The newly emerged lineage 1 porcine reproductive and respiratory syndrome viruses (PRRSVs) (especially the NADC30‐like and NADC34‐like viruses) have posed a direct threat to the Chinese pig industry since 2013. The phylogenetic, epidemic, and recombinant properties of these viruses have not yet systematically analyzed in China. This report presents regular surveillance and field epidemiological studies for PRRSV across China from 2007 to 2019. From over 4000 detected clinical samples, 70 open reading frame 5 sequences and 4 complete genomes of lineage 1 viruses were successfully obtained. Combined with global data, we conducted an extensive and systematic molecular phylogeny analysis using a maximum likelihood tree. The Chinese lineage 1 viruses were clustered and their temporal and spatial distribution was further explored. Multiple viral introductions of lineage 1 virus from the United States to China were detected, and some became endemic in China. There are three sub‐lineage 1 clusters: lineage 1.5 (NADC34‐like), lineage 1.6, and New Intro cluster (NADC30‐like). These viruses show high genetic diversity and a wide distribution in China, with Henan province showing the highest diversity. Moreover, Chinese lineage 1 viruses have developed an endemic NADC30‐like cluster. The demographic feature of this cluster showed a more or less constant population expansion history with a recent decreasing trend. Moreover, the genome recombination of Chinese lineage 1 with two dominant clusters (Chinese HP‐PRRSVs: lineage 8.7 and VR2332‐like: lineage 5.1) was frequently detected, both of which have commercial vaccine strains available. Furthermore, recombination hotspots were discovered near NSP9 and ORF2‐4 regions of the genome. Overall, these findings provide important insights into the evolution and geographical diversity of Chinese lineage 1 PRRSV. These results will facilitate the development of programs for the control and prevention of the emerging lineage 1 viruses in China.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.

Lameness has significant effects on the health, welfare, and productivity of dairy cows. Common challenges in farm environments, such as uneven lighting and occlusion, reduce the accuracy of keypoint detection, which in turn affects the precise extraction of motion features. Moreover, a single motion feature is often insufficient to comprehensively reflect lameness behavior. This study explores a lameness detection method for dairy cows based on the integration of keypoint-derived motion features. First, to enhance the accuracy of cow keypoint detection, improvements were made to YOLOv8-Pose-the keypoint detection module in the YOLOv8 framework-to boost performance under complex environmental conditions, and its positive effect on lameness classification was validated. Next, the improved model was used to detect keypoints on the hooves, knees, hips, and head-neck region of the cows. From these, 3 types of temporal motion features were extracted: relative displacement between fore and hind hooves, hoof movement speed, and head-neck motion trajectory. Each feature type was individually used for lameness classification using a Conv2D-LSTM structure, which combines convolutional operations with a long short-term memory (LSTM) network for temporal modeling. Finally, to achieve more robust lameness detection results, the stacking method from ensemble learning was applied to fuse the predictions based on the 3 types of features. Results show that the improved YOLOv8-Pose model can effectively detect cow keypoints, achieving a precision of 99.4%, recall of 96.9%, mAP@0.5 of 97.8%, mAP@0.75 of 88.0%, and mAP@0.5:0.95 of 79.3% (where mAP refers to mean average precision, a standard metric for detection accuracy). Among 141 cow samples with a lameness prevalence of 52.5%, the average classification accuracy for each of the 3 motion features exceeded 85%, whereas the integrated method based on keypoint motion features achieved an overall accuracy of 97.2%. Cross-validation further confirms the accuracy and generalization capability of the proposed algorithm, offering a feasible path for intelligent lameness monitoring in dairy cows.

Side plate offset is one of the grate system faults. If it is not dealt with in time, some accidents will occur and economic losses will be made. Aiming at the problems like time‐consuming, labour‐wasting, and low intelligent by the side plate offset detection method manually, an autoside plate offset detection method is proposed, based on You Only Look Once version 4 (YOLOv4). Two cameras were fixed to collect the image information of the grate trolley’s side plate. With reference to the grate trolley’s operation, the offset judgment rules were set. YOLOv4 object detection algorithm was used to detect the side plate and trolley’s chassis frame in video frame images. A baseline was set according to the position information of the trolley’s chassis frame output by detection, and then, the position intervals between side plates and the baseline could be determined by calculation. According to the judgment rules, the scheme in this paper could detect the offset fault of the trolley’s side plate timely, and an alarm would be made automatically when faults are detected. Our video images of the trolley’s side plate were collected and sorted in Baogang Group sintering plant for testing. In this experiment, no error judgment was made, and the average detection and judgment time was 0.024 s. In this paper, rather than manually, the real‐time automatic detection was realized to detect the offset fault of the trolley’s side plate so as to provide a new solution for offset detection of the grate trolley’s side plate.

With the development of digitalization and intelligence, the heating, ventilation and air conditioning (HVAC) system is facing the challenges of changing environments, stricter quality standards and increased energy efficiency requirements. In this paper, a digital twin generation method for environmental temperature and humidity control based on time series prediction (TSP) is proposed. The system is comprised of a forecasting model attention-based temporal convolutional network (ATCN) and a control model proximal policy optimization (PPO). Through the analysis of temperature sensor data, we propose a regulatory method underpinned by PPO. The effectiveness of the forecasting model has been verified on five datasets, and the control model can provide strategies that are superior to manual methods in the real environment.