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Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.

To achieve accurate identification and efficient segmentation of adhesive tobacco shreds, a method for adhesive tobacco shreds recognition based on an improved Mask R-CNN (Mask Region-based Convolutional Neural Network) model was proposed. Firstly, adhesive tobacco shreds images were collected, and the dataset was augmented through image enhancement operations to expand it to the required sample size for training the model. Secondly, edge feature extraction and fractal feature extraction were performed on the adhesive tobacco shreds images in the training set based on Mask R-CNN, resulting in clearer and more continuous image edge features and texture feature information. Subsequently, the original features, edge features, and fractal features were fused to fully utilize features at different levels and enrich low-level features. Finally, by introducing a hybrid attention mechanism that focused on both channel and spatial dimensions of feature maps, the efficiency and accuracy of tobacco shred recognition were improved. Experimental results showed that the mean intersectionover union (Avg.MIoU) of the recognition method based on the improved Mask R-CNN model was 85.29%, and the mean class pixel accuracy (Avg.MPA) was 84.33%, under different adhesion conditions enabling precise identification of tobacco shreds and outperforming the original Mask R-CNN and DeepLabV3+ models. This method could rapidly and accurately identify and segment adhesive tobacco shreds, providing technical support for subsequent tobacco shred width detection. [ABSTRACT FROM AUTHOR]

Taking into account the wind speed distribution and correlation, the units in the field are first clustered according to the wind speed distribution, and the wind speed distribution modeling scheme is proposed for practical applications. Then, each area is selected according to the European distance. Finally, considering the uncertainty between wind speed and available inertia, neural network is used to predict the available inertia distribution of each cluster. A wind farm in China is selected as the simulation example. The simulation results show that compared with the equivalent machine method based on average wind speed, the prediction accuracy of the proposed method in the low wind speed range is significantly improved, and the calculation efficiency of the proposed algorithm based on the clustered central unit is higher.

[Objectives] In order to study on the dynamic motion of detonation products generated by gas cloud explosion inside the multi-cabin,[Methods] the fifth-order WENO finite difference scheme is adopted to solve the coupling of mass fraction transport equation and compressible Euler equation for the detonation products,and then a two-dimensional numerical simulation program for the gas cloud explosion in the multi-cabin is developed successfully. The reliability and correctness of this numerical simulation program is verified by virtue of the plane shock diffraction rectangular step test,after which the verified program is used to carry out the numerical simulation of the gas cloud explosion under different explosion conditions, so as to explore the influence of venting hole and afterburning energy on the dynamic motion process of the detonation products during the explosion inside the multi-cabin.[Results] The results show that increasing the size of the venting hole can accelerate the emission process of the detonation products; increasing the reaction rate constant can accelerate the expansion of the detonation products to the adjacent cabin.[Conclusions] This study can provide a reference and guidance for further exploring the expansion process of the detonation products generated by the complex multi-component combustion and the evaluation of the explosion fireball damage.

[Objectives] In this paper, an improved third-order WENO scheme named ‘WENO-N+3’ is constructed by adding a suitable term to the weights of the conventional WENO-N3 scheme. [Methods] The precision near the critical points of WENO-N3 and the WENO-N+3 schemes is derived using the Taylor expansion, and accuracy tests are also conducted. Shock-entropy wave interaction, interacting blast waves and Rayleigh-Taylor instability problems are selected in order to verify that the improved scheme WENO-N+3 has less dissipation than the conventional WENO-JS3, WENO-Z3 or WENO-N3 schemes, with high-resolution flow field structures. Finally, the proposed WENO-N+3 scheme is applied to simulations of explosion loads in a confined space. [Results] The simulation results indicate that the present scheme can provide a higher shockwave peak. [Conclusions] The findings of this paper can provide valuable references for research into the numerical calculation of internal explosion loads.

[Purpose/Significance] Generative artificial intelligence (AI) technology has been widely used in many fields, and the application of this technology has become popular among researchers. However, there are few studies on the willingness of researchers willingness to accept generative AI. This leads to an insufficient understanding of the psychological mechanism, cognitive process and behavioral pattern of users' acceptance of generative AI, which limits the ability of theoretical innovation and practical exploration in user information behavior. This study focuses on researchers acceptance of generative AI. By studying the evaluation process of ChatGPT by college students, it explores the acceptance behavior of generative AI. At the same time, it verifies the applicability of the AIDUA model in the new context, and introduces the new variable of school identity, which further extends the AIDUA model. [Method/Process] Based on the cognitive assessment theory and the AI acceptance framework (AIDUA), this paper constructs a theoretical model of the intention to use generative artificial intelligence, and develops and empirically tests the theoretical model of the intention to use generative AI. Taking college students as the main research object, based on the maturity scale in authoritative literature at home and abroad, 8 variables and 29 observation variables such as social influence, hedonic motivation and anthropomorphism were designed. College students with experience in using generative AI were invited to participate in the questionnaire survey. SPSS26.0 was used to analyze the data from 294 valid questionnaires collected. SmartPLS 3.2.9 was used to construct a structural equation model to test the hypothesis, and the JN method was used to detect the regulatory effect. [Results/Conclusions] The study found that users went through three stages of decision making before using generative AI. The PLS-SEM results show that: 1) Social influence, hedonic motivation and anthropomorphism significantly affect performance expectancy and effort expectancy, and anthropomorphism is the strongest variable affecting performance expectancy and effort expectancy. 2) Performance expectancy and effort expectancy are significantly negatively correlated with negative emotions, while hedonic motivation has no significant effect on negative emotions. 3) Negative emotions are significantly negatively correlated with users' intension to use. 4) School identity moderates the relationship between effort expectancy and negative emotions. This study combines anthropomorphic research on college students' acceptance of generative AI, and provides a framework for the acceptance of generative AI. Researchers can use this framework to better study the acceptance of AI. This study has some limitations. In the future, we will focus on the following three aspects: 1) to evaluate the users' acceptance of generative AI in different usage scenarios. 2) to use samples of other groups to test the applicability of the model, such as civil servants, librarians, researchers and other groups. 3) to incorporate variables from other technology acceptance models into the model, such as ease of use and practicality. [ABSTRACT FROM AUTHOR]

[Objective] With the rapid popularization of new energy vehicles and the vigorous development of autonomous driving technology, 3D object detection algorithms play a pivotal role in real road scenes. The LiDAR point clouds contain precise position and geometric structure information of the object, which can accurately describe the 3D space position of the target. Moreover, LiDAR makes environmental perception and route planning of unmanned vehicles a reality. However, cars in real scenes often fall into complex and difficult situations, such as occlusion and truncation of objects, which contribute to highly sparse clouds and incomplete contours. Therefore, the effective use of disordered and unevenly distributed point clouds for accurate 3D object detection has important research significance and practical value for the safety of autonomous driving. [Methods] This paper uses LiDAR point clouds in an autonomous driving scene to conduct in-depth research on a high-performance 3D object detection algorithm based on deep learning. The 3D object detection algorithm based on multibranch feature fusion (PSANet) is designed to improve the capacity and viability of autonomous driving technology. After the disordered point clouds are divided into regular voxels, the voxel feature coding module and convolutional neural network are used to learn the voxel features, and the sparse 3D data are compressed into a dense 2D bird's-eye view. Furthermore, the multiscale bird's-eye view features are deeply fused through the coarse and fine branches of the 2D backbone network. The splitting and aggregation feature pyramid module in the fine branch splits and aggregates the bird's-eye view features at different levels and realizes the deep fusion of semantic information, texture information, and context information of multiscale features to obtain more expressive features. The multiscale features in the coarse branch are fused after transposed convolution, and the precise original spatial location information is preserved. After the feature extraction of coarse and fine branches, the element-wise addition method can obtain more accurate and complete features for object classification, position regression, and orientation prediction. [Results] The experimental results on the KITTI dataset show that the average precision of PSANet in 3D object detection and bird's-eye view object detection tasks reach 81.72% and 88.25%, respectively. The inference speed on a single GTX 1080Ti GPU can reach 24 frames per second, and it shows strong robustness in complex scenes. Compared with the two-stage target detection algorithms MV3D, AVOD-FPN, F-PointNet, and IPOD, the average accuracy of 3D target detection of this algorithm increased by 18.21%, 5.89%, 8.94%, and 3.12%, respectively. Compared with the one-stage target detection algorithms VoxelNet, SECOND, PointPillars, and VoTr-SSD, the average accuracy of 3D target detection of this algorithm increased by 11.63%, 4.05%, 3.19%, 0.98%, 1.16%, and 0.7%, respectively. The detection speed of this algorithm improved by 14 frames per second compared with the two-stage algorithm PointRCNN with similar accuracy. [Conclusions] In comparison with other advanced algorithms, this algorithm exhibits strong performance, in which it can better balance the accuracy and speed of target detection in autonomous driving scenarios. Higher accuracy is required in the algorithm, whether one- or two-stage, for novel applications. The method with the highest efficiency must be used in an autonomous car. [ABSTRACT FROM AUTHOR]

Variable cross-section spherical groove structure had high surface hardness and complex 3D shape. ECM had better technical and economic advantages, but the processing accuracy and processing stability were affected by the flow field obviously. In order to improve the uniformity of the electrolyte flow field, the influences of the electrolyte flow mode and channel structure on the flow field distribution were discussed based on the flow field simulation analysis, and the ECM experiments were carried out. The simulation and test results show that the zonal liquid flow in directional channel is conducive to improving the stability of the flow field and the consistency of the flow rate distribution, which may improve the processing stability. Optimization of the inlet and outlet flow channel structures may further improve the surface quality of the spherical groove, which may meet the requirements of machining stability and machining accuracy. [ABSTRACT FROM AUTHOR]

The style design of clothing plays an important role in enhancing the overall aesthetics of clothing and customer satisfaction. Using celadon images, Dunhuang frescoes and Yangliuqing New Year paintings as style maps and clothing images as content maps, this study used VGG-19 network model to extract features of content images and style images. It introduced a pixel-mean loss function to perform style migration on the luminance channel of content images. It introduced ResNet - B, ResNet - C and ResNet - D variants in the image segmentation model YOLACT, replacing the activation function ReLU with Swish and introducing the Soft-NMS-based candidate frame screening algorithm. Further more, it enhanced the edge effect of garments by an omnidirectional gradient-based stylized edge extraction algorithm. The stylized images were denoised by using a denoising convolutional neural network. The experimental results show that the generated stylized images achieve a more coordinated view of global color information; the image segmentation accuracy is improved; the edge information of the images is enhanced; the noise is eliminated significantly; and the Clarity is still maintained. The proposed series of methods can assist in garment style design and widen the application range of style migration. [ABSTRACT FROM AUTHOR]

Carbon emission pressure on the optimal operation strategy of integrated energy system is intensified by environmental issues and low carbon policies. To address this issue,a multi-objective optimal scheduling model under the operation of the integrated energy system in the park was established,which considered the economics and environmental friendliness of the scheduling strategy described in terms of total operating cost and carbon emission,respectively. To address the problem that the multi-objective weight selection is prone to subjective bias,the multi-objective optimization problem was transformed into a cooperative game of economy and environmental protection,and a multi-objective Pareto front solution method based on the Karush-Kuhn-Tucker,KKT,condition was proposed,and then the cooperative game solution was calculated using the Andersonacceleration- based alternating direction method of multipliers,ADMM,. The cooperative game approach was used to determine the compromise between the economic and environmental objectives to achieve the optimal scheduling and operation strategy. In the final example section,the proposed method was verified in multiple scenarios by analyzing the optimal scheduling strategy of the integrated energy system in the park through four typical days of the season,and the results show that the proposed method can make a reasonable trade-off between operating costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Modern antiques and Internet + were the development trends of high-quality ceramics. It was of great significance to introduce the artistic style of ceramic decoration into contemporary products and realize the fusion of time and space and artistic re-creation. Taking blue-and-white porcelain as the research object, the near-modern landscape painting was used as the content to superimpose random Gaussian white noise to obtain the initial style transfer image. The pre-trained convolutional neural network was used to extract the content image and the content feature of the generated image, and the style image of the porcelain decoration and the style feature of the generated image were extracted. The content loss and style loss were calculated separately. By the calculation of the loss function and iterating to minimize it, a style transfer image with a strong blue-and-white porcelain flavor was finally generated. The experimental results showed that the deep learning method could realize the integration of blue-and-white porcelain decoration and landscape paintings, and successfully transfer the learned characteristics of blue-and-white porcelain decoration to landscape paintings, which not only expanded the application scope of image style transfer, but also produced a new visual experience, with a certain artistic appreciation and innovation. [ABSTRACT FROM AUTHOR]

In order to enhance the stability of power grid operation and improve power supply capacity of power grid, a power supply capacity simulation method based on harmonic reactive compensation was proposed. The harmonic data in the power network equipment was calculated through DIgSILENT/PowerFactory software, including generator, transformer, load, transmission line, shunt capacitor bank and other harmonic parts; according to the acquired harmonic data, the active power filter (APF) as the core was combined with static var compensator (SVC), so that the thyristor switched capacitor (TSC) and the APF connected by the H bridge were connected with the grid, and the inductance parameter was set to realize current tracking; according to the open-loop control, TSC and APF were worked in parallel under stable conditions; through TSC's autonomous classification, the constantly changing reactive power was compensated, and APF was used to compensate again the parts that cannot be completed between different levels, so as to complete the comprehensive suppression of harmonic reactive power compensation in the grid. The simulation results show that the proposed method can effectively suppress the harmonics, and the harmonic current content is within the range of national standard value, effectively enhance the power supply performance of the grid. [ABSTRACT FROM AUTHOR]

The performance of mechanical seal has an important influence on the thermal control efficiency and stable operation of marine core equipment of submarine hybrid pump. Based on the working state of the hybrid pump and the service environment of the mechanical seal, the three-dimensional model of seal ring-liquid film multi-body structure was established. Considering the multi-field synergy of thermal effect, force effect and fluid effect, the performance change law of seal ring-liquid film multi-body structure under simulated actual working conditions was studied by using thermal-fluid-solid coupling numerical simulation technology, and the lubrication characteristics, mechanical characteristics and temperature characteristics of seal ring-liquid film multi-body structure under different working conditions were obtained. The results show that in the multi-body structure of the seal ring-liquid film, the maximum deformation and maximum temperature of seal ring appear in the spiral groove area. With the increase of speed and pressure, the seal opening force and leakage increase, but the influence of speed is obviously greater than that of pressure. The influence of pressure on stress is significantly greater than that of speed, especially when the pressure exceeds 6 MPa, the contact pressure of sealing face is relatively uneven. Due to the effect of convective heat transfer and air flow viscous shear,the speed has a greater effect on the temperature of the seal end face than the pressure, especially in the low speed region of 500 r/min, the temperature of the ring-liquid film multi-body structure exceeds 75 T, and the temperature difference at different radial positions is also very obvious, which will cause damage to the mechanical seal. [ABSTRACT FROM AUTHOR]

Tests include chemical composition analysis, compaction test, the unconfined compression strength test to the titanium gypsum and amendment. Titanium gypsum need to add some cement or lime because it will appear the similar character of rubbery clay that titanium gypsum reach the high water ratio. The value of unconfined compressive strength is proportional to the weight of cement and lime, and it will be higher when we add cement and lime together, but it won't change nearly when addition no more than one percent of weight of dry ground. Titanium gypsum and amendment compressed present coniform brittle failure. [ABSTRACT FROM AUTHOR]

The current situation of fruit recognition based on machine vision was reviewed from fruit recognition based on color threshold, shape and texture, three-dimensional fruit shape recognition, nocturnal fruit recognition, fruit recognition based on machine learning, fruit recognition under the influence of shadow and occlusion. It's thought that the algorithms involved in the above research were more complicated and features were very powerful. However, in view of the limitations of visual theory, image processing technology and hardware conditions, as well as the complex and varied environment of apple garden, there was no more ideal technology for machine vision-based fruit recognition, and it needed to be improved. Future research focuses include: 1) Strengthening more effective algorithms for image enhancement, image segmentation, and feature extraction to effectively address the effects of fruit overlap, occlusion, color, and light changes; and improving the identification algorithms for day and night orchard field operations for the construction of an all-weather operation picking robot. 2) Strengthening the research on fruit recognition based on self-supervised learning to increase the feedback information received by the model and the complex applicable task types of model representation, reduce the proportion of human manual labor involved in the task, and improve the degree of automation. 3) Strengthening the research of automatic image acquisition and fruit recognition, combined with computer vision and near-infrared, laser radar and other detection technologies, integrating multi-modal image and non-image information for fruit recognition, improving processing speed and real-time, and identifing accuracy and system robustness to provide reference for apple's automatic picking and precise management of orchard. [ABSTRACT FROM AUTHOR]

[Objectives] In order to study how dynamic motion of explosive, initial interface disturbance and grid size affect the overpressure load of internal explosion,[Methods] the spatial and temporal terms of the two-dimensional compressible Euler equations are discretized numerically using the fifth-order WNEO finite difference scheme and the third-order TVD Runge-Kutta scheme. Based on Fortran platform, an in-house high-precision numerical calculation program of blast wave in confined space was developed. The impacts of the overpressure load of the internal explosion were preliminarily discussed adopting the developed program.[Results] The results show that, increasing charge movement speed, selecting a specific interface disturbance and decreasing the size of the grid can significantly increase shock wave peaks of internal explosion, but has little effect on quasi-static overpressure load.[Conclusions] The research in this paper can provide certain reference and basis for the design of anti-explosion structure and the evaluation of internal explosion damage.

Liver failure is a clinical syndrome with severe disorders of liver cells for biosynthesis, detoxication, excretion, and biological transformation, which presents with coagulation disorders, jaundice, hepatic encephalopathy, and ascites. Liver failure progresses rapidly, so the prediction of clinical outcome is significant for the diagnosis and treatment. In recent years, there have been numerous reports on the prediction of clinical outcome in patients with liver failure. The study and application of serological and comprehensive models are reviewed, which provides a reference for the rational therapy for liver failure patients. [ABSTRACT FROM AUTHOR]

A low-cycle thermal fatigue test at heat shock is carried out on a simple specimen of cast iron, as the material of cylinder head. Meanwhile a finite element (FE) model for specimen is built and a FE simulation on the temperature, stress and strain fields of specimen is conducted with the fatigue life of the specimen predicted. The fatigue life predicted well agrees with test result, indicating the correctness of the model built and the simulation scheme used. The low-cycle thermal fatigue damage of cylinder head has a certain similarity to that of cast iron specimen, so the test and simulation methods used in the paper are also suitable for the study on the thermal fatigue life of real cylinder head. [ABSTRACT FROM AUTHOR]

Background Rifampicin-resistant/multidrug-resistant tuberculosis (RR/MDR-TB) is featured by challenges in the treatment, low cure rate, and high infectivity. Fluoroquinolones (FQs), as the core drugs for the treatment of RR/MDR-TB, have a severe trend of resistance. Analyzing influencing factors for FQs can help to increase the cure rate of RR/MDR-TB and to control the occurrence of the pre-extensive drug resistance (pre-XDR) and extensive drug resistance. Objective To analyze the drug resistance of FQs in hospitalized patients with pulmonary tuberculosis and the influencing factors, and to construct and validate a Nomogram prediction model for the risk factors of drug resistance of FQs. Methods A total of 583 patients with pulmonary tuberculosis who were hospitalized in Guiyang Public Health Clinical Center from January 2021 to February 2022 and tested for drug sensitivity were retrospectively selected as study subjects. They were divided into the initial treatment group (296 patients) and the retreatment group (287 patients) according to the history of previous treatment. Moreover, they were divided into the FQs-resistant group (63 patients) and FQs-sensitive group (520 patients) according to their FQs-resistance status. The distribution of total resistance to 13 antituberculosis drugs in 583 patients was analyzed, and the baseline characteristics of patients in the FQs-resistant group and FQs-sensitive group were compared. After screening the characteristic variables using least absolute shrinkage and selection operator (LASSO) regression model, multivariate Logistic regression was performed to analyze the independent risk factors for the resistance of FQs. A Nomogram prediction model was constructed, and its performance was validated by calculating the area under the curve (AUC) of receiver operating characteristic (ROC), and plotting the calibration curve. Results Among 583 patients, 520 cases were sensitive to FQs and 63 cases were resistant (resistance rate of 10.81%). The resistance rate of FQs was secondary to the total resistance rate of first-line antituberculosis drugs, including the isoniazid (36.36%), rifampicin (32.76%), streptomycin (21.61%), and ethambutol (12.86%). The resistance rates of rifampicin, isoniazid, ethambutol, streptomycin, levofloxacin, moxifloxacin and rifampicin resistance (RR), multidrug resistance (MDR), and pre-XDR were significantly higher in patients of the retreatment group than those of the initial treatment group (P

ObjectiveThe detection of lameness in dairy cows is an important issue that needs to be solved urgently in the process of large-scale dairy farming. Timely detection and effective intervention can reduce the culling rate of young dairy cows, which has important practical significance for increasing the milk production of dairy cows and improving the economic benefits of pastures. Due to the low efficiency and low degree of automation of traditional manual detection and contact sensor detection, the mainstream cow lameness detection method is mainly based on computer vision. The detection perspective of existing computer vision-based cow lameness detection methods is mainly side view, but the side view perspective has limitations that are difficult to eliminate. In the actual detection process, there are problems such as cows blocking each other and difficulty in deployment. The cow lameness detection method from the top view will not be difficult to use on the farm due to occlusion problems. The aim is to solve the occlusion problem under the side view.MethodsIn order to fully explore the movement undulations of the trunk of the cow and the movement information in the time dimension during the walking process of the cow, a cow lameness detection method was proposed from a top view based on fused spatiotemporal flow features. By analyzing the height changes of the lame cow in the depth video stream during movement, a spatial stream feature image sequence was constructed. By analyzing the instantaneous speed of the lame cow's body moving forward and swaying left and right when walking, optical flow was used to capture the instantaneous speed of the cow's movement, and a time flow characteristic image sequence was constructed. The spatial flow and time flow features were combined to construct a fused spatiotemporal flow feature image sequence. Different from traditional image classification tasks, the image sequence of cows walking includes features in both time and space dimensions. There would be a certain distinction between lame cows and non-lame cows due to their related postures and walking speeds when walking, so using video information analysis was feasible to characterize lameness as a behavior. The video action classification network could effectively model the spatiotemporal information in the input image sequence and output the corresponding category in the predicted result. The attention module Convolutional Block Attention Module (CBAM) was used to improve the PP-TSMv2 video action classification network and build the Cow-TSM cow lameness detection model. The CBAM module could perform channel weighting on different modes of cows, while paying attention to the weights between pixels to improve the model's feature extraction capabilities. Finally, cow lameness experiments were conducted on different modalities, different attention mechanisms, different video action classification networks and comparison of existing methods. The data was used for cow lameness included a total of 180 video streams of cows walking. Each video was decomposed into 100‒400 frames. The ratio of the number of video segments of lame cows and normal cows was 1:1. For the feature extraction of cow lameness from the top view, RGB images had less extractable information, so this work mainly used depth video streams.Results and DiscussionsIn this study, a total of 180 segments of cow image sequence data were acquired and processed, including 90 lame cows and 90 non-lame cows with a 1:1 ratio of video segments, and the prediction accuracy of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features reaches 88.7%, the model size was 22 M, and the offline inference time was 0.046 s. The prediction accuracy of the common mainstream video action classification models TSM, PP-TSM, SlowFast and TimesFormer models on the data set of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features reached 66.7%, 84.8%, 87.1% and 85.7%, respectively. The comprehensive performance of the improved Cow-TSM model in this paper was the most. At the same time, the recognition accuracy of the fused spatiotemporal flow feature image was improved by 12% and 4.1%, respectively, compared with the temporal mode and spatial mode, which proved the effectiveness of spatiotemporal flow fusion in this method. By conducting ablation experiments on different attention mechanisms of SE, SK, CA and CBAM, it was proved that the CBAM attention mechanism used has the best effect on the data of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features. The channel attention in CBAM had a better effect on fused spatiotemporal flow data, and the spatial attention could also focus on the key spatial information in cow images. Finally, comparisons were made with existing lameness detection methods, including different methods from side view and top view. Compared with existing methods in the side-view perspective, the prediction accuracy of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features was slightly lower, because the side-view perspective had more effective cow lameness characteristics. Compared with the method from the top view, a novel fused spatiotemporal flow feature detection method with better performance and practicability was proposed.ConclusionsThis method can avoid the occlusion problem of detecting lame cows from the side view, and at the same time improves the prediction accuracy of the detection method from the top view. It is of great significance for reducing the incidence of lameness in cows and improving the economic benefits of the pasture, and meets the needs of large-scale construction of the pasture.

ObjectiveReal-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases. Currently, various strategies have been proposed for monitoring cow ruminant behavior, including video surveillance, sound recognition, and sensor monitoring methods. However, the application of edge device gives rise to the issue of inadequate real-time performance. To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior, a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.MethodsAutonomously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time. Based on these six-axis data, two distinct strategies, federated edge intelligence and split edge intelligence, were investigated for the real-time recognition of cow ruminant behavior. Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence, the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism. Additionally, a federated edge intelligence model was designed utilizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm. In the study on split edge intelligence, a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.Results and DiscussionsThrough comparative experiments with MobileNet v3 and MobileNet-LSTM, the federated edge intelligence model based on CA-MobileNet v3 achieved an average Precision rate, Recall rate, F1-Score, Specificity, and Accuracy of 97.1%, 97.9%, 97.5%, 98.3%, and 98.2%, respectively, yielding the best recognition performance.ConclusionsIt is provided a real-time and effective method for monitoring cow ruminant behavior, and the proposed federated edge intelligence model can be applied in practical settings.

Multi-criteria priority ranking involves determining priorities through calculations for a multitude of tasks as per potentially conflicting criteria, to aid decision-makers in efficient allocation of resources and prioritization of tasks based on their criticality. This process is essential for optimizing resource allocation and improving overall work efficiency. Over the past two decades, the field of priority ranking has seen the development of numerous mature classic methods through continuous modifications and expansions. Furthermore, new sorting methods rooted in advancing technologies in the era of big data have also emerged. To assist decision-makers in evaluating and selecting the most suitable multi-criteria prioritization methods for priority ranking needs in specific application scenarios, this paper first elucidates differences across various multi-criteria prioritization methods regarding criteria substitutability, advantages and disadvantages, data input/output requirements, and applicable scenes. It further categorizes existing multi-criteria task prioritization methods into four main types: full aggregation methods, inferiority ranking methods, reference level methods, and data processing-based prioritization methods. Additionally, it discusses in detail the basic principles, implementation steps, latest research findings, and pros and cons of representative methods within each category. Finally, this paper summarizes current research hotspots, including the analytic hierarchy process, fuzzy theory, and AI-assisted ranking technologies, while suggesting future research pathways covering AI-assisted big data ranking optimization, ranking stability enhancement, and intelligent preference identification.

Objective: Metabolomics based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was applied to explore the metabolic changes in the urine of mice with Alzheimer’s disease (AD) after intervention with camellia oil. Methods: Mice were randomly divided into four groups: normal control, AD model, positive drug and camellia oil treatment. In all mice except for those in the control group, AD was induced by amyloid β-protein (Aβ) 25-35 peptide, and urine samples were collected from each group for UPLC-Q/TOF-MS analysis after protein precipitation. The obtain urine metabolite data were analyzed by the SIMCA-P14.1 software with principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Using the online databases of HMBD, MassBank, MetaboAnalyst and Kyoto Encyclopedia of Genes and Genomes (KEGG), biomarker identification, pathway enrichment analysis, and metabolic network construction were conducted. Results: In the PCA score plots, the camellia oil group was clearly separated from the model group and closer to the normal group. Totally 48 biomarkers were identified and the major pathways involved were taurine and hypotaurine metabolism, arginine and proline metabolism, cysteine and methionine metabolism, alanine, aspartate and glutamate metabolism. Conclusion: Camellia oil can improve the metabolism of AD mice by regulating energy metabolism, amino acid metabolism and other pathways, which provides a reference for further study of camellia oil intervention against AD.

Cross-domain computing power networks wish to integrate computational and data resources from multiple computing centers, but existing methods do not pay enough attention to cross-domain file and data management.In this paper, a lightweight data access scheme for cross-domain computing power networks was proposed: (1) accessing parallel file system storage resources of remote computing centers through file system protocol conversion; (2) local caching as a supplement to cope with high IOPS applications; and (3) mounting remote or local storage to specified directories through container binding technology.The prototype system based on this scheme had been deployed on highperformance computing centers in multiple universities.The measured data and user experience showed that the scheme in this paper could meet the requirements of common high-performance computing applications.

Objective To investigate the correlation between heart rate variability (HRV) and the degree of nervousness before blood donation. Methods The psychological state of 253 blood donors before blood donation was assessed by the self-rating anxiety scale (SAS) and the degree of nervousness and their HRV were measured. The correlation between the SAS score, the degree of nervousness and the HRV parameters was analyzed, and the differences were compared among different types of donors by multivariate linear regression. Results A total of 247 blood donors were included in the study. Five HRV parameters in blood donors aged 18-24 were higher than in those aged 25 years and above(all P

In recent years, ducted fans, as a kind of propeller with excellent performance, have been widely used in various vertical take-off and landing(VTOL) unmanned aerial vehicles(UAVs). However, there is less research on its ground effect in China. Therefore, in this paper, a solution method based on Reynolds mean Navier-Stokes (RANS) equations and multiple reference coordinate system(MRF) was used. Through numerical simulation, the force and torque changes of each part of the ducted fans with three different blade numbers at different elevations were calculated. The changing mechanism of aerodynamic characteristics was analyzed by calculation results of computational fluid dynamics. The results show that when the altitude near the ground reaches a certain value, the aerodynamic characteristics of the ducted fans change significantly. The lift force of the propeller increases, but that of the duct decreases, and the reverse torque of the whole system increases, which were finally quantified as the variation of lift coefficient, power coefficient, and quality factor. The power coefficient increases significantly with the decrease in the elevation, and the variation of lift coefficient and quality factor of the ducted fans is related to the number of propeller blades. A preliminary conclusion on the influence of ground effect on the aerodynamic characteristics of ducted fans is thus obtained.

Aiming at the problem that clustering routing algorithm in a three-dimensional (3D) scene has high energy consumption and short network lifetime, a clustering routing algorithm IFCRA for 3D wireless sensor networks based on improved honey badger algorithm optimized fuzzy C-means was proposed.The network clustering, cluster head election, and data transmission stages were optimized respectively.Firstly, the improved honey badger algorithm was used to optimize the fuzzy C-means algorithm, solving the problem of fuzzy C-means easily falling into local optima, and the topological structure was divided based on the distance and energy characteristics of nodes.Secondly, the optimal cluster head function was constructed by combining the energy and relative distance of nodes within the cluster to balance the cluster head load.Finally, an adaptive transmission mechanism was used to search for relay nodes, and the optimal transmission path function was constructed by combining node distance and energy.The improved honey badger algorithm was used to optimize the data transmission energy consumption.Simulation results show that IFCRA has reasonable clustering, balanced energy consumption, and long lifespan in a 3D heterogeneous scene.

[Objectives]The process of explosion in confined space involves not only the propagation and reflection of shock waves,but also complex afterburning effect. The afterburning effect not only enhances the shock wave intensity,but also has a great influence on the final quasi-static pressure. In order to give a prediction of the quasi-static overpressure for explosion inside confined space,[Methods]a theoretical model for calculation of the peak valve of the quasi-static overpressure for confined explosions considering afterburning effects was proposed based on the analysis of chemical reactions and the law of conservation of energy. The results calculated by the model were then analyzed and compared with the literatures' as well as the empirical formulas' to verify the reliability and correctness of the proposed model.[Results]The results show that,the peak value of the quasi-static overpressure calculated by theoretical model is in good agreement with the results of the experimental measurement and from the empirical formulas in the literature;the Anderson's empirical formula is inapplicable to the confined explosion condition in case of a small mass volume ratio of less than 0.5 kg/m3,and the formula given in the reference[8]may not be extrapolated to the confined explosion conditions with a large ratio of greater than 8.87 kg/m3.[Conclusions]The study in this paper can provide a better understanding of the physical mechanism of the contribution of afterburning effect to quasi-static pressure,and provide some reference and guidance for the design of explosion-resistant structures and damage assessment.

[Objectives] Since the afterburning effect on explosion in confined space involves complex multi-component chemical combustion process, describing the afterburning effect approximately with a simplified reaction rate model has certain rationality and engineering application value. However,it is still required to determine the means of predicting the time history of reaction rate in the model and determine an energy release constant. Therefore [Methods],a theoretical prediction method for the time history of afterburning reaction rate is proposed,and a chemical reaction kinetics method is used to determine the time of chemical reaction in internal explosion so as to determine the energy release constant.[Results] The results show that although there are certain differences between the theoretical prediction and the numerical calculation results in the early and later stages of explosion,the overall trend is in good agreement,and it is reasonable and reliable to approximately determine the final time of chemical reaction according to the chemical reaction kinetics analysis method.[Conclusions] The research results in this paper can provide certain reference and basis for the design of anti-explosion structure and assessment of damage.

[Objectives] In order to study the influence rule of JWL Equation of State(JWL-EOS)and its equivalent polytropic EOS on internal explosion parameters.[Methods]An independently developed procedure was used to carry out the numerical calculation of explosive blasting process in the cabin. Firstly,the influence rule of different values of parameter ω in the JWL-EOS and the specific heat ratio of ambient gas on the calculated explosion parameters was discussed. Secondly,based on the JWL-EOS, the way to determine parameters in the equivalent polytropic EOS of gas was proposed,and the feasibility and reliability of this method is verified through the algorithm case.[Results]The results show that the increase of parameter ω and specific heat ratio of ambient gas can increase the specific heat ratio of the final mixture,the peak values of shock wave and make the initial arrival time of shock wave to the cabin wall ahead of schedule. The increase of parameter ω can accelerate the expansion rate of detonation products at the initial stage of explosion and increase the peak of quasi-static pressure. While,the increase of specific heat ratio of ambient gas slows down the expansion rate of detonation products at the initial stage of explosion and has little effect on the final quasi-static pressure peak.[Conclusions]It is concluded that the proposed equivalent EOS is of some reliability and the study result in this paper can provide certain reference and basis for the design of anti-explosion structure and damage assessment.

[Objectives] In order to study the afterburning effect on blast load in confined space,[Methods] the reaction rate model is coupled to the compressible Euler equations,and the afterburning energy is added in the form of source term. The space term and time term of the coupled equations are solved discretely using the fifth-order WENO finite difference scheme and the third-order TVD-RK scheme. Then an in-house two-dimensional numerical calculation code is developed for afterburning effect in confined space,on the base of which the numerical calculations of the afterburning effects in different explosion cases in confined space are carried out,and the influences of reaction rate and afterburning energy on the blast load are discussed.[Results] The study shows that,1) in the case of constant afterburning energy,when the reaction rate increases,the shock wave arrives earlier,the shock wave peak and impulse increase,and the final quasi-static overpressure peak remains unchanged;2)in the case of constant reaction rate,when the afterburning energy increases,the shock wave peak,impulse and final quasi-static overpressure peak increase. The addition of the afterburning energy can significantly enhance the strength of the blast load.[Conclusions] The study in this paper can provide reference and guidance for the design and damage assessment of anti-explosion structures.

The propagation and evolution characteristics of blast waves in confined spaces are complicated due to the constraint of the surrounding walls, by which the enhanced reflected shock waves will cause more serious damage to the internal structures, facilities and personnel. In order to investigate the characteristics of explosions in confined spaces, an in-house high-resolution hydrocode was developed in this present work. The third-order WENO finite difference scheme (weighted essentially non-oscillation scheme) was implemented in the code to capture the shock waves generated by cylindrical explosives. The Sod shock tube problem, interacting blast wave problem and blast in air problem were simulated to validate the code. The validated code was then used to simulate the blast waves generated by condensed explosives in closed, vented and connected spaces. The propagation of blast waves and the characteristics of blast load were subsequently investigated. The developed code appears to accurately predict the process of explosions in confined spaces. This high-resolution hydrocode can be used to study the propagation paths of blast waves in complicated spaces and evaluate the internal blast load, which can provide reliable input for the design of explosion-resistant structures.

In order to avoid the design of the rotor of the dry gas seal high speed test stand occur resonance phenomenon during high speed running,the transfer matrix method was applicated to calculate the natural frequency.Then it obtains that its natural frequency is 14044.586r/min when the dry gas seal static and ring `s quality is 33.78 kg in rotor system,and using the finite element method analysis the modes of the rotor system when it is operating in the vicinity of its natural frequency,then it will get the modal figure with four modal value.Comparative and analysis the results of the two methods,though simplified model are not the same,the results are similar.And the design of speed can meet the requirements,so it can describe the design of the rotor system is reasonable.

The paper introduced composition and working mechanism of driving circuit and echo processing circuit of mine-used intrinsic safety ultrasonic ranging circuit system. By analyzing output voltage waveform, it calculated the maximum energy storage of each energy storage element in ranging circuit system. The experimental results show that the largest energy storage of each energy storage element is less than minimum ignition energy in ranging circuit system, which reaches stipulated intrinsic safety request of national standards.

Along with the constant growth of massive online social networks such as Facebook,Twitter,Weixin and Weibo,a tremendous amount of network data sets are generated.How to represent the data is an important aspect when we apply machine learning techniques to analyze network data sets.Firstly,the research background was introduced and the definitions of NRL (network representation learning) were related.According to the categories of different algorithms,five kinds of primary NRL algorithms were introduced.Particularly,a detailed introduction to NRL algorithms based deep learning techniques was given emphatically.Then the evaluation methods and application scenarios of NRL were discussed.Finally,the research prospect of NRL in the future was discussed.

A fluid-structure coupled model for the exhaust manifold of an engine is built, in which the radiative heat transfer and material nonlinearity are taken into consideration, and a simulation on its thermal load is conducted. In addition, a bench test is also performed to measure the temperature and strain of exhaust manifold. The results of both simulation and test show that the highest temperature is located at the confluence of manifolds, plastic deformation occurs in some areas, and the location of maximum plastic strain coincides with fracture spot. [ABSTRACT FROM AUTHOR]