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The inherent ambiguity in reconstructed images from coherent diffraction imaging (CDI) poses an intrinsic challenge, as images derived from the same dataset under varying initial conditions often display inconsistencies. This study introduces a method that employs the Noise2Noise approach combined with neural networks to effectively mitigate these ambiguities. We applied this methodology to hundreds of ambiguous reconstructed images retrieved from a single diffraction pattern using a conventional retrieval algorithm. Our results demonstrate that ambiguous features in these reconstructions are effectively treated as inter‐reconstruction noise and are significantly reduced. The post‐Noise2Noise treated images closely approximate the average and singular value decomposition analysis of various reconstructions, providing consistent and reliable reconstructions. Ambiguity, which is an intrinsic characteristic of coherent diffraction imaging during image retrieval, can be reduced using machine learning.

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6‐month‐old infants, the extremely low‐intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single‐scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed‐scale asymmetric convolutional segmentation network (3D‐MASNet) framework for brain MR images of 6‐month‐old infants. We replaced the traditional convolutional layer of an existing to‐be‐trained network with a 3D mixed‐scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1‐ and T2‐weighted images of 23 6‐month‐old infants from iSeg‐2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC‐3D‐FCN) and the highest performance in the Dense U‐Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg‐2019 Grand Challenge. Thus, the proposed 3D‐MASNet can improve the accuracy of existing CNNs‐based segmentation models as a plug‐and‐play solution that offers a promising technique for future infant brain MRI studies. Precise tissue segmentation of 6‐month‐old infant brain MR images is challenging. We propose a 3D mixed‐scale asymmetric convolutional segmentation network (3D‐MASNet) framework for this task by replacing the traditional convolutional layer of an existing to‐be‐trained network with a 3D mixed‐scale convolution block consisting of asymmetric kernels (MixACB). Our framework is flexible plug‐and‐play and reaches the level of state‐of‐the‐art.

Large eddy simulations of a three-dimensional (3D) compressible parallel jet flow at Mach number of 0.9 and Reynolds number 2000 are carried out. Four subgrid-scale (SGS) models, namely, the standard Smagorinsky model (SM), the selective mixed scale model (SMSM), the coherent-structure Smagorinsky model (CSM) and the coherent-structure kinetic-energy model (CKM) are employed, respectively, and compared. The purpose of the study is to compare the SGS models and to find their suitability of predicting the flow transition in the potential core of the jet, and so as to provide a reference for selecting SGS models in simulating compressible jet flows, which is a kind of proto-type flow in fluid dynamics and aeroacoustics. A finite difference code with fourth-order spatial and very low storage third-order explicit Runge-Kutta temporal schemes is introduced and employed for calculation. The code, which was previously designed for simulating shock/boundary-layer interactions and had been widely validated in simulating a variety of compressible flows, is rewritten and changed into parallelized using the OpenMP protocol so that it can be run on memory-shared multi-core workstations. The computational domain size and the index of LES resolution quality are checked to validate the simulations. Detailed comparisons of the four SGS models are carried out. The results of averaged flow-field including the velocity profiles and the developments of shear-layer, the instantaneous vortical flows and the viscous dissipation, the predicted turbulence statistics and the balances of momentum equation are studied and compared. The results show that although the normalized developed velocity profiles are well predicted by the four SGS models, the length of the potential core and the development of the shear-layer reveal that the SM has excessive SGS viscosity and is therefore too dissipative to correctly predict the flow transition and shear-layer expansion. The model smears small vortical scales and lowers down the effective Reynolds number of the flow because of the over-predicted SGS viscosity and dissipation. The turbulence statistics and the balances of momentum equation have also confirmed the excessive dissipation of the SM. The CKM is also found to over-predict the SGS viscosity. Compared with these two models, the SMSM and the CSM have performed well in predicting both the averaged and the instantaneous flow-fields of the compressible jet. And they are localized models which are computationally efficient and easy for coding. Therefore, the SMSM and the CSM are recommended for the LES of the compressible Jet.

The discharge of large quantities of organic dyes into the environment causes significant harm to humans and the environment. Thus, there is an urgent need to develop cost-effective adsorbents for removing these dyes. In the present study, the synthesis of activated carbon (AC) derived from mixed fish scale waste using KOH activation was investigated for Congo red (CR) dye removal. The finding shows that the obtained biocarbon has a fixed carbon of 42.9% with a crystallinity index of 15.01%. N2 adsorption-desorption isotherm was found to be type IV, signifying mesoporous structure with a surface area and total pore volume of 150.049 m2 g-1 and 0.119 cm3.g-1. Batch adsorption was carried out by various adsorbent doses, initial concentration, contact time, and pH to comprehend the effect of operating parameters on its removal efficacy. The isotherm studies fitted well for Freundlich with an R2 of 0.99%. Adsorption kinetics was best fitted by the pseudo-second-order model and thermodynamic studies revealed the adsorption process to be exothermic and spontaneous. The efficiency of AC was also studied by an amount of sorption and desorption cycles which showed its potential for reusability up to the sixth cycle. Thus, the findings suggest that activated carbon derived from mixed fish scale waste is a promising adsorbent for removing Congo red dye from aqueous solutions.

Tobacco brown spot disease caused by fungal species is a major threat to tobacco growth and yield. Thus, accurate and rapid detection of tobacco brown spot disease is vital for disease prevention and chemical pesticide inputs. Here, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network. As a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS). Therefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.

To address the demand for high spatial resolution data in the water color inversion task of multispectral satellite images in island sea areas, a feasible solution is to process through multi-source remote sensing data fusion methods. However, the inherent biases among multi-source sensors and the spectral distortion caused by the dynamic changes of water bodies in island sea areas restrict the fusion accuracy, necessitating more precise fusion solutions. Therefore, this paper proposes a pansharpening method based on Hybrid-Scale Mutual Information (HSMI). This method effectively enhances the accuracy and consistency of panchromatic sharpening results by integrating mixed-scale information into scale regression. Secondly, it introduces mutual information to quantify the spatial–spectral correlation among multi-source data to balance the fusion representation under mixed scales. Finally, the performance of various popular pansharpening methods was compared and analyzed using the coupled datasets of Sentinel-2 and Sentinel-3 in typical island and reef waters of the South China Sea. The results show that HSMI can enhance the spatial details and edge clarity of islands while better preserving the spectral characteristics of the surrounding sea areas.

We consider nonparametric estimation of a mixed discrete-continuous distribution under anisotropic smoothness conditions and a possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates. Next, we consider a nonparametric mixture of normals model that uses continuous latent variables for the discrete part of the observations. We show that the posterior in this model contracts at rates that are equal to the derived lower bounds up to a log factor. Thus, Bayesian mixture of normals models can be used for (up to a log factor) optimal adaptive estimation of mixed discrete-continuous distributions. The proposed model demonstrates excellent performance in simulations mimicking the first stage in the estimation of structural discrete choice models.

In the ecosystem, birds are important indicators that can sensitively reflect changes in the ecological environment and its health. However, bird monitoring has challenges due to species diversity, variable behaviors, and distinct morphological characteristics. Therefore, we propose a parallel dual-branch hybrid CNN–Transformer architecture for feature extraction that simultaneously captures local and global image features to address the “local feature similarity” issue in dual tasks of bird species and behaviors. The dual-task framework comprises three main components: the Token Re-segmentation Module (TRM), the Multi-scale Adaptive Module (MAM), and the Feature Interleaving Structure (FIS). The designed MAM fuses hybrid attention to address the problem of different-scale birds. MAM models the interdependencies between spatial and channel dimensions of features from different scales. It enables the model to adaptively choose scale-specific feature representations, accommodating inputs of different scales. In addition, we designed an efficient feature-sharing mechanism, called FIS, between parallel CNN branches. FIS interleaving delivers and fuses CNN feature maps across parallel layers, combining them with the features of the corresponding Transformer layer to share local and global information at different depths and promote deep feature fusion across parallel networks. Finally, we designed the TRM to address the challenge of visually similar but distinct bird species and of similar poses with distinct behaviors. TRM adopts a two-step approach: first, it locates discriminative regions, and then performs fine segmentation on them. This module enables the network to allocate relatively more attention to key areas while merging non-essential information and reducing interference from irrelevant details. Experiments on the self-made dataset demonstrate that, compared with state-of-the-art classification networks, the proposed network achieves the best performance, achieving 79.70% accuracy in bird species recognition, 76.21% in behavior recognition, and the best performance in dual-task recognition.

We develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov chain Monte Carlo. A key computational advantage of our method, Moment Estimation for latent Dirichlet models (MELD), is that parameter estimation does not require instantiation of the latent variables. Moreover, performance is agnostic to distributional assumptions of the observations. We derive population moment conditions after marginalizing out the sample-specific Dirichlet latent variables. The moment conditions only depend on component mean parameters. We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application to several datasets. Supplementary materials for this article are available online.

Skin lesion detection is crucial in diagnosing and managing dermatological conditions. In this study, we developed and demonstrated the potential applicability of a novel mixed-scale dense convolution, self-attention mechanism, hierarchical feature fusion, and attention-based contextual information technique (MSHA) model for skin lesion detection using digital skin images of chickenpox and shingles lesions. The model adopts a combination of unique architectural designs, such as a mixed-scale dense convolution layer, self-attention mechanism, hierarchical feature fusion, and attention-based contextual information, enabling the MSHA model to capture and extract relevant features more effectively for chickenpox and shingles lesion classification. We also implemented an effective training strategy to enhance a better capacity to learn and represent the relevant features in the skin lesion images. We evaluated the performance of the novel model in comparison to state-of-the-art models, including ResNet50, VGG16, VGG19, InceptionV3, and ViT. The results indicated that the MSHA model outperformed the other models with accuracy and loss of 95.0% and 0.104, respectively. Furthermore, it exhibited superior performance in terms of true-positive and true-negative rates while maintaining low-false positive and false-negative rates. The MSHA model’s success can be attributed to its unique architectural design, effective training strategy, and better capacity to learn and represent the relevant features in skin lesion images. The study underscores the potential of the MSHA model as a valuable tool for the accurate and reliable detection of chickenpox and shingles lesions, which can aid in timely diagnosis and appropriate treatment planning for dermatological conditions.