Saline-alkali land is the main reserve soil resource. The amount of arable farmland soil can be increased through improvement and utilization of saline-alkali land. Bio-organic fertilizer is an effective saline-alkali soil conditioner, but there are few studies on the improvement of saline-alkali soil with traditional Chinese medicine residues (TCMRs). In this study, an organic fertilizer made from Ganoderma lucidum residue (GLR) was mixed with saline-alkali soil at different proportions; physicochemical properties, enzyme activity, and microbial community characteristics of the soil were investigated. The results showed that the soil pH, as well as bulk density and electrical conductivity were significantly reduced, while the soil moisture content and porosity were significantly increased after the GLR organic fertilizer incorporation. Soil invertase and soil amylase activities significantly increased, as well as the diversity and richness of the soil microbial community structure. The abundance of the dominant phyla, Bacteroidota, Actinobacteriota, Chloroflexi, Firmicutes, and Basidiomycota, increased, while the abundance of the dominant Proteobacteria and Ascomycota phyla decreased. The best improvement effect is obtained when the application ratio of GLR organic fertilizer is 25%. The findings showed that TCMRs have positive application prospects in saline-alkali soil improvement.

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.

The NSGA-II algorithm was used to establish a multi-objective optimization model for the oxygen enrichment rate of a blast furnace in terms of achieving a lower fuel ratio and higher pulverized coal ratio. The model has the hearth temperature as the constraint condition and the oxygen enrichment rate as the decision variable. The NSGA-II algorithm was used to obtain the Pareto optimal solution of the multi-objective optimization scheme. The prediction effect of the optimization scheme was then tested in industrial experiments. The results show that the optimal setting of the oxygen enrichment rate predicted by the model was 2.70%, which provides an optimal fuel ratio and pulverized coal ratio of 553.86 kg·tHM⁻¹ and 144.58 kg·tHM⁻¹, respectively. In actual production, when the oxygen enrichment rate was set at 2.71%, an optimal fuel ratio and pulverized coal ratio of 553.74 kg·tHM⁻¹ and 148.73 kg·tHM⁻¹ were obtained. The relative error in the oxygen enrichment rate between the model prediction and the actual prediction was 0.003%. The prediction results of the model suggest a reduction in CO₂ emissions by 25,770.71 tons per year. The CO₂ emission reduction in actual production was approximately 1.09 times the prediction of the model.

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.