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.

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.

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.

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.

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.

A multi-morphology design method based on conformal mapping is proposed to design triply periodic minimal surface (TPMS) microchannels with freeform boundary constraints. This method first maps the boundary of a freeform surface to a plane, allowing for channel topology design in the 2D parametric domain; Then, a Beta growth function algorithm based on loop is proposed to achieve smooth transitions of various TPMS morphological features; Finally, by mapping the designed microchannels to the 3D space constrained by the free surface, the microchannels meet the design requirements. Our results show that the microchannels constructed by this method have good adaptability to complex surface boundaries and can achieve the design goals of internal morphological features.

Overhangs are usually inevitable when fabricating a part of complex shape in 3D printing. Meanwhile, the geometric error on the side surface of an overhang (i.e. the side loss) after fabricating is often significant, which seriously affects the accuracy of the overhang as well as its container (i.e. a part). To solve the above problem, a double-level intelligent improvement approach for overhangs on side loss (i.e. process parameter optimization and geometry pre-compensation) is proposed in this paper. Firstly, a series of experiments with different values concerning the critical design parameter and process parameters are designed based on the Taguchi method. Then, a deliberate measurement method is designed to get the side loss data from the fabricated inverted 'L'-shaped parts. Secondly, two types of side loss prediction networks are respectively constructed for the two sides (that is the overhang side and the non-overhang side) of each inverted 'L'-shaped part. They are mainly designed according to the requirements of support structures on an overhang. Aided with these networks, the geometric error of both sides of an overhang on an inverted 'L'-shaped part (with various values of the critical design parameter) can be predicted accurately. Thirdly, aiming at minimizing the side losses on both sides of an overhang, a single-objective and multiple-variables nonlinear programming problem is formulated. Hereby, the corresponding optimized side losses as well as their counterpart values of key process parameters can be determined. Finally, we compensate the geometries on the two sides of an overhang based on the above-optimized side losses by conducting an inverse modification first and then fabricate the overhang adopting the above-optimized values of key process parameters. Based on fused deposition modeling, experiments were implemented on various inverted 'L'-shaped parts except the ones used in constructing prediction networks, which verified the effectiveness of the proposed approach. Meanwhile, comparative analyses with state-of-the-art works were also carried out. The results show that our method is suitable for overhangs and has great potential to significantly improve their side losses.

The identification of minerals in petrographic thin sections is essentially required in petrological research, and is a prerequisite for further understanding of rock classification, petrogenesis, material flow and evolution history. Traditional methods rely on manual identification with optical microscope, which is costly, time-consuming, and subject to expert judgment and personal experience. Following the development of deep learning technology, it is possible for computer to automatically extract more accurate semantic information from images of petrographic thin sections. This paper proposes a deep learning-based method on petrographic thin section images for automatic mineral identification, which not only utilizes the deep convolutional neural network to extract different mineral features in the images for semantic segmentation and recognition, but also takes into account the plane polarized light images and cross polarized light images for comprehensive automatic identification. Our paper used the photomicrograph dataset of rocks for petrology teaching at Nanjing University for mineral identification and achieved the overall accuracy of 86.7% and Kappa coefficient of 0.818 demonstrating the advantage of the proposed approach compared with those of the traditional image classification methods.

Artificial intelligence generated content (AIGC) has received significant attention at present. As the numerous generative models proposed, the emerging diffusion model has attracted extensive attention due to its highly interpretable mathematical properties and the ability to generate high-quality and diverse results. Nowadays, diffusion models have achieved remarkable results in the field of condition-guided image generation. This achievement promotes the development of diffusion models in other conditional tasks and has various applications in areas such as movies, games, paintings, and virtual reality. For instance, the diffusion model can generate high-resolution images in text-guided image generation tasks while ensuring the quality of the generated images. In this paper, we first introduce the definition and background of diffusion models. Then, we present a review of the development history and latest progress of conditional image generation based on diffusion models. Finally, we conclude this survey with discussions on challenges and future research directions of diffusion models.

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.

The numerical solution of hyperbolic equation is a well-know hot topic in the field of numerical solution of partial differential equation, among which the discontinuous capturing of hyperbolic equation is always a difficult problem. Inspired by physical-informed neural networks (PINN), this paper presents a PINN-type algorithm to approximately solve discontinuity problem of hyperbolic equations. It takes the data set constructed by coordinate as the input of neural network. The loss function in PINN algorithm is converted to the error between the output value of the training network and the reference solution (entropy compatible format data based on the fine grid) or the exact solution. Then the loss function is minimized by network optimization to obtain the optimal network parameters. Finally, some numerical examples are demonstrated to verify the feasibility of the proposed algorithm. The numerical results show that the proposed algorithm can capture shock waves, and it has high resolution, without nonphysical oscillations.

As an important form of expressing the relationship among entities, graph networks have been widely used in data analysis, relational reasoning, and information services. For these applications, how to reasonably represent network characteristic information is the primary task of network analysis research. Graph embedding technology solves the problem of how to efficiently and reasonably map massive, heterogeneous, and complex high-dimensional graph data to low-dimensional vector space while still retaining the original data feature information. This paper aims to survey the algorithm and research progress of graph embedding in recent years, analyze the development status of this field, and explore the direction for subsequent research. First, it reviews the principle and basic theory of graph embedding technology, then systematically investigates the current mainstream graph embedding algorithms, including graph embedding approaches based respectively on dimensionality reduction, matrix decomposition,network topology,neural network, generative adversarial network, and hypergraph. Then we show the application scenarios of graph embedding technology and introduce the commonly used test data sets and evaluation criteria. Finally, we highlight the future research trends and directions of graph embedding, such as dynamic graph embedding, graph embedding scalability and interpretability.

The equation of rotating surface is one of the key contents in the teaching of vector algebra and spatial analytic geometry in higher mathematics. The existing higher mathematics textbooks mostly concern the solution methods of the surface equation formed by the rotation of the coplanar curve on the coordinate plane around the coordinate axis. Based on the equation of the such rotating surfaces, this paper deduces the equation of the general rotating surface formed by rotating a space curve around a fixed space line by using the formula of direction angle and rotation axis. It determines the rotation axis by looking for attitude and its relative position between the two coordinate systems. The method for solving the general equation of rotating surface proposed in this paper not only is a useful supplement to the current teaching content, but also provides a practical reference for constructing the surface rotation.

In recent years, artificial intelligence technologies such as machine learning have been applied to protein engineering, and have shown unique advantages in studies on as protein structure, function prediction, and catalytic activity. In the absence of protein structure, combining protein sequence and functional properties with machine learning is a new research direction. In this papers, based on a new sequence-activity relationship (ISAR) method, the mutant library of gloeobacter violaceus rhodopsin (GR) and the maximum absorption wavelength of the spectrum are modeled by machine learning. It can fit the best model even in the case of a small number of data sets. The proposed method digitizes the protein amino acid sequence, preprocesses it through fast Fourier transform (FFT), and then performs partial least squares regression (PLSR) modeling. Finally, the best model of the amino acid sequence of the rhodopsin mutant protein and the maximum absorption wavelength of the spectrum is obtained. Modeling with the best index LEVM760106, the coefficient of determination is that R² is 0.944, and the minimum mean square error E is 11.64. In contrast, when the wavelet transform was used to preprocess the data, the coefficient of determination is close to 0.944, but the E is greater than 11.64, not as good as the result of FFT preprocessing. It is shown that, this method effectively solves the mathematical model relationship between protein sequence and functional characteristics, and provides support for predicting better mutants in later protein engineering.

The influences of brushwork and structure on the aesthetic quality of Chinese characters is an important issue in eastern calligraphy culture and is also related to the aesthetic evaluation technology in the field of digital calligraphy. However, in the past, this issue was mainly discussed qualitatively, mostly based on personal aesthetic experience. This study proposed an experimental method to quantitatively study it, which combined technologies in several disciplines, such as spline curve modeling and aesthetic scales. Firstly, six representative regular scripts of 78 high-frequency characters were selected as raw materials, participants were recruited to mark the structural key points using a customized annotation software, and the character images after the "brushwork feature removal" operation were modeled using Bezier curve. Further, the strictly defined "structure unification" operation was applied to these character images. Therefore, three groups of stimuli were obtained and each contains 468 character images. Then, an aesthetic scale was designed, and participants were recruited to evaluate the preference and style of the three groups of stimuli. Each participant was randomly assigned to one of the three. Finally, statistical analysis on the evaluation data showed that the influence of brushwork on the style of regular script was much higher than that of structure, and the influence of brushwork on the preference was slightly higher than that of structure. However, the above finding is limited to the several classical regular scripts selected in the experiment, and "brushwork feature removal" and "structure unification" can not fully cover the scope of relevant concepts in previous classical theories. The study shows that the scientific method can be used to study the propositions in calligraphy theory.

The Yangtze River basin plays an important role in Chinese water resources allocation. What proves common knowledge is that it is particularly important to predict the water quality in the Yangtze River basin. Based on the existing research, the recurrent neural network (RNN) model with gate recurrent unit (GRU) and fully connected neural network (FCNN) are combined in this study to improve a multiple water quality parameter prediction (MWQPP) model. It is proposed to predict the four water quality parameters, such as pH, dissolved oxygen (DO), permanganate index (CODMn) and ammonia nitrogen (NH₃-N) in the Yangtze River basin. Based on 7 566 raw data of 23 water quality monitoring points in the Yangtze River basin from 2011 to 2018, the comparative experiments show that the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R²) obtained from the MWQPP model's prediction results are better than traditional models, such as the multiple linear regression model, the random forest model, FCNN model and LSTM model, and the MWQPP model also has better robustness than these traditional water quality prediction models. As we can say, the MWQPP model can provide scientific, reasonable and effective support for water quality assurance and water management in Yangtze River basin.

To generalize the concept of classic limit of sequence, this paper introduces the definition of almost convergent sequence and proves several important properties together with a necessary and sufficient condition of almost convergence, hence building an equivalent relation between almost and strictly convergence. Moreover, based on the Lebesgue measure and by introducing the conception of density of subsets on {\mathbf{R}}^n,we also provide the definition of almost convergence of measurable functions, including some properties and a basic theorem of almost convergence similar to sequence. Then we introduce the definition of almost continuous function. At last, based on the Lebesgue differential theorem, it is proved that any measurable function is almost continuous, almost everywhere on {\mathbf{R}}^n.

Based on Baidu index, this study takes 'Thailand tourism network attention' of 31 provinces (autonomous regions and municipalities) as the research object, and explores the spatial-temporal evolution of domestic residents' attention to Thailand tourism network and its influencing factors based on seasonal intensity index, geographic concentration index, Herfindahl-Hirschman index and with geographic detector. The results can be concluded as below: from the perspective of time series evolution, Thailand tourism network attention shows a fluctuating upward trend from 2011 to 2019, which can be divided into two stages: rapid upward stage and stable development stage. The seasonal differences are extremely obvious. Over the years, March, July and December are the peak time of Thailand tourism network attention. From the perspective of spatial differentiation, the spatial structure of Thailand tourism network attention exhibits a similar spatial distribution every year. It appears as a ladder-like distribution of "high in the east and low in the west" as a whole. The area with high attention are mainly concentrated in the eastern region and Sichuan Province, while area with low attention are mainly distributed in the western provinces. From the perspective of influencing factors, the overall economic level (GDP), per capital disposable income, transportation convenience, trade openness and international tourism openness jointly affect the spatial distribution of Thailand's tourism attention, and the fundamental cause for the spatial differentiation on network attention lies in the level of economic development.