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Simple Summary: Through the non-destructive recognition and tracking algorithm of multi-objective behaviors of beef cattle, practitioners are able to obtain beef cattle feeding information in an all-round way, which lays the foundation for intelligent farming. This study is based on the deep learning recognition algorithm YOLOv8 and tracking algorithm Deep SORT. Through optimizing the convolution module, introducing the attention mechanism, improving the re-identification network, and the trajectory generation and matching process, a new method for the non-destructive identification and tracking of multi-target behaviors of beef cattle is constructed. The average accuracy of nine behaviors can be up to 96.5%, and the accuracy of multi-target tracking is up to 92.1%, which can provide technical support for beef cattle management. The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model's receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm's capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming. [ABSTRACT FROM AUTHOR]

Individual identification of pigs is a critical component of intelligent pig farming. Traditional pig ear-tagging requires significant human resources and suffers from issues such as difficulty in recognition and low accuracy. This paper proposes the YOLOv5-KCB algorithm for non-invasive identification of individual pigs. Specifically, the algorithm utilizes two datasets—pig faces and pig necks—which are divided into nine categories. Following data augmentation, the total sample size was augmented to 19,680. The distance metric used for K-means clustering is changed from the original algorithm to 1-IOU, which improves the adaptability of the model's target anchor boxes. Furthermore, the algorithm introduces SE, CBAM, and CA attention mechanisms, with the CA attention mechanism being selected for its superior performance in feature extraction. Finally, CARAFE, ASFF, and BiFPN are used for feature fusion, with BiFPN selected for its superior performance in improving the detection ability of the algorithm. The experimental results indicate that the YOLOv5-KCB algorithm achieved the highest accuracy rates in pig individual recognition, surpassing all other improved algorithms in average accuracy rate (IOU = 0.5). The accuracy rate of pig head and neck recognition was 98.4%, while the accuracy rate for pig face recognition was 95.1%, representing an improvement of 4.8% and 13.8% over the original YOLOv5 algorithm. Notably, the average accuracy rate of identifying pig head and neck was consistently higher than pig face recognition across all algorithms, with YOLOv5-KCB demonstrating an impressive 2.9% improvement. These results emphasize the potential for utilizing the YOLOv5-KCB algorithm for precise individual pig identification, facilitating subsequent intelligent management practices. [ABSTRACT FROM AUTHOR]

Taper angle is an important factor for characterizing a hyrocyclone's separation performance. Appropriate setting of the taper angle can contribute to particle grading and separation performance of the hydrocyclone. Through numerical analysis, this study investigated the flow fields and separation performances of the hydrocyclones with different taper angles in depth. Moreover, the numerical simulation results were compared with experimental data of the conventional hydrocyclone for accuracy validation. The results showed that the tangential velocity, axial velocity, and the diameter of air core increased with the increasing taper angle. At a too large or a too small taper angle, the inner flow field would become unstable, thereby leading to the increasing number of dislocated particles and reducing the particle separation precision. For the hydrocyclone with a diameter of 75 mm, the separation precision of fine particles can be remarkably enhanced by setting the taper angle of 15°, accompanied with the improvement of product quality. The present data can provide the related enterprises with favorable guidance and a theoretical foundation for further designing the taper angle of novel hydrocyclones. [ABSTRACT FROM AUTHOR]

The rapid expansion of coastal aquaculture has led to an increase in the coverage of aquaculture ponds, where intense feed-derived nitrogen is causing significant emissions of nitrous oxide (N 2 O). Multiple N 2 O production pathways and the relative importance of water column vs. sedimentary production in aquaculture ponds remain uncertain. Clarifying these pathways is vital for sustainable aquaculture development. Using 15 N-labeled dissolved inorganic nitrogen, the pathways and rates of N 2 O production in subtropical aquaculture ponds located in south China, cultivating whiteleg shrimp, Japanese seabass, and giant river prawn, were successfully characterized. Total N 2 O production rates ranged from 6 to 70 µmol-N m -2 d -1 , with the shrimp pond exhibiting the highest total N 2 O production rates, followed by ponds for seabass and prawn. These differences are primarily due to varying feed amounts causing differences in dissolved nutrients in water column and sediment. Particularly, nutrient and organic matter accumulation at the surface sediment stimulated N 2 O production. The oxygenated sediment on a centimeter scale could produce substantially more N 2 O compared to the water column above on a meter scale. Partial denitrification, i.e., nitrate and nitrite reduction to N 2 O, was more important (> 60 %) for N 2 O production in aquaculture ponds. The availability of nitrite is likely a major factor driving partial denitrification for both sedimentary and water column N 2 O production., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Objective: To summarize the clinical and genetic characteristics, treatment and prognosis of four children with Steroid-resistant nephrotic syndrome (SRNS) due to variants of TRPC6 gene., Methods: Clinical data of four children with SRNS admitted to Children's Hospital Affiliated to Zhengzhou University between May 2020 and August 2022 were collected. Peripheral blood samples were collected from the children and their parents, and whole exome sequencing was carried out. Sanger sequencing was used to verify the pathogenicity of the candidate variants among the children and their parents., Results: All of the four children were found to harbor heterozygous variants of the TRPC6 gene, including c.523C>T (p.R175W), c.1327T>A (p.F443I), c.430G>C (p.E144Q) (unreported previously), and c.523C>T (p.R175W), which were all missense variants. Two of the children have shown a simple type, whilst two have shown a nephritis type, none had extrarenal phenotype. Comprehensive renal pathology of three children revealed focal segmental glomerulosclerosis (FSGS). Two children were treated with steroids combined with calcineurin inhibitors (CNIs), among whom one showed significant improvement in symptoms., Conclusion: Discoveries of the novel c.430G>C variant and the new SRNS phenotype of the c.1327T>A variant have expanded the mutational and phenotypic spectrum of the TRPC6 gene, which has provided a reference for clinical diagnosis and genetic counseling for the families.

A trench-assisted ring few-mode erbium-doped fiber amplifier (FM-EDFA) supporting eight spatial modes is designed and proposed in this work. The gain equalization for the FM-EDFA is achieved by selecting the appropriate doping radius and concentration using a particle swarm optimization (PSO) algorithm when only the pump in the fundamental mode ( L P 01 ) is applied. When the signals in the eight spatial modes are simultaneously amplified, the average modal gain is about 20 dB, and the DMG is less than 0.3 dB for a signal at 1550 nm. Considering the gain competition of six wavelength signals, the modal gain and DMG are more than 20 and 1 dB, respectively. In addition, the tolerance analysis for manufacturing with this design is also discussed. For a fluctuation in the refractive index, the average modal gain is about 19.5 dB, and the DMG is 0.77 dB, indicating that the structure has good fabrication tolerance.