Cold atmospheric plasma can effectively disinfect SARS-CoV-2 in the wastewater.

COVID-19 is currently pandemic and the detection of SARS-CoV-2 variants in wastewater is causing widespread concern. Herein, cold atmospheric plasma (CAP) is proposed as a novel wastewater disinfection technology that effectively inactivates SARS-CoV-2 transcription- and replication-competent virus-like particles, coronavirus GX_P2V, pseudotyped SARS-CoV-2 variants, and porcine epidemic diarrhoea virus in a large volume of water within 180 s (inhibition rate > 99%). Further, CAP disinfection did not adversely affect the viability of various human cell lines. It is identified that CAP produced peroxynitrite (ONOO-), ozone (O3), superoxide anion radicals (O2 -), and hydrogen peroxide (H2O2) as the major active substances for coronavirus disinfection. Investigation of the mechanism showed that active substances not only reacted with the coronavirus spike protein and affected its infectivity, but also destroyed the nucleocapsid protein and genome, thus affecting virus replication. This method provides an efficient and environmentally friendly strategy for the elimination of SARS-CoV-2 and other coronaviruses from wastewater.

Open-Set Recognition of Individual Cows Based on Spatial Feature Transformation and Metric Learning.

The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the frame. This study proposes an open-set method for individual cow recognition based on spatial feature transformation and metric learning to address these issues. Initially, a spatial transformation deep feature extraction module, ResSTN, which incorporates preprocessing techniques, was designed to effectively address the low recognition rate caused by the diverse orientation distribution of individual cows. Subsequently, by constructing an open-set recognition framework that integrates three attention mechanisms, four loss functions, and four distance metric methods and exploring the impact of each component on recognition performance, this study achieves refined and optimized model configurations. Lastly, introducing moderate cropping and random occlusion strategies during the data-loading phase enhances the model's ability to recognize partially visible individuals. The method proposed in this study achieves a recognition accuracy of 94.58% in open-set scenarios for individual cows in overhead images, with an average accuracy improvement of 2.98 percentage points for cows with diverse orientation distributions, and also demonstrates an improved recognition performance for partially visible and randomly occluded individual cows. This validates the effectiveness of the proposed method in open-set recognition, showing significant potential for application in precision cattle farming management.

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.

Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.