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In light of the question of how to select the best noise control position and measures in the large noise transmission path of the cabins of a ship, based on the acoustic ray-tracing method in the theory of geometrical acoustics, and by considering the effect of the sound transmission of the bulkhead, this paper proposes the sound line search method. It is used to calculate the sound pressure of a ship's cabin, allowing the sound field distribution of multiple compartments to be simulated. The paper proposes a sound ray-searching method in which the acoustic sensitivity of different positions of the bulkhead to the noise of the target cabin is calculated by searching for the sound ray passing the target cabin. According to this, a cabin noise reduction plan can be designed to optimize medium and high frequency cabin noise. With this method, the noise of a typical cabin can be optimized and reduced by 7.3 dB. Through comparative analysis with the statistical energy method, it is proven that the method is feasible and can guide the refined design of noise reduction in ships' cabins.

Crop yield prediction is helpful for farmers to make reasonable agronomic decisions such as crop insurance, planning of harvest, storage requirement, cash flow budgeting and the determination of input factors. This paper presented an application for cotton yield prediction based on the remote visual and spectral images captured by a UAV (unmanned aerial vehicle) remote sensing system. Field experiments were carried out at a cotton field approximately 5 hm2. Images of the cotton field were captured by the UAV remote sensing system at a 50 m flight height before fully blooming. One RGB panoramic image and one CIR (color-infrared) panoramic image were obtained by stitching the collected partial images with 75% overlap. Yield data with GPS (global position system) information were registered respectively to the RGB and CIR images according to the GCPs (ground control points). Then 3 features including color feature, coverage rate and vegetation index were extracted from the images and used to develop predication models. Color feature was the hue channel in HSV (hue, saturation, value) color model. Coverage rate was calculated by using Excess Green value based on RGB (red, green, blue) color model. Vegetation index was NDVI (normalized difference vegetation index) value usually used to monitor the crop growth. The datasets including yield data and 3 features were randomly divided into training data (90% of raw data) for modeling and test data (10% of raw data) for evaluating the model accuracy. The training data were preprocessed though removing 10% partial outliers based on probability distribution of yield data and being smoothed by mean filter respectively. Then multiple linear regression models were built to predict cotton yield, and parameters of the models were estimated based on the least square method and calculated by SAS software. 3 parameters were used to evaluate the models including P value in F-test statistic, coefficient of determination R2, and MAPE (mean absolute percentage error) between predicted values and the ground truth data in acre area level. The results showed that 7 linear regression models built by 1 variable (color feature, coverage rate or vegetation index), 2 variables (color feature and coverage rate, color feature and vegetation index or coverage rate and vegetation index) and 3 variables (color feature, coverage rate and vegetation index) were all statistically significant at significance level 5%. However, the adequacy of the multiple linear regression model with 3 variables was the best due to the highest R2=0.977 3 compared to other 6 regression models built by 1 variable and 2 variables. So the multiple linear regression model with 3 variables was used to predict yield. And the average MAPEs on the multiple linear regression models built by 3 variables was 4.0% in acre area level. The results from this study indicate that combining visual and spectral features from image sensors can be used effectively to predict yield. [ABSTRACT FROM AUTHOR]

Between 2013 and 2018, the novel A/Anhui/1/2013 (AH/13)-lineage H7N9 virus caused at least five waves of outbreaks in humans, totaling 1,567 confirmed human cases in China. Surveillance data indicated a disproportionate distribution of poultry infected with this AH/13-lineage virus, and laboratory experiments demonstrated that this virus can efficiently spread among chickens but not among Pekin ducks. The underlying mechanism of this selective transmission remains unclear. In this study, we demonstrated the absence of Neu5Gc expression in chickens across all respiratory and gastrointestinal tissues. However, Neu5Gc expression varied among different duck species and even within the tissues of the same species. The AH/13-lineage viruses exclusively bind to acetylneuraminic acid (Neu5Ac), in contrast to wild waterbird H7 viruses that bind both Neu5Ac and N-glycolylneuraminic acid (Neu5Gc). The level of Neu5Gc expression influences H7 virus replication and facilitates adaptive mutations in these viruses. In summary, our findings highlight the critical role of Neu5Gc in affecting the host range and interspecies transmission dynamics of H7 viruses among avian species.IMPORTANCEMigratory waterfowl, gulls, and shorebirds are natural reservoirs for influenza A viruses (IAVs) that can occasionally spill over to domestic poultry, and ultimately humans. This study showed wild-type H7 IAVs from waterbirds initially bind to glycan receptors terminated with N-acetylneuraminic acid (Neu5Ac) or N-glycolylneuraminic acid (Neu5Gc). However, after enzootic transmission in chickens, the viruses exclusively bind to Neu5Ac. The absence of Neu5Gc expression in gallinaceous poultry, particularly chickens, exerts selective pressure, shaping IAV populations, and promoting the acquisition of adaptive amino acid substitutions in the hemagglutinin protein. This results in the loss of Neu5Gc binding and an increase in virus transmissibility in gallinaceous poultry, particularly chickens. Consequently, the transmission capability of these poultry-adapted H7 IAVs in wild water birds decreases. Timely intervention, such as stamping out, may help reduce virus adaptation to domestic chicken populations and lower the risk of enzootic outbreaks, including those caused by IAVs exhibiting high pathogenicity.