D-2,3-butanediol has many industrial applications such as chiral reagents, solvents, anti-freeze agents, and low freezing point fuels. Traditional D-2,3-butanediol producing microorganisms, such as Klebsiella pneumonia and K. xoytoca, are pathogenic and not capable of producing D-2,3-butanediol at high optical purity. Bacillus licheniformis is a potential 2,3-butanediol producer but the wild type strain (WX-02) produces a mix of D- and meso-type isomers. BudC in B. licheniformis is annotated as 2,3-butanediol dehydrogenase or acetoin reductase, but no pervious experiment was performed to verify this hypothesis.

Backfat thickness (BF) is closely related to the service life and reproductive performance of sows. The dynamic monitoring of sows’ BF is a critical part of the production process in large-scale pig farms. This study proposed the application of a hybrid CNN-ViT (Vision Transformer, ViT) model for measuring sows’ BF to address the problems of high measurement intensity caused by the traditional contact measurement of sows’ BF and the low efficiency of existing non-contact models for measuring sows’ BF. The CNN-ViT introduced depth-separable convolution and lightweight self-attention, mainly consisting of a Pre-local Unit (PLU), a Lightweight ViT (LViT) and an Inverted Residual Unit (IRU). This model could extract local and global features of images, making it more suitable for small datasets. The model was tested on 106 pregnant sows with seven randomly divided datasets. The results showed that the CNN-ViT had a Mean Absolute Error (MAE) of 0.83 mm, a Root Mean Square Error (RMSE) of 1.05 mm, a Mean Absolute Percentage Error (MAPE) of 4.87% and a coefficient of determination (R-Square, R²) of 0.74. Compared to LviT-IRU, PLU-IRU and PLU-LviT, the CNN-ViT’s MAE decreased by more than 12%, RMSE decreased by more than 15%, MAPE decreased by more than 15% and R² improved by more than 17%. Compared to the Resnet50 and ViT, the CNN-ViT’s MAE decreased by more than 7%, RMSE decreased by more than 13%, MAPE decreased by more than 7% and R² improved by more than 15%. The method could better meet the demand for the non-contact automatic measurement of pregnant sows’ BF in actual production and provide technical support for the intelligent management of pregnant sows.

Aiming to address the problem that the existing methods for detecting sow backfat thickness are stressful, costly, and cannot detect in real time, this paper proposes a non-contact detection method for sow backfat with a residual network based on image segmentation using the feature visualization of neural networks. In this paper, removing the irrelevant information of the image to improve the accuracy of the sow backfat thickness detection model is proposed. The irrelevant features in the corresponding image of the feature map are found to have the same high brightness as the relevant feature regions using feature visualization. An image segmentation algorithm is then used to separate the relevant feature image regions, and the model performance before and after image segmentation is compared to verify the feasibility of this method. In order to verify the generalization ability of the model, five datasets were randomly divided, and the test results show that the coefficients of determination (R²) of the five groups were above 0.89, with a mean value of 0.91, and the mean absolute error (MAE) values were below 0.66 mm, with a mean value of 0.54 mm, indicating that the model has high detection accuracy and strong robustness. In order to explain the high accuracy of the backfat thickness detection model and to increase the credibility of the application of the detection model, using feature visualization, the irrelevant features and related features of the sow back images extracted by the residual network were statistically analyzed, which were the characteristics of the hip edge, the area near the body height point, the area near the backfat thickness measurement point (P2), and the lateral contour edge. The first three points align with the previous research on sow backfat, thus explaining the phenomenon of the high accuracy of the detection model. At the same time, the side contour edge features were found to be effective for predicting the thickness of the back. In order to explore the influence of irrelevant features on the accuracy of the model, UNet was used to segment the image area corresponding to the irrelevant features and obtain the sow contour image, which was used to construct a dorsal fat thickness detection model. The R² results of the model were above 0.91, with a mean value of 0.94, and the MAE was below 0.65 mm, with a mean value of 0.44 mm. Compared to the test results of the model before segmentation, the average R² of the model after segmentation increased by 3.3%, and the average MAE decreased by 18.5%, indicating that irrelevant features will reduce the detection accuracy of the model, which can provide a reference for farmers to dynamically monitor the backfat of sows and accurately manage their farms.

This article is concerned with a strong unique continuation property of a forward differential inequality abstracted from parabolic equations proposed on a convex domain $ \Omega $ prescribed with some regularity and growth conditions. Our results show that the value of the solutions can be determined uniquely by its value on an arbitrary open subset $ \omega $ in $ \Omega $ at any given positive time $ T $. We also derive the quantitative nature of this unique continuation, that is, the estimate of a $ L^2(\Omega) $ norm of the initial data, which is majorized by that of solution on the bounded open subset $ \omega $ at terminal moment $ t = T $.

This article is concerned with a strong unique continuation property of solutions for a diffusive SIS (Susceptible - Infected - Susceptible, or SI) model, which belongs to a type of observability inequalities in a time interval $ [0, T] $. That is, if one can observe solution on a convex and connected bounded open set $ \omega $ in a bounded domain $ \Omega $ at time $ t = T $, then the norms of solution on $ [0,T) $ on $ \Omega $ are observable. In our discussion, boundary condition is a homogeneous Dirichlet one (hostile boundary condition).