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.

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.

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.

In order to effectively remove low-concentration antimony in the printing and dyeing wastewater, this study adapted FeSO₄ enhanced activated sludge method, applying high-throughput sequencing, together with scanning electron microscope (SEM), Fourier transform infrared spectrometer (FTIR) and X-ray photoelectron spectroscopy (XPS) to reveal the mechanism of removing antimony. The results showed that: (1) FeSO₄ enhanced activated sludge group had strong antimony removal performance, the initial antimony concentration was 250 μg·L^-1, pH=8, Fe²⁺ dosage was 90 mg·L^-1, the removal efficiency of Sb (Ⅴ) was stable above 60%. At the same time, the different coexisting ions can promote or inhibit the removal rate of Sb (Ⅴ). (2) During the domestication process of adding FeSO₄, the abundance and diversity of activated sludge microorganisms decreased, activated sludge particles became looser, specific surface area increased, particle size decreased, and the contact areas between sludge flocs and antimony in water increased, improving the efficiency of antimony removal. Fe²⁺ was oxidized to α-FeO(OH) in situ in the biochemical process, and combined with a large number of carboxyl or amino groups on the surface of activated sludge, therefore adhered to the surface of activated sludge to realize the adsorption of antimony pollutants.

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.

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.

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.

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.