Space Research Articles

The Evaluation of Relationships between Milk Composition Traits and Breeds with Categorical Principal Component Analysis in Akkaraman and Awasi Sheep

Background: This study aims to determine the relationship between milk composition traits and breed in the Akkaraman and Awasi sheep as well as to provide ease of interpretation by showing the relationships structure between variables and between categories of variables in two-dimensional space with Categorical principal component analysis. Methods: Categorical principal component analysis determines relationships between continuous and categorical variables as well as ordinal variables. It aims to reduce system dimensionality through optimal scaling while maintaining variable measurement levels (nominal, multiple nominal, ordinal and interval). In this research, data obtained from Akkaraman and Awasi Breed Sheep Raised by Public Hands in Tuşba District of Van Province were used. In order to determine relationship with breed, the traits were divided into two categories, “low” and “high” and all variables (9 variables) were considered together and a Categorical principal components analysis was performed. Result: As a results, Dimension 1 accounted for 35.58% of the total variation while dimension 2 accounted for 15.21%. Two dimensions together accounted for 50.79% of the variation. Thus it can be noted that Categorical principal component analysis can be used in the analysis of data sets containing a large number of different types of variables with linear or non-linear relationships between them.

Color image encryption scheme for distributed architecture with SCFP chaotic map

Image protection mechanism in distributed cloud network is an essential component of information security field. In this paper, a novel one-dimensional sine-cosine fractional power chaotic map (SCFP) is proposed. Results of various dynamical system tests illustrate that SCFP exhibits superior chaotic behavior over its infinite positive real parameter range, whose complexity and unpredictability can guarantee the strength of image cryptosystem. Furthermore, a color image encryption scheme tailored for distributed architecture is devised. Firstly, a hybrid cryptographic mechanism is designed to perform diffusion and confusion encryption for image data and ECC public key encryption for intermediate keys. Secondly, the diffusion structure elevates processing units to row-column level, and the diffusion order is dictated by a pseudo-random sequence generated by SCFP. Thirdly, the confusion structure extends the unbiased and efficient Fisher-Yates algorithm into a 2D space, and adopts a design of dual plaintext-related key. Lastly, three techniques namely QOI lossless compression, DE information embedding and threshold secret sharing are integrated to resolve issues of data volume inflation, key synchronization difficulty and poor fault tolerance. Simulation experiments conducted on multiple color images demonstrate that the proposed scheme offers significant ciphertext randomness, sufficiently large key space and strong key sensitivity, which can ensure the integrity of image data and resist various typical cryptographic attacks, and outperforms existing schemes oriented to centralized architecture in terms of security and efficiency.

3D spatial distribution of soil pollutants based on geo-shadowing anisotropic RBF-PCA

Research on soil contamination has become increasingly important, but there is limited information about where to sample for pollutants. Thus, the use of three-dimensional (3D) spatial interpolation techniques has been promoted in this area of study. However, the application of traditional interpolation methods is limited in geography, especially in the expression of anisotropy, and it is not associated with geographical properties. To address this issue, we used a test site (a factory in Nanjing) to develop a new research method based on the geographical shading radial basis function (RBF) interpolation method, which considers 3D anisotropy and geographical attribute expression. Drilling and uniform sampling were used to sample the contaminated area at this test site. This approach included two steps: i) An ellipsoid with anisotropic properties was constructed. Thus, the first step was to determine the shape of the ellipsoid using principal component analysis (PCA) to determine the main orientations and construct a rotational and stretched matrix. The second step was determining the ellipsoid size by computing the range using the variogram method for orientations. ii) During field measurement, the geospatial direction influences soil attribute values, so a shadowing calculation method was derived for quadratic weight determination. Then, the weight of the attribute value of known points can be assigned to meet the field conditions. Lastly, the model was evaluated using the root mean square error (RMSE). For the 2D space, the RMSE values of Kriging, RBF, and the proposed method are 6.09, 7.12, and 5.02, respectively. The R2 values of Kriging, RBF, and the proposed method are 0.871, 0.832, and 0.946, respectively. For the 3D space, the RMSE values of Kriging, RBF, and the proposed method are 2.65, 2.23, and 2.58, respectively. The R2 values of Kriging, RBF, and the proposed method are 0.934, 0.912, and 0.953, respectively. The resulting fitted model was relatively smooth and met experimental needs. Thus, we believe that the interpolation method can be applied as a new method to predict the distribution of soil pollutants.

Real-time position and pose prediction for a self-propelled undulatory swimmer in 3D space with artificial lateral line system

This study aims to investigate the feasibility of using an artificial lateral line (ALL) system for predicting the real-time position and pose of an undulating swimmer with Carangiform swimming patterns. We established a 3D computational fluid dynamics simulation to replicate the swimming dynamics of a freely swimming mackerel under various motion parameters, calculating the corresponding pressure fields. Using the simulated lateral line data, we trained an artificial neural network to predict the centroid coordinates and orientation of the swimmer. A comprehensive analysis was further conducted to explore the impact of sensor quantity, distribution, noise amplitude and sampling intervals of the ALL array on predicting performance. Additionally, to quantitatively assess the reliability of the localization network, we trained another neural network to evaluate error magnitudes for different input signals. These findings provide valuable insights for guiding future research on mutual sensing and schooling in underwater robotic fish.

Depth-enhancement network for monocular 3D object detection

Abstract To accurately obtain 3D information, the correct use of depth data is crucial. Compared with radar-based methods, detecting objects in 3D space in a single image is extremely challenging due to the lack of depth cues. However, monocular 3D object detection provides a more economical solution. Traditional monocular 3D object detection methods often rely on geometric constraints, such as key points, object shape relationships and 3D to 2D optimization, to address the inherent lack of depth information. However, these methods still make it challenging to extract rich information directly from depth estimation for fusion. To fundamentally enhance the ability of monocular 3D object detection, we propose a monocular 3D object detection network based on depth information enhancement. The network learns object detection and depth estimation tasks simultaneously through a unified framework, integrates depth features as auxiliary information into the detection branch, and then constrains and enhances them to obtain better spatial representation. To this end, we introduce a new cross-modal fusion strategy, which realizes a more reasonable fusion of cross-modal information by exploring redundant, complementary information and their interactions between RGB features and depth features. Extensive experiments on the KITTI dataset show that our method can significantly improve the performance of monocular 3D object detection.

Enhancing seismic porosity estimation through 3D sequence-to-sequence deep learning with data augmentation, spatial constraints, and geologic constraints

Estimating porosity from seismic data is critical for studying underground rock properties, assessing energy reserves, and subsequent reservoir exploration and development. For reservoirs with strong heterogeneity, the endeavor to accurately and stably characterize spatial variations in porosity often encounters considerable challenges due to the rapid lateral changes in formations. In view of this, establishing a robust mapping relationship from seismic data to reservoir properties in 3D space is important in addressing this challenge. We transform the conventional 1D sequence-to-point (STP) prediction paradigm into a 3D sequence-to-sequence (STS) prediction paradigm to enable machine learning to extract 3D seismic data features. The 3D STS prediction presents valuable potential for enhancing the geologic continuity and vertical characterization ability of porosity compared to STP. Building upon the 3D STS prediction model, three strategies from different perspectives are introduced to further enhance the performance of seismic porosity estimation. First, we apply a translation-based data augmentation (DA) strategy to mitigate the problem of sparsely labeled data. Second, we develop spatial constraints (SCs) considering absolute coordinates and relative time to boost the spatial delineation of porosity. Third, to incorporate geologic insights into machine learning, we impose geologic constraints (GCs) by measuring the data distribution similarity between around-the-well predictions and well labels. Compared with DA strategies, incorporating SCs and GCs to STS yields more substantial improvements, which illustrates the importance of prior knowledge for physical parameter inversion. Finally, the combined application of these three strategies and the 3D STS method gives better generalization performance and geologic plausibility in the porosity prediction for investigated carbonate reservoirs, outperforming other methods and decreasing error by an average of 8% across 48 wells compared to STP.

On causality violation in different classes of Ricci inverse gravity

In this paper, we investigate a causality-violating four-dimensional space-time, an extension of the two-dimensional Misner space within the framework of modified gravity theories, specifically in the context of Ricci-inverse gravity. To explore this, we focus on two classes of models denoted as f(R,AμνAμν)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f({\\mathcal {R}}, A^{\\mu \ u }\\,A_{\\mu \ u })$$\\end{document} and f(R,A,AμνAμν)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f({\\mathcal {R}}, {\\mathcal {A}}, A^{\\mu \ u }\\,A_{\\mu \ u })$$\\end{document}, and derive the modified field equations using the four-dimensional space-time under consideration. Here, Aμν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A^{\\mu \ u }$$\\end{document} is the anti-curvature tensor defined by inverse of the Ricci tensor Rμν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{\\mu \ u }$$\\end{document}, that is, Aμν=Rμν-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A^{\\mu \ u }=R^{-1}_{\\mu \ u }$$\\end{document}. Additionally, A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {A}}$$\\end{document} denotes its scalar, defined as A=gμνAμν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {A}}=g_{\\mu \ u }\\,A^{\\mu \ u }$$\\end{document}. Our analysis shows that in both models, the modified field equations can be solved by considering the matter energy content as a pure radiation field including the cosmological constant. Furthermore, we demonstrate that the energy density of the pure radiation field and the cosmological constant undergo modifications due to the coupling parameters, and these modifications converge to the results of general relativity when these parameters are set to zero. This observation confirms the possibility of causality violation in the form of closed time-like curves within the framework of Ricci-inverse gravity theory.

“Is What We See Always Real?” A Comparative Study of Two-Dimensional and Three-Dimensional Urban Green Spaces: The Case of Shenzhen’s Central District

This paper takes the central area of Shenzhen as an example to explore the correlation and differences between 2D and 3D green spaces on urban roads during the summer of 2023. By collecting street view image data and using convolutional neural networks for image semantic segmentation, the Green View Index (GVI) was calculated and combined with the Normalized Difference Vegetation Index (NDVI) for analysis. The results show that the road greening levels in Nanshan District, Futian District, and Luohu District of Shenzhen are relatively high, with GVI exceeding 25%. The Pearson correlation coefficient between the 2D and 3D greening data is 0.5818, indicating a moderate correlation. By analyzing four typical greening scenarios (high NDVI and high GVI, high NDVI and low GVI, low NDVI and high GVI, and low NDVI and low GVI), the study found specific reasons for the differences in green data in different dimensions; the analysis revealed that factors such as building height, density, and elevated transportation facilities significantly affect the accuracy of NDVI in urban spaces. The study suggests that in urban greening assessments, the complementarity and differences between street view data and remote sensing data should be comprehensively considered to improve the accuracy and comprehensiveness of the analysis.

CrossKeys: Text Entry for Virtual Reality Using a Single Controller via Wrist Rotation

Text entry has long been an indispensable part of people’s lives; notwithstanding, in virtual reality (VR), an efficient and handy text entry method for such an environment is still wanted. There are two negative factors in the very majority of existing text entry methods: 1) constrained by two-dimensional mapping; 2) must manipulate with both hands to ensure efficiency. Few are employing the three-dimensional space a virtual environment provides, with which text entry could perform much better; also, even with several methods enabling one-hand manipulation, the trade-off is off-balance when sacrificing performance. Therefore, we propose an innovative text entry method to achieve a faster speed, higher accuracy, and better user experience. We design a cross-like layout to reduce the average distance of spatial displacement when selecting characters. In selecting and entering characters, a user simply rotates the wrist and points the embodied controller to one of the seven character blocks in the virtual environment; afterward, within the selected block, the user enters the target character or auto-completion candidate word via two basic trackpad interactions, touching and clicking. We evaluate our CrossKeys mainly based on two criteria: efficiency, task load, and three tasks: learnability and performance test, fatigue test, and evaluation of in-motion performance. To evaluate efficiency, we analyze words per minute (WPM) and error rate (NCER, TER); to evaluate task load, we analyze NASA-TLX and Simulator Sickness Questionnaire (SSQ) results. According to the data from our participants after only 2 hours of first-time training, our CrossKeys performs well with an average WPM of 17.73, a peak WPM of 24.73, and an error rate (NCER) of 0.30% along with a low task load.

Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images

AbstractExisting deep learning‐based defect inspection results on images lack depth information to fully demonstrate the sewer, despite their high accuracy. To address this limitation, a novel attention‐optimized three‐dimensional (3D) segmentation and reconstruction system for sewer pipelines is presented. First, a real‐time sewer segmentation method called AM‐Pipe‐SegNet is developed to inspect defects (i.e., misalignment, obstacle, and fracture) efficiently. Attention mechanisms (AMs) are introduced to improve the performance of segmentation. Second, an attention‐optimized and sparse‐initialized depth estimation network called AM‐Pipe‐DepNet is presented to generate depth maps from multi‐view images. Third, a 2D‐to‐3D mapping algorithm is proposed to remove noise and transform the sewer segmentation results into 3D spaces. Comparison experiments reveal that incorporating AMs into the network significantly enhances pipe segmentation and 3D reconstruction performance. Finally, two digital replicas of real sewer pipes are built based on photos taken by probes, providing valuable insights for sewer maintenance.

Head pose estimation with particle swarm optimization‐based contrastive learning and multimodal entangled GCN

AbstractHead pose estimation is an especially challenging task due to the complexity nonlinear mapping from 2D feature space to 3D pose space. To address the above issue, this paper presents a novel and efficient head pose estimation framework based on particle swarm optimized contrastive learning and multimodal entangled graph convolution network. Firstly, a new network, the region and difference‐aware feature pyramid network (RD‐FPN), is proposed for 2D keypoints detection to alleviate the background interference and enhance the feature expressiveness. Then, particle swarm optimized contrastive learning is constructed to alternatively match 2D and 3D keypoints, which takes the multimodal keypoints matching accuracy as the optimization objective, while considering the similarity of cross‐modal positive and negative sample pairs from contrastive learning as a local contrastive constraint. Finally, multimodal entangled graph convolution network is designed to enhance the ability of establishing geometric relationships between keypoints and head pose angles based on second‐order bilinear attention, in which point‐edge attention is introduced to improve the representation of geometric features between multimodal keypoints. Compared with other methods, the average error of our method is reduced by 8.23%, indicating the accuracy, generalization, and efficiency of our method on the 300W‐LP, AFLW2000, BIWI datasets.

A 3D ray traced biological neural network learning model

Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another dataset. However, current choices of transfer learning algorithms are limited because the transferred models always have to adhere to the dimensions of the base model and can not easily modify the neural architecture to solve other datasets. On the other hand, biological neural networks (BNNs) are adept at rearranging themselves to tackle completely different problems using transfer learning. Taking advantage of BNNs, we design a dynamic neural network that is transferable to any other network architecture and can accommodate many datasets. Our approach uses raytracing to connect neurons in a three-dimensional space, allowing the network to grow into any shape or size. In the Alcala dataset, our transfer learning algorithm trains the fastest across changing environments and input sizes. In addition, we show that our algorithm also outperformance the state of the art in EEG dataset. In the future, this network may be considered for implementation on real biological neural networks to decrease power consumption.

Creating color patterns using optical manipulation and scattering of silicon nanoparticles

The utilization of color patterns has been widely employed in encryption and displays. Printing-based nanostructures are gaining traction in color displays, showcasing remarkable resolution but facing limitations in reconfigurability. Here, we demonstrate a flexible scanning process using optical tweezers to trap silicon nanoparticles (SiNPs) for converting their trajectories into vibrant dynamic color patterns. In this process, the optical potential well stably captures a single SiNP while moving in three-dimensional space at a speed of about 1000 ​μm/s, leading to the display of dynamic color patterns due to persistence of vision (POV). Leveraging the tunable ability provided by Mie resonances within the visible band, the scattering color can be altered simply by adjusting the number of trapped SiNPs, thereby enabling the creation of tunable high-saturation color patterns. This strategy is further explored for flexible design of composite images with potential applications in anti-counterfeiting and dynamic display.

A high-precision hazard ranging method for transmission lines based on laser point cloud

Abstract To ensure the safe operation of the line, it is necessary to discover the hidden danger in time and accurately. This paper proposes a high-precision hidden danger-ranging method for transmission lines based on the laser point cloud. This method uses LiDAR to form a data acquisition system, which is installed in the fixed position of the transmission line channel power tower to obtain the point cloud data of the channel field at regular intervals, according to a certain rule. Two ends of the suspension points and some of the conductor point cloud data are used to fit the whole conductor in three-dimensional space and carry out the precise hidden danger ranging through the fitted conductor combined with the point cloud data and ranging. By comparing the original conductor point cloud data with the fitted conductor data to verify the reliability of the conductor fitting, and verifying the feasibility of the conductor arc prolapse measurement method based on the measured arc prolapse of the conductor and the distance to the ground, it can provide more timely and effective early warning information for the safety of the transmission line channel.

Sedimentation of a spherical squirmer in a square tube under gravity

In this study, we used a three-dimensional lattice Boltzmann method to simulate the settling motion of a spherical squirmer in a square tube under the effect of gravity. A spherical squirmer model with chirality was chosen to simulate the motion of a real microswimmer in a three-dimensional space and to systematically analyze its kinematic properties. According to the results of this study, we identified seven different motion modes: diagonal plane large-amplitude oscillation, central stable sedimentation, bidirectional spiral motion, rebound motion, unidirectional spiral motion, corner stable motion, and near-wall attraction oscillation. It was shown that the formation of different motion modes is caused by the effects of squirmer-type factor and chirality. squirmer-type factor determines the stable motion position of the squirmer in the channel. Chirality makes the head direction of the squirmer more susceptible to change, thus changing the motion trajectory of the squirmer. In addition, it was found that the self-propelling strength determines the speed of squirmer’s motion, which affects the motion frequency of squirmer’s periodic oscillations.

Parameter-Efficient Person Re-Identification in the 3D Space.

People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting the understanding of people. In this work, we address this limitation by exploring the prior knowledge of the 3D body structure. Specifically, we project 2D images to a 3D space and introduce a novel parameter-efficient omni-scale graph network (OG-Net) to learn the pedestrian representation directly from 3D point clouds. OG-Net effectively exploits the local information provided by sparse 3D points and takes advantage of the structure and appearance information in a coherent manner. With the help of 3D geometry information, we can learn a new type of deep re-id feature free from noisy variants, such as scale and viewpoint. To our knowledge, we are among the first attempts to conduct person re-id in the 3D space. We demonstrate through extensive experiments that the proposed method: (1) eases the matching difficulty in the traditional 2D space; 2) exploits the complementary information of 2D appearance and 3D structure; 3) achieves competitive results with limited parameters on four large-scale person re-id datasets; and 4) has good scalability to unseen datasets. Our code, models, and generated 3D human data are publicly available at https://github.com/layumi/person-reid-3d.

Neural Radiance Selector: Find the best 2D representations of 3D data for CLIP based 3D tasks

Representing the world in 3D space provides vivid texture and depth information. However, 3D datasets currently do not match the scale of 2D datasets. There is a growing trend in representing 3D data as multi-view 2D images and using large-scale 2D models, to solve 3D tasks. In this work, we present the Neural Radiance Selector, a method that automatically selects the optimal 2D representations of 3D data. Instead of indiscriminately sampling multi-view 2D images, we define the optimal 2D views as those capable of reconstructing the entire 3D scene with a conditional neural radiance field. We propose two distinct methods for 3D point cloud data and 3D implicit models to achieve faster inference. We demonstrate the efficacy of our methods in various 3D tasks, including zero-shot 3D point cloud classification, 3D implicit model classification, and language-guided NeRF editing.

3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection.

Vascular structure segmentation plays a crucial role in medical analysis and clinical applications. The practical adoption of fully supervised segmentation models is impeded by the intricacy and time-consuming nature of annotating vessels in the 3D space. This has spurred the exploration of weakly-supervised approaches that reduce reliance on expensive segmentation annotations. Despite this, existing weakly supervised methods employed in organ segmentation, which encompass points, bounding boxes, or graffiti, have exhibited suboptimal performance when handling sparse vascular structure. To alleviate this issue, we employ maximum intensity projection (MIP) to decrease the dimensionality of 3D volume to 2D image for efficient annotation, and the 2D labels are utilized to provide guidance and oversight for training 3D vessel segmentation model. Initially, we generate pseudo-labels for 3D blood vessels using the annotations of 2D projections. Subsequently, taking into account the acquisition method of the 2D labels, we introduce a weakly-supervised network that fuses 2D-3D deep features via MIP to further improve segmentation performance. Furthermore, we integrate confidence learning and uncertainty estimation to refine the generated pseudo-labels, followed by fine-tuning the segmentation network. Our method is validated on five datasets (including cerebral vessel, aorta and coronary artery), demonstrating highly competitive performance in segmenting vessels and the potential to significantly reduce the time and effort required for vessel annotation. Our code is available at: https://github.com/gzq17/Weakly-Supervised-by-MIP.

Study on the properties of polypyrrole (PPy) coated 3D spacer conductive fabric

Abstract In this study, the 3D spacer fabric with electrical conductivity was prepared and its properties make an inquiry. The synthesis of polypyrrole (PPy) on the spacer fabric surface demonstrated by Infrared curves and SEM images. The test results of tensile properties of conductive fabric show that when the fabric is deformed by stress, its conductivity decreases, and the change of resistance shows a trend of slow increase and then rapid increase. The tribological properties of conductive fabric show that the tribological fastness of conductive fabric after alkali treatment is better than that of untreated fabric. The research on the properties of 3D conductive fabric provides theoretical foundation for its application in intelligent spinning, electromagnetic shielding, and other fields.

Sequential diethylaminoethyl dextran-grafting and diethylaminoethyl modification improve the adsorption performance of anion exchangers

Ion exchangers with high adsorption capacity, fast mass transfer, and high salt-tolerance synchronously are highly desired for high-performance protein purification. Here, we propose a sequential diethylaminoethyl dextran-grafting and diethylaminoethyl chloride modification strategy to achieve high-performance anion exchangers. The advantages of the double-modification strategy lie in: (1) the introduction of diethylaminoethyl in the second modification has no diffusion limitation due to the small molecular size, thus a high ionic capacity; (2) the grafting ligands not only provide three-dimensional adsorption space for high adsorption capacitybut alsofacilitate surface diffusion of protein by chain delivery. The maximum adsorption capacity of the obtained anion exchangers for bovine serum albumin reaches 333 mg/mL, the ratio of effective pore diffusivity (De) to free solution diffusivity (D0) reaches 0.69, and the adsorption amount reaches 97 mg/mL even in 100 mmol/L NaCl concentration,. All these results demonstrate the proposed sequential modification strategy are promising for the preparation of high-performance ion exchangers.