times for an event pair in hypoDD calculation if there were over 12 phase pairs presented with an average squared coherency ≥ 0.8 in the 1–8 Hz frequency bands. Finally, we obtained 5,041,816 and 3,904,866 differential travel times for P- and S-waves from the waveform analysis, respectively. Consequently, we precisely relocated 6705 events. The root mean square of the travel time residuals decreased from 0.1362 to 0.1002 s for the catalog data and from 0.1785 to 0.0238 s for the waveform analysis data.We also calculated precise travel time differences of the P- and S-waves using velocity waveforms in the 1–8 Hz frequency bands55. We used the VMF1 model for tropospheric correction and applied higher-order ionosphere correction. To correct ocean tide loading, we used FES2014b56 by applying parameters downloaded from the Onsala Space Observatory . The daily coordinates were transformed into the IGS14 reference frame using the Helmert transformation parameters provided by the Jet Propulsion Laboratory. To extract the transient deformation associated with the earthquake swarm, we removed secular, seasonal, and long-term postseismic deformation associated with the 2011 Mw9.0 Tohoku-oki earthquake before November 2020. For the GEONET sites, we fitted linear, logarithmic, exponential57, and sinusoidal functions to the daily coordinates from March 12, 2011, to October 31, 2020, and removed them from the observed coordinates to extract the transient deformation. However, we could not estimate the pre-swarm deformation from data collected at SoftBank and university sites because the data did not cover a sufficiently long time period. Therefore, we removed only the linear component by interpolating the velocities at the surrounding GEONET sites from November 2017 to October 2020 using basis function expansion58. Finally, we calculated common-mode noise by stacking daily coordinates at five stable sites located 50–100 km away from the swarm area and applied spatial filtering59 to remove common-mode noise from all stations.The daily coordinates of 30 GNSS stations  w6,2) earthquake occurred in the Noto Peninsula. This earthquake resulted in 38 casualties and damage to more than six hundred buildings. Its aftershock area overlapped Clusters N and NE and further expanded to a northern offshore region . Further analysis of the transient deformation and seismicity associated with the Noto swarm is essential to resolve the mechanism and possible triggering processes of the earthquake.On May 5, 2023, during the revision of this paper, an M6.5 (MSupplementary Information.


PLN) p.Arg14del variant carriers. A DNN was trained to discriminate PLN variant carriers (n = 278) from control subjects (n = 621) using raw deformation curves obtained by 2D-speckle tracking in the longitudinal axis. A visualization technique was used to identify the parts of these curves that were used by the DNN for classification. The PLN variant carriers were clustered according to the output of the visualization technique. The DNN showed excellent discriminatory performance (C-statistic 0.93 [95% CI 0.87–0.97]). We identified four clusters with PLN-associated disease features in the deformation curves. Two clusters showed previously described features: apical post-systolic shortening and reduced systolic strain. The two other clusters revealed novel features, both reflecting delayed relaxation. Additionally, a fifth cluster was identified containing variant carriers without disease features in the deformation curves, who were classified as controls by the DNN. This latter cluster had a very benign disease course regarding development of ventricular arrhythmias. Applying an explainable DNN-based pipeline to myocardial deformation curves enables automated detection and visualization of disease features. In PLN variant carriers, we discovered novel disease features which may improve individual risk stratification. Applying this approach to other diseases will further expand our knowledge on disease-specific deformation patterns.Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.Arg14del variant carriers and controls were selected and a deep neural network (DNN) was trained to detect the PLN variant carriers. Subsequently, a clustering-based approach was performed on the attention maps of the DNN, which revealed 4 distinct phenotypes of PLN variant carriers with different prognoses. Moreover, a cluster without features and a benign prognosis was detected.Overview of the deep neural network-based pipeline for feature detection in myocardial deformation curves. Firstly, phospholamban (The online version contains supplementary material available at 10.1007/s10554-023-02924-9. Mechanical deformation of the myocardium is the major determinant of cardiac function, and may be disrupted in a wide variety of cardiac diseases . ChangesSeveral non-invasive techniques exist for quantification of myocardial deformation, of which echocardiography-based 2D speckle tracking is now the most robust and most widely used technique . The assDeep neural networks (DNNs) are computer algorithms based on the structure and function of the human brain. Their hidden layers of neurons can be trained to discover complex patterns in signals such as deformation curves. DNNs are increasingly applied to electrocardiograms (ECGs), which has led to both classification of ECGs with very high diagnostic accuracy and detection of novel ECG features , 7. To dPLN) p.Arg14del variant, who are at high risk of developing dilated and/or arrhythmogenic cardiomyopathy (DCM/ACM) , 0.90 [95% CI 0.85–0.96], 0.89 [95% CI 0.83–0.95], 0.88 [95% CI 0.79–0.96] and 0.93 [95% CI 0.85–0.98], respectively.PLN phenotype clusters .Using the relevance maps generated by the Integrated Gradients visualization technique, we identified four PLN variant carriers were located in the apical septal segments, mid septal segments and apical lateral segments. Temporally, these features were particularly located at end-systole and in the early diastolic phase. Figure As shown in Fig. 2 and 24.7 [IQR 20.8–27.9] ml/m2, respectively) and a particularly high rate of low QRS voltages (38.1%). Finally, cluster D consisted of the oldest subjects (54 [IQR 43–61] years) with the most advanced disease, with high rates of heart failure and VA, and severely impaired conventional echocardiographic LV and RV measurements.Additional clinical data of the phenotype clusters can be found in Table PLN variant carriers (86%). During a median follow-up duration of 3.0 years [IQR 1.4–5.2 years], 34 patients (14%) experienced the sustained VA endpoint. These were 4 (5%) from cluster A, 3 (8%) from cluster B, 4 (7%) from cluster C and 23 (46%) from cluster D. None of the subjects that were allocated to cluster O experienced a sustained arrhythmia during follow-up. Figure Follow-up data was available for 240/278 PLN p.Arg14del variant carriers, (ii) confirm previously described disease features which are characteristic for this genetic variant, and (iii) discover novel disease features that were, up to now, concealed in the deformation curves of these variant carriers. These novel features can be used to reclassify deformation curves that were previously considered normal. Importantly, the phenotype clusters identified by this approach seem to have distinct prognostic differences, which could potentially lead to improvement of individual risk stratification. Applying this approach to other patient populations will likely enrich our knowledge on deformation patterns in a broad variety of other diseases.This study is the first one to use a DNN for discovery of novel disease features in myocardial deformation curves. By using a completely novel pipeline for deformation curves that combines an explainable DNN with a clustering approach, we were able to (i) identify five distinct phenotype clusters among Myocardial deformation curves contain a large amount of information on intrinsic mechanical myocardial properties. However, the interpretation of deformation curves is challenging, which hampers the routine clinical use of these curves. Previously, attempts have been made to classify deformation curves by extracting certain parameters manually and applying disease-specific cut-off values . HoweverPLN p.Arg14del variant carriers who are in early stages of disease [This study was specifically designed to explore the unique strain characteristics in patients with a homogeneous genetic background. In a recent observational study, we found that post-systolic shortening in the LV apex is a typical deformation pattern in  disease . In morePLN p.Arg14del variant, since it is thought that this variant causes PLN to inhibit calcium reuptake into the sarcoplasmic reticulum, leading to a diastolic calcium overload in the cardiomyocyte [Strikingly, the results of this study also added novel data on specific diastolic myocardial behavior that has not been recognized before. In clusters A and B, we observed specific patterns of delayed relaxation, where overt post-systolic shortening is not (yet) present. It is conceivable that these disease features in early diastole can be explained by the pathophysiological mechanism of the omyocyte .PLN variant. In future studies, it would be of great interest to include patients with a broad variety of variants into a DNN, to characterize variant-specific disease features and personalize diagnostic protocols for carriers of all different variants. This was beyond the scope of the present explorative study.Since disease mechanisms vary among carriers of different variants, we assume that the identified features are specific for this Since the prognostic value for developing VA was similar among clusters A, B and C, one could argue that there is no clinical relevance in distinguishing these clusters. However, it is important to note that by manual classification, a significant part of the deformation curves from clusters A, B and C would be classified as normal . With our approach, we could reclassify the deformation curves from these individuals from normal to abnormal. This highlights the clinical relevance of our approach, considering the difference in development of VA between the normal cluster O and the abnormal clusters A, B and C.PLN variant carriers who were correctly classified by the DNN. In addition, we described cluster O which contained variant carriers who were not recognized by the DNN as variant carriers due to the absence of disease features. It is known that the PLN p.Arg14del variant has age-related penetrance with symptoms often beginning around the fifth decade, which implies that there is a pre-phenotypical phase in which disease expression is absent [Clusters A to D all contained s absent . TherefoPLN p.Arg14del variant is characterized by large phenotypical variability, this novel DNN-based approach may be useful for classifying subjects with this particular variant into one of the phenotype clusters. Subjects with this variant who do not exhibit disease features (cluster O) can perhaps undergo low-frequency follow-up, whereas subjects who are classified in the most advanced disease cluster (cluster D) may possibly benefit from more intensive follow-up and appropriate therapeutic intervention, for example ICD implantation. Future studies should elaborate on the prognostic value of these clusters, also considering other clinical variables [Since the ariables .Besides using this approach for classification purposes, this approach is very useful to gain insight into characteristic disease features that are concealed in the deformation curves. Ideally, this approach should be applied to other diseases, which will expand our knowledge on disease-specific deformation patterns and potentially improve the interpretation of deformation curves in clinical practice. In this proof-of-concept study we only used the deformation curves from the apical 4-chamber view, but in future studies it would be of interest to include the deformation curves from all apical views, including the atrial and right ventricular deformation curves. Combining the deformation curves with ECG data in a DNN model would also be of interest in future studies.This study has several limitations to address. First, our control group was derived from a population-based cohort, while the group of variant carriers were scanned and analyzed in other centers. This may have induced center-specific differences between the controls and the variant carriers. Second, the developed DNN was not validated in an external cohort. However, the aim of this study was not to investigate the performance of the algorithm, but to propose a novel way to detect diseases-specific features in deformation curves. Third, the visualization technique that was used in this study only shows the temporal location of important features in the deformation curve, but it does not specify what the feature exactly is. The actual description of the features was performed by visual assessment and should be validated in future studies. Fourth, since detection of regional abnormalities by deformation imaging is limited by inter-vendor variability, it remains unknown whether the results can be generalized to other vendors . Fifth, PLN p.Arg14del variant, this approach detected two previously described features , and more importantly, revealed two novel features reflecting delayed relaxation. The different phenotype clusters seem to have distinct prognostic differences, which could lead to improvement of individual risk stratification in this group of variant carriers. Applying this novel pipeline to other patient populations will enrich our knowledge on deformation characteristics in a broad variety of diseases, which could improve the assessment of deformation curves in clinical practice.Applying an explainable DNN-based pipeline to myocardial deformation curves allows for automated discovery of (novel) disease features. In subjects with the genetic Supplementary file1 (DOCX 186 KB)Below is the link to the electronic supplementary material.


Bimetallic COF-CuNiPc with an asymmetric synergistic effect achieves a fast adsorption/desorption process to NO2. It is demonstrated that the COF-CuNiPc can detect 50 ppb NO2 with a recovery time of 7 s assisted by ultraviolet illumination. Compared with single-metal phthalocyanine-based COFs (COF-CuPc and COF-NiPc), the bimetallic structure of COF-CuNiPc can provide a proper band gap to interact with NO2 gas molecules. The CuNiPc heterometallic active site expands the overlap of d-orbitals, and the optimized electronic arrangement accelerates the adsorption/desorption processes. The concept of a synergistic effect enabled by bimetallic phthalocyanines in this work can provide an innovative direction to design high-performance chemiresistive gas sensors.Two-dimensional (2D) phthalocyanine-based covalent organic frameworks (COFs) provide an ideal platform for efficient and rapid gas sensing—this can be attributed to their regular structure, moderate conductivity, and a large number of scalable metal active centers. However, there remains a need to explore structural modification strategies for optimizing the sluggish desorption process caused by the extensive porosity and strong adsorption effect of metal sites. Herein, we reported a 2D bimetallic phthalocyanine-based COF (COF-CuNiPc) as chemiresistive gas sensors that exhibited a high gas-sensing performance to nitrogen dioxide (NO Massive industrial waste gas emissions seriously harm the environment and lead to human diseases 8, CuPcF8 (building blocks-1 and blocks-3), and NiPc[OH]8, NiPcF8 (building blocks-4 and blocks-2) were introduced into COF-CuPc and COF-NiPc according to the same preparation scheme described above.COF-CuNiPc was prepared by the nucleophilic substitution of the hydroxyl group, according to the previous report . BrieflyThe morphology and crystalline structure of the obtained samples were examined by field-emission scanning electron microscope , and powder X-ray diffraction (PXRD) measurements  with Cu Kα source (λ = 0.15418 nm). Fourier transform infrared spectroscopy (FT-IR) spectra were recorded using a Thermo Fisher Nicolet 6700 FT-IR spectrometer . The element composition and internal bonding were probed by X-ray photoelectron spectroscopy . UV–vis diffuse reflectance spectroscopy (UV-Vis DRS) spectra were detected by a spectrophotometer  with a wavelength range from 200 to 800 nm.2 cylinders were replaced by corresponding standard cylinders including H2, NH3, acetone, formaldehyde, CHCl3, ethanol, EtOAc, and n-hexanal. The effect of humidity on the gas sensor was tested at 20% to 80% relative humidity (RH) by a simple bubbling method 8 as building blocks via the nucleophilic aromatic substitution reaction , and the peak at 1469 cm−1 is ascribed to ν(C=N) 8 could respond to NO2, while the NiPcF8-based device did not show any response signal. We speculate that the coordination metal species and hydroxyl functional groups of CuPc[OH]8 play a key role in the COF-CuNiPc-sensing performance 8 and COF-CuNiPc fluxed with 100 ppb NO2, and both exhibited response signals that could not be saturated  of NO2 was 5.4 ppb for the gas sensor based on COF-CuNiPc 2− also has a remaining empty 4pz orbital in the vertical plane, which is more accessible to NO2 interactions. This is the reason that the gradual increase in the cycle recovery process time occurs for sensors based on COF-NiPc. As we know, [Cu(CN)4]2− contains a d9 structure. The 4p orbital of the central ion Cu2+, which does not participate in the hybridization, forms −, resulting in improvement in the stability [d-orbitals, and the resulting heterometallic active sites help to optimize the electronic structure of COFs [The comparison of the band gaps of 2D phthalocyanine-based COFs e furthertability . Cu-Ni b of COFs . Hence,  of COFs  and achi2 chemiresistive gas sensing. Based on the heterometallic active sites of bimetallic phthalocyanine , the band gap of COFs is optimized, and the electronic arrangement can be rationally assigned to create a rational environment for enhancing the response as well as boosting the gas adsorption/desorption of NO2 gas molecules. The testing results reveal that the recovery time of the as-designed COF-CuNiPc-based sensor exposed to 50 ppb NO2 is 7 s with the assistance of UV illumination, and the LOD can reach 5.4 ppb. XPS and UV–vis DRS partially confirm that the ligand bi-metal species in phthalocyanine-based COFs significantly influenced their performance. This synergistic effect of bimetallic active centers can provide an innovative route for the development of high-performance chemiresistive gas-sensing devices.In summary, bimetallic phthalocyanine-based COFs have been reported for high-performance NO


Additionally, a thorough inquiry into the unusual cyclic voltammetry pattern exhibited by TQ was studied and was associated with formation of a 1:1 TQ-superoxide radical species, [TQ-O2]−•. DFT calculations reveal this radical species to be involved in the π-π mechanism describing TQ reactivity with superoxide. The crystal structures of TQ and THQ were analyzed, and the experimental data reveal the presence of stacking intermolecular interactions that can be associated with formation of the radical species, [TQ-O2]−•. All three of these methods were essential for us to arrive at a chemical mechanism that explains TQ antioxidant activity, that incorporates intermolecular features found in the crystal structure and which correlates with the measured superoxide scavenging activity.Black cumin seeds and seed oil have long been used in traditional foods and medicine in South Asian, Middle Eastern and Mediterranean countries and are valuable flavor ingredients. An important ingredient of black cumin is the small molecule thymoquinone (TQ), which manifests low toxicity and potential therapeutic activity against a wide number of diseases including diabetes, cancer and neurodegenerative disorders. In this study, the antioxidant activities of black seed oil, TQ and a related molecule found in black cumin, thymohydroquinone (THQ), were measured using a direct electrochemical method to experimentally evaluate their superoxide scavenging action. TQ and the black seed oil showed good superoxide scavenging ability, while THQ did not. Density Functional Theory (DFT) computational methods were applied to arrive at a chemical mechanism describing these results, and confirmed the experimental Rotating Ring Disk Electrode (RRDE) findings that superoxide oxidation to O Nigella sativa L. is native to the Mediterranean region, northern Africa and south Asia. Historical evidence of its use by ancient civilizations in those areas exists and seeds of the plant (black cumin) were even found in King Tutankhamen’s tomb • HO2−} is +4.3 Kcal/mol higher than the reagent, thus indicating no further reactivity of the thymohydroquinone-η-2O2 complex.However, since TQ has no hydroxyls available, an alternative process (b) can be envisioned involving interaction between the quinone and available protons. This route can induce reduction of a C=O carbonyl to a C-OH moiety and has been described for the quinone natural product embelin . The iniAs recently described, molecular oxygen is an important product when polyphenols act as mimics of superoxide dismutase enzymes, following reaction (3) .2 O2•− 2 and H2O2, are not produced by process (b). However, the independent process (a), shown in 2 formation. In any case, H2O2 is not produced, and so TQ does not mimic SOD action, unlike behavior recently described for some polyphenols −•, 2 by a radical species is uncommon, and the resulting species, [TQ-O2]−•, is therefore associated with its oxidation  peak in  peak was strongly decreased, consistent with decreasing O2, although not completely eliminated because of the presence of some residual non-purged oxygen. Thus, the anomalous increase in efficiency, 2]−• concentration, formed as TQ aliquots are added and react with bubbled O2.To assign the first oxidation peak, labelled 2]−• seems not to be important at low concentrations of TQ, as seen in the first five data points in 4) for the linear expression y = −21104x + 20.9 (R2 = 0.9965), is a good indicator of superoxide scavenging ability of TQ. We conclude that RRDE experiments on TQ show a mixed effect: on one hand, at low concentrations, TQ is able to scavenge superoxide, and shows the expected decreasing efficiency while at higher TQ concentrations, an unexpected increase in efficiency can be associated with formation of the [TQ-O2]−• radical. This radical species is oxidized after reversing the potential at about −0.45 V in the electrochemistry experiment using one working electrode, or alternatively, is detected at the ring electrode, whose setting is 0.0 V in the RRDE experiment.However, it is interesting to compare the peaks associated with reactions (1) and (2) in 4 falls between chrysin and eriodyctiol, but is closer in value to the latter.Our results support earlier investigations that describe TQ superoxide scavenging ability  as well THQ was also explored with the RRDE method, and results are shown in 2 = 0.9763), demonstrating a good scavenging ability by the black seed oil.Black seed oil was also analyzed, and its RRDE cyclovoltammetry data are shown in 2 = 0.99348) for all 11 runs, , and shows a slightly better scavenging of superoxide by olive oil.Earlier RRDE studies by our group on another cold-pressed vegetable oil, extra virgin olive oil , allow uThe molecular mechanisms that underlie the therapeutic effects of TQ are not completely understood, and literature reports show that TQ likely interacts with a number of receptors. Its low toxicity and the fact that TQ as well as the oil and seeds from which it is isolated have been used for thousands of years, make studying its therapeutic capacity appealing .2 as a product, and 2) superoxide σ proton attack on a TQ O(carbonyl), which gets reduced to C-OH, and is not sensitive to further attack by superoxide.In this work, we have measured the superoxide scavenging ability of cold-pressed black cumin oil and two of its ingredients, TQ and THQ. Our results show that the oil and TQ are strong scavengers of the superoxide radical, whereas THQ has no effect on the scavenging, despite the presence of two para-hydroxyl groups. Structural X-ray diffraction data support subsequent DFT calculations that show a path of energetically feasible molecular transformations which corroborate cyclovoltammetry experimental features, i.e., the scavenging of superoxide by TQ, and no scavenging by THQ. The suggested stoichiometric reactivity of TQ when reacting with superoxide involves 1) direct π-π attack, involving superoxide oxidation to produce OThis theoretical assessment is confirmed using two cyclovoltammetry techniques, cyclic voltammetry and hydrodynamic voltammetry at an RRDE. Lastly, our work shows the efficacy of combining multidisciplinary approaches, including experimental X-ray crystallography and voltammetry as well as computational DFT methods, to provide understanding of the mechanism of action of small molecule antioxidants. This research supports further exploration of other natural products having important biomedical activities.


The flow behaviour of aluminium alloy AA5083 at 450  This alloy is affordable and has reasonable environmental resistance and good mechanical properties, making it ideal for aerospace, marine and automotive applications2.Aluminium AA5083 is a non-heat-treatable aluminium alloy with excellent cold formability and can reach moderate levels of superplasticity3. Both the mechanical properties  and the microstructural characteristics of superplastic aluminium alloys have been extensively investigated10. There are three main aspects necessary for the achievement of superplasticity: high forming temperatures , fine and stable grain size . Furthermore, high levels of grain boundary sliding are accompanied by an additional accommodating deformation mechanism, and traditional GBS models are separated into two categories: diffusionally accommodated and dislocationally accommodated GBS  14. Despite the very large deformations that can be achieved, the main drawback to the widespread use of superplastic forming is the significant time required to form an industrial part, ranging from two to ten minutes15. This limits the number of parts that can be formed, especially in the automotive industry, which significantly increases the cost of the part.Of particular interest, published research has shown the effectiveness and results of superplastically deforming AA5083 sheets16. This process has successfully reduced the weight of several parts used in the automotive and aerospace industries.Blow-forming processes can take advantage of superplastic materials to plastically form complex parts having a relatively uniform thickness distribution. In this process, a metallic blank may be preheated and then placed in a die that is heated to the prescribed forming temperature. A pressure vs. time curve is used to control the pressure of the hot sheet against the surface of the die cavity; with air or argon gas, forcing the blank into its final shape  17 performed uniaxial tensile tests on AA5083 specimens that were thermo-mechanically processed to achieve a grain size 18 achieved elongation greater than 1000% using an aluminum alloy that had been processed by friction stirring to achieve a grain size 19 utilised grain refinement through friction stir welding to achieve superplasticity at elevated strain rates up to 10If superplasticity could be achieved at strain rates faster than those of typically slow superplastic-forming strain rates, industrial forming processes could be enhanced. High Strain Rate (HSR) superplasticity has been the target of much research to boost superplastic forming rates. As a result of the correlation between increased maximum elongation and reduction of grain size, HSR research is primarily concerned with the refinement of grains in the microstructure. In order to refine grains and theoretically demonstrate superplasticity at high strain rates, it is the examination of severe plastic deformation processes. Although efficient, this procedure can be costly and has limited industrial application. Yan et al.21. Ultrasonic welding uses high-frequency vibrations to weld two clamped components, and the result is a highly repeatable and high-strength weld. Moreover, superimposing ultrasonic oscillations in tensile tests to improve deformation has also been investigated27. Superimposed oscillations hypothetically improve the effectiveness of deformation due to the oscillatory stress generating a mean stress lower than that of the stress strain curve for a static tensile load.The effectiveness of superposition of an oscillating load to optimise both metal plastic deformation and several metal forming processes has been investigated for decades, as seen in ultrasonic welding27 investigated the use of ultrasonic energy during metal forming, resulting in a reduction of the force to iron and deep-draw copper shells from 220 to 70 lb. In other metal forming processes, the success of inducing ultrasonic oscillations was attributed to the oscillations reducing the friction between the part and the forming tools. Reducing frictional effects in superplastic forming processes is of great value to reduce post-forming costs and processes to improve post-formed surface finishes, which are traditionally negatively impacted by sliding during forming. The consensus for oscillations/vibrations ability to aid in deformation is energy being preferentially absorbed at dislocation sites, allowing them to then overcome slip obstacles. This is especially valuable for a part that deforms superplastically to aid in dislocation-accommodated grain boundary sliding. In addition, ultrasonic vibration has been shown to aid in refining the microstructure of the material and, in turn, improving the properties of the material. The effect of introducing ultrasonic vibrations as a means of grain refinement was investigated in an ultrasonic assisted upsetting process and then compared to a conventional upsetting process25. This process produced results similar to those of severe plastic deformation grain refinement processes and could also be an underlying mechanism occurring during metal forming. Parallel research has been conducted into the design and utilisation of a supersonic fluidic oscillator capable to be used under superplastic conditions28. In conjunction, the prospective tensile behaviour improvement along with a fluidic oscillator embedded in a superplastic die can greatly expand the incorporation of superplastically formed parts because of the forming time and prospective forming temperature reduction. The effectiveness of an oscillative pressure forming process has been recently investigated by Anaraki et al resulting in an expanded forming domain when implementing an oscillative pressure forming procedure 29.The effectiveness of imposing ultrasonic oscillations onto a metal that is plastically deformed can be related to the effectiveness of heat as it pertains to thermal softening. However, the results in the literature indicate increased effectiveness compared to thermal softening because energy is preferentially absorbed in localised areas which typically impede deformation; vacancies, dislocations, and grain boundaries, while the entire volume of the material absorbs the thermal energy. Due to its effectiveness in optimising deformation capabilities, research on the application of oscillation energy during manufacturing has been under development. Langenecker et al.Due to the use of variable pressure over time in industrial SPF processes, it is of great value to develop constitutive models of the flow stress that can be used to simulate the superplastic forming process. Thus, the objective of this research is to carry out experimental testing, and to use the data obtained to develop a constitutive model of superplastic deformation with and without oscillations.30. Despite previous models using a constant m value, recent studies have indicated that this is inaccurate and can negatively skew the predictions. Additionally, despite the power law being frequently utilised in FE simulations due to its simplicity, it has limitations due to its inability to predict either the softening or the damage behaviours which typically occur during superplastic forming.Superplastic deformation is regarded as a creep process that lies within region II of the sigmodal stress vs. strain rate curve. This region is often defined by a strain rate sensitivity index  and solute drag creep (SDC). For high temperature material deformation, GBS is more prevalent at slower strain rates but at higher strain rates, the major deformation mechanism shifts to SDC34 GBS failure is accompanied by void nucleation, void growth, and void coalescence, which then leads to fracture. In SDC, failure occurs due to flow localisation and necking in the material. It has been observed by Mary-Anne et al.34 that for low impurity Al-Mg materials that have undergone SDC, failure can occur by necking up till a sharp point, but for high impurity aluminum like AA5083, necking occurs under SDC deformation, however, failure still occurs by cavity growth and coalescence in the necked area.As seen in the literature36 who indicate that fibre formation at grain boundaries occurs during GBS at elevated temperatures. Gali et al.36 also obtained an Energy Dispersive X-ray (EDS) spectrum in conjunction with SEM analysis on the fibres which confirmed that they consist mainly of AlSEM images of the specimens were taken before and after SPF and show deformation in the grains, cavitation development, and GBS. The image in Fig. As previously mentioned, the power law  for the computed model. The calculated AARE and RMSE of 11.11 % and 4.7 and 15.59 % and 5.0 with and without oscillations, respectively, are also indicative of a strong correlation between the experimental data and the data computed using the proposed model in Fig. In addition to the qualitative and comparative analysis of the fit, a correlation analysis was conducted to evaluate the fit to the experimental data with and without oscillations. The visualisation of the quantitative correlation analysis is shown in Fig. mentclass2pt{minimSince the magnitude of the residuals as well as the overall correlation of the material model have been evaluated and deemed suitable, further investigation was conducted to evaluate the normality of the residuals to confirm a fully appropriate fit. To assess the normal distribution of the error terms, the probability plot of the residuals generated is shown in Fig.  For further numerical investigation on the process of superplastic deformation, the commercial finite element software LS-DYNA was employed. The aforementioned material model was added to LS-DYNA as a user-defined material model. Belytschko-Tsay shell elements consisting of five integration points through the thickness were used to model the tensile specimen. Rather than applying limitations as boundary conditions, the deformation was applied by allowing contact between the tensile specimen and the solid components of the test fixture. This approach takes into account the movement of the material from the gripping region to the gauge region that occurred during the conducted experimental work. The stress vs effective plastic strain in uniaxial tension was provided as input to LS-DYNA. Due to the specimen design in this instance, the experimental flow curves were not obtained in uniaxial tension, so a series of repeated simulations were run to scale the input flow curve until the experimental force displacement data could be reliably predicted. The constant load conditions shown in Fig. The formability of AA5083 at a temperature of 450 The addition of oscillations with amplitudes and frequencies ranging from 0.02 to 0.5-N and 5 to 100 Hz respectively allowed for a relative improvement for the true strain at fracture of more than 30%The stability of the flow stress and expansion of the steady state region as a function of strain rate were improved as a result of imposed oscillations during tensile deformationTensile test specimens showed a significantly lower thickness reduction when an oscillating load was superimposedThe experimental hardening and softening behaviour was accurately modelled using a quadratic strain relationship and power strain rate constitutive model for tensile data obtained both with and without oscillations.The constructed model showed a strong correlation for the constant rate tensile tests as indicated by Using LS-DYNA’s user-defined material subroutine, the simulation results showed a strong correlation with the constant load test data done experimentally in terms of the thickness distribution.Finally, the addition of a minor oscillation during superplastic deformation leads to significantly lower rates of thickness reduction compared to conventional processes without oscillations.The following conclusions can be drawn from this study.