With the increasing richness of online fitness resources, autonomous fitness has become a new sporting trend. However, due to the lack of action guidance and correction by professional fitness coaches, autonomous fitness usually cannot guarantee the fitness effect and is easy to cause sports injuries, so real-time monitoring of fitness actions is required. Existing fitness monitoring equipment usually relies on professional hardware such as big screens, stereo camera and other sensors. As a result, they fail to satisfy common needs for virtual fitness due to high cost, complexity of installation and limited application scenario. With gradual maturing of human pose estimation technique, identification of human face and movements of limbs can be realized through easily accessible cell phone camera with high accuracy and speed. Low cost, multi-scenario virtual fitness monitoring on mobile terminal thus it made possible. Based on the background above, this work designs a fitness action evaluation algorithm based on angle thresholds in 3D scenes which relies on cell phone monocular camera and 3D human key point detection technology. The algorithm can detect whether the user's fitness actions are standard in real time and give corresponding responses through voice. The work has implemented a prototype system on an Android phone. This work verifies the usability and real-time performance of the algorithm and the system through a series of user experiments. It also verifies the accuracy of the action evaluation algorithm through comparative experiments with relevant work in recent years. Results shows that the algorithm and functions of the prototype system were greatly recognized by the users with high accuracy, reasonable responding speed for real time usage.

Converting renewable resources such as walnut shells into energy or chemical products is one of the targets for peak carbon dioxide emission and carbon neutrality. Walnut shell char is a solid byproduct of the rapid pyrolysis of walnut shells to create biological oil. Due to its limited porosity and low specific surface area, it is difficult to be used directly as carbon material. FeCl₃ was utilized to activate carbon extracted from walnut shells, whereas KOH was used as a reference. Through SEM, TEM, BET, Raman, N₂-adsorption, and other series of characterization, it was found that activation improves the degree of graphitization compared to that without activation. The specific surface area increased from 1.6 cm²·g^-1 to 377 cm²·g^-1 and the pore volume increased from 0.004 6 cm³·g^-1 to 0.074 cm³·g^-1 when the FeCl₃/WSC mass ratio was 10%. The structure of pores was abundant, and the carbon yield (61.2%) was higher than that by using the KOH activation method (35.8%). As a support for the corn oil hydrodeoxidation reaction, carbon derived from walnut shells was treated with FeCl₃ to produce the molybdenum carbide catalyst. The corn oil conversion rate and the yield of hydrocarbon were 89.7% and 87.2%, respectively. After 8 cycles, neither the corn oil conversion rate nor the yield of hydrocarbon decreased, and the catalyst remained stable. This method of modifying FeCl₃ has the advantages of cost-effectiveness, high efficiency, and environmental friendliness, as well as the potential for extensive use.

3D-power diagrams have a wide range of applications in the fields of graphics and fluid simulation. To overcome the poor time performance of the existing 3D-power diagram construction algorithms, a novel GPU-based construction algorithm is proposed. We introduce an estimation algorithm for computing the area between power cells. The estimation algorithm enables the construction algorithm to be coupled with the Lloyd algorithm and Newton's algorithm to generate a 3D-centroidal capacity constrained power diagram(3D-CCCPD). Experiment results show that our approach raises the efficiency of the 3D-power diagram construction up to several orders of magnitude.

The fuel data collected by aircraft sensors is the basis for various follow-up studies on aircraft. However, due to various factors, the data collected by sensors always have many missing values. The direct use of such data for subsequent processing will lead to a decrease in the accuracy of the algorithm. Therefore, the effect of processing missing values in the data is crucial. The existing missing value filling algorithm has two problems. On the one hand, it ignores the historical dependence of time series in the time dimension; on the other hand, some algorithms require a complete data set for model training. In order to solve the above problems, this paper uses a method of filling missing values based on adversarial networks. This method can effectively solve the problems of traditional methods, and can achieve the best filling effect compared with other algorithms.

The sensory quality of Ovalipes punctatus, Ovalipes punctatus reconstituted crab meat products, crab meat pieces and crab sticks were studied by using intelligent sensory analysis technology combined with traditional sensory evaluation. The four types of products were all low-fat foods that can be eaten with confidence. The electronic tongue combined with the principal component analysis method was used to distinguish and identify the four types of crab meat products, and the palatability of various types of crab meat (including hardness, elasticity, cohesiveness, gumminess, chewiness, resilence). The results showed that there was a significant difference in taste between imitation crabmeat and real crabmeat. Compared with imitation crabmeat, real crabmeat had a stronger umami aftertaste, but the bitterness and astringency and miscellaneous taste were relatively higher; TGase had a positive effect on the sensory quality of crab meat, especially on its taste and texture, the whole texture measurement results and human sensory evaluation results showed good consistency.

Virtual garment display provides an online solution for offline apparel ordering meetings and fashion shows, alleviating the limitations of space and providing a convenient and effective new way for companies and users to try on clothes. To address the problem that most of the existing virtual clothing fitting systems are unable to generate personalized clothing effect of the customers due to the preset uniform body size and display effect, a personalized virtual clothing demonstration system with automatic garment adaption is developed. Our approach is mainly divided into three steps: first, a three-dimensional human body reconstruction framework based on human body size parameters is set up. Then the relationship between the mannequin vertex and the three-dimensional garment vertex is established using mesh skinning. Automatic garment adaption is finally realized by driving the garment deformation according to human body deformation using dual quaternion interpolation. A VR-based system for clothing display is developed for general users using Unity 3D to validate the presented method.

Using computer vision and image technology to digitally reconstruct plants is an important means for plant phenotyping. In this paper, we take the common grain crop legume plants in China as the research object, study the rapid three-dimensional digital reconstruction of the bean plants based on the depth images collected by the RGB-D depth camera for accurate phenotype identification. Firstly, the skeleton points are extracted from the point cloud of bean plant by employing a hierarchical clustering algorithm. Secondly, the backbone skeleton points of the first stage are connected according to the shortest distance of each skeleton point to the root node. According to the morphological characteristics, the connection points of the subgraph and the backbone graph are filtered out and the path growth of the subgraph is performed. Finally, the 3D plant model is reconstructed based on the connected skeleton. Experiments show that, under a single frame and registration data of multiple real soybean plants point cloud, the method of this article can carry out rapid three-dimensional reconstruction of the soybean plants with different morphological characteristics, and it is robust for situations such as low resolution, large noise interference, and large registration error.

In this paper it is reported firstly that fermented cellulose was anionized by one pot method with sodium 3-chloro-2-hydroxypropane sulfonate in aqueous phase, and sodium hydroxide solution was used as reactant, solvent, and catalyst. The reaction conditions were mild, the degree of substitution was moderate, and the conversion rate was acceptable. The characterization of cellulose derivative was studied by means of elemental analysis, nuclear magnetic resonance,FTIR and so on. The antifungal biological evaluation of cellulose derivative was carried out, and the results showed that it had certain antifungal properties. The ecological compatibility of cellulose derivatives confirms that they can be applied in archaeology, agriculture and epidemic prevention, etc.

The catalysts PtxTi/MCF and Pt/yTi-MCF were obtained by adding Ti element to mesocellular silica foam (MCF) support in two different ways. The catalytic activities of these catalysts for the complete oxidation of benzene, n-hexane and ethyl acetate were studied. The effects of different catalyst preparation methods, Ti addition amount and calcination temperature were investigated in detail. In addition, the structure and physicochemical properties of some of the support and catalyst were characterized by nitrogen isothermal adsorption-desorption, Raman spectrum analysis, small and wide angle XRD. The results show that, Pt8Ti/MCF has good catalytic activity and stability for the complete oxidation of benzene, n-hexane, and especially ethyl acetate. It was prepared simply by impregnating the mixture solution of platinum acetylacetone and titanium acetylacetone and then calcination, which exhibits a good application prospect in future.

This paper proposes to employ matrix weighted NURBS surfaces to fit and fair quad meshes. For a quadrilateral mesh with given or estimated unit normals at vertices, a matrix weighted NURBS surface can be constructed by choosing the mesh vertices as control points and employing normals at each vertex for computing matrix weights. Compared with traditional NURBS surfaces, matrix weighted NURBS surfaces have quasi-cylindrical accuracy. When the input data is uniformly sampled from a smooth surface, the constructed matrix weighted NURBS surface has good smoothness and fits the mesh model well; if the input grid data contain noise, a fair fitting surface that approximates the original grid well can still be obtained by resampling control vertices on current fitting surfaces and re-calculating vertex normals based on the new quad meshes iteratively.

To improve the catalytic ability of nano zero-valent iron in a Fenton like reaction system and reduce the agglomeration of nano zero-valent iron materials, the liquid phase reduction method was used to prepare biochar loaded nano zero valent iron materials (nZVI@BC). The material was characterized and the methylene blue was degraded to explore the material properties, the optimal degradation conditions and the action mechanism. The results showed that nano zero-valent iron had been successfully and evenly distributed on the surface of biochar. The prepared material was of high purity and good stability. In the degradation optimization experiment, the optimal reaction condition for degradation of 100 mg·L^-1 methylene blue solution was 25 ℃, the initial pH=3, the dosage of nZVI@BC is 30 mg·L^-1 and the dosage of H₂O₂ is 4 mmol·L^-1. Under this condition, methylene blue solution can be almost completely degraded in 10 minutes. It had found that hydroxyl radicals played a major role in the degradation process. The chromogenic functional groups first broke, followed by the destruction of the aromatic ring structure, and ultimately completed degradation. And the material had a good recycling performance. After three cycles, the removal rate of methylene blue was still above 85%. In actual printing and dyeing wastewater treatment experiments, nZVI@BC had a good wastewater treatment effect. When the dosage of H₂O₂ was 0.9 mmol·L^-1, the dosage of nZVI@BC was 60 mg·L^-1 and the pH≤ 4.5, the COD_cr decreased to below 50 mg·L^-1, up to the standards for reuse water.

In order to improve the efficiency and quality of parametric tread pattern retrieval, a novel method is proposed. Firstly, the tread pattern model in B-rep format is converted into an attribute adjacency graph, in which the edge compatibility is used for inexact matching of two attribute adjacency graphs and for the calculation of graph similarity. The geometric features reflected by the design parameters are used to define similarity of tread pattern models. Secondly, to improve query efficiency, various design parameters are used for rough space division and recursive clustering on the tread pattern database. An index structure based on the cluster tree is constructed to speed up model retrieval. Our experimental results show the superiority of the proposed method over the general model retrieval methods, both in search efficiency and quality. This demonstrates the advantage of utilizing design parameters and geometric information of the tread pattern in CAD model retrieval.

Improving the segmentation quality of 3D meshes is always an important problem to computer graphics. To handle this problem, this paper proposes a shape-aware graph attention network. The shape-aware graph attention coefficient is defined to better reflect the similarity between nodes, which not only expands the attention coefficient obtained by network learning with the help of edge features between nodes, but also introduces the attention coefficient related to the local shape information of nodes. On the other hand, the network architecture is adjusted by taking both shape features and labels of 3D mesh model as the input of graph attention network, which enables the participation of labels in network training and verification stages. Residual connection is further employed to make the network output more stable. A large number of experiments show that the proposed algorithm can obtain accurate segmentation boundaries. Compared with the existing classical segmentation algorithms on PSB dataset, the proposed algorithm improves 2% in accuracy, and achieves better Rand index. The reasonableness of the algorithm is proved by ablation experiment.

Photobiological hydrogen production by green algae exhibits a bright application prospect in solar energy utilization and hydrogen energy production due to the advantages of high energy conversion efficiency, environmental friendliness as well as abundant raw materials. This paper analyzes the potential factors limiting photobiological hydrogen production by green algae based on the mechanism, and summarizes various methods to improve the efficiency of photobiological hydrogen production by green algae. The main problems and development trends in the commercial application of photobiological hydrogen production by green algae are briefly reviewed, which are referable for the large-scale application of photobiological hydrogen production by green algae in the future.

Herein, a novel one pot synthesis of chiral δ-substituent caprolactam strategy which applies 1-substituent homoallylic amine as the starting material is reported. This method combines the enzymatic dynamic kinetic resolution (DKR) and ring cross metathesis. Moreover, the configuration of caprolactam can be controlled by using lipase and protease as the DKR catalyst. The enantiomeric excess value of obtained R- and S-caprolactam can reach up to 90% and 82%, the yield can also be above 82% and 70%.

This paper discusses the effect of ex-situ ecological combined remediation (ESECR) system on the purification of phosphorus of different forms in the polluted water of Pulin Creek, a tributary of Jiulong River, and each unit of ESECR treatment system is analyzed on the correlation between different phosphorus forms and environmental factors. Results showed that the average total removal rate of TP, DTP, DP and PP of ESECR treatment system were 84.3%, 84.4%, 90.8% and 78.9% respectively, among them the DP purification effect was the best. In each unit of ESECR treatment system, when the phosphorus content in the raw water was low, the sludge-membrane symbiotic (SSM) high efficiency coagulation water purification system had good TP, DTP and DP purification effect, the average removal rate were 81.4%, 76.0% and 84.8% respectively, the average removal rate of PP was 66.7%; when the content of phosphorus in the raw water was high, the average removal rate of TP, DTP and DP of the SSM high efficiency coagulation water purification system were 37.8%, 44.6% and 45.8%, nevertheless, the purification effect of PP was significant, and the average removal rate was 93.2%. When the processed water of the SSM high efficiency coagulation water purification system with low phosphorus content, the purification effect of ecological pond Ⅱ on TP and DTP was better than that of ecological pond Ⅰ, but the results on DP was opposite. For PP, the purification effect in ecological pond Ⅱ was better than that in ecological pond Ⅰ regardless of the impact of phosphorus content. The purification effect of ecological pond Ⅲ fluctuated, the purification effect on DP is relatively significant, and the average removal rate was 94.1%. In addition to the SSM high efficiency coagulation water purification system, the form of phosphorus in the polluted water of Pulin Creek and purified water by ESECR technology was mainly PP, and the percentage of each form in total phosphorus appeared as PP>DTP>DP. Pearson correlation analysis showed that different phosphorus forms were correlated with pH, water temperature and dissolved oxygen (DO) in ESECR treatment system.

Methoxypolybrominated diphenyl ethers (MeO-PBDEs) were widely present in marine organisms and the marine environment. An analytical method of six methoxypolybrominated diphenyl ethers for biological samples and sediment samples from Xiangshan Sea area using solid-phase extraction and gas chromatography coupled with negative chemical ionization mass spectrometry was developed and optimized. The developed method exhibited satisfying linearity in the range of 0.1-20.0 μg?L^-1 (R² >0.999). The detection limit (LOD) and limit of quantitation (LOQ) for MeO-PBDEs were 0.13-0.22 μg?kg^-1 and 0.42-0.72 μg?kg^-1 respectively. The spiked recovery was 71.2%-116.2%. The method was applied to the analysis of marine organisms and sediments collected from Xiangshan sea area. All kinds of MeO-PBDEs were not detected in sediment samples. Only 6-MeO-BDE-47 was detected in algae samples with low concentration. Three MeO-PBDEs were detected in other biological samples, and the ratio of detection was 31.3% with a concentration range of 0.21-2.72 μg?kg^-1 MeO-PBDEs were not detected in sediment samples.

Advanced oxidative processes of persulfate (PS-AOPs) have promising applications in antibiotic wastewater treatment due to their strong oxidation capacity and good selectivity. In this study, Sargassum fusiforme was selected as a raw biomass, and ZnCl₂ carbonation-pyrolysis was adopted to prepare zinc chloride modified sheep sorrel biochar which was used as a catalyst to catalyze persulfate (PDS) to degrade the norfloxacin (NOR) in the water. The results showed that different ratios of ZnCl₂ doping would significantly affect the performance of biochar, and the catalytic efficiency of the prepared biochar was optimal when the ratio of algal powder to ZnCl₂ was 1∶1. Moreover, SEM, BET, XRD characterizations were employed to investigate the physical and chemical properties of YQZn in order to further explore the modification mechanism of ZnCl₂. The catalytic efficiency result of YQZn under different conditions showed that the biochar had the best NOR removal rate of 95.09% under the conditions of PDS concentration was 1 mmol·L^-1, modified biochar addition was 0.3 g·L^-1, pH=9 and NOR concentration was 10 mg·L^-1. \mathrm{S}{\mathrm{O}}_{\mathrm{4}}^{·-}, HO· radicals, and ¹{\mathrm{O}}_{\mathrm{2}} played a leading role in activating PDS in the system by free radical quenching. To solve the difficulties in biochar powder recovery YQZn geopolymer composite catalyst (GB) was prepared by using biochar loaded on porous geopolymer. The results showed that GB had excellent degradation activity and reuse performance, which successfully realized the loading and recycling of biochar, and thus had great potential for continuous treatment of norfloxacin in water.

V-system is a kind of complete orthogonal piecewise polynomial function system on L^{\mathrm{2}}[\mathrm{0,1}], because of the discontinuous nature of its basis functions, it has significant advantages in the expression and analysis of discontinuous signals. However, in the current V-system transformation algorithm, for a signal with a length of N, it not only needs to generate and store an N-order orthogonal matrix in advance, but also its time complexity is as high as \mathrm{{\rm O}}(N^{\mathrm{3}}). Therefore, in order to adapt to the efficient processing needs, this paper designs and implements a fast decomposition and reconstruction algorithm for V-systems from the perspective of multi-resolution analysis of V-systems. This fast algorithm does not need to store additional information, and its time complexity is only \mathrm{{\rm O}}(N^{\mathrm{2}}). The test results show that the fast algorithm proposed in this paper can meet the requirements of high-efficiency processing of large-scale data, which lays the foundation for the application of V-system in more fields.

To address the limitations of current mainstream networks, which rely solely on local neighborhoods for feature aggregation and suffering from insufficient feature extraction capabilities and information loss due to max-pooling, we propose an attention-based point cloud processing network that combines both local and global information. First, we introduce channel attention for local feature aggregation to minimize information loss. Next, we design a dynamic key point learning method to capture the remote dependency information of points and obtain global information. Finally, we develop a spatial attention fusion module to allow each point to learn the global con-textual information. Our proposed method has been benchmarked on several point cloud analysis tasks. It achieved an overall classification accuracy of 94.0% and an average classification accuracy of 91.7% on the ModelNet40 classification task. On the ScanObjectNN classification task, our method reached an overall class fication accuracy of 81.5% and an average classification accuracy of 78.1%. In the ShapeNet segmentation task, we obtained a mean intersection over union of 86.5%. The experimental results show that the proposed network has significantly improved accuracy compared to classical networks such as PointNet, PointNet++, and DGCNN in classification and segmentation tasks, and has also achieved improvement in deferent degree compared to other point cloud processing networks.