Phylogeography and biological characterization of H12N2 virus isolated from whooper swan in Central China

Wild birds and waterfowl serve as the natural reservoirs of avian influenza viruses (AIVs). When AIVs originating from wild birds cross species barriers to infect mammals or humans, they pose a significant threat to public health. The H12 subtype of AIVs primarily circulates in wild birds, with relatively few isolates reported worldwide, and the evolutionary and biological characteristics of H12 subtype AIVs remain largely unknown. In this study, we analyzed the spatiotemporal distribution of H12 subtype AIVs worldwide and conducted a comprehensive investigation into the evolutionary and biological characteristics of an H12N2 virus isolated from a whooper swan in Central China. Phylogenetic analysis revealed that the H12N2 isolate belongs to the Eurasian lineage, with its HA gene likely originating from a duck-derived H12N5 virus and its NA gene potentially derived from an H9N2 virus, indicating that it is a complex reassorted virus. Animal experiments in domestic ducks and chickens demonstrated that the virus replicates at low levels in the respiratory tract of poultry and exhibits moderate horizontal transmission in ducks. However, it is capable of efficient horizontal transmission in chickens. Mouse infection experiments revealed that the virus could be detected in the nasal turbinates and lungs of mice, indicating that the H12N2 virus can infect mice without prior adaptation. In vitro studies revealed that the virus replicates efficiently in MDCK cells, with significantly higher titers than those in DF1 cells. These findings, combined with the mouse infection results, suggest that the H12N2 virus poses a potential risk of mammalian infection. This study provides valuable insights regarding the characteristics of the H12N2 virus and highlights the importance of ongoing surveillance and risk assessment of AIVs originating from wild birds

Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation

Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud and corresponding shadow detection have achieved a promising accomplishment in remote sensing imagery processing. The limited capability of these methods to delineate the boundaries of clouds and shadows, however, is still referred to as a central issue of precise cloud and shadow detection. In this paper, we focus on the issue of rough cloud and shadow location and fine-grained boundary refinement of clouds on the dataset of Landsat8 OLI and therefore propose the Refined UNet to achieve this goal. To this end, a data-driven UNet-based coarse prediction and a fully-connected conditional random field (Dense CRF) are concatenated to achieve precise detection. Specifically, the UNet network with adaptive weights of balancing categories is trained from scratch, which can locate the clouds and cloud shadows roughly, while correspondingly the Dense CRF is employed to refine the cloud boundaries. Eventually, Refined UNet can give cloud and shadow proposals sharper and more precisely. The experiments and results illustrate that our model can propose sharper and more precise cloud and shadow segmentation proposals than the ground truths do. Additionally, evaluations on the Landsat 8 OLI imagery dataset of Blue, Green, Red, and NIR bands illustrate that our model can be applied to feasibly segment clouds and shadows on the four-band imagery data

An Empirical Model of Angle-of-Arrival Variance for a Gaussian Wave Propagation through Non-Kolmogorov Turbulence

This paper proposes an empirical model of the angle-of-arrival (AOA) variance for a Gaussian wave propagating through the weak non-Kolmogorov turbulence. The proposed model is approximately expressed as the linear weighted average between the AOA variances of the plane and spherical waves. The Monte Carlo method is applied to validate the proposed model. The numerical simulations indicate that, under the geometrical optics approximation, the AOA variance for a Gaussian wave is insensitive to the change of the diffraction parameter and can be closely approximated by a simple linear relationship in the refraction parameter. These two properties ensure the validity of the empirical model.</jats:p

An Improved Particle Swarm Algorithm with Heuristic Factors and Empirical Operators

Abstract An improved particle swarm algorithm (PSA) is proposed based on the universal framework of the intelligence optimization algorithms (IOA). Regarded as increment to the standard PSA, the improved PSA takes the essence of the standard PSA, and additionally utilizes various heuristic factors and different empirical operators introduced by multiple other IOA. The capability of global and local optimization can be strengthened by the free search, the differential evolution and the quantum rotation comprehensively. The genetic variation is adopted to avoid premature convergence, while the greed strategy is adopted to guarantee monotonous convergence. The numerical simulation indicates that the improved PSA achieves better performance than the standard PSA.</jats:p

Frequency Increments Optimization Based on IPSO to Extend Beam Dwell Time for Frequency Diverse Array

Abstract Compared with traditional phased array radar, frequency diverse array (FDA) can form a range-dependent beampattern and overcome the shortcoming that the traditional phased array radar cannot effectively control the range direction of the transmit beam. Nonuniform linear frequency increments has been widely applied in FDA to promote the performance of FDA. However, few methods can effectively extend the beam dwell time. In this paper, we propose an improved particle swarm optimization algorithm (IPSO) that can adjust the frequency increment of every array element to extend the dwell time of the beam within a period of time. Numerical simulation results show that our method can outperform other existing methods, which implies that a more stable and flexible FDA communication system can be achieved for the proposed method.</jats:p

An Empirical Model of Angle-of-Arrival Variance for a Gaussian Wave Propagation through Non-Kolmogorov Turbulence

This paper proposes an empirical model of the angle-of-arrival (AOA) variance for a Gaussian wave propagating through the weak non-Kolmogorov turbulence. The proposed model is approximately expressed as the linear weighted average between the AOA variances of the plane and spherical waves. The Monte Carlo method is applied to validate the proposed model. The numerical simulations indicate that, under the geometrical optics approximation, the AOA variance for a Gaussian wave is insensitive to the change of the diffraction parameter and can be closely approximated by a simple linear relationship in the refraction parameter. These two properties ensure the validity of the empirical model

Towards Edge-Precise Cloud and Shadow Detection on the GaoFen-1 Dataset: A Visual, Comprehensive Investigation

Remote sensing images are usually contaminated by opaque cloud and shadow regions when acquired, and therefore cloud and shadow detection arises as one of the essential prerequisites for restoration and prediction of the objects of interest underneath, which are required by further processing and analysis. Cutting-edge, learning-based segmentation techniques, given a well-labeled, sufficient sample set, are significantly developed for such a detection issue and can already achieve region-accurate or even pixel-precise performance. However, it may possibly be problematic to attempt to apply the sophisticated segmentation techniques to label-free datasets in a straightforward way, more specifically, to the remote sensing data generated by the Chinese domestic satellite GaoFen-1. We wish to partially address such a segmentation problem from a practical perspective rather than in a conceptual way. This can be performed by considering a hypothesis that a segmentor, which is sufficiently trained on the well-labeled samples of common bands drawn from a source dataset, can be directly applicable to the custom, band-consistent test cases from a target set. Such a band-consistent hypothesis allows us to present a straightforward solution to the GaoFen-1 segmentation problem by treating the well-labeled Landsat 8 Operational Land Imager dataset as the source and by selecting the fourth, the third, and the second bands, also known as the false-color bands, to construct the band-consistent samples and cases. Furthermore, we attempt to achieve edge-refined segmentation performance on the GaoFen-1 dataset by adopting our prior Refined UNet and v4. We finally verify the effectiveness of the band-consistent hypothesis and the edge-refined approaches by performing a relatively comprehensive investigation, including visual comparisons, ablation experiments regarding bilateral manipulations, explorations of critical hyperparameters within our implementation of the conditional random field, and time consumption in practice. The experiments and corresponding results show that the hypothesis of selecting the false-color bands is effective for cloud and shadow segmentation on the GaoFen-1 data, and that edge-refined segmentation performance of our Refined UNet and v4 can be also achieved.</jats:p