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For the efficient utilization of CO2 into valuable product, the attractive carbon nitride catalysts have been widely studied. In this work, heptazine‐related materials with varying degree of polymerization were designed by an intrinsically modification strategy and employed in the cycloaddition of CO2 with the bisepoxide 1, 4‐butanediol diglycidyl ether (BDODGE). We initially figured out that the sample prepared at 450 °C contained more melem hydrate, exhibiting the best performance. The epoxides conversion and corresponding cyclic carbonates selectivity could achieve 93.1 % and 99.3 % at 140 °C for 20 h without any cocatalyst and solvent, respectively. Results of the catalytic tests suggested that the high catalytic activity was dependent on big size porous structure and the synergetic effect of active amino groups and −OH groups. The role of water in maintaining the specific structure and providing active site has been proved. Moreover, the CN‐450‐W catalyst exhibited outstanding recycling stability. And finally, a plausible reaction mechanism was proposed. [ABSTRACT FROM AUTHOR]

Long-term exposure to a relatively high concentration of airborne bacteria emitted from intensive livestock houses could potentially threaten the health and welfare of animals and workers. There is a dual effect of air sterilization and promotion of vitamin D synthesis for the specific bands of ultraviolet light. This study investigated the potential use of A-band ultraviolet (UVA) tubes as a clean and safe way of reducing airborne bacteria and improving calf health. The composition and emission characteristics of airborne bacteria were investigated and used to determine the correct operating regime of UVA tubes in calf houses. Intermittent exceedances of indoor airborne bacteria were observed in closed calf houses. The measured emission intensity of airborne bacteria was 1.13 ± 0.09 × 107 CFU h−1 per calf. Proteobacteria were the dominant microbial species in the air inside and outside calf houses. After UVA radiation, the indoor culturable airborne bacteria decreased in all particle size ranges of the Anderson sampler, and it showed the highest reduction rate in the size range of 3.3–4.7 μm. The results of this study would enrich the knowledge of the source characteristics of the airborne bacteria in intensive livestock farming and contribute to the environmental control of cattle in intensive livestock production. [ABSTRACT FROM AUTHOR]

Background: Differentially expressed genes/proteins (DEGs/DEPs) play critical roles in pulmonary tuberculosis (PTB) diagnosis and treatment. However, there is a scarcity of reports on DEGs/DEPs in lung tissues and blood samples in PTB patients. Objective: We aim to identify the DEGs/DEPs in lung tissues and blood samples of PTB patients and investigate their roles in PTB. Materials and Methods: The lung granulomas and normal tissues were collected from PTB patients for proteomic and transcriptomic analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses annotated the functions of DEGs/DEPs. The GSE107994 data set was downloaded to identify the DEGs/DEPs in peripheral blood. The common DEGs and DEPs were identified. A nomogram was established. Pearson correlation analysis was conducted. Results: Eighty‐three DEGs/DEPs were identified. These DEGs/DEPs were mainly enriched in the movement of cell or subcellular components, regulation of cellular component biogenesis, and actin filament‐based process as well as in the pathways of inositol phosphate metabolism, adherens junction, phosphatidylinositol signaling system, leukocyte transendothelial migration, regulation of actin cytoskeleton, and tight junction. There were eight common DEGs/DEPs (TYMP, LAP3, ADGRL2, SIL1, LMO7, SULF 1, ANXA3, and PACSIN3) between the lung tissues and blood samples. They were effective in predicting tuberculosis. Moreover, the activated dendritic cells, macrophages, monocytes, neutrophils, and regulatory T cells were significantly positively correlated with TYMP (r >.50), LAP3 (r >.50), SIL1 (r >.50), ANXA3 (r >.5), and PACSIN3 (r <.50), while negatively correlated with LMO7 (r < −0.50) (p <.05). ADGRL2 and SULF1 did not have a significant correlation (p >.05). Limitations: The sample size was small. Conclusions: Eight common DEGs/DEPs of lung tissues and blood samples were identified. They were correlated with immune cells and demonstrated predictive value for PTB. Our data may facilitate the diagnosis and treatment of PTB. [ABSTRACT FROM AUTHOR]

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11–17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments. [ABSTRACT FROM AUTHOR]

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods. [ABSTRACT FROM AUTHOR]

Purpose: To investigate and characterize the performance of a novel orthogonal dual‐layer alpha multileaf collimator (αMLC) mounted on the LinaTech VenusX linac. Methods: We evaluated leaf positioning accuracy and reproducibility using an electronic portal imaging device through the picket fence test. The average, interleaf, intraleaf, and leaf tip transmissions of the single and dual layers were measured using an ionization chamber. Square and rhombus fields were used to evaluate the leaf penumbra of αMLC. To investigate the advantages of the orthogonal dual‐layer multileaf collimator (MLC) in field shaping, right triangular and circular pattern fields were formed using both the dual layers and single layers of the αMLC. Results: The average maximum positioning deviations of the upper and lower αMLC over 1 year were 0.76 ± 0.09 mm and 0.62 ± 0.07 mm, respectively. The average transmissions were 1.87%, 1.83%, and 0.03% for the upper‐, lower‐ and dual‐layer αMLC, respectively. The maximum interleaf transmissions of the lower‐ and dual‐layer were 2.43% and 0.17%, respectively. The leaf tip transmissions were 9.34% and 0.25%, respectively. The penumbra of the square field was 6.2 mm in the X direction and 8.0 mm in the Y direction. The average penumbras of the rhombus fields with side lengths of 5 and 10 cm were 3.6 and 4.9 mm, respectively. For the right triangular and circular fields, the fields shaped by the dual‐layer leaves were much closer to the set field than those shaped by single‐layer leaves. The dose undulation amplitude of the 50% isodose lines and leaf stepping angle change of the dual‐layer leaves were smaller than those of the single‐layer leaves. Conclusions: The αMLC benefits from its orthogonal dual‐layer design. Leaf transmission, dose undulations at the field edge, and MLC field dependence of the leaf stepping angle of the dual‐layer αMLC were remarkably reduced. [ABSTRACT FROM AUTHOR]

In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig's behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model's ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model's segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds. [ABSTRACT FROM AUTHOR]

Background: Although many molecules have been investigated as biomarkers for spinal cord injury (SCI) or ischemic stroke, none of them are specifically induced in central nervous system (CNS) neurons following injuries with low baseline expression. However, neuronal injury constitutes a major pathology associated with SCI or stroke and strongly correlates with neurological outcomes. Biomarkers characterized by low baseline expression and specific induction in neurons post‐injury are likely to better correlate with injury severity and recovery, demonstrating higher sensitivity and specificity for CNS injuries compared to non‐neuronal markers or pan‐neuronal markers with constitutive expressions. Methods: In animal studies, young adult wildtype and global Atf3 knockout mice underwent unilateral cervical 5 (C5) SCI or permanent distal middle cerebral artery occlusion (pMCAO). Gene expression was assessed using RNA‐sequencing and qRT‐PCR, while protein expression was detected through immunostaining. Serum ATF3 levels in animal models and clinical human samples were measured using commercially available enzyme‐linked immune‐sorbent assay (ELISA) kits. Results: Activating transcription factor 3 (ATF3), a molecular marker for injured dorsal root ganglion sensory neurons in the peripheral nervous system, was not expressed in spinal cord or cortex of naïve mice but was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Additionally, ATF3 protein levels in mouse blood significantly increased 1 day after SCI or ischemic stroke. Importantly, ATF3 protein levels in human serum were elevated in clinical patients within 24 hours after SCI or ischemic stroke. Moreover, Atf3 knockout mice, compared to the wildtype mice, exhibited worse neurological outcomes and larger damage regions after SCI or ischemic stroke, indicating that ATF3 has a neuroprotective function. Conclusions: ATF3 is an easily measurable, neuron‐specific biomarker for clinical SCI and ischemic stroke, with neuroprotective properties. Highlights: ATF3 was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively.Serum ATF3 protein levels are elevated in clinical patients within 24 hours after SCI or ischemic stroke.ATF3 exhibits neuroprotective properties, as evidenced by the worse neurological outcomes and larger damage regions observed in Atf3 knockout mice compared to wildtype mice following SCI or ischemic stroke. [ABSTRACT FROM AUTHOR]

Pasquier and Perrin discovered that the G2-horospherical manifold X of Picard number 1 can be realized as a smooth specialization of the rational homogeneous space parameterizing the lines on the 5-dimensional hyperquadric; in other words, it can be deformed nontrivially to the rational homogeneous space. We show that X is the only smooth projective variety with this property. This is obtained as a consequence of our main result that X can be recognized by its VMRT, namely, a Fano manifold of Picard number 1 is biregular to X if and only if its VMRT at a general point is projectively isomorphic to that of X. We employ the method the authors developed to solve the corresponding problem for symplectic Grassmannians, which constructs a flat Cartan connection in a neighborhood of a general minimal rational curve. In adapting this method to X, we need an intricate study of the positivity/negativity of vector bundles with respect to a family of rational curves, which is subtler than the case of symplectic Grassmannians because of the nature of the differential geometric structure on X arising from VMRT. [ABSTRACT FROM AUTHOR]

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the main problems faced by modern agricultural production, especially cucumber production. This study screened the salt–alkali-tolerant cultivar 'D1909' and the salt–alkali-sensitive cultivar 'D1604' from 32 different cucumber ecological types on the basis of morphological indicators and salt–alkali indices combined with relevant physiological and biochemical indices. By performing a transcriptome metabolome analysis, the key gene CsSRG1, which is responsive to salt–alkali stress in cucumber, was screened, and its function was verified. The role of CsSRG1 in reducing salt–alkali stress in cucumber was clarified, and the mechanism of salt–alkali tolerance in cucumber was preliminarily explored. This study provides germplasm resources for cucumber salt–alkali tolerance breeding and a theoretical basis for the effective use of saline alkali soil to achieve high quality and high yield in other crops. [ABSTRACT FROM AUTHOR]

Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement. [ABSTRACT FROM AUTHOR]

To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition. [ABSTRACT FROM AUTHOR]

A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing methods still face some challenges, such as: (1) the lack of a substantial high-precision pig-counting dataset; (2) creating a dataset for instance segmentation can be time-consuming and labor-intensive; (3) interactive occlusion and overlapping always lead to incorrect recognition of pigs; (4) existing methods for counting such as object detection have limited accuracy. To address the issues of dataset scarcity and labor-intensive manual labeling, we make a semi-auto instance labeling tool (SAI) to help us to produce a high-precision pig counting dataset named Count1200 including 1220 images and 25,762 instances. The speed at which we make labels far exceeds the speed of manual annotation. A concise and efficient instance segmentation model built upon several novel modules, referred to as the Instances Counting Network (ICNet), is proposed in this paper for pig counting. ICNet is a dual-branch model ingeniously formed of a combination of several layers, which is named the Parallel Deformable Convolutions Layer (PDCL), which is trained from scratch and primarily composed of a couple of parallel deformable convolution blocks (PDCBs). We effectively leverage the characteristic of modeling long-range sequences to build our basic block and compute layer. Along with the benefits of a large effective receptive field, PDCL achieves a better performance for multi-scale objects. In the trade-off between computational resources and performance, ICNet demonstrates excellent performance and surpasses other models in Count1200, A P of 71.4% and A P 50 of 95.7% are obtained in our experiments. This work provides inspiration for the rapid creation of high-precision datasets and proposes an accurate approach to pig counting. [ABSTRACT FROM AUTHOR]

Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics. [ABSTRACT FROM AUTHOR]

To improve the detection speed of cow mounting behavior and the lightness of the model in dense scenes, this study proposes a lightweight rapid detection system for cow mounting behavior. Using the concept of EfficientNetV2, a lightweight backbone network is designed using an attention mechanism, inverted residual structure, and depth-wise separable convolution. Next, a feature enhancement module is designed using residual structure, efficient attention mechanism, and Ghost convolution. Finally, YOLOv5s, the lightweight backbone network, and the feature enhancement module are combined to construct a lightweight rapid recognition model for cow mounting behavior. Multiple cameras were installed in a barn with 200 cows to obtain 3343 images that formed the cow mounting behavior dataset. Based on the experimental results, the inference speed of the model put forward in this study is as high as 333.3 fps, the inference time per image is 4.1 ms, and the model mAP value is 87.7%. The mAP value of the proposed model is shown to be 2.1% higher than that of YOLOv5s, the inference speed is 0.47 times greater than that of YOLOv5s, and the model weight is 2.34 times less than that of YOLOv5s. According to the obtained results, the model proposed in the current work shows high accuracy and inference speed and acquires the automatic detection of cow mounting behavior in dense scenes, which would be beneficial for the all-weather real-time monitoring of multi-channel cameras in large cattle farms. [ABSTRACT FROM AUTHOR]

To solve the problem that individual visual features could not be accurately extracted from low-light and high-noise pig face images in intensive farming, the optimal fitting curve parameters of image brightness enhancement were defined, and the Zero-DCE model was improved and Denoise-Net was introduced to achieve brightness enhancement and high-noise suppression of a single low-light pig face image. The experimental results show that, compared with EnlightGAN, Zero-DCE, Retinex, and SSE, the algorithm in this paper (DCE-Denoise-Net) has good results on image quality metrics such as information entropy, Brisque, NIQE, and PIQE in the absence of reference images. The image quality is improved. On the basis of improving the low visibility of low-light images, denoising was achieved. It is more suitable for low-light pig face image enhancement in a real breeding environment. [ABSTRACT FROM AUTHOR]

To improve the detection speed of cow mounting behavior and the lightness of the model in dense scenes, this study proposes a lightweight rapid detection system for cow mounting behavior. Using the concept of EfficientNetV2, a lightweight backbone network is designed using an attention mechanism, inverted residual structure, and depth-wise separable convolution. Next, a feature enhancement module is designed using residual structure, efficient attention mechanism, and Ghost convolution. Finally, YOLOv5s, the lightweight backbone network, and the feature enhancement module are combined to construct a lightweight rapid recognition model for cow mounting behavior. Multiple cameras were installed in a barn with 200 cows to obtain 3343 images that formed the cow mounting behavior dataset. Based on the experimental results, the inference speed of the model put forward in this study is as high as 333.3 fps, the inference time per image is 4.1 ms, and the model mAP value is 87.7%. The mAP value of the proposed model is shown to be 2.1% higher than that of YOLOv5s, the inference speed is 0.47 times greater than that of YOLOv5s, and the model weight is 2.34 times less than that of YOLOv5s. According to the obtained results, the model proposed in the current work shows high accuracy and inference speed and acquires the automatic detection of cow mounting behavior in dense scenes, which would be beneficial for the all-weather real-time monitoring of multi-channel cameras in large cattle farms. [ABSTRACT FROM AUTHOR]

Rumination behavior is closely associated with factors such as cow productivity, reproductive performance, and disease incidence. For multi-object scenarios of dairy cattle, ruminant mouth area images accounted for little characteristic information, which was first put forward using an improved Faster R-CNN target detection algorithm to improve the detection performance model for the ruminant area of dairy cattle. The primary objective is to enhance the model's performance in accurately detecting cow rumination regions. To achieve this, the dataset used in this study is annotated with both the cow head region and the mouth region. The ResNet-50-FPN network is employed to extract the cow mouth features, and the CBAM attention mechanism is incorporated to further improve the algorithm's detection accuracy. Subsequently, the object detection results are combined with optical flow information to eliminate false detections. Finally, an interpolation approach is adopted to design a frame complementary algorithm that corrects the detection frame of the cow mouth region. This interpolation algorithm is employed to rectify the detection frame of the cow's mouth region, addressing the issue of missed detections and enhancing the accuracy of ruminant mouth region detection. To overcome the challenges associated with the inaccurate extraction of small-scale optical flow information and interference between different optical flow information in multi-objective scenes, an enhanced GMFlowNet-based method for multi-objective cow ruminant optical flow analysis is proposed. To mitigate interference from other head movements, the MeanShift clustering method is utilized to compute the velocity magnitude values of each pixel in the vertical direction within the intercepted ruminant mouth region. Furthermore, the mean square difference is calculated, incorporating the concept of range interquartile, to eliminate outliers in the optical flow curve. Finally, a final filter is applied to fit the optical flow curve of the multi-object cow mouth movement, and it is able to identify rumination behavior and calculate chewing times. The efficacy, robustness, and accuracy of the proposed method are evaluated through experiments, with nine videos capturing multi-object cow chewing behavior in different settings. The experimental findings demonstrate that the enhanced Faster R-CNN algorithm achieved an 84.70% accuracy in detecting the ruminant mouth region, representing an improvement of 11.80 percentage points over the results obtained using the Faster R-CNN detection approach. Additionally, the enhanced GMFlowNet algorithm accurately identifies the ruminant behavior of all multi-objective cows, with a 97.30% accuracy in calculating the number of ruminant chewing instances, surpassing the accuracy of the FlowNet2.0 algorithm by 3.97 percentage points. This study provides technical support for intelligent monitoring and analysis of rumination behavior of dairy cows in group breeding. [ABSTRACT FROM AUTHOR]

Since the celebrated work by Cartan, distributions with small growth vector $(2,3,5)$ have been studied extensively. In the holomorphic setting, there is a natural correspondence between holomorphic $(2,3,5)$ -distributions and nondegenerate lines on holomorphic contact manifolds of dimension 5. We generalize this correspondence to higher dimensions by studying nondegenerate lines on holomorphic contact manifolds and the corresponding class of distributions of small growth vector $(2m, 3m, 3m+2)$ for any positive integer m. [ABSTRACT FROM AUTHOR]

Studying the distribution characteristics of bioaerosols and their interaction with the environment is crucial for dairy farms. The distribution of aerosols differs in dairy farming from farming of other livestock, and their sensitivity to environmental factors varies across sites. Field experiments were conducted in an intensive commercial dairy farm in Northern China to investigate the horizontal and vertical distribution of culturable bacterial bioaerosols. Concentration levels and particle size ranges were analyzed, and the impact of multiple environmental factors on culturable bacterial bioaerosols was assessed. Significant variations in culturable bacterial bioaerosol concentrations were observed across eight functional zones, ranging from 1.14 × 103 to 7.35 × 103 CFU/m3. Culturable bacterial bioaerosols exhibited consistent carrier distribution patterns across six different size ranges. Vertical analysis revealed significantly higher culturable bacterial bioaerosol concentrations at a 1 m height compared to 4 m (p < 0.05), while similar size distributions were observed at different heights of the same sampling location. The top three environmental factors influencing culturable bacterial bioaerosol concentrations were PM100 concentration, wind direction, and air temperature. This study provides insights into the distribution characteristics of culturable bacterial bioaerosols on dairy farms and their response to environmental factors. The findings serve as a reference for evaluating bioaerosol emissions and establishing daily disinfection management measures on dairy farms. [ABSTRACT FROM AUTHOR]

In the multi-agent cooperative decision-making process, an agent needs to learn cooperatively with its neighbors to obtain the optimal strategy. The actions of agents can be classified into independent actions and interactive actions. The overall information and neighbor information obtained by the agent are different in guiding the selection of the two types of actions. Generally, real-time interaction between the representations of overall information and neighborhood information can facilitate the cooperative decision-making of agents. Therefore, this paper proposes a bidirectional real-time gain representation (BRGR) mechanism, which explicitly enables such real-time interactions. On the one hand, real-time effective neighborhood information representation is incorporated into the overall information representation via an attention module to achieve the gain of the overall information. The gains provide a better understanding and utilization of neighborhood information and guide the agents to make independent action selections. On the other hand, real-time overall information representation is integrated into the neighborhood information representation to achieve the gain of neighborhood information, which guarantees that the interactive actions are based on the current state of agent. The gains make the agents select proper interactive actions. Thus, the proposed BRGR mechanism enables the agents to effectively learn the optimal cooperative strategies. The BRGR is applied to state-of-the-art multi-agent reinforcement learning algorithms. The experimental results show that the BRGR significantly improves the performance of the base algorithms, and have more advantage in complex environments. [ABSTRACT FROM AUTHOR]

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Earthen sites are a kind of constructure with significant historical and cultural value. However, the destruction of earthen sites caused by erosion occurs frequently. The solidification of calcined ginger nuts can improve the strength of the soil so that it can be used to protect the earthen sites. However, the strength degradation of solidified soil by calcined ginger nuts after dry–wet and freeze–thaw cycles is unclear. To reveal the deterioration pattern of solidified soil strength, the effects of its dosage and cycle number on the strength of solidified soil were analyzed through shear strength, dry–wet cycle, and freeze–thaw cycle tests. The results showed that the solidified soil strength decreased first and increased with dosage increase. With the number of dry–wet cycles increasing, the strength of the plain loess decreased rapidly and gradually turned flat. The strength loss of solidified soil was small in the dry–wet process. With freeze–thaw cycle numbers increasing, the strength of the plain loess decreased first and then tended to be flat, the strength of solidified soil decreased first and then increased slightly, and the change in the strength had a clear inflection point. With the increasing dosage, freeze–thaw cycle numbers corresponding to the inflection point were significantly reduced. These results indicate that calcined ginger nuts could enhance the resistance of earthen sites loess to dry–wet and freeze–thaw cycles. [ABSTRACT FROM AUTHOR]

Simple Summary: The H9N2 avian influenza virus has spread to the whole world and become one of the dominant subtype influenza viruses in chickens in China. H9N2 virus could not only result in great economic losses by reducing egg production but also serve as a gene vector to provide its gene segments to other emerging severe influenza A viruses to cause higher mortality and more serious consequences. Thus, developing models to predict H9N2 status should be given priority. Our main aim was to use the machine learning method (XGBoost classification algorithm) and regular production data (laying rate and mortality) to predict the occurrence and development of H9N2 in laying hen houses and evaluate their performance in disease prediction. Additionally, we assessed the working ability of the framework with different time frames to predict H9N2 status in advance within a 3-day period. We found that this framework could work well in prediction with high accuracy and sensitivity, and with more information introduced into the model, more "don't care values" would be added into datasets to affect model performance by forming attribute noise. Besides, our study recommended efficient frameworks and models for H9N2 status prediction and provided practical potential uses in the livestock and poultry industry. The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months' laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day − 1, day − 2, day − 3, day − 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable. [ABSTRACT FROM AUTHOR]

Introduction: We investigated the molecular mechanism by which B lymphocytes regulate Th1/Th2 imbalance to participate in the pulmonary fibrosis in hypersensitivity pneumonia induced by pigeon shedding in rats. Methods: CD19+ rats and CD19− rats were used to construct animal models of fibrotic hypersensitivity pneumonia. DAPT was used to inhibit the Notch signaling pathway. The pathological changes were assessed with HE and Masson staining. Protein level was detected with Western blot. Th1/Th2 ratio was analyzed with flow cytometry. Cytokine levels were measured with ELISA. Results: The pathological changes of pulmonary fibrosis were not obvious in the CD19− rats and after DAPT treatment. Notch signaling pathway proteins, including Notch1, Notch2, Jag1, Jag2, DLL1, and DLL4, in lung tissues of model rats were all significantly upregulated than those in control rats. However, these proteins in CD19− rats were lower in CD19+ rats, suggesting that B cells play a key role in inducing pneumonia. Besides, the Th1/Th2 ratio in the BALF of model rats decreased, which was further reversed by DAPT. However, we found that in CD19− rats, the regulation of the Th1/Th2 ratio by the Notch signaling pathway was lost. Conclusion: Deleting B lymphocytes or blocking the Notch pathway both reversed the Th1/Th2 imbalance in fibrotic hypersensitivity pneumonia and inhibited pulmonary fibrosis. [ABSTRACT FROM AUTHOR]

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm. [ABSTRACT FROM AUTHOR]

The procoagulant effect of microparticles (MPs) contributes to hypercoagulability-induced thrombosis. We provide preliminary findings of the MPs-Activated Clotting Time (MPs-ACT) assay to determine the procoagulant activity of MPs. MPs-rich plasma was obtained and recalcified. Changes in plasma viscoelasticity were evaluated and the time to the peak viscoelastic changes was defined as the MPs-ACT. MPs concentration was measured by flow cytometry. Coagulation products produced during plasma clotting were identified by fibrin and fibrinopeptide A. MPs were prepared in vitro and added to standard plasma to simulate pathological samples. In addition, reproducibility and sensitivity were evaluated. We confirmed the linear relationship between MPs-ACT and MP concentrations. Dynamic changes in fibrin production were depicted. We simulated the correlation between MPs-ACT and standard plasma containing MPs prepared in vitro. The reproducibility of high-value and low-value samples was 6.0% and 10.8%, respectively. MPs-ACT sensitively detected hypercoagulable samples from patients with pre-eclampsia, hip fractures, and lung tumors. MPs-ACT largely reflects the procoagulant effect of MPs. MPs-ACT sensitively and rapidly detects hypercoagulability with MPs-rich plasma. It may be promising for the diagnosis of hypercoagulable states induced by MPs. [ABSTRACT FROM AUTHOR]

As machine vision technology has advanced, pig face recognition has gained wide attention as an individual pig identification method. This study establishes an improved ResNAM network as a backbone network for pig face image feature extraction by combining an NAM (normalization-based attention module) attention mechanism and a ResNet model to probe non-contact open-set pig face recognition. Then, an open-set pig face recognition framework is designed by integrating three loss functions and two metrics to finish the task with no crossover of individuals in the training and test sets. The SphereFace loss function with the cosine distance as a metric and ResNAM are combined in the framework to obtain the optimal open-set pig face recognition model. To train our model, 37 pigs with a total of 12,993 images were randomly selected from the collected pig face images, and 9 pigs with a total of 3431 images were set as a test set. 900 pairs of positive sample pairs and 900 pairs of negative pairs were obtained from the images in the test set. A series of experimental results show that our accuracy reached 95.28%, which was 2.61% higher than that of a human face recognition model. NAM was more effective in improving the performance of the pig face recognition model than the mainstream BAM (bottleneck attention module) and CBAM (convolutional block attention module). The research results can provide technological support for non-contact open-set individual recognition for intelligent farming processes. [ABSTRACT FROM AUTHOR]

An optimal static voltage stability margin (SVSM) control model considering the uncertain wind farm output is proposed, in which the probability distribution of SVSM is required to satisfy the chance and variance constraints. A probabilistic distribution control method is proposed to solve the model. First, a linear weighted sum of B-spline basic functions is introduced to approximate the analytical function between the probability density function (PDF) of SVSM and control variables. Next, the analytical expression of the PDF of SVSM corresponding to a control variables’ sample is derived, and the sample of corresponding B-spline function weights is obtained, thus forming a series of ‘control variables-weights’ samples. Then, the analytic functions between weights and control variables are obtained using the generated samples to train a radial basis function neural-network, and the analytical function between the PDF of SVSM and control variables is obtained. After that, the chance and variance constraints are transformed into general nonlinear constraints with respect to control variables. Moreover, a method of selecting key control variables by calculating sensitivities is proposed to increase computational efficiency. Test results on the IEEE-39 bus system and an actual provincial power grid demonstrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Objective: A stable animal model was needed to study bone non‐union caused by insufficient blood supply, the main object of this paper is to develop a medial malleolar fracture model with controllable arterial vascular injuries in rats for revealing the biochemical mechanism of non‐union by insufficient blood supply. Methods: A total of 18 rats were randomly divided into three equal groups: the Sham group, the Fracture group, and the Fracture + Vascular group. The animals were subjected to unilateral medial malleolar bone fracture and vascular injury using customized molding equipment. The fracture site was scanned by micro‐CT, and vascular injury was evaluated by laser Doppler flowmetry (LDF) 24 h after modeling. Histological examination (HE), alkaline phosphatase (ALP) and tartrate‐resistant acid phosphatase (TRAP) staining, immunohistochemistry and immunofluorescence were conducted on the medial malleolar fracture tissues of three rats randomly selected from each group 24 h after modeling. Subsequently, to further confirm the success of fracture modeling, the fracture sites of three other rats in each group underwent micro‐CT scanning again 6 weeks after surgery. Results: The results of a 24 h micro‐CT showed that all rats used to create the fracture models showed controlled injury of the medial malleolus. The model was stable, and the satisfaction of the homemade equipment agreed with the expectation. LDF showed that the blood flow of rats in the Fracture + Vascular group decreased significantly 24 h after fracture injury, while collateral blood flow perfusion increased by 50% on average. The results of HE, ALP and TRAP staining in the medial malleolus showed that the number of osteoblasts (OBs) and osteoclasts (OCs) in the Fracture group increased significantly, but the number of OBs and OCs in the Fracture + Vascular group decreased sharply relative to the number in the Sham group 24 h later. Furthermore, immunohistochemistry and immunofluorescence results showed that the number of neovessels in the Fracture group was significantly increased, while the number of neovessels in the Fracture + Vascular group was significantly decreased, which was consistent with the above results. After 6 weeks of modeling, the micro‐CT results showed that the fractures in the Fracture group had healed substantially, while those in the Fracture + Vascular group had not. Conclusion: This study provided a reproducible and stable experimental animal model for medial malleolar fractures with arterial injury. [ABSTRACT FROM AUTHOR]

Natural breeding scenes have the characteristics of a large number of cows, complex lighting, and a complex background environment, which presents great difficulties for the detection of dairy cow estrus behavior. However, the existing research on cow estrus behavior detection works well in ideal environments with a small number of cows and has a low inference speed and accuracy in natural scenes. To improve the inference speed and accuracy of cow estrus behavior in natural scenes, this paper proposes a cow estrus behavior detection method based on the improved YOLOv5. By improving the YOLOv5 model, it has stronger detection ability for complex environments and multi-scale objects. First, the atrous spatial pyramid pooling (ASPP) module is employed to optimize the YOLOv5l network at multiple scales, which improves the model's receptive field and ability to perceive global contextual multiscale information. Second, a cow estrus behavior detection model is constructed by combining the channel-attention mechanism and a deep-asymmetric-bottleneck module. Last, K-means clustering is performed to obtain new anchors and complete intersection over union (CIoU) is used to introduce the relative ratio between the predicted box of the cow mounting and the true box of the cow mounting to the regression box prediction function to improve the scale invariance of the model. Multiple cameras were installed in a natural breeding scene containing 200 cows to capture videos of cows mounting. A total of 2668 images were obtained from 115 videos of cow mounting events from the training set, and 675 images were obtained from 29 videos of cow mounting events from the test set. The training set is augmented by the mosaic method to increase the diversity of the dataset. The experimental results show that the average accuracy of the improved model was 94.3%, that the precision was 97.0%, and that the recall was 89.5%, which were higher than those of mainstream models such as YOLOv5, YOLOv3, and Faster R-CNN. The results of the ablation experiments show that ASPP, new anchors, C3SAB, and C3DAB designed in this study can improve the accuracy of the model by 5.9%. Furthermore, when the ASPP dilated convolution was set to (1,5,9,13) and the loss function was set to CIoU, the model had the highest accuracy. The class activation map function was utilized to visualize the model's feature extraction results and to explain the model's region of interest for cow images in natural scenes, which demonstrates the effectiveness of the model. Therefore, the model proposed in this study can improve the accuracy of the model for detecting cow estrus events. Additionally, the model's inference speed was 71 frames per second (fps), which meets the requirements of fast and accurate detection of cow estrus events in natural scenes and all-weather conditions. [ABSTRACT FROM AUTHOR]

Ubiquitous surface wrinkling has been well‐studied theoretically and experimentally. How to modulate the stress state of a liquid‐supported system for the unexploited wrinkling capabilities remains a challenge. Here we report a simple linearly‐polarized‐light illumination to spatiotemporally trigger ultrasensitive in situ dynamic wrinkling on a floating azo‐film. The smart combination of the liquid substrate with photoresponsive azo‐moieties leads to the light‐induced ultrafast wrinkling evolution, accompanied by unprecedented sequential wrinkling orientation conversion (from polarization‐parallel to polarization‐perpendicular). The involved different polarization‐dependent sequential photo‐orientation for azo side chains and azo‐grafted main chains of azopolymers is disclosed experimentally for the first time. Meanwhile, programmable dynamic wrinkling with all‐optical switchable surface topographies is available, which has wide application potentials in photoresponsive soft photonics. [ABSTRACT FROM AUTHOR]

Ubiquitous surface wrinkling has been well‐studied theoretically and experimentally. How to modulate the stress state of a liquid‐supported system for the unexploited wrinkling capabilities remains a challenge. Here we report a simple linearly‐polarized‐light illumination to spatiotemporally trigger ultrasensitive in situ dynamic wrinkling on a floating azo‐film. The smart combination of the liquid substrate with photoresponsive azo‐moieties leads to the light‐induced ultrafast wrinkling evolution, accompanied by unprecedented sequential wrinkling orientation conversion (from polarization‐parallel to polarization‐perpendicular). The involved different polarization‐dependent sequential photo‐orientation for azo side chains and azo‐grafted main chains of azopolymers is disclosed experimentally for the first time. Meanwhile, programmable dynamic wrinkling with all‐optical switchable surface topographies is available, which has wide application potentials in photoresponsive soft photonics. [ABSTRACT FROM AUTHOR]

Body dimensions are key indicators for the beef cattle fattening and breeding process. On-animal measurement is relatively inefficient, and can induce severe stress responses among beef cattle and pose a risk for operators, thereby impacting the cattle's growth rate and wellbeing. To address the above issues, a highly efficient and automatic method was developed to measure beef cattle's body dimensions, including the oblique length, height, width, abdominal girth, and chest girth, based on the reconstructed three-dimensional point cloud data. The horizontal continuous slice sequence of the complete point clouds was first extracted, and the central point of the beef cattle leg region was determined from the span distribution of the point cloud clusters in the targeted slices. Subsequently, the boundary of the beef cattle leg region was identified by the "five-point clustering gradient boundary recognition algorithm" and was then calibrated, followed by the accurate segmentation of the corresponding region. The key regions for body dimension data calculation were further determined by the proposed algorithm, which forms the basis of the scientific calculation of key body dimensions. The influence of different postures of beef cattle on the measurement results was also preliminarily discussed. The results showed that the errors of calculated body dimensions, i.e., the oblique length, height, width, abdominal girth, and chest girth, were 2.3%, 2.8%, 1.6%, 2.8%, and 2.6%, respectively. In the present work, the beef cattle body dimensions could be effectively measured based on the 3D regional features of the point cloud data. The proposed algorithm shows a degree of generalization and robustness that is not affected by different postures of beef cattle. This automatic method can be effectively used to collect reliable phenotype data during the fattening of beef cattle and can be directly integrated into the breeding process. [ABSTRACT FROM AUTHOR]

The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake. [ABSTRACT FROM AUTHOR]

We study the question whether rational homogeneous spaces are rigid under Fano deformation. In other words, given any smooth connected family π : X → Z of Fano manifolds, if one fiber is biholomorphic to a rational homogeneous space S, is π an S-fibration? The cases of Picard number one were answered by Hwang and Mok. The manifold F (1 , Q 5) is the unique rational homogeneous space of Picard number one that is not rigid under Fano deformation, and a Fano degeneration of it is constructed by Pasquier and Perrin. For higher Picard number cases, one notices that the Picard number of a rational homogeneous space G/P satisfies ρ (G / P) ≤ rank (G) . Weber and Wiśniewski proved that the rational homogeneous spaces G/P with ρ (G / P) = rank (G) (i.e. complete flag manifolds) are rigid under Fano deformation. In this paper, we show that the rational homogeneous spaces G/P with ρ (G / P) = rank (G) - 1 are rigid under Fano deformation, provided that G is a simple algebraic group of type ADE, and G/P is not biholomorphic to F (1 , 2 , P 3) or F (1 , 2 , Q 6) . We also show that F (1 , 2 , P 3) has a unique Fano degeneration, which is explicitly constructed. Furthermore, the structure of possible Fano degenerations of F (1 , 2 , Q 6) is also described explicitly. Our main result is obtained by applying the theory of Cartan connections and symbol algebras. [ABSTRACT FROM AUTHOR]

1-Flat irreducible G-structures, equivalently, irreducible G-structures admitting torsion-free affine connections, have been studied extensively in differential geometry, especially in connection with the theory of affine holonomy groups. We propose to study them in a setting in algebraic geometry, where they arise from varieties of minimal rational tangents (VMRT) associated to families of minimal rational curves on uniruled projective manifolds. We prove that such a structure is locally symmetric when the dimension of the uniruled projective manifold is at least 5. By the classification result of Merkulov and Schwachhöfer on irreducible affine holonomy, the problem is reduced to the case when the VMRT at a general point of the uniruled projective manifold is isomorphic to a subadjoint variety. In the latter situation, we prove a stronger result that, without the assumption of 1-flatness, the structure arising from VMRT is always locally flat. The proof employs the method of Cartan connections. An interesting feature is that Cartan connections are considered not for the G-structures themselves, but for certain geometric structures on the spaces of minimal rational curves. [ABSTRACT FROM AUTHOR]

With the development of modern agricultural facilities, crop diseases recognition, nutritional status and morphology achieved rapid growth. To avoid yield loss caused by the delay of disease detection, digital images that contain information with respect to crop growth, disease type and nutrition deficiency have been studied by some researchers. However, traditional image processing methods fail to extract typical disease features of crop images with ambiguous disease information. In this paper, a crop disease image recognition technique based on the salient region and hierarchical indexing was proposed. Improved Harris algorithm and maximum radius were used to calculate the widest salient region. In order to eliminate the effect of different salience distribution ranges between different features, a group of images in the cucumber disease image library were normalized. Experiment results indicate that the time complexity of each algorithm will go up as the size of the dataset increase. Especially when testing large datasets, nonhierarchical and nonclustering, hierarchical and nonclustering and hierarchical based on points all tend to raise the algorithm's time complexity. Plant Village dataset and AI Challenger 2018 dataset were utilized to compare the recognition performances among the models based on machine learning, neural network, deep learning and our methods. The experiment results show that the method proposed in this paper is capable of recognizing local similar images effectively rather than global similar images, therefore, it has better recognition performance than the model learning methods in the early detection stage of crop disease. [ABSTRACT FROM AUTHOR]

Purpose: Zaozigou gold mine is located in the Qinghai-Tibet Plateau, and it is one of the largest gold (Au) mines in China. Agricultural soil pollution of toxic trace elements (TTEs) in Au mining area has become an increasingly common issue and potentially harms the environment and human health. In this paper, 78 samples were collected from a typical Au mining area to evaluate pollution degree of TTEs in the soils and assess related human health risk. Materials and methods: In this study, a total of 78 samples including 36 agricultural soil, 21 sediment, and 21 river samples were collected from the Au mining area. The soil chemical properties of soil samples were analysed. The geo-accumulation (Igeo), contamination factor (CF), and pollution loading index (PLI) were used to analyse the degree of contamination by TTEs. In addition, potential ecological and health risks to local residents via different three exposure routes were revealed using potential ecological risk index and US-EPA health risk assessment models. Pearson correlations between soil chemical properties and toxic elements concentration were calculated. Results and discussion: Results showed that the mean concentrations of As, Cd, Zn, Pb, Cr, and Hg in soils were 27.02, 4.22, 1.57, 1.46, 1.17, and 4.88 times higher than the background values, respectively. The Igeo index and CF values indicated that As pollution was the most severe and Hg and Cd pollution was moderate. Additionally, PLI indicated that soil As and Hg posed extremely high risks. The average potential ecological risk value of all TTEs was 602.34, and As had a higher contribution rate than the other elements, suggesting that the study area was under very high risk, mainly posed by soil As. The hazard index values for Pb, Cr, Cd, Hg, and Zn were below safe value of 1. However, the hazard index values of As for local adults and children were 1.66 and 10.25, respectively. Meanwhile, As presented the unacceptable carcinogenic risks for local residents. The presence of As was related to mining activities, based on the Pearson correlation (between soil chemical properties and TTE contents) and As levels in rivers and sediments near the mining area. Conclusions: As pollution from Au mining activities has posed potential ecological risks to the ecosystem and caused human health risk in the Au mining area. Therefore, more efficient policies to control As contamination in the soils should be implemented as soon as possible. [ABSTRACT FROM AUTHOR]

Though gold mines provide significant economic benefits to local governments, mining causes soil pollution by potentially toxic trace elements (PTEs) in mining areas, especially in the Qinghai-Tibet Plateau. Screening of native plant species from mining areas is now an effective, inexpensive, and eco-friendly method for the remediation of PTEs in situ. In the present study, we conducted experiments to assess the accumulation of As, Cd, Pb, and Zn in 12 native plant species growing on a typical gold mining area in the Qinghai-Tibet Plateau. Our results showed that rhizosphere soils have high soil organic matter content, high levels of As, and moderate levels of Cd. Geranium pylzowianum accumulated relatively higher As in its shoots and exhibited translocation factor (TF) higher than 1 for As (4.65), Cd (1.87), and Pb (1.36). Potentilla saundersiana had bioconcentration factor of shoot (BCF-S) higher than 1 for Cd (4.52) and Pb (1.70), whereas its TF was higher than 1 for As, Cd, Pb, and Zn. These plant species exhibit strong tolerance to these PTEs. Furthermore, Elymus nutans accumulated low levels of As, Cd, Pb, and Zn in their shoots and exhibited TF values lower than 1 for the four PTEs. Therefore, G. pylzowianum is a promising candidate for the in situ phytoextraction of As, and P. saundersiana can be used as an effective plant for Cd and Pb phytoextraction. E. nutans is better suited for the phytostabilisation of multiple PTEs. This work is of significant importance for screening native plant species that can provide a reference for phytoremediation of PTE-contaminated soils in this area or other place with similar climate, and has a good potential for developing PTE phytoremediation strategies at mining sites. [ABSTRACT FROM AUTHOR]

This paper investigates a comprehensive optimization method of determining the size and location of dynamic voltage restorers (DVRs) in distribution network considering customers’ demand for sag mitigation. The objective is to minimize the investment of DVRs subject to customers’ satisfaction. In the objective function, the unit price per kVA of DVR was described by a continuous function of the capacity to calculate the investment. In the constraints, a mitigation expectation index (MEI) was proposed to assess the customers’ demand for mitigation. The MEI indicates the proportion among all sag events affecting customers, which were expected to be mitigated by the customers. It is represented by the cumulative probability of residual voltage of sag events. To improve the computational efficiency, the binary encoding for the locations and real encoding for the output voltage of DVRs are combined as chromosomes in multiple population Genetic Algorithm (MPGA). The proposed method was testified with the IEEE 33 bus distribution system with different sensitive loads. Optimal results were obtained to satisfy different customers’ demand for sag mitigation efficiently. [ABSTRACT FROM AUTHOR]

Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails. [ABSTRACT FROM AUTHOR]