Table of Content

25 November 2025, Volume 53 Issue 11

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Computer Science & Technology

Light Field Depth Estimation with Information Entropy-Based Pre-Computed Occlusion Masks

CHEN Zhong, CHEN Changfeng, ZHANG Xianmin

2025, 53(11):  1-8.  doi:10.12141/j.issn.1000-565X.250074

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Light field cameras capture synchronized spatial-angular information of lights, offering a new paradigm for 3D visual perception. Depth estimation, as a fundamental task in light field analysis, underpins the critical applications such as 3D reconstruction and visual odometry. However, occlusion-induced estimation errors remain a persistent challenge. This paper proposes an occlusion-aware depth estimation framework featuring two novel mo-dules: an entropy-based occlusion mask pre-computation method and a viewpoint screening-driven depth estimation algorithm. In the investigation, first, the light field occlusion in the polar plane diagram is modeled by analyzing the information entropy of micro-images array, and a local entropy extremum-based occlusion mask pre-computation approach is constructed, thus overcoming the limitations of conventional techniques in characterizing occluded regions. Subsequently, viewpoint screening is employed to eliminate the interference from occluded perspectives, thus effectively reducing estimation errors and decreasing the proportion of disparity outliers exceeding 0.03 pixels compared to other methods. The core contribution of this paper lies in establishing a theoretical connection between information entropy and light field occlusion, enabling a robust occlusion-aware depth estimation framework based on the information entropy of micro-images array. Experimental results on light field benchmark dataset demonstrate that the proposed method achieves superior performance in mean absolute error and 25th error percentile. Comparative ablation studies confirm the efficacy of the entropy-driven occlusion mask, thus highlighting the critical role of information entropy theory in the framework of light field depth estimation.

Design and Optimization of Fast Fourier Transform Algorithm Based on Ascend NPU

LU Lu, WANG Yuanfei, LIANG Zhihong, SUO Siliang

2025, 53(11):  9-17.  doi:10.12141/j.issn.1000-565X.240524

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As a fundamental algorithm in scientific computing and signal processing, fast Fourier transform (FFT) has been widely applied to such fields as digital signal processing, image processing, deep learning. With the growth of data scale and the increasing demand for processing power, optimizing FFT algorithms on emerging hardware platforms has become particularly crucial. This paper conducts an in-depth analysis of the architectural cha-racteristics of Ascend NPU and their impacts on FFT algorithm optimization. Based on the matrix-computation-based Stockham FFT algorithm, a series of innovative optimization strategies are proposed: (1) A heuristic radix selection algorithm is designed to provide effective radix sequence combinations for different input sizes; (2) An efficient computation flow for single-iteration FFT without real-imaginary separation is developed, significantly reducing the global memory access overhead; (3) An on-chip cache-based data reading optimization strategy is proposed, greatly improving data access speed; (4) A data layout optimization method for multiple iterations is designed, effectively enhancing overall memory access efficiency. Experimental results on Ascend Atlas 800 platform equipped with Ascend 910 AI processor demonstrate that the proposed optimization strategies achieve an average speedup of 4.61 compared to non-optimized implementations. Independent performance analysis and validation of each optimization strategy demonstrate that the individual average speedup ratio ranges from 1.42 to 3.52. This research provides a technical references for implementing efficient FFT algorithms on emerging NPU architectures.

Unpaired Cross-Modal Retrieval Re-Ranking Based on Neighbor Information Aggregation

WO Yan, LIANG Zhanyang

2025, 53(11):  18-26.  doi:10.12141/j.issn.1000-565X.240598

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As a post-processing technique, re-ranking has demonstrated significant effectiveness in cross-modal retrieval tasks. By mining and processing the information between initial ranking lists, re-ranking process effectively improves retrieval accuracy. The current mainstream cross-modal retrieval re-ranking methods re-rank the initial list based on paired datasets. However, they have poor flexibility because they cannot be easily plugged into existing systems without modifying the original framework and retraining, which makes it difficult to transfer them to other frameworks. Moreover, they cannot be applied in unpaired scenarios. At present, cross-modal retrieval has achieved significant progress by relying on large-scale paired datasets, but it overlooks the problem that labeling such large-scale datasets in practical scenarios requires substantial resources. To address these issues, this paper proposes an unpaired cross-modal retrieval re-ranking method based on neighbor information aggregation. The method improves retrieval performance by mining and utilizing the neighbor information of samples, pushing incorrect answers away from the query input. It searches for local neighbors in the Euclidean neighborhood and for global neighbor expressions through collaborative expression, and then integrates these two types of neighbor information to generate new features for re-calculating semantic similarity with the retrieval input, thus completing a re-ranking process. Finally, the proposed method is applied as a post-processing technique in several cross-modal retrieval model frameworks and is tested on MSCOCO dataset, with its effectiveness and superiority over other re-ranking methods being demonstrated.

An Atomic Structure Segmentation Method for High-Noise STEM Image Based on Materials Structural Priors

GUO Lihua, LIN Yanyu, CHEN Ke

2025, 53(11):  27-36.  doi:10.12141/j.issn.1000-565X.250032

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Scanning transmission electron microscopy (STEM) can perform electron imaging of material properties at the atomic picometer level and interpret the atomic structure using the obtained images. However, obtaining high-quality atomic-scale STEM images requires advanced STEM equipment and skilled operators. Various environmental factors can introduce unpredictable non-uniform noise during the STEM imaging process, thereby significantly affecting image quality and consequently influencing the results of atomic structure analysis. The prediction model based on deep neural networks can reduce the impact of noise through denoising or data fitting, but there exists a problem of overfitting. This paper introduces materials structure conditions as priors in the deep neural network model and designs a method for atomic structure segmentation of high-noise STEM images based on materials structural priors. In this method, the materials structural priors are modelled as the attention (including self-attention and cross-attention) of the segmentation network and are calculated, which not only enables the segmentation network to adaptively focus on the key regions of the image but also to adaptively focus on the control information from the structural coordinate vector modalities. In the simulation test set, as compared with AtomAI Segmentor method, the proposed method improves the chamfer distance, Jaccard and F1 metrics by 175%, 49.7% and 42.7%, respectively; as compared with the early multi-scale method proposed by the research group, it improves the chamfer distance, Jaccard and F1 metrics by 167%, 28% and 23.9%, respectively. In the laboratory sample test set, as compared with AtomAI Segmentor method, the proposed method improves the chamfer distance, Jaccard and F1 metrics by 63%, 9.3% and 7.4%, respectively; as compared with the early multi-scale method proposed by the research group, it improves the chamfer distance by 12.8%, and the Jaccard and F1 metrics remain largely unchanged. The introduction of materials structural priors enables the segmentation network model to more accurately segment the atomic structure in high-noise STEM images and predict the secondary structure information that is affected by noise or top-level occlusion.

3D Hand Pose Estimation with Multimodal Feature Fusion Guided by Depth Geometric Features

GUAN Xin, LIU Chenxi, LI Qiang

2025, 53(11):  37-51.  doi:10.12141/j.issn.1000-565X.250072

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Owing to the inherent instability in data acquisition quality, the reliance on either RGB or depth images alone in 3D hand pose estimation tasks frequently results in the loss of critical features. In contrast, multimodal approaches that integrate the complementary semantic and structural strengths of both modalities exhibit significantly enhanced robustness. However, existing multimodal 3D hand pose estimation methods face significant challenges in effectively fusing RGB and depth information, primarily due to issues of feature redundancy, modality misalignment, and the loss of local features. These limitations significantly degrade the accuracy and stability of keypoint localization. To address these challenges, this paper proposes a depth feature-guided multimodal keypoint feature enhancement and fusion method. In this method, first, depth structural features are leveraged to capture hand contour and geometric information, thus providing an initial estimation of keypoint positions. Subsequently, RGB modal information is employed to locally enhance depth features, thus effectively addressing the inherent limitations of depth modal in capturing structural features being lost due to voids and occlusions. Furthermore, a framework integrating the localized depth-based 3D structural features of keypoint is proposed to refine the initial RGB features, thus enhancing the spatial structure understanding of the hand in the RGB modal. To optimize the fusion process, a global cross-modal attention mechanism is introduced to facilitate interactive learning, thus ensuring the global alignment of locally enhanced depth and RGB features while dynamically enhancing the complementarity between modalities. Compared with existing mainstream deep learning methods, the proposed approach helps to achieve the lowest errors of 7.52, 1.80 and 7.40 mm on DexYCB, HO-3D and InterHand2.6M datasets, respectively.

CO₂ Emission Measurement and Forecasting for Power Plants Based on Multi-Source Data and Machine Learning

SUN Zunqiang, TIAN Yichun, SU Nan, ZHENG Chenghang, ZHANG Zhen, YANG Hongmin, GAO Xiang

2025, 53(11):  52-61.  doi:10.12141/j.issn.1000-565X.250086

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Accurate CO₂ emission measurement and dynamic forecasting are crucial for achieving China’s “dual carbon” goals (carbon peak and carbon neutrality). This study integrates the emission factor method, CO₂-CEMS and machine learning technologies to propose a CO₂ emission measurement and forecasting method based on multi-source data fusion, for the purpose of providing an efficient and accurate carbon emission monitoring tool for coal-fired power plants. In the investigation, by comparing the CO₂ emission calculation results obtained by different methods, the performances of different machine learning algorithms are evaluated, and a dynamic forecasting model based on multi-source data is developed. Experimental results in Units 1 and 3 of a power plant of Guoneng Group Co., Ltd. in Hebei, China show that the relative deviations of CO₂ emission calculations between the emission factor method and CO₂-CEMS for the two units are 1.63% and -1.27%, respectively, meaning that the two methods are of good cross-validation. By comparing the performance of various machine learning models (such as XGBoost, LightGBM, and AdaBoost), beyond the two conventional evaluation metrics, namely the determination coefficient (R²) and the mean absolute percentage error (MAPE), a new selection criterion, namely the mean deviation (x_c), is proposed by applying trained models to other units. Then, x_c is used to assess the generalization capability of machine learning algorithms for further model screening. The results reveal that AdaBoost exhibits superior performance in prediction accuracy and stability, along with higher generalization capability and robustness. The dynamic CO₂ emission forecasting using the optimized AdaBoost algorithm achieves R² values greater than 0.99 on both the training and the testing sets, with a MAPE below 2%, which indicates that the algorithm is of high prediction accuracy, stability, generalization ability and robustness. The proposed multi-source data fusion method not only effectively overcomes the limitations of traditional methods in dynamic scenarios but also enables precise hourly CO₂ emission forecasting based on real-time data.

Intelligent Transportation System

Research on the Mechanism Influencing the Coupling Coordination Between Road Significance and Traffic State

DENG Yajuan, SONG Siliang, CUI Liangbin, LI Yu, NIU Xiaolu, WU Qi

2025, 53(11):  62-76.  doi:10.12141/j.issn.1000-565X.240388

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Urban road significance and traffic state have a complex interaction relationship, which affects people’s travelling efficiency and satisfaction. In this paper, two types of traffic state indicators are extracted from the GPS trajectory data of taxis, namely the number of GPS points and the ratio of average speed during the morning traffic peak to the free-flow speed at night. Based on the theory of complex networks, the node degree, betweenness centrality, closeness centrality and eigenvector centrality in the topological structure are obtained to characterize the road significance at different levels, and the coupling relationship between traffic state and the four significance indexes is explored by using the coupling coordination degree model. With the coupling relationship as the dependent variable, thirteen independent variables are selected to regress the mechanism of the coupling degree using CatBoost algorithm combined with SHAP. The results show that the number of intersections, the green area, and the number of bus stops promote the coupling coordination between traffic state and road significance, and that the road separation type has a significant positive effect on the degree of coupling between traffic state and betweenness centrality, closeness centrality as well as node degree. In addition, for the positively uncoordinated roads, the intensities of public management land and residential land have a negative effect on the coupling coordination between traffic state and closeness centrality, and the intensity of residential land has a negative effect on the coupling coordination between traffic state and eigenvector centrality; while for the negatively uncoordinated roads, the intensity of residential land has a negative effect on the coupling coordination between tra-ffic state and closeness centrality.

Path Planning Method Integrating Curve Combination and Numerical Optimization for Parallel Parking on Narrow Road

CHENG Guozhu, XUE Daoan, GU Shuang

2025, 53(11):  77-89.  doi:10.12141/j.issn.1000-565X.240581

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To address the challenges of abrupt curvature changes, high collision risks, and repeated adjustments in parallel parking within narrow urban passages, this study proposes a three-segment one-step path planning method integrating curve combination and numerical optimization to enhance vehicle’s steering performance and dynamic obstacle avoidance capabilities, and to improve the parking efficiency and safety. Firstly, based on the curvature characteristics of arcs and spiral curves, two types of curve combinations, namely CAC (Clothoid Curve-Arc-Clothoid Curve) and CC (Clothoid Curve-Clothoid Curve), are designed. Secondly, the parking process is reversed, and the CAC curve combination is used to design the path of the end section. Then, a constrained optimization model based on the CC curve combination is constructed to plan the path of the starting section, and the middle section path is designed by introducing the quintic polynomial curve. Finally, multi-condition simulation tests are carried out in the quasi-feasible area through Matlab. The results show that the proposed method can plan a collision-free and continuously curved feasible path in a narrow scene with a channel width of only 3 meters, without occupying wide channel. Comparative analysis reveals significant advantages of the proposed method over quintic polynomial curve and arc-clothoid curve-straight methods in terms of path smoothness, obstacle avoidance and space utilization efficiency. Model predictive control validation confirms that the vehicle adopting the proposed method is of precise path tracking performance with continuous front-wheel steering angle changes. The proposed three-segment one-step path planning method provides an efficient and safe solution to narrow-space parallel parking, and effectively improves the parking efficiency, thus alleviating road congestion caused by roadside parking operations and improving urban traffic efficiency.

A Review of Researches on Operation Management, Simulation, and Evaluation of Urban Autonomous Delivery Vehicles

LI Haijian, LI Yuxuan, WANG Weijie, TONG Shengjun, GAO Jingbo, LI Tongfei, CHEN Na

2025, 53(11):  90-100.  doi:10.12141/j.issn.1000-565X.240367

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The operation simulation and evaluation constitute the basis of theoretical research in the field of autonomous delivery vehicles, and are conducive to the high-quality ground operation of autonomous delivery vehicles. This paper first reviews the current state of autonomous delivery vehicle technology and the domestic and international safety operation management standards, then analyzes the driving characteristics of autonomous delivery vehicles (including vehicle safety attributes, speed and energy consumption profiles, as well as following and lane-changing behaviors) and the operational characteristics of delivery services. The study highlights the need to strike an appropriate balance among vehicle design, environmental factors and delivery tasks, and summarizes the current state and limitations of simulation methodologies for testing autonomous delivery vehicle technology, operational impact assessments, and network simulations. On this basis, by drawing on the operation evaluation methodologies for autonomous delivery vehicles at home and abroad and considering the operational characteristics, an indicator system for assessing the performance of autonomous delivery vehicles operating in non-motorized lanes is established, and the key evaluation metrics and future research directions of autonomous delivery vehicles are outlined. There comes to the conclusions that (1) the existing simulation technologies for the operation of autonomous delivery vehicles lack unified and authoritative management standards; (2) the researches on simulation-based evaluation of the operational impacts of autonomous delivery vehicles are limited; (3) current studies on the operational characteristics of autonomous delivery vehicles are insufficiently detailed and lack consideration of real-world deployment scenarios; and (4) drawing on road level-of-service evaluation indicators, average speed variation rate and average density variation rate are two appropriate evaluation metrics for assessing the impact of autonomous delivery vehicle deployment.

Evaluation of Multi-Dimension Accessibility of Intercity Road Networks from the Perspective of Regional Coordinated Development

YE Yichen, WEN Huiying, ZHANG Lin

2025, 53(11):  101-111.  doi:10.12141/j.issn.1000-565X.240608

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The accessibility of intercity road network is a key indicator for measuring the efficiency of regional flows of economic and social resources. However, the existing researches mainly focus on macro-level connectivity, while local services and economic interactions between nodes receive less attention, thus providing insufficient support for regional coordinated development. To address this issue, this paper proposes a multi-dimension accessibi-lity evaluation framework for intercity road networks. First, a node classification method is developed to balance regional coordinated development goals with data availability, systematically defining the positions and functions of economic activity centers, transportation hubs and tourist attractions within the network. Secondly, expressways, national highways and provincial highways are integrated into a unified topological model, and a time impedance adjustment mechanism that accounts for both travel efficiency and cost differences is established, which is used to capture the dynamic balance characteristics of different road types in path selection. On this basis, three accessibility evaluation models are constructed respectively from the dimensions of connectivity, service and economy, and are used to quantify the overall network connectivity, residents’convenience in accessing hubs and attractions, and the strength of economic interactions between nodes, respectively. An empirical analysis of the Guangdong-Hong Kong-Macao intercity road network at the end of 2021 shows that the Greater Bay Area significantly outperforms Eastern, Western, and Northern Guangdong in terms of multi-dimension accessibility. Based on the principles of evidence-based design, the selection of key nodes and the planning and construction of high-standard roads lead to significant improvements in connectivity, service, and economic accessibility, both across the entire region and within Eastern, Western, and Northern Guangdong. Specifically, 99.2% of nodes in Eastern, Western, and Northern Guangdong show improved connectivity accessibility, and 83.3% exhibit enhanced economic accessibility, thus fully validating the effectiveness of the proposed evaluation framework and the optimization scheme. This study provides a syste-matic tool and decision-making reference for regional road network optimization and resource allocation.

Hotspot Segments Identification of Collisions Between Electric Vehicles and Pedestrians/Non-Motor Vehicles

YANG Miaoyan, DONG Chunjiao, XIONG Zhihua, ZHUANG Yan, XU Bo

2025, 53(11):  112-121.  doi:10.12141/j.issn.1000-565X.240592

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To explore in-depth the spatiotemporal distribution characteristics of collisions between electric vehicles and vulnerable road users, this paper proposes a method for identifying spatiotemporal hotspot segments of collisions between electric vehicles and pedestrians/non-motor vehicles. First, based on the collision data involving electric vehicles and pedestrians/non-motor vehicles, the analytic hierarchy process is employed to determine the weights of the influencing factors, and a weighted network kernel density estimation method is employed to reveal the spatial clustering of traffic accidents. On this basis, the density peaks clustering (DPC) algorithm is utilized as a spatial clustering model for accidents, and a spatiotemporal-DBSCAN (ST-DBSCAN) model is constructed to incorporate the temporal dimension, thereby accurately characterizing the spatiotemporal distribution characteristics of collision accidents between electric vehicles and pedestrians/non-motor vehicles. Finally, an empirical study is conducted using the electric vehicle accident data from a city over 11 consecutive months. The results indicate that the collisions between electric vehicles and pedestrians/non-motor vehicles exhibit 3 temporal peaks, differing from the traditional bimodal characteristic observed in traffic accidents, while spatially demonstrating localized clustering characteristics. For identifying spatial hotspot segments, as compared with the optimal values of DBSCAN, OPTICS and Mean Shift algorithms, DPC algorithm shows improvements of 42.9%, 74.5% and 11.1% in terms of silhouette coefficient, Davies-Bouldin index (DBI) and Calinski-Harabasz Index (CHI), respectively. For identifying spatiotemporal hotspot segments, under similar DBI conditions, ST-DBSCAN algorithm achieves silhouette coefficient and CHI values that are 2.25 times and 57.3% higher, respectively, than the optimal values of ST-OPTICS, ST-DPC and ST-Mean Shift algorithms.

Vehicle Engineering

Aerodynamic Performance Optimization of Vehicle-Mounted Photovoltaic System for Net Power Maximization

LUO Yutao, LIN Zhiqiang

2025, 53(11):  122-131.  doi:10.12141/j.issn.1000-565X.250070

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The present researches on vehicle-mounted photovoltaic systems mainly focus on increasing the installation area of photovoltaic panels by optimizing the folding mechanism to enhance the power generation capacity, while neglecting the issue of the synergistic optimization of power generation capacity and the additional drag energy consumption of the system. To enhance the net power of vehicle-mounted photovoltaic systems, by optimizing the aerodynamic performance of the system, this paper presents a new methodology to reduce the additional drag energy consumption imposed by the system on the vehicle, and thereby to increase the net power of the system. Firstly, a foldable vehicle-mounted photovoltaic system was designated as the object, and a high-transmittance fairing and a tail wing that conform to the aerodynamic principle were designed. Subsequently, three design variables, namely the front tilt angle of the fairing, the back angle, and the system height, were selected to optimize the shape of the fairing. Through the construction of an orthogonal test scheme and the analysis of polar deviation, the influence degree of the three design variables on the system aerodynamic drag was obtained as system height > front tilt angle > back angle. From the analysis of the main effect plot, the three variables are found exhibiting monotonic effects on the aerodynamic drag of the system, thus determining the structural parameters of the fairing shape as follows: a front tilt angle of 70°, a back angle of 0°, and a system height of 100mm. Subsequently, the tail attack angle of the vehicle-mounted photovoltaic system was optimized, a cubic spline interpolation approximation model was constructed based on the experimental data, and the tail attack angle with optimal lift-to-drag ratio was obtained as 33.96°. In addition, the vehicles equipped with the on-board photovoltaic system proposed in this paper were compared with those without the system. It is found that the air resistance coefficient decreases by 44.59%, the aerodynamic resistance decreases by 22.45% and the lift coefficient decreases by 226.15%, and that the direction of the lift undergoes a transformation from upward to downward. This transformation serves to mitigate the adverse impact of upward aerodynamic lift on the handling and safety performance of the entire vehicle. A comparison of the original vehicle model with the modified version reveals a significant decrease of air resistance coefficient of 17.35% and a modest aerodynamic resistance increase of 3.14 N, which means that the modification effectively mitigates the adverse effects of the on-board photovoltaic system on vehicle’s aerodynamic performance. Finally, a comparison and analysis of the net power of the on-board photovoltaic system before and after the optimization was conducted. It is found that the proposed optimization scheme can effectively increase the net power generation of the vehicle-mounted photovoltaic system during vehicle operation. When the vehicle speed is 40.0 m/s, the net power difference reaches 7 723.62 W.

Time Series Prediction of Vehicle Crash Based on Process Parameters Analysis of Friction Stir Welding

XIE Zhengchao, LIU Jincan, LI Shuang, LI Wenfeng, ZHAO Jing

2025, 53(11):  132-140.  doi:10.12141/j.issn.1000-565X.240364

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Under the demands for vehicle lightweighting and high safety, friction stir welding has become the core technology for the manufacturing of key vehicle body structures, and it is crucial for the enhancement of collision energy absorption capacity. However, when the traditional explicit dynamic model is used to optimize the process pa-rameters of friction stir welding, a large number of repeated finite element calculations are required, thus leading to such problems as long calculation time and high resource consumption, which restricts the design efficiency. To solve these problems, this paper proposes a time series prediction method of vehicle crash based on the process parameters analysis of friction stir welding, which balances the optimization efficiency and crash safety. During the investigation, first, the mapping relationships between rotational speed, welding speed and the elastic modulus of welded parts are summarized, and a parameter set is constructed. Next, by taking a body-in-white as the object, the set of body-in-white components with mixed shell elements is discretized and the frontal crash condition is set to establish a vehicle explicit dynamic model. Then, a time series prediction surrogate model is designed, and is trained with explicit dynamic response data, with the combination of a high-dimension data decoupler and a penalty function, thus finally forming a surrogate model update process toward the goal of minimizing the deformation and strain of observation points. After iteration, the root mean square error and loss of the surrogate model’s prediction results approach zero, which means that the model accuracy is reliable. In addition, compared with the traditional method, the proposed method saves 50% of the calculation time. This method achieves the collaborative optimization of lightweighting and high safety of vehicles, provides an efficient technical means for vehicle body design and the iteration of friction stir welding process parameters, and has engineering value for shortening the research and deve-lopment cycle and improving the safety performance of vehicles.

Chemistry & Chemical Engineering

Controlled Anionic Polymerization of Propylene Sulfide and Strategy for Synthesis of Sulfur-Rich Copolymers

ZHANG Hongxin, ZHU Hongxuan, SUN Duzheng, WANG Guohu, LIU Fengzhuang

2025, 53(11):  141-149.  doi:10.12141/j.issn.1000-565X.250115

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Ring-opening polymerization of cyclic sulfur compounds is one of the important methods to synthesize sulfur-containing polymers. However, in traditional anionic polymerization techniques, the high reactivity of sulfur-centered anions tends to induce chain transfer side reactions, leading to products with broad molecular mass distributions and structural variations. These issues severely restrict the precise control over polymer chain structure and compromise the controllability and reproducibility of material properties. To solve this problem, this study presents a synergistic catalytic system that combines triethylborane (Et₃B), phosphazene base ^t BuP₁ and a thiol initiator to facilitate efficient and controlled anionic ring-opening polymerization of propylene sulfide. Experimental results show that adding Et₃B at 0 ℃ effectively blocks the chain transfer reaction of sulfur anions to monomers and prevents the formation of disulfide bonds. Density functional theory calculations confirm that Et₃B stabilizes the active sulfur-centered anion intermediates through strong B-S coordination, significantly reducing their nucleophilic reactivity and enabling precise control over the polymerization process. Based on these findings, a “one-pot, two-step” strategy for efficient synthesis of sulfur-rich polymers is developed. By using the dual-component catalytic system, an alternating copolymer of carbon disulfide and propylene sulfide can be synthesized as a macro-chain transfer agent, followed by the direct addition of styrene monomer to successfully create a well-defined polystyrene-co-poly(carbon disulfide-alt-propylene sulfide) terpolymer without isolating the intermediate. This work presents a novel pathway for the precise synthesis of sulfur-rich polymers.

Experimental Investigation into Suspension Crystallization Refining of Coal-to-Ethylene Glycol

XUE Yuyuan, ZHAO Miao, ZHAO Zhitong, YUAN Dongyong, LI Gang

2025, 53(11):  150-156.  doi:10.12141/j.issn.1000-565X.240368

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Suspension crystallization coupled with centrifugal separation can be used to refine polyester-grade coal-to-ethylene glycol. Compared with the traditional distillation purification, suspension crystallization has the advantages of low energy consumption, no need for solvents and environmental friendliness. It is particularly effective in reducing energy consumption and emissions when purifying the narrow-boiling, azeotropic and heat-sensitive systems formed by impurities and ethylene glycol during the coal-to-ethylene glycol production process. To optimize the operation process of suspension crystallization for coal-to-ethylene glycol and provide a reference for the deve-lopment of coupled centrifugal separation technology, this paper investigated the effects of seed dosage, filter cake thickness, stirring intensity, programmed cooling and mother liquid circulation operation process on the purification efficiency. The results show that seed dosage affects crystal size distribution, and that increasing the dosage may improve the product purity, with the effective distribution coefficient decreasing by 15.8%. Reducing the filter cake thickness allows for more sufficient contact between crystals and sweating gas, thus enhancing the sweating effect and resulting in a maximum reduction of 56.8% in the effective distribution coefficient. Stirring intensity directly affects mass and heat transfer. Higher stirring intensity leads to higher product purity but lower yield, with maximum decreases in the effective distribution coefficient and the yield of 39.4% and 30.8%, respectively. Programmed cooling directly affects crystallization time. A slower cooling rate prolongs the crystallization time but reduces product purity. Mother liquid recycling increases the water content in the system, reduces the degree of supercooling, and thereby improves product purity. Compared with fresh mother liquid, the mother liquid cycled for 7 times leads to an effective distribution coefficient decrease by 72.0%.