Rice is a widely consumed food across the world. Whilst the world recovers from COVID-19, food manufacturers are looking to enhance their quality inspection processes for satisfying exportation requirements and providing safety assurance to their clients. Rice cultivation is a significant process, the yield of which can be significantly impacted in an adverse manner due to plant disease. Yet, a large portion of rice cultivation takes place in developing countries with less stringent quality inspection protocols due to various reasons including cost of labor. To address this, we propose the development of lightweight convolutional neural network architecture for the automated detection of rice leaf smut and rice leaf blight. In doing so, this research addresses the issue of data scarcity via a practical variance modeling mechanism (Domain Feature Mapping) and a custom filter development mechanism assisted through a reference protocol for filter suppression.  Rice is considered as one of the most widely consumed foods across the globe, boasting a total consumption rate of 493.13 million metric tons between 2019 and 2020 [Timely and accurate detection of rice plant diseases is fundamental to preserving the quality and quantity of rice production. At present, the de facto approach for quality inspection of rice production is purely manual . This apThe idea of automating quality inspection processes within various agricultural domains is a well-researched topic expanding across the complete spectrum from conventional image processing  utilizinZhou et al.  proposedQin et al.  presentePhadikar et al.  researchRahman et al.  focused Huang et al.  focused Islam et al.  proposedAsfarian et al.  based thAlbattah et al.  proposedAtila et al.  also basShah et al.  focused Le et al.  proposedTm et al.  proposedSumming the literature, we find that although various existing CNN architectures are implemented for leaf disease detection, there is a lack of research based on the development of custom architectures that are tailored toward lightweight application with respect to internal architectural complexity, making them suitable for deployment within a constrained environment.Furthermore, we observe from various works ,19,20 thThe contribution of this paper is based on addressing the issues highlighted in the summary of the literature section. The first contribution is based on the representative modeling of variance caused by internal and external factors during the process of rice leaf cultivation. In order to achieve this, various regional surface enhancement augmentations are proposed, tailored toward manifesting variances that may occur in the production environment, i.e., farmland. The proposed augmentations not only result in an increased dataset for addressing the issue of data scarcity but, more importantly, the generated images introduce representative variance and assist with the further accentuating of defective features, resulting in a more generalized architecture.The first contribution provides a nice segue into the second research objective. That is, the modeling of representative data augmentations facilitates the development of a custom CNN architecture that uses only two convolutional blocks for the classification of input images as containing leaf smut or leaf blight. Furthermore, a reference protocol mechanism is introduced, enabling the suppression of the number of filters used within each block by referencing against the overall number of learnable parameters for state-of-the-art image classification architectures. In doing so, particular care was taken to make sure that the developed CNN architecture was computationally lightweight when compared to other architectures such as the popular ResNet and VGG.This research was based on the detection of two types of rice leaf diseases: blight and smut. Although there are other classes that can also be investigated, the two selected classes provide both ends of the spectrum in terms of their impact on the rice leaf. Rice leaf smut is categorized as a fungus known as Entyloma oryzae. The dataset was created via an internet ‘search and extract’ copyright-free process using Google image search. The images consisted of varying resolution; as a preprocessing step, all images were capped at a resolution of 224 × 224 pixels. The main visual characteristic of this disease is the emergence of black spots on the leaves. Although leaf smut by itself does not cause any significant damage to the rice leaves, it can increase the vulnerability of the rice leaves to other diseases. Bacterial blight, on the other hand, is categorized as one of the more deadly diseases that can have a destructive impact on the cultivation of rice leaves. With regard to its emergence, bacterial blight is initiated as water-soaked streaks spread from the margins and leaf tips of the leaf, becoming larger in size, and releasing a milky-looking ooze that consolidates into yellowish droplets. During its progression, gray-whitish lesions emerge on the leaf surface, signifying the final stage, as the leaf dries and eventually dies. The original dataset is presented in Two fundamental motivations led to the development of a specific data transformation mechanism presented as ‘Domain Feature Mapping (DFM)’, shown in The first phase of the DFM, named class accentuation, aimed at essentially enhancing the contrast between defective pixels and the background. This was due to potential misclassification that may be caused by the arbitrary size and non-uniform distribution of the two classes internal characteristics. The class accentuation process was initiated by transforming the input image into greyscale for mitigating the background color interference. It can be seen from the conversion in To address the issues highlighted with global equalization, regional equalization based on pre-defined pixels as equalization regions was implemented. This was performed via the Contrast-Limited Adaptive Histogram Equalization (CLAHE) technique. The second DFM technique was aimed at generating variance that may occur due to the hardware utilized. The aim of the research was to provide a lightweight architecture that could be deployed on computationally constrained hardware devices rather than requiring cloud-based inference. The rationale for this was due to the fact that rice leaf cultivation is carried out mostly in hot Middle Eastern countries, which may not necessarily have the communications infrastructure to facilitate cloud AI inference. Producers that do have the capacity in terms of the communications infrastructure may still be reluctant due to cyber security issues with their business-related data being processed in a remote location. For example, a Raspberry-Pi-based system would carry out inference by processing the images via the camera and running the onboard CNN model. The rice leaf plants running down the production line can come in various orientations. Hence, if the model is not trained on a dataset containing images at various orientations, it may falsely assume that they belong to a particular class. After realizing the importance and impact hardware-induced variance can have on the model’s performance, an induced shift mechanism was implemented for generating images with a pre-defined pixel shift parameter. Thus, specifying the shift parameter in the form of  is attained via the matrix:The generated images, as a result of pixel-shifting, captured variance caused by the aspect ratio and hardware placement, resulting in losing random marginal-based pixel data. However, this transformation also resulted in some cases of excluding pixels containing the defective regions. As mentioned earlier, the defects are indiscriminate in their locality selection with respect to the surface of the rice leaf plant. Hence, to introduce additional variations by preserving the defective regions that may occur at the margins of the image, center-based rotations were introduced. Traditionally, the rotation of an image with respect to an angle (centre = rotation center (input image), ϴ = rotation angle (degrees), and scale = isotropic scale factor.Yet, our objective is on anchoring the center of the image for the rotation; hence, the matrix becomesThe final technique proposed in the DFM for scaling the original dataset in a representative manner was bilateral filtering. It was observed during data inspection that both defects smut and blight contain boundaries around the defective regions. Although these may be fuzzy in certain images, it would be these boundaries coupled with the color contrast that would enable the differentiation of the defects from the background surface, as shown in Brown spots (smut) and yellowish regions (blight) appearing on the leaf surface are non-uniform in their shape but do consist of margins, as shown in Bilateral filtering presents a weighting component dependent on the tonal distance of Notice that the weights depend only on the image values; hence, normalization is necessary to make sure the sum of all weights is equal to one.This section of the research focuses on the development of the lightweight CNN architecture for the classification of smut and blight rice leaves. As mentioned earlier, rice cultivation is usually undertaken in hot countries, especially in the Middle East/Asia continent. Countries that come under the category of developing nations may not necessarily have the infrastructure for facilitating automated quality inspection process, especially in the rural areas. This was the inspiration for developing a custom lightweight architecture rather than subscribing to the transfer learning approach via state-of-the-art architectures. By doing so, rice producers could utilize the automated inspection system on a constrained device such as a Raspberry Pi rather than requiring cloud connectivity for hosting computationally demanding architectures. The process was initiated by developing a two-layered CNN, i.e., containing two convolutional blocks and two fully connected layers. The parameters of the model were calculated based on the number of filters used within each convolutional block and fully connected layers. The total number of parameters Gz were compared with the parameters for the models within the RP block. If the parameters were greater than those for any of the architectures in the RP, then the number of filters within the convolutional blocks would be reduced until the developed architecture was smaller in computational parameters as compared to those architectures in the RP. Once this was achieved, the developed architecture was ready for training on the dataset presented earlier in Looking deeper at the convolutional blocks, each block consisted of a number of filters (learnable parameters), an activation function, and feature map aggregation. Max-pooling was selected for resultant feature map aggregation with the aim of mitigating the impact of positional dependency as the defects were indiscriminate in their locality selection.We noted that several activation functions have been utilized in the development of deep learning architectures. The rationale for selecting ReLu as the activation function for our CNN was due to its effectiveness in combatting the vanishing gradient issue. This was a major limitation for the sigmoid and tanh activation functions. Furthermore, diving deeper into the mathematical structure of the ReLu, we observe that it is simply a ‘Max’ operation and, hence, computationally more lightweight.γ, β = Learnable parameters.Additionally, batch normalization was introduced within the two convolutional blocks. The rationale was to overcome the internal covariate shift, which may be caused due to the internal class variance, impacting the performance of the architecture in terms of convergence speed. For example, the severity of the smut class, i.e., number of brown spots and their intensity, would differ from image to image, although they belong to the same class. In the case of no batch normalization, this internal variance would result in the projection of the two images onto a different feature space, requiring more training effort (time) for the model to converge.The developed CNN architecture and the resultant computational parameters are presented in This section of the article presents the performance of the trained CNN across various metrics. In order to train the CNN, a set of hyperparameters had to be defined. The hyper-parameters used for training on the dataset are provided in Before undergoing the training of the CNN, as per the framework presented earlier in It can be observed that our proposed CNN architecture was significantly smaller in its computational parameters, compared to all RP architectures. ResNet-18 was the second most effective model in terms of learnable parameters with a difference of 1.76 million parameters. VGG-19 consisted of a significantly higher number of learnable parameters (143.67 million) compared to all other architectures. This demonstrates the reason why the VGG-19 architecture is not deployed onto edge devices, as its computational demand is too high, requiring specific GPU hardware for reasonable inference performance post-deployment.After validating the lightweight architecture of our proposed CNN, the next stage was the training and validation of the architecture. The architecture was trained on the training dataset and validated on the validation set  as per tFirst, the fact that the difference between the training and validation accuracies is small (2.8%) signifies that the proposed augmentations were successful in generating a representative sample for the model to generalize without overfitting. Secondly, the size of the dataset post-augmentations was still small in conventional terms . This demonstrates that it was not the increased size of the dataset but rather the effectiveness of the augmentations and selection of internal CNN layers that contributed toward the high performance of the architecture. Further inspection of the internal class performance via the confusion matrix is shown in After achieving high performance on the training and validation data, the final performance check was based on the introduction of test data. In general, the objective is to hide the test data throughout the training and validation process in order to mitigate the risk of data leakage. Once the final architecture has been trained and the validation performance is sufficient, the test data can then be introduced to validate if the model had in fact truly generalized.In order to achieve this, first, the training and validation datasets were merged into one folder known as the training dataset. The architecture would be retrained on the merged training dataset based on the same hyperparameters outlined in Starting with the accuracy, it can be stated that the accuracy on the test data had actually improved from the accuracy presented in the validation stage by a margin of 1.2%. This demonstrates the high generalizability of the developed architecture. Furthermore, we observe that all metrics are in close conformity with respect to their results. This again is a manifestation of the successful data augmentation strategy for generating representative data samples and also the proposed CNN development framework for developing a lightweight yet highly performant architecture.The inception of this research was with the investigation of the limited dataset for understanding the practical variance that may occur within the cultivation process of rice leaf both due to internal and external factors. As a result, various augmentation techniques were proposed under the Domain Feature Mapping (DFM) mechanism. The aim of DFM was not to simply increase the dataset in size but rather to concentrate on producing representative samples to facilitate the proposed CNN toward true generalization. This is evident from the fact that, post-DFM, the dataset had increased from 80 to 560 samples as opposed to acquiring thousands of images via applying of arbitrary augmentations. We feel that the proposed DFM process will provide researchers focusing on automation in the rice cultivation domain with a set of techniques that can output representative samples, helping them to address the issue of data scarcity without compromising on the quality of the data.Moving onto the proposed CNN development framework, it can be said that based on the performance of the trained architecture on the test dataset, the CNN, although containing only 2 convolutional blocks with limited filters, was able to highly generalize on the dataset. The proposed RP mechanism and its assistance in developing a lightweight architecture consisting of a limited number of computational parameters can again be utilized by developers for designing architectures that are computationally friendly and can be deployed without requiring specific GPU hardware. It is also important to mention that the constrained environment for undertaking this research is also a manifestation of the high efficacy of the proposed mechanisms. For example, the development and training of the CNN were carried out in the Google Colab environment. Due to the limited availability of computational resources for the free tier, the training was limited to 125 epochs, whereas, conventionally, CNN architectures are trained for several hundreds or thousands of epochs for achieving acceptable performance. Based on the training of the proposed CNN for 125 epochs with a limited dataset in terms of size, we achieved an F1-score of 94.0% on the test data.In summary, we review our research output with respect to the research objective. The objective was the development of a lightweight deep learning architecture that could be deployed in constrained environments for automated detection of leaf smut and leaf blight, assisting with the quality inspection process in rice leaf cultivation. In order to achieve this objective, the first barrier we had to overcome was the data scarcity issue with the original dataset consisting of 80 images. We considered this as a practical issue that other developers may face due to the difficulty in procuring certain datasets based on the application. Hence, we provided a series of processing techniques under DFM. The proposed techniques were a result of careful domain modeling based on variances that may occur from external and internal factors.Furthermore, the CNN development framework utilizing the RP as the filter reference protocol enabled the development of a CNN architecture consisting of only 9.43 million learnable parameters compared to 143.67 million for VGG-19. Coupling the two proposed frameworks together, we were able achieve a highly accurate architecture with an overall test accuracy 94.1%We feel that our proposed methodology will enable developers in the field of CNN-based quality inspection and fault detection via AI in general ,22 acros


Large-scale industrial processes usually adopt centralized control and optimization methods. However, with the growth of the scale of industrial processes leading to increasing computational complexity, the online optimization capability of the double-layer model predictive control algorithm is challenged, exacerbating the difficulty of the widespread implementation of this algorithm in the industry. This paper proposes a distributed double-layer model predictive control algorithm based on dual decomposition for multivariate constrained systems to reduce the computational complexity of process control. Firstly, to solve the problem that the original dual decomposition method does not apply to constrained systems, two improved dual decomposition model prediction control methods are proposed: the dual decomposition method based on the quadratic programming in the subsystem and the dual decomposition method based on constraint zones, respectively. It is proved that the latter will certainly converge to the constraint boundaries with appropriate convergence factors for the controlled variables. The online optimization ability of the proposed two methods is compared in discussion and simulation, concluding that the dual decomposition method based on the constraint zones exhibits superior online optimization ability. Further, a distributed double-layer model predictive control algorithm with dual decomposition based on constraint zones is proposed. Different from the objective function of the original dual decomposition model predictive control, the proposed algorithm’s dynamic control-layer objective function simultaneously tracks the steady-state optimization values of the controlled and manipulated variables, giving the optimal solution formulation of the optimization problem consisting of this objective function and the constraints. The algorithm proposed in this paper achieves the control goals while significantly reducing the computational complexity and has research significance for promoting the industrial implementation of double-layer model predictive control. Modern industrial processes are characterized by large-scale components, extensive spatial structure, and strong sub-unit coupling . The hieThe premise of implementing hierarchical optimization and control structure is based on a decentralized control system, realizing the coordination optimization among subsystems with control algorithms. Compared to centralized control systems, decentralized control systems can reduce computational complexity. Tamás Keviczky et al., facing a class dynamic decoupling systems’ optimal control problem, designed a decentralized rolling horizon control (RHC) scheme, describing the coupling between systems through diagrams, which decomposes centralized RHC controllers into some small RHC controllers to reduce the complexity of the problem and analyzes the sufficient stability conditions for the proposed solution . For extAlthough powerful algorithms quantify the interactions of decentralized control loops, decentralized control systems still lack the coupling between multiple variables . In contMany scholars have proposed distributed or decentralized MPC algorithms based on dual decomposition. Joseph J. Yame et al. proposed a new DMPC containing both decomposition subsystem and integrated coordination sub-controller stages . Takumi Centralized control optimization is often employed for industrial process control. However, the high computational effort of centralized control optimization challenges the MPC’s online optimization capabilities, exacerbating the difficulty of the widespread implementation of DLMPC algorithms in the industry. Reducing the more considerable computational complexity with a bit of sacrifice of control performance under satisfying the control goals is of greater significance in promoting the popularization of the industrial implementation of DLMPC algorithms. To reduce the computational complexity of industrial process control, research in this paper is oriented towards constrained multivariable distributed control systems. Based on decomposition–coordination MPC, we propose two strategies for adding constraints, and based on one of them we propose decomposition–coordination of DLMPC for constrained systems, with the following main contributions: Firstly, two methods are proposed to add variable constraints based on the original decomposition–coordination  MPC algorithm to solve the problem that the original method is not applicable to multivariable systems with constraints. In the first method, based on the dual decomposition method, the suboptimization problem with constraints for each subsystem forms a quadratic programming (QP) problem. The second method is the dual decomposition method based on the constrained zones, which analyzes the convergence relationship between the variables and the constraints. It proves that if the convergence factor is small enough, the solutions will eventually definitely converge to the boundaries of the constraints. Then, both proposed methods are discussed and analyzed based on their performances, concluding that the second method has superior online optimization capabilities, which are validated by Simulation 1. Further, a distributed DLMPC algorithm based on the dual decomposition of constraint zones is proposed, where the decomposition–coordinated dynamic control layer simultaneously tracks the steady-state optimized values of the controlled variable (CV) and manipulated variable (MV), which is also different from the original decomposition–coordinated dynamic control objective function, giving an optimal solution expression that added the tracking of the steady-state optimized values of the MV, and proving the effectiveness of the proposed algorithm through Simulation 2. The improved DLMPC algorithm in this paper satisfies the control goals and constraints while greatly reducing the computational complexity of the dynamic control layer, thus improving the online optimization capability of the algorithm. It is of interest and value to provide a fundamental theoretical study for the industrial implementation of distributed DLMPC.This paper is arranged as follows: Partial abbreviations and notations are shown in DLMPC contains a steady-state optimization layer and dynamic control layer, and the structure is shown in The steady-state optimization layer calculates the steady-state target through the steady model of plants, and the steady-state targets are tracked in the dynamic control layer. There are two modes in the steady-state optimization layer: economic optimization (EO) and target tracking (TT). The economic optimization mode characterizes the benefit maximization or energy consumption minimization as a linear programming problem with the manipulated variables, as shown in Equation (1).The optimal steady-state operating point calculated by the steady-state optimization layer is used as the setpoint for tracking by the dynamic control layer, which forms the online optimization problem shown in Equation (3).The dynamic prediction model The online optimization problem for an unconstrained multi-input multi-output (MIMO) system is shown in Equation (7).Decomposed–Coordinated MPC originated from large-system control theory , a methoFirstly, by introducing Lagrange multipliers The whole problem is decomposed into In the first stage, the Lagrange function of the original problem is minimized for a given coordination factor According to the extreme value necessary condition, we can obtain In the second stage, the coordination factor Once the difference between the two neighboring iterations’ coordination factors is sufficiently small, it means that The relation between the solution of the decomposition–coordination method and the optimal solution of the original problem has been shown in , where aetail in .The original DCMPC is oriented towards multivariable systems with no constraints (only one equation constraint). However, most systems have constraint requirements on the controlled and manipulated variables. This paper proposes an improved DCMPC method for constrained multivariable systems, incorporating the simple handling of inequality constraints to the original method. The improved DCMPC can meet the control requirements of systems with inequality constraints.Suppose a constrained MIMO system, with Decomposing the original system into The first improved dual decomposition method proposed in this paper for constrained MIMO distributed systems introduces CV, MV, and MV incremental constraints into the solving process of each distributed system to form the subsystem QP problem.The equation constraint that contains the association of Therefore, the association of Using the QP solution method, The second improved dual decomposition method proposed in this paper for constrained MIMO distributed systems introduces CV, MV, and MV incremental constraints into each distributed system after the solution has been solved, and the zones formed by the constraints limit the solution.Firstly, the problem is dealt with as an optimization problem with equality constraints, equivalent to Equation (9). Then, the steps of forming the Lagrange function are the same as the original method, as shown in Equations (10)–(12). In solving the optimal of the sub-problem stage, the proposed strategy adds the inequality constraints after obtaining In the second stage, updating the coordination factor is the same as the original method based on calculated Suppose When When we substitute the equality constraints of Equations (9) and (14) into Equation (27), we obtain that seen in Equation (28).We can notice that the sum in parentheses in Equation (28) is the predicted value of the controlled variable in the future This paper proposes two improved methods based on the decomposition–coordination method for constrained distributed MPC systems. As demonstrated by the simulations in The first method introduces the equation constraint containing the association of This paper proposes two improved dual decomposition methods for constrained multivariate systems. After the above discussion and analysis and subsequent validation, the online optimization performance of the dual decomposition method based on the constrained zone is more advantageous, so an improved PDLMPC method is introduced into the DLMPC architecture. In this method, the steady-state optimization layer adopts the centralized optimization method, and the dynamic control layer adopts the constrained decomposition–coordination method. The structure is shown in Different from the matrix dimension of the dynamic model, the steady-state model has a smaller one, and it is a one-step optimization. The prediction and control horizon are both one, so the centralized optimization will not generate a large computational burden. The centralized optimization problem for the steady-state layer of a constrained multivariable system is shown in Equation (29).Firstly, the inequality constraints are ignored, and only the dual problem under equality constraints is considered, as shown in Equation (31).At the first level, the dual problem The steps for updating the coordination factor in the second stage are the same as the original method. That is, Equations (15)–(17) are performed. When the iteration is stopped, the optimal manipulated variable is applied to the controlled process. The deviation between the sampling result In summary, the improved DLMPC algorithm based on the decomposition–coordination method for constrained multivariable systems is complete. The algorithm adopts the overall optimization mode in the steady-state layer, which can give a more comprehensive steady-state target value. Taking the steady-state target value as the setting point of the dynamic control layer can provide more global information for each subsystem and more reasonable tracking targets. The distributed architecture of the dynamic control layer ensures the information transmission between subsystems through decomposition and coordination. At the same time, a simple constraint method is proposed. Through theoretical analysis, it is proved that the method can ensure that MV and CV run in the constraint conditions. However, this method is also incomplete and needs specific skills and experience when setting coefficients such as weight of control, error weight, and iteration stop accuracy.The simulation object used in this paper is the Shell heavy oil fractionation tower model, which is a typical, large, constrained, multivariable process, the structure of which is shown in To verify the performance and effectiveness of the two algorithms proposed in Simulations are first performed in the unconstrained case to ensure that the constraints in Comparing the centralized optimization method with constraints, the PDD1 and PDD2 are proposed in this paper. The results show that the three algorithms’ optimal solutions are consistent in the presence of constraints, as shown in As for the algorithm running time, PDD1 took about 14.68 s, OC took about 0.522 s, and PDD2 took about 0.0259 s. We noticed that PDD1 takes the longest time and PDD2 has an obvious advantage in reducing the amount of computation and shortening the running time, which verifies the conjecture in In summary, Simulation 1 demonstrates that the constrained decomposition–coordination method proposed in The above simulation results show the effectiveness of the two improved dual decomposition methods proposed in this paper. Both methods can ensure that the variables are constrained and the control goal can be achieved under positive constraints. The optimal solution trajectory is consistent with the constrained OC algorithm. At the same time, the calculation time of the three algorithms is compared. PDD2 takes the shortest time and shows excellent online optimization performance.Although PDD1 achieves results consistent with OC, it takes longer. It is not advantageous for small- to medium-sized solutions, so we use a modified double-layer model predictive control algorithm (PDLMPC) based on PDD2 for subsequent simulation validation and a comparison algorithm using the original double-layer model predictive control algorithm (ODLMPC) with centralized optimization, using the Shell heavy oil fractionation model as shown in Equation (35). The steady-state optimization layer is required to track the external value for a given MV and CV and constitutes the steady-state optimization problem, as shown in Equation (18). The dynamic control layer tracks the steady-state targets The ODLMPC and DCDLMPC  algorithms were first run without constraints to prove that the constraints in Results from the ODLMPC and the PDLMPC algorithms presented in This paper proposes an improved distributed DLMPC approach facing a complex class of constrained systems in modern, extensive, industrial processes. Firstly, two methods for adding constraints are proposed based on the decomposition–coordination MPC. The first improved dual decomposition method introduces the constraints of MV and CV into solving the distributed subsystem, forming the QP problem with the dual function of the subsystem to solve it. The second is a dual decomposition method based on constrained zones, which introduces the constraints of MV and CV into the distributed subsystem after solving


Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research. Autism spectrum disorder (ASD) is a neurodevelopmental disorder with impaired social function as its core deficit  not only played an important role in the “social brain” of ASD  is an exploratory data-driven approach , to improve the reproducibility and statistical power of the results. It includes two large-scale collections: ABIDE I and ABIDE II, each of which is composed of independent functional and structural brain imaging data collected from more than 24 different laboratories worldwide ]; and (4) participants with right-handedness. The demographic information is shown in http://fcon_1000.projects.nitrc.org/indi/abide/.The experimental data of this study were obtained from the ABIDE dataset (ABIDE I and ABIDE II), which included multiple neuroimaging data sites. Before data collection, all sites were required to confirm that their local institutional review board (IRB) or ethics committee approved both the initial collection and retrospective sharing of a fully anonymized version of the datasets , was used to preprocess the data on MATLAB 2017b (https://www.mathworks.cn/products/matlab.html). The first 10 time points were removed to promote equilibrium magnetization and allow the participants to adapt to the MRI environment. Subsequently, slice-timing was used to correct the acquisition time of differences between slices, and realignment was performed to adjust the time series of images to make every image at the same position  isotropic Gaussian kernel to perform spatial smoothing to reduce spatial noise  to extract the region-specific HRF and deconvolve the observed BOLD signal into the real neural signal , we used Resting-State Hemodynamic Response Function Retrieval and Deconvolution . Continuous variables were compared using two-sample t-test between the ASD and TD groups with age, gender, and mean frame displacement .For the statistical analysis of the GCA, we used RESTplus to conduct a two-sample https://www.sdmproject.com) was used to conduct a meta-analysis on the original uncorrected statistical parametric maps of the GCA results calculated from the ABIDE dataset , 188 not being right-handed, and four sites (55 participants) were excluded due to no TD participants. Finally, 618 individuals with ASD and 836 TD participants were included. Demographic information is shown in p < 0.0005, The results of the meta-analysis of the GCA showed that, compared with the TD group, the GCA values of the ASD group from the RTPJ to the right fusiform, the left inferior temporal gyrus (ITG), the right middle temporal gyrus (MTG), and the left precuneus were higher. However, the left insula, the left anterior cingulate cortex (ACC), the left middle frontal gyrus (MFG), and the left superior frontal gyrus (SFG) were lower than those of the TD group  to the RTPJ were significantly lower than those of the TD group  and are jointly involved in the processing of visual stimuli, especially in relation to face recognition of ASD  signals  and the Youth Science and Technology Plan of Soochow Science and Technology Bureau and Soochow Health Planning Commission (KJXW2020065).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Magnetic systems governed by exchange interactions between magnetic moments harbor frustration that leads to ground state degeneracy and results in the new topological state often referred to as a frustrated state of matter (FSM). The frustration in the commonly discussed magnetic systems has a spatial origin. Here we demonstrate that an array of nanomagnets coupled by the real retarded exchange interactions develops a new state of matter, time frustrated matter (TFM). In a spin system with the time-dependent retarded exchange interaction, a single spin-flip influences other spins not instantly but after some delay. This implies that the sign of the exchange interaction changes, leading to either ferro- or antiferromagnetic interaction, depends on time. As a result, the system’s temporal evolution is essentially non-Markovian. The emerging competition between different magnetic orders leads to a new kind of time-core frustration. To establish this paradigmatic shift, we focus on the exemplary system, a granular multiferroic, where the exchange transferring medium has a pronounced frequency dispersion and hence develops the TFM. The exchange interaction is of the quantum mechanical origin and stems from the intertwined effect of the Coulomb interaction and Pauli exclusion principle governing the behavior of indistinguishable fermions with overlapping wave functions. Remarkably, a rich lore narrating how the geometric frustration developing from the exchange effects leads to degeneracies and the emergent FSM, neglects the temporal component: local magnetic moments are supposed to instantly interact with each other without delays4. However, the flip of electron spin in an atom in a crystal implies a rearrangement of electron density distribution in space, which, in turn, affects the strength of interactions between the atom with its neighbors. The rearrangement of electron clouds occurs at optical frequencies  nature.Macroscopic magnetism forms due to microscopic exchange interactions7, where interactions occur not directly but through the mediating active dielectric or ferroelectric environment. In such materials11 the retardation effects are relatively large and cannot be ignored. There, the polarization, 13 in a linear response approximation. The superexchange interaction of magnetic moments in granular multiferroics14, where electric and magnetic degrees of freedom mutually influence each other, acquires retardation as well. In our work we reveal the time retardation of the exchange interaction and investigate the time-frustrated state of matter emerging due to this retardation in an array of magnetic moments immersed into the ferrolectric environment.There is an abundance of new functional materials, like granular multiferroics23. One of the most discussed examples has been relaxation of the system after an instantaneous external action of, for example, laser irradiation finite-duration pulse. Here, we address a different situation where the retardation emerges due to internal properties of the system. The resulting relaxation manifests a variety of the non-trivial effects appearing even without the external finite-time impacts. We are confident that our finding on the retarded nature of exchange would contribute to investigations of relaxation processes in the time-dependent exchange.Relaxation of the exchange has recently been intensely studied, both experimentally and theoretically24, that the dielectric constant appears in the effective exchange between two magnetic moments as24 and also accounting for the spacial dispersion effects. Yet, even in this first approximation, the frequency dispersion of the exchange integral 25. Accordingly, we arrive at the model of a magnetic system with the effectively delayed exchange. In a temporal representation, this implies the delay in the interaction of magnetic moments: To reveal how the exchange retardation results in the TFM we focus first on an elemental building block of a granular-multiferroic, two adjacent magnetic granules interacting via a ferroelectric medium as schematically shown in Fig. In typical ferroelectrics  and lead zirconate titanate (PZT)), J(t) is alternating in sign with time. Hence we arrive at the “time frustration” of the exchange interaction.Accordingly, we putLet us consider now two adjacent magnetic moments i-th moment is27 implying that the exchange energy assumes the usual form, The magnetic granules are supposed to be semiclassical, hence granule’s magnetization should obey the non-local in time Landau-Lifshitz-Gilbert (LLG) equation. We consider an array of localized magnetic moments satisfying Frustrated exchange with or forA non-frustrated exchange with We see that for stationary solutions, frustrated and non-frustrated cases differ qualitatively. Hereafter we focus on a dynamically frustrated case.Let us consider a stationary solution to Eq.  in whichs to Eq.  in the ai-th magnetic moment Let us derive the adjustments to the stationary solution discussed above arising due to time dependence of the magnetic moments. We consider the time-dependent part of the The above general reasoning holds for any arbitrary regular magnetic structure and, in particular, is not restricted to systems subject to nearest neighbor interactions constraint. To illustrate how the formation of the time-frustrated state occurs, we consider the simplest particular system, two interacting magnetic moments Taking the simplest form of the exchange satisfying all the above-defined conditionsG, To simplify further notations, we set it in that the magnetic moments and energy are properly normalized and are measured in dimensionless units. Thus, Having established the ranges of stability within the linear approximation, let us turn to detailed investigating the time evolution of our system. The zero-frequency limit was discussed above. At high frequencies the ferroelectric degrees of freedom are frozen and the exchange is provided by the conventional electron clouds overlapping.The time evolution appears radically different in the isotropic case and in the presence of the even weak uniaxial anisotropy. In the isotropic case, the asymptotic, mentclasspt{minimaThis whole evolution picture is noise-resistant, it does not transform under the delta-correlated noise with the amplitude In the presence of the anisotropy, both states, the AFM and the FM, become stable. There exist two ways of switching the final destination of the system between these states. The first way is changing the parameters of the dynamically frustrated potential. The example of such a switch by changing the characteristic frequency The revealed behaviours are of a general character and maintain for a general case of the system with the arbitrary number of the magnetic moments. The behaviour of the exemplary four-site cluster is presented in the We have studied spin system with retarded spin-spin interaction 31, and further aspects and implications of the non-Hermitian behavior of the system in hand will be a subject of the forthcoming publication.It is important to stress that the retardation causes the non-Hermiticity of the effective Hamiltonian of the interacting magnetic moments, therefore, the considered system is an effectively dissipative. Non-Hermitian quantum mechanics describing open dissipative systems is currently enjoying an intense explosive development34. At the same time, G in Eq.  logics54. We demonstrated that owing to the dynamic frustration, the ferromagnetic and antiferromagnetic are stable and long-living states, hence having, in fact, the same energies. In particular, the system that we have considered renders the tunnel junction magnet-ferroelectric-magnet with the time frustrated exchange having four different stable states, comprising two ferroelectric and two magnetic states. Hence the computer element based on the TFM holds high potential for the four-valued logic hardware realizations.Mutliferroics, the systems with interacting magnetic and electric degrees of freedom, broaden the scope of the existing current hardware conceptst, and Eq. (i-th for the Fourier transform of magnetic moment becomes Eq. (Taking magnetization as and Eq.  of the m and Eq. , one getomes Eq.  of the mFor the two-site cluster the system of equations  for \docTaking the exchange in the form Eq.  that notFor the AFM configuration Supplementary Information.


The cerebrospinal fluid (CSF) plays an important role in delivering nutrients and eliminating the metabolic wastes of the central nervous system. An interrupted CSF flow could cause disorders of the brain and eyes such as Alzheimer's disease and glaucoma. This review provides an overview of the anatomy and flow pathways of the CSF system with an emphasis on the optic nerve. Imaging technologies used for visualizing the CSF dynamics and the anatomic structures associated with CSF circulation have been highlighted. Recent advances in the use of computational models to predict CSF flow patterns have been introduced. Open questions and potential mechanisms underlying CSF circulation at the optic nerves have also been discussed. The cerebrospinal fluid (CSF) is primarily composed of water (99%)  and occuIn the ON, interrupted CSF circulation, also referred to as compartment syndrome of the ON , 8, causIn this review, the anatomy and circulation route of the intracranial and ON CSF system have been introduced with an emphasis on the ON. Interstitial fluid (ISF) in parenchyma can be rapidly transported by AQP4 at the apex of the astrocytes , and theVarious tissues and structures are involved in the generation, circulation, and absorption of CSF, including the choroid plexus, ependyma, cisterna magna, subarachnoid spaces, Virchow–Robin space (VRS), brain parenchyma, the hypothalamus in the intracranial space, dura, ON subarachnoid space, and ON parenchyma . In thisMost of the CSF is produced in the choroid plexus. The choroid plexus is located in the ventricular system, which is a set of interconnected cavities composed of two lateral ventricles, the third ventricle, the cerebral aqueduct, and the fourth ventricle. Capillaries, enfolded by fenestrated endothelial cells, form the core of the choroid plexus . The epeCSF flowing from cisterna magna enters the subarachnoid space. The subarachnoid space is the space between the arachnoid mater and the pia mater, which is filled with CSF. It is occupied by delicate connective trabeculae and intercommunicating channels as well as branches of the arteries and veins of the brain .The VRS  are regaThere are three layers of meninges covering the central nervous system. The pia mater directly enfolds the parenchyma. Dura mater is the exterior most layer of the brain meninges. Between the dura and the pia is the arachnoid layer. Studies have shown that a network of lymphatic vessels exists in the dura, which is capable of draining CSF out of the brain , 24. How18F) fluoro-D-glucose ([18F]FDG) secreted by the hypothalamus takes part in the regulation of metabolic processes and sleep processes . Du. Du40). It is speculated that ON dural sheath is involved in CSF transport through the lymphatic vessels in the dura . HoweverInside the ON parenchyma, a perivascular transport system exists to allow CSF communications between the ON subarachnoid space and the ON parenchyma . Cells, Human eyes exhibit frequent movements in daily activities with approximately 170,000 saccades per day . The angInteractions between the eye globe and the ON –53 durinOf note, the eye rotates frequently during rapid eye movement (REM) sleep, which is a sleep stage, despite the eyes being closed. This state persists for 10–30 min at a time . ThroughFluid in the lymphatic systems  plays a Microscopically, the metabolic waste of brain tissues is cleared by a brain-wide perivascular pathway along which there is an exchange of CSF and ISF through AQP water channels , which iThe glymphatic system provides novel insights into the pathology of AD. The deposition of β-amyloid plaques  in AD reThe ON is part of the central nervous system. Therefore, the glymphatic system should also exist in the ON to assist in the clearance of metabolic wastes. The ON glymphatic system can assist in understanding the development of glaucoma . In WangA higher trans-lamina cribrosa pressure difference (TLCPD) will cause deformation of the intraocular portion of the ON and distort the pathways of the ON glymphatic system, which could hinder the metabolic clearance mechanism and result in glaucoma . The intOwing to the limitations of currently used imaging technologies and anatomical findings, whether the glymphatic system has an impact on those diseases is still unknown. Further research is thus required.Various imaging technologies with corresponding indicator elements or substances can be utilized to visualize the pathways of CSF to understand its properties and functions. These techniques include magnetic resonance imaging (MRI), two-photon imaging, computer-assisted cisternography (CT cisternography), and scanning electron microscopy (SEM). Moreover, phase contrast techniques are also widely used in the CSF study .Phase-contrast MRI (PCMRI) is a commonly used method in studying CSF dynamics . PCMRI iBesides PCMRI, time-spatial labeling inversion pulse (Time-SLIP) is another technique that can visualize the CSF movement . It is uEcho planar imaging (EPI) is another technique frequently used in MRI. EPI is capable of acquiring an image in milliseconds, allowing it to image dynamic physiologic processes such as CSF movement. In 1990, Stehling and associates showed that EPI with a snap-shot imaging time of 128 ms allows detailed demonstration of transient intraventricular CSF flow patterns . It alsoBecause a contrast agent is not necessary for MRI, potential artifacts induced by contrast agent injection, such as the increase in the CSF volume, can be avoided. However, in a complex CSF flow pattern, because anterograde and retrograde flows mix, they may not be detectable with fMRI as a result of spatial overlap.in vivo two-photon imaging to detect the perivascular CSF pathway through the brain parenchyma. In this study, it is found that CSF enters the parenchyma along perivascular spaces, which surround the penetrating arteries and the ISF is cleared along perivenous pathways.Two-photon imaging is a type of fluorescence imaging with a limited imaging depth of approximately 300 μm . Becausein vivo. The study conducted by Wang used this technique and found that the ocular β-amyloid clearance was affected by glial water channel AQP4  is a branch of fluid mechanics that utilizes equations governing a fluid motion to simulate complex fluid flows. Generally, four steps are required in a CFD simulation that are as follows: 1) constructing the geometry of the problem, usually through computer-aided design or reconstruction from medical images. 2) dividing the volume of fluid and the boundaries into discrete cells (elements). 3) defining the physical modeling such as the equations of fluid motion. 4) defining the boundary conditions . All comComputational models are versatile in terms of predicting the CSF properties in perivascular spaces , CSF volIntracranial CSF circulation has been investigated to a considerable extent. However, the corresponding flow in the ON is largely unexplored . The narAccording to Killer's research, the subarachnoid space in the ONs is divided into different cavities by trabeculae and septae . HoweverMoreover, the relationship between eye movements and the CSF circulation in the ON deserves further investigation. The functions of eye movements in human REM sleep are still unknown. Eye movements have been hypothesized to drive the CSF flow in the ONs.This review described the anatomy and CSF flow in the ON and cranium, as well as research tools for CSF dynamics. The role of the glymphatic system in the brain and the relationship between CSF flow and ophthalmic and neurological diseases were emphasized. Potential mechanisms and driving forces of CSF circulation in the ON are still uncertain. Several questions about ON CSF dynamics remain to be answered. The eye movement was speculated to be the driving force of CSF exchange between the ON and cranial subarachnoid spaces. We are interested in analyzing the pressure gradients in the ON and their impact on the visual pathway.XW and JS contributed to the conception of the manuscript and wrote the first draft of the manuscript. QL and TL wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This study was funded by the National Natural Science Foundation of China (12002025 and U20A20390) and the 111 Project (B13003).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To the best of our knowledge, this is the first case reporting PLD in the G7® cup (Zimmer-Biomet). This case report, along with a review of the literature, highlights the clinical presentation, radiological imaging, treatment options, and technical tips to avoid PLD in the early postoperative period.Polyethylene liner dissociation (PLD) is a rare but catastrophic complication following total hip arthroplasty (THA). When it occurs in the early postoperative period, it can be easy to miss the diagnosis. Liner dissociation has been reported previously with the Pinnacle Modularity in total hip arthroplasty (THA) is very advantageous as it increases the intraoperative flexibility to adjust the length, offset, and anteversion. However, the modularity of the acetabular components can introduce different mechanisms of failure, like backside wear and disassembly of polyethylene from the acetabular shell. Polyethylene liner dissociation (PLD) from the acetabular shell is a rare event, and early diagnosis prevents the need for acetabular component revision. There are several different locking mechanisms designed to fixate polyethylene liners to the acetabular shell. A polyethylene liner is locked into the acetabular shell by locking rings and tabs, taper lock, circular fit, or by increasing the friction fit between the metal and polyethylene component, or by increasing the conformity between the metal shell and PE liner . MultiplA 67-year-old male patient presented to our clinic with significant right hip pain and imaging consistent with end-stage right hip arthritis Figure . The patThe patient was discharged two days after surgery and followed up at regular intervals for up to 12 months. He had an uneventful recovery and his Hip Disability and Osteoarthritis Outcome Score (HOOS) improved from 16.3 (at the time of PLD) to 63.6 at 12 months.PLD is a rare but catastrophic complication with a reported incidence of 15/10,000 ,9. ThereUsually, when PLD occurs, the patient feels a sudden clunk and mild discomfort. The event can be associated with trauma but can also occur spontaneously. In our patient, there was no history of injury or sudden clunk. Patients typically complain of abnormal sounds and squeaking, a grating sensation, relative shortening of the operative leg, or a change in the gait pattern. When radiographs are obtained, they reveal abnormal eccentric articulation with superolateral migration of the femoral head into the shell on the antero-posterior view (AP), and medial migration of the femoral head in the lateral view is usually confirmatory. Unlike PLD associated with polyethylene wear usually, these changes are abrupt. The polyethylene can be seen as a radiolucent ring in some cases (bubble sign/crescent sign). When in doubt, CT scans, MRIs, and ultrasounds are utilized for confirmation of the diagnosis. A CT scan can be helpful to locate the displaced radiolucent polyethylene, however, beam-hardening artifacts can obscure some details. The PE can be seen inferiorly below the capsule or posterior joint recess/greater trochanteric bursa or may be partially associated with the shell. Metal Artifact Reduction Technique Sequence (MARS) can increase the diagnostic confidence relative to routine pulse sequences. MRI with MARS can usually demonstrate polyethylene more clearly than any other investigation. An arthrogram can also help to confirm the diagnosis . SonograIf this complication is diagnosed early without damage to the locking mechanism, an isolated liner revision can be performed, making treatment substantially easier. In the case of a late diagnosis, treatment includes either revision of the entire acetabular shell or modification of the causative factors.Even though we could not conclude the reason for PLD in our case, improper seating of the implant is likely the cause, but other causes, likely femoral neck impingement secondary to the use of high-wall liner, could not be ruled out. PLD could happen during the immediate post-operative period, medium-term (5-10 years), or can be delayed (15 to 20 years) from the primary procedure. The early or immediate PLD is usually technique-related, such as improper seating of the liner, or could be from internal impingement between the neck and the prosthesis liner. The medium-term PLD could be from internal impingement from an abnormal activity or position, or a new onset of impingement from progressive degenerative spine disorder or heterotopic ossification (HO). The late onset of PLD could be a wear-related or a new onset of impingement from DJD of the spine. Backside wear can damage the locking mechanism, resulting in this catastrophic complication.Technical factors that can impact the incidence of PLD include failing to ensure that the liner is cleared of the soft tissue, blood, or fat at the interface; not being collinear with the shell while impacting poly; damage to the tabs during initial insertion; and prominent screw heads. Occasionally, the acetabular shell can deform during insertion, resulting in improper seating of polyethylene into the shell. Malposition of the acetabular component and femoral component can predispose to impingement leading to PLD. A combination of face-changing or offset acetabular liners with vertically mal-positioned acetabular components can cause impingement, resulting in PLD. The liner should be inspected and palpated circumferentially, and should be manually checked to confirm the locking of the polyethylene into the liner. Some implants have a recess (R 3) along the edge of the acetabular shell to check the proper seating and stability of the locking mechanism. However, it is not present in most of the other existing systems. Most modern acetabular shells have two locking rings and 12 grooves to receive tabs of the liner along the inner surface, and the neutral liner sits flush with the acetabular shell. However, this design feature is not uniformly present in all modern implant companies, and it is important for surgeons to understand the different locking mechanisms (Table In the current case, the linear and horizontal grooves of the articular surface of the shell appeared intact, so we elected to retain the acetabular shell and proceed with head and liner exchange. The original lipped liner and 36 mm ceramic head were swapped with a neutral liner and a 40 mm head. The increased head diameter and neutral liner increased impingement free range of motion. After the new implants were placed, there were no signs of impingement or instability with functional ROM. We also carefully evaluated if there was any micromotion between the liner and the shell before proceeding with this plan. Since this incident happened, 360 degrees of visualization of the liner seating flush with the acetabular shell, and checking the stability of the locking of the liner into the shell with a hemostat at multiple places has become routine during THA.Overall, this case brings to attention the importance of having a high index of suspicion for PLD. Although it is not a common complication, early detection can significantly reduce the complexity of revision surgery.Dissociation of the polyethylene liner from the acetabular shell is a rare complication of THA. When it occurs in the early postoperative period, early recognition is critical in potentially avoiding an acetabular component revision. This study presents a case of PLD in the early postoperative period and discusses the diagnostic challenges in recognizing this complication. Furthermore, we discuss the technical tips to avoid this rare complication and various treatment options described in the literature.


The Matter-wave laser Interferometric Gravitation Antenna (MIGA) is an underground instrument using cold-atom interferometry to perform precision measurements of gravity gradients and strains. Following its installation at the low noise underground laboratory LSBB in the South-East of France, it will serve as a prototype for gravitational wave detectors with a horizontal baseline of 150 meters. Three spatially separated cold-atom interferometers will be driven by two common counter-propagating lasers to perform a measurement of the gravity gradient along this baseline. This article presents the cold-atom sources of MIGA, focusing on the design choices, the realization of the systems, the performances and the integration within the MIGA instrument. Besides the current  and future7 ground-based laser interferometers operating in the 8 probing the nHz region of the spectrum, the space-based laser interferometer LISA is planned to be launched in 2034 to probe the 10 or in space12. Ground based cold-atom interferometers represent a promising candidate solution owing to their large immunity to seismic noise13 and their potential to reject the gravity gradient noise to a sufficient extent14 in that frequency band. Several projects around the world are designed to explore that potential18Gravitational wave astronomy requires the development of detectors addressing the various frequency bands of interest to the astrophysics and fundamental physics communities19. An overview of the instrument is also shown on Fig.  The Matter-wave laser Interferometric Gravitation Antenna (MIGA) aims at providing a platform to study the key techniques for GW detection with matter wave interferometry. The project is described in details in Ref.The characteristics of the MIGA instrument  require an adapted design of the atomic preparation and detection. An important feature of the instrument is the ability to produce cold atom clouds with the appropriate quantum state which are injected in the AI region, and to detect the state of the atoms at the output of the interferometer. A Bragg interferometer requires a narrow velocity selection for the input state, and the ability to detect the output velocity state. This article presents the working principle of the cold-atom systems (excluding the interferometer part), their design and realization, and their performances.21 where three light pulses are used to split, deflect and recombine the atomic waves. The cold atoms enter the interferometer from the bottom and interact with two horizontal pairs of counterpropagating Bragg lasers22 separated by a distance of 30.6 cm in the vertical axis. The splitting and recombination , laser cooled using polarization gradient cooling and vertically launched. On their way up before the interferometer, the atoms are prepared in the desired quantum state . On the way down, the probability for an atom to be in a given output momentum state is detected using a momentum-to-internal state labelling technique based on a Raman transitionA two-dimensional magneto-optical trap 2D MOT) fed by a rubidium alloy dispenser is realized in a vacuum chamber connected to the atom preparation chamber by a differential vacuum tube  of the atoms is prepared on their way up, before the interrogation region. A first counter-propagating velocity-selective Raman pulse  detected, for a sample in a state of equal repartition between the two hyperfine levels . Further characterisation and optimisation will be carried out during integration of the subsystem to the instrument. A possible design evolution may include the use of top-hat beam shapers to increase the contrast of Raman transitions


As one of the most widely planted fruit trees in southern China, citrus occupies an important position in the agriculture field and forestry economy in China. There are many kinds of citrus diseases. If citrus infected with diseases cannot be controlled in time, it easily seriously affects citrus production and causes large economic losses. Timely monitoring of disease characteristics in the citrus growth process is important for implementing timely control measures. Citrus images are easily disturbed by environmental factors such as dust, low light, clouds or leaf shadows. This makes it easy for some disease spot features in citrus pictures to be obscured. Occluded lesions cannot be effectively extracted and recognized. Second, similar characteristics of different diseases also make it difficult to distinguish the different types of diseases. However, the existing machine vision technology for identifying citrus diseases still has some difficulties in dealing with the above problems.This paper proposes a new citrus disease identification framework. First, a citrus image enhancement algorithm based on the MSR-AMSR algorithm is proposed, which can enhance the image and highlight the disease characteristic information. The AMSR algorithm can also greatly alleviate the interference of clouds and low light on image lesions, making the image features clearer. Second, an MF-RANet network is proposed to recognize citrus disease images. MF-RANet is composed of a main feature frame and a detail feature frame. The main feature frame uses the cross stacking structure of ResNet50 and RAM to extract the main features in the citrus image dataset. RAM is used to extract the attention weight in the feature layer, which enables RAM to give higher weight to disease features. The detailed feature frame path uses AugFPN to extract features from multiple scales and fuse the main feature frame path. AugFPN enables the network to retain more detailed features, so it can effectively distinguish similar features in different diseases. In addition, we use the ELU activation function not only to solve the problem of gradient explosion and gradient disappearance but also to effectively use the negative input of the network. Finally, we use the label smoothing regularization method to prevent overfitting the network in the classification process. Finally, the experimental results show that the highest detection accuracy of the network for Huanglong disease, Corynespora blight of citrus, fat spot macular disease, citrus scab, citrus canker and healthy citrus is 96.77%, 96.22%, 95.96%, 95.93%, 94.04% and 97.55%, respectively.The citrus disease algorithm based on AMSR and MF-RANet can effectively perform the disease detection function. It has a high recognition rate for different kinds of citrus diseases. With the addition of AMSR preprocessing, RAM, AugFPN, ELU activation function and other structures, the MF-RANet network performance improves. As one of the most widely planted fruit trees in southern China, citrus occupies an important position in the field of agriculture and forestry economy in China , 1. WithTraditional disease recognition technology is usually based on global features such as the colour, shape and texture of the disease spot and multiple indicators are selected to reflect the gap between the diseases. For example, Stegmayer et al.  used 10 To solve the problem that the disease features in the image are vulnerable to environmental interference, LV et al.  used pulTherefore, this paper proposes the AMSR algorithm to enhance citrus disease images. The AMSR algorithm is improved based on the traditional MSR  algorithTo solve the problem that the feature similarity and detail feature loss of citrus diseases affect neural network recognition, Sankaran et al.  used theTo prevent the loss of detailed features and extract similar features among different citrus diseases, a new MF-RANet network is proposed to identify citrus diseases. The design is as follows:We use RAM for MF-RANet. RAM cross cascades the 1– 4 layers of ResNe50 with RAM blocks with the same residual structure, which can realize the residual structure with high depth and high width. This residual structure is conducive to extracting important features.We use the AugFPN feature fusion algorithm for MF-RANet. AugFPN can effectively reduce the loss of small citrus disease features by extracting and fusing features of multiple scales. The AugFPN structure and ResNet50 extract features together and can jointly realize size feature extraction.In MF-RANet network, we use ELU activation function instead of ReLU activation function. The ELU activation function can not only solve the problems of gradient explosion and gradient disappearance in the network but also effectively use the negative input part of the network. Second, the effective activation brought by the ELU activation function can improve the network citrus disease recognition rate and convergence speed.We add label smoothing regularization after the softmax function of MF-RANet network. Label smoothing regularization can effectively prevent overfitting in the label smoothing network classification process. Introducing label smoothing regularization can effectively improve citrus disease recognition network accuracy.The average test recognition accuracy of the six citrus pictures was 96.87%, and the F1 score was 96.56%. This method not only has a good effect on identifying citrus health and disease but also has a good effect on classifying citrus diseases with similar disease spots. The method in this paper can also be used in public datasets with good effect. It can reduce the agricultural citrus loss due to disease. Rapid and accurate identification and classification of diseases can effectively reduce the loss of citrus to diseases in agricultural production.Therefore, aiming at the problem that there are similar characteristics among different citrus disease species and the details are easily lost, this paper constructs an MF-RANet model. The MF-RANet model contains two frame paths for feature extraction: the main feature frame path and the detail feature frame path. The main feature frame takes ResNet50 as the main structure of the network and adds the attention mechanism –20—RAM tA schematic diagram of citrus disease recognition is shown in Fig. The dataset used in the experiment comes from 2 sources: the dataset website and orchard collection. The dataset website includes the China Science Data Network  and digiA variety of citrus plants are planted in mountainous and hilly areas, which are prone to cloudy and rainy weather. In the process of collecting citrus disease images, the images may be disturbed by dust, clouds, low light and other environments. This may result in some lesions in the dataset being blocked or unclear. For example, in Fig. In citrus image enhancement based on ASMR, first, the original image of citrus diseases is decomposed to obtain three RGB colour channels. Second, the incident component of a single channel is estimated, and three Gaussian surround functions are constructed by using three scale parameters. The Gaussian surround function is used to filter the image channels, and then the weight coefficient is introduced to obtain the three-channel incident component weight of each region. Next, the reflection component is calculated. The reflection component is obtained by subtracting the original image from the illumination component in the logarithmic domain. Finally, the R, G and B channels of the whole picture are restored, and the brightness compensation factor is added to repair and adjust the defect of colour distortion caused by contrast enhancement in local areas of the image. The schematic diagram of the AMSR algorithm is shown in Fig. The resulting citrus disease image is decomposed into three colour channels: R, G and B. Subsequent calculations are implemented in each channel.The incident component reduces the high-frequency difference of the original fluctuating citrus diseases image, covers the disease features, blurs the edge details, and visually becomes a large black (homogeneous area). Due to the influence of incident light, the resulting picture deviates from the inherent properties of the object, so it is necessary to eliminate the interference of incident light. Next, the Gaussian surround function is used to filter the three channels of the citrus disease image to estimate the incident light. The three-channel Gaussian surround function can be expressed by the following formula:In the formula, σ is the Gaussian surround scale, which is related to the overall smoothness of the Gaussian function. The incoming and outgoing images are calculated by Fourier transform.Three different weights represent different incident components in different regions. Because different pixels are located in different regions, they have different weights. The pixel weight is determined by judging whether the pixel is in the image edge area carrying high-frequency information or in the homogeneous area carrying low-frequency information. If the pixel is in the edge area (carrying high-frequency information), the effect image with a smaller A given citrus disease image S  can be decomposed into two different images: reflected image R and incident image L . The incident light is reflected on the reflected object and is reflected into the human eye after reflection of the object. The final formed image can be represented by the following formula:The grey reflection components are combined and brightness compensated to restore the three colour channels of R, G and B, and the increased brightness compensation factor is The comparison of citrus disease images in the AMSR enhancement algorithm before and after enhancement is shown in Fig. Many kinds of citrus diseases have similar characteristics. For example, both corynespora blight of citrus and fat spot macular disease in Fig. The MF-RANet overall model built in this article is shown in Fig. The main feature box is composed of each layer of the ResNet50 network and attention module. The following focuses on the ResNet50 and RAM structures.ResNet is a network model proposed by He et al.  in 2015.Each layer of the ResNet50 network contains 2 modules, an identity block and a convolution block. Convolution blocks can change the network dimension, but they cannot be connected in series; identity blocks are used to deepen the network and can be connected in series. With the deepening of the network level, the learned things become more complex, and the number of output channels increases. Therefore, while using identity blocks to deepen the network, it is also necessary to use convolution blocks to convert dimensions so that the features of the part in front can be transmitted to the feature layer in the back. Compared with previous networks, it is still one of the classic and used networks because of its few parameters, deep layers and excellent classification and recognition effect. However, for the problems that small features are easy to ignore and similar features are not easy to distinguish in the identification of citrus diseases, a single ResNet50 structure is still not enough. Therefore, this article improves upon ResNet50.When people observe and recognize the target, they will focus on the prominent part of the target and ignore some global and background information. This selective attention mechanism is consistent with the characteristics of the discrimination part in fine-grained image classification. Then, in order to focus on monitoring the different features of the citrus diseases image, this paper cross adds the residual attention mechanism in each layer of resnet50. The RAM can give higher weight distribution to the features containing disease identification information. It can effectively improve the effect of fine-grained classification. There are two branches in RAM, namely, the mask branch and trunk branch. The trunk branch is convolution, and the mask branch outputs the attention feature map with the same dimension through feature map processing. Then, the characteristic graphs of the 2 branches are combined by point multiplication to obtain the final output characteristic graph. Finally, the RAM model is formed, as shown in Fig. In the trunk branch structure, there are two convolutions in the main RAM branch structure, and the input features are directly processed into the same size as the mask branch structure 7 × 7.In the mask branch structure, the processing operation of the feature map includes a forward downsampling process and an upsampling process. The downsampling process ensures fast coding and obtains the global features of the feature map. Upsampling combines the extracted global high-dimensional features after upsampling with the features without downsampling to fuse the features of high and low latitudes. The specific operations are as follows: mask branch for fixed input, after multilayer convolution calculation, use maximum pooling to reduce the feature map dimension. The dimension is reduced until the width and height of the feature map reach the minimum size of the network output feature map 7 × 7. Then, the width and height dimensions of the feature graph are expanded layer by layer by using the bilinear difference method and added to the previous features under the same dimension. The mask branching structure combines global and local features to enhance the expression ability of the feature map.The RAM model built from these 2 parts is described below. The trunk branch output characteristic diagram is The detail feature box is used to extract the detail features by AugFPN feature fusion. As mentioned above, a network for identifying citrus diseases is proposed based on ResNet50. Although the features are extracted by convolution, after resampling again, some small pixel object features have been lost and cannot be recognized effectively. To ensure that the detailed features are not lost in the citrus disease recognition, the object features of any size can be effectively detected, and the correct recognition results can be obtained. Based on the main feature frame path, this article uses the AugFPN after the improved FPN  to add fFirst, the feature pyramid is constructed based on the multiscale features  in the main feature box. The ROI characteristics of each level  are obtained through ROI align. Then, the ROI features of  are convoluted by 3 × 3 to obtain the feature pyramid  to generate multiple ROIs. A detector and a classifier are added after  each feature before fusion. These classification and regression parameters are shared at different levels, which can further force different feature maps to learn similar semantic information outside the same monitoring signal. For more stable optimization, the weight is used to balance the auxiliary loss and original loss caused by consistent supervision. Formally, the final loss function formula is as follows:AugFPN fusion proposes residual feature enhancement to reduce the loss of semantic information caused by the reduction in the number of channels through spatial information compensation. First, B4 is downsampled into three parts as large as B4 through adaptive pooling. Then, the four layers are fused into P5. The weight of each layer is First, because  each layer contains ROI features, we use the adaptive spatial fusion module (ASF) to adaptively fuse ROI features. ASF generates different spatial weight maps for different levels of region of interest features and weights and fuses the region of interest features. The specific fusion process of different levels of features and the framework of adaptive fusion (ASF) are shown in Fig. Based on the above principles, AugFPN reduces the semantic gap between different scale features before feature fusion through consistency monitoring. In feature fusion, the ratio invariant context information is extracted by residual feature enhancement to reduce the information loss of feature mapping at the highest pyramid level. The soft ROI selection method is used to better realize feature extraction and fusion through adaptive spatial fusion. Then, they are integrated with the full connection layer of the network, which can effectively solve the common problem of losing small image features in the main feature frame.The introduction of an activation function increases the nonlinearity of the neural network model. The nonlinear expression ability of the activation function is strong. When the linear input is large, the output will not expand infinitely, which does not easily lead to gradient explosion. In addition, gradient descent can be effectively realized because the nonlinear activation function is differentiable. Traditional saturation activation functions such as sigmoid and tanh have the problem of gradient disappearance. This will make the convergence of the training network increasingly slower.The activation function used in the original ResNet50 model is the ReLU  functionIn view of the above shortcomings of the ReLU function, we choose another improved ReLU-ELU activation function. The image and its derivative function are shown in Fig. The expression of the ELU function is:The ELU function is consistent with the part where the ReLU function is greater than 0. When it is less than 0, the ELU function expression is In neural network, because of too many model parameters, it is easy to cause overfitting of the model. Typical regularization methods such as L1, L2 and dropout  are usedIn the citrus disease classification task, the confidence scores of citrus disease images corresponding to various diseases are obtained through the MF-RANet network. These scores are normalized by the softmax function , and finLabel smoothing changes the probability distribution into simple uniformly distributed noise. The formula is as follows: The cross entropy isWhen training the MF-RANet network, the cross entropy of the prediction probability and label real probability is minimized to obtain the optimal prediction probability distribution. The prediction probability distribution of the optimal fitting effect of label smoothing is:The essence of label smoothing regularization is to suppress the output difference between positive and negative samples and smooth the label. The smoothed label can prevent the network from overlearning. This can effectively alleviate the overfitting phenomenon.ResNet50 is the basic network of the MF-RANe network. Before the hidden layer of the ResNet50 network, there is a batch normalization layer to normalize the data. Therefore, the overfitting phenomenon of the network in training citrus disease samples is inhibited by batch normalization. Unlike batch normalization regularization, the use of label smoothing regularization can effectively alleviate the overfitting phenomenon in the classification process.To analyse the effectiveness of the MF-RANet network in citrus disease identification and classification. We design experiments to compare the effectiveness of different models. However, because there are no clear standards and instructions for the specific codes and data segmentation of other models, we have to reproduce their models independently and conduct comparative experiments on the datasets we collected. In the comparative experiment, the test sets of different models are completely consistent. This part includes the experimental environment, experimental device, effectiveness analysis of each module of the MF-RANet network, evaluation index, ablation experiment and comparison experiment between different models.This paper constructs a neural network framework on Colaboratory, edits, compiles and runs the MF-RANet network on Colaboratory. The programming environment of the code is Python 3.7 and PyTorch. In addition, we use MATLAB to perform AMSR image enhancement. The hardware environment of the simulation experiment is a Google cloud disk GPU and Windows 10 (64 bit), in which the system memory is 32 GB.The homemade dataset used in this paper contains 6 categories of citrus: Huanglong disease, Corynespora blight of citrus, fat spot macular disease, citrus scab, citrus canker and healthy citrus. The size of the input image is 224*224. This can improve the efficiency of image processing technology and reduce the time for model training and classification. After image enhancement and image amplification, we obtained a total of 10,100 citrus images. The number of six types of images is evenly distributed, all in the range of 15–20%. The dataset in this paper is divided into a training set: verification set: test set = 6:2:2. There are 6060 citrus images in the training set. There are 2020 citrus images in the test set.In deep learning training, the selection of super parameters is difficult and time-consuming. This is because the best combination of super parameters depends not only on the model itself but also on the software and hardware environment. The super parameters of the MF-RANet network in this paper are shown in Table .To verify the impact of the dataset after AMSR image enhancement on the classification performance, we use the MF-RANet network to train the dataset with image enhancement and the dataset without image enhancement simultaneously. The image divisions in the training set, the dataset verification set and the test set with and without image enhancement are the same. When other configurations are the same, the experimental results are shown in Fig. 9It can be seen in Fig. Therefore, after the dataset is enhanced by AMSR, the training accuracy of the MF-RANet network is significantly improved. This shows that the AMSR algorithm makes the disturbed lesion features clearer after image enhancement. The clearer lesion features are easier to learn and recognize by the MF-RANet network.To verify that among the different activation functions, the ELU activation function is the most helpful for improving the recognition rate of citrus diseases in the MF-RANet network. We designed a comparative experiment of the influence of 5 activation functions on the network recognition rate and recorded the experimental results. The 5 activation functions selected in the comparative experiment are the tanh function , sigmoidThe data in Table To verify the performance of the improved MF-RANet network, we used the original unmodified ResNet50 and the improved MF-RANet to experiment on the same training set, verification set and test set of AMSR in the same test environment. Table Table To verify the effectiveness of each part of the MF-RANet network, we performed ablation experiments on the citrus image dataset enhanced by AMSR images. Taking ResNet50 as the backbone, RAM (R), AugFPN (a) the ELU activation function (E)  and label smoothing regularization (L) are added. By comparing the test accuracy of the network for each kind of disease and the training time of the network, the performance of each module is analysed. The overall ablation experiment is shown in Table As seen from Table Compared with the recognition accuracy of 6 kinds of citrus images in the MF-RANet network, the recognition rate of healthy citrus images is the highest. The image recognition rates of the MF-RANet network for citrus canker and citrus scab are similar and lower than those of other diseases. This is mainly due to the high similarity of disease texture features between citrus canker and citrus scab. However, the recognition accuracy of the MF-RANet network for these 2 diseases is still higher than that of the other networks in the table for citrus canker and citrus scab. It still shows that the detailed feature frame designed in this paper has an effect on the identification of these 2 diseases with high similarity and improves the identification degree of similar features.To compare the classification performance between the MF-RANet model and the neural network model. We compared the performance of eight models and tested the same dataset in the same experimental environment (the dataset has been enhanced by AMSR). During the experiment, we trained 8 networks for comparison from 0. Because there is no description of all codes and specific data of all networks, it creates repeatability problems. We have to reproduce most of these models independently and compare them when the datasets are completely consistent. We built the environment, reproduced the code, sorted out the dataset, trained the model and counted the training results we needed. The recognition accuracy, training time and model size of each model corresponding to each citrus image are filled in Table Compared with the CNN, AlexNet, VGG16, DenseNet121, NTS-Net, DFL-Net, BSNet and MF-RANet models, the disease identification accuracy of the MF-RANet model proposed in this paper is generally higher than that of the other networks. This shows that the disease identification ability of the MF-RANet model built in this paper is higher than that of other common networks. Although the GFLOP values of VGG16, DenseNet121, NTS-Net, and DFL-Net are higher than those of the MF-RANet network, the final training time is much longer than that of the MF-RANet network because the network parameters of the above four models are too large. Thus, the value of the MF-RANet model in the current neural network model is verified.To better verify the performance of the MF-RANett model, we found a total of 1,860 images of the same 6 kinds of citrus diseases on the PlantVillage  dataset.The experimental results are shown in Fig. In fact, it is one-sided to only rely on classification accuracy to determine whether the model is truly effective. Therefore, this article selects the F1-score and its corresponding recall, precision and corresponding confusion matrix of 6 different citrus images as the evaluation index. The F1-score is the measurement function of precision and recall, and its calculation formula is as follows:In Formula , P repreIn this experiment, recall, precision and F1-score are selected to verify the performance of the model. This article selects the above 12 models compared with MF-RANet and calculates their recall rate, precision and F1-score.Figure In the machine learning field, the confusion matrix is also called the possibility table or error matrix. It is a specific matrix used to visualize the performance of the algorithm, usually supervised learning . Each column represents the predicted value, and each row represents the actual category. This is very important because in the actual classification, the TP value and FP value are the most direct indicators for determining whether the classification is correct. The F1-score value is the comprehensive embodiment of these two indicators. We selected and compared the confusion matrices of MF-RANet, BSNet, ResNet50, and CNN, as shown in Fig. From the confusion matrix of the above four models, it can be seen that the MF-RANe model proposed in this paper has a good image recognition effect of citrus diseases. Compared with the ResNet50 network, CNN network and BSNet, the FP values of citrus canker and citrus scab with a large number of complex and similar characteristics in the MF-RANet model are significantly lower than those of the other models. This is because the algorithm of the MF-RANet model tends to pay more attention to the main features and the features that retain the detailed features and filter out the useless information. The decrease in the FP value increases the recall rate of these 2 kinds of disease identification. According to Eq. , under tThrough the above experiments, it can be seen that the AMSR preprocessing + MF-RANet network designed in this article has a good effect in realizing the identification of citrus diseases. However, the network designed in this article still has some defects, so the recognition rate of all types of diseases cannot reach 100%. AMSR preprocessing improves the retinex algorithm to achieve image enhancement, and the retinex algorithm also has a certain denoising effect. 2. Therefore, in AMSR preprocessing, the denoising process causes the loss of some features. Although there are detail feature frames in the MF-RANet network to reduce the loss of detail features, it can only reduce the details lost in the main feature frame. It cannot reduce the detailed features lost before entering the MF-RANet network. 2. Due to the imperfection of transmission media and recording equipment, digital images are often polluted by a variety of noise in the process of transmission and recording. Although the retinex algorithm in AMSR preprocessing can play a certain role in denoising, it cannot completely filter the noise, and the greater the noise generated by the picture is, the greater the impact on the recognition accuracy.Therefore, this article adds different levels of noise to the test set of the dataset collected in this article and selects four models, AMSR + MF-RANet, BSNet, ResNet50 and CNN, for experimental analysis. After adding salt noise and Gaussian noise, the accuracy of these models for citrus disease recognition is shown in Fig. Figure To eliminate the interference of environmental factors such as low local illumination or shadows caused by clouds, dust and leaves, the AMSR enhancement algorithm designed in this paper can not only brighten the citrus disease image overall but also enhance the local illumination where the local illumination is low, make the features of the input image more obvious and reduce the difficulty of recognition.The RAM and AugFPN feature fusion method is used to enhance the model parameters and improve the detection ability of the model. In the MF-RANet network, RAM used in the main feature frame and AugFPN used in the detail feature frame have a residual structure, which can effectively broaden the width of ResNet50. In this article, label smoothing regularization is used to improve the generalization ability of the whole network. Additionally, the ELU function is selected to improve the anti-interference ability of the network.Through the comparison of MF-RANet and many other models, it is found that the citrus disease recognition effect of MF-RANet combined with AMSR pretreatment is the best in the selected network, and the recognition rate of different kinds of citrus diseases is the highest.According to the experimental results in Fig. With the progress of deep learning and computer vision technology, image recognition, image analysis and other fields have been widely studied. In recent years, crop disease image recognition and management based on deep learning have also been widely used. To improve citrus yield, ResNet50 was improved in this paper. From the test results of the improved model, we can see that.In life, the network designed in this article is beneficial for fruit farmers to accurately control the disease situation of citrus orchards. For example, some diseases are not easily detected by human beings. Through machine identification, the spread of diseases can be strangled at the onset in time to reduce economic losses as much as possible. In addition, the model can also play an important role in foreign trade exports and economic growth. In the follow-up, the model will be tested in real life, improved and applied to society as soon as possible to contribute to the economic production of citrus.