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Facile synthesis of W-Mo bimetallic oxides with high adsorption properties from secondary resources

Tungsten 2024年第4期6卷 806-820页

作者： Jia-Li Cheng Yang-Si Liu Xiao-Li Xi Zuo-Ren Nie Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and EngineeringBeijing University of TechnologyBeijing100124China National Engineering Laboratory for Industrial Big-Data Application Technology Beijing University of TechnologyBeijing100124China Key Laboratory of Advanced Functional Materials Ministry of EducationBeijing University of TechnologyBeijing100124China

A simple solvothermal method was used to obtain W-Mo bimetallic oxides from W-Mo alloy scrap,and pure metal powders were also used as the raw materials to simulate *** products had a sea urchin-like structure with abundant oxygen vacancies and the products prepared at low temperatures forms a sosoloid resembling orthorhombic W_(0.4)Mo_(0.6)O_(3).The WMo bimetallic oxide prepared at the reaction temperature of 120℃exhibited excellent selective adsorption performance for methylene blue(MB),which the adsorption rate of MB reached 99%in 12 min and the adsorption rate reached 90%after6 adsorption *** the W-Mo molar ratio is 1:3,the maximum adsorption capacity of sample for MB can reach1148 mg·g^(-1).The adsorption process followed the Langmuir and pseudo-second-order models,which is surface-controlled monolayer *** experimental results show the feasibility of preparing W-Mo bimetal oxide products from pure materials and *** process is simple and effective,which offered a potential approach for secondary resource recycling and reusing.

关键词： W-Mo bimetallic oxide Oxygen vacancy Selective adsorption W-Mo alloy scrap Solvothermal

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Research on Equipment Operation and Maintenance Management technology of Large Railway Passenger Station 5th

Research on Equipment Operation and Maintenance Management T...

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5th International Conference on Smart Vehicular technology, transportation, Communication and applications, VTCA 2022

作者： Wang, Bozhou Li, Lexi Ni, Shaoquan Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Sichuan Chengdu610031 China

ISBN: (纸本)9789819908479

With the development of informationization and intelligence of railway passenger stations, problems such as inconvenient information interaction, missing operation and maintenance data, and difficulty in accurate positioning of equipment under the existing equipment operation and maintenance management mode have become the focus. In this paper, the operation and maintenance management process of equipment is divided into three stages: fault prediction and warning, fault diagnosis and processing, and fault rule summary. The implementation schemes of key technologies such as data warehouse, data mining, 5G fusion positioning, and electronic fence are given to realize functions such as condition assessment, fault prediction, fault diagnosis, precise positioning, fence warning, and auxiliary decision-making, which can meet the needs of managers and operations people. Research will help to improve the efficiency and safety of equipment operation and maintenance, and have a reference significance for integrated intelligent operation and maintenance technology and the construction of modern passenger stations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Collaborative optimization of vehicle formation and timetable for modular autonomous bus considering demand uncertainty:A data-driven continuous approximate method

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transportation Research Part E: Logistics and transportation Review 2025年 200卷

作者： Yao, Zhihong Zhang, Qi Fu, Chengxin Wu, Yunxia Jiang, Yangsheng School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu Sichuan610031 China

The emerging modular autonomous vehicle (MAV) provides new opportunities to address the imbalance between supply and demand in the public transportation system. The continuous approximation (CA) model can efficiently solve the optimal time-varying headway of bus corridors, but the current CA model for MAV corridors does not consider the demand uncertainty, and its vehicle formation method still has limitations. To solve these gaps, this paper proposes collaborative optimization of vehicle formation and timetable for modular autonomous bus considering demand uncertainty based on a data-driven continuous approximate method. First, a time-dependent passenger flow disturbance parameter is introduced to capture the uncertainty demand, and the CA model is extended under demand uncertainty. Second, data-driven stochastic optimization methods (i.e., stochastic programming and distributed robust optimization) are developed to address the loss function term with the random passenger flow in the CA model. Then, based on the proposed CA model, a mixed integer linear programming (MILP) model is developed to obtain the optimal vehicle formation. Finally, two numerical experiments were conducted to verify the effectiveness and superiority of the proposed model. Results show that, (1) the proposed vehicle formation model achieves up to a 9.8% reduction in average total system cost compared to the benchmark model. (2) stochastic programming and distributed robust optimization do not significantly reduce the average system total cost when demand is uncertain, but can significantly improve the robustness of timetables and vehicle formation. Compared to the deterministic model, the proposed method achieves a reduction of over 90% in both the sample standard deviation and the interquartile range on the test dataset. In summary, the proposed method can provide theoretical support for modular bus operation and scheduling under passenger flow uncertainty. © 2025 Elsevier Ltd

关键词： Stochastic programming

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Improved Sparse Activity Detection Based on Optimized Measurement Matrix in mMTC 100

Improved Sparse Activity Detection Based on Optimized Measur...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wang, Jianyu Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

Device activity detection and channel estimation are central problems in massive Machine Type Communication (mMTC) scenarios. Given the sparse distribution of active devices, compressive sensing (CS) emerges as a viable solution at the gNB to mitigate interference stemming from non-orthogonal preamble sets. This paper proposes an algorithm for optimizing measurement matrix, thereby elevating the performance of signal recovery in CS. Within the proposed algorithm, a optimization model that integrates eigenvalue averaging and equiangular tight frame (ETF) theory is formulated, and an iterative algorithm is devised to approximate the optimal solution utilizing singular value decomposition (SVD). Further, a weight-based strategie is discussed with respect to the feasibility of the proposed algorithm, accompanied by the introduction of iteration termination condition. Experimental results corroborate the algorithm's swift convergence, underpinning the efficient production of high-performance matrices that refine the accuracy of joint device activity detection and channel estimation. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Coordinated Congestion Pricing for Bi-Modal Multi-Region Urban Networks

Coordinated Congestion Pricing for Bi-Modal Multi-Region Urb...

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International Conference on Intelligent transportation

作者： Xiangyu He Yu Zhao Yafei Liu Zhanbo Sun National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Transportation and Logistics Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Extensive studies have explored designing pricing schemes for single-mode or homogeneous networks. The development of bi-modal Macroscopic Fundamental Diagrams (3D-MFD) facilitates real-time coordinated control strategies for bi-modal multi-regional urban networks. This research explores the traffic dynamics of mixed networks with urban networks and expressways, proposing two 3D-MFD-based coordinated pricing schemes for bi-modal multi-region urban networks. The first scheme utilizes a Model Predictive Control (MPC) approach to integrating toll rates into the 3D-MFD-based optimization problem. The second scheme applies feedback control theory to simultaneously determine the dynamic price and estimate the value of Time (VOT) and trip length distribution of commuters. The proposed coordinated pricing schemes are further compared with uncoordinated pricing schemes. Numerical examples indicate that coordinated pricing by comprehensively considering the traffic flow and demand across different regions of the urban network achieves more effective congestion alleviation.

关键词： Sensitivity Pricing Real-time systems Feedback control Optimization Intelligent transportation systems Predictive control

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LightST: A Simplifying Spatio-Temporal Graph Neural Network for Traffic Flow Forecasting

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IEEE Transactions on big data 2025年

作者： Hu, Jie Zheng, Taichuan Peng, Lilan Teng, Fei Du, Shengdong Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology China

Traffic flow forecasting task plays an essential role in intelligent transportation systems. Accurately capturing the intricate spatio-temporal dependencies in traffic network signals is the core of precise prediction. Recently, a paradigm that models spatio-temporal dependencies through graph neural networks and time series models has become one of the most promising methods to solve this problem. However, existing methods still have limitations due to ineffectively modeling dynamic spatial dependencies and high time and space complexity. To address these issues, we propose a simplifying and powerful general spatio-temporal traffic flow forecasting model called LightST. Specifically, LightST first embeds temporal covariates and spatial position information to enhance the spatio-temporal modeling capabilities. Then, stacked temporal linear layers are introduced to capture temporal dependencies efficiently. Finally,we propose a concise adaptive spatio-temporal embedding graph convolution method to extract implicit spatial dependencies over time via dynamic graph convolution with adaptive spatio-temporal embedding graph generation. Extensive experiment results on four public traffic flow datasets demonstrate the superiority of our LightST concerning computational efficiency and prediction performance. © 2025 IEEE. All rights reserved,

关键词： Graph embeddings

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Efect of Pre‑stretch Strain at High Temperatures on the Formability of AZ31 Magnesium Alloy Sheets

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Acta Metallurgica Sinica(English Letters) 2023年第1期36卷 48-60页

作者： Yongqiao Li Lifei Wang Xiaohuan Pan Qiang Zhang Guangsheng Huang Bin Xing Weili Cheng Hongxia Wang Kwang Seon Shin College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China Chongqing Innovation Center of Industrial Big-Data Co.Ltd. National Engineering Laboratory for Industrial Big-Data Application TechnologyChongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National UniversitySeoul 881416Republic of Korea

High-temperature pre-stretching experiments were carried out on the AZ31 Mg alloy at 723 K with strain levels of 2.54%,6.48%,10.92%,and 19.2%to alter the microstructure and texture for improving room-temperature *** results showed that the strain-hardening coefcient increased,while the Lankford value *** addition,the Erichsen values of all pre-stretch sheets were enhanced compared with that of the as-received *** maximum Erichsen value increased from 2.38 mm for the as-received sample to 4.03 mm for the 10.92%-stretched sample,corresponding to an improvement of 69.32%.This improvement was mainly attributed to the gradual increase in grain size,and the(0001)basal texture was weakened due to the activated non-basal slip as the high-temperature pre-stretching strain levels *** visco-plastic self-consistent analysis was performed on the as-received and high-temperature pre-stretched *** confrmed the higher activity of the prismatic slip in 10.92%-stretched sample,leading to divergence and weakening of basal texture *** results in an augmentation of the Schmid factor under diferent slip ***,it can be concluded that high-temperature pre-stretching technology provided an efective method to enhance the formability of Mg alloy sheets.

关键词： High-temperature deformation Grain growth Magnesium alloys Texture Visco-plastic self-consistent modeling Formability

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Travel Time Delay Model of Mixed Traffic Flow Considering Different Dedicated Lane Management Policies for Cavs

SSRN

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SSRN 2024年

作者： Ren, Tingting Yao, Zhihong Jiang, Yangsheng School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu611756 China

To investigate the impact of dedicated lane management policies for connected automated vehicles (CAVs) on travel time delay, this paper proposes a general framework of the travel time delay model considering different dedicated lane management policies for CAVs. First, the car-following modes and their proportions in CAVs dedicated lanes and normal lanes with different dedicated lane management policies are analyzed, and the average time headways are further derived. Second, the travel time delay model with sufficiently high demand and low/medium demand is derived based on the point queue (P-Q) model, respectively. Then, the relevant properties of the travel time delay model with different dedicated lane management policies for CAVs are proposed and proved. Finally, multiple parameters, such as traffic saturation, the penetration rate of CAVs, and the arrival rate of vehicles, are adopted to discuss their impact on the optimal split ratio, traffic capacity, average reduced delay, optimal CAVs penetration rate interval, optimal CAVs penetration rate, and optimal lane management policy. Results show that (1) when the traffic saturation is less than or equal to 1, the optimal split ratio is always 0;when the traffic saturation is greater than 1, the optimal split ratio remains 0 first and then increases with the CAVs penetration rate;(2) the average reduced delay of (C, M) and (C, H) policies is always 0 when the traffic saturation is less than or equal to 1, and the average delay of (C, M) and (C, H) policies is minimum 0.63 times that of the mixed traffic flow without CAVs dedicated lanes;(3) the optimal CAVs penetration rate to reduce the average delay most for both (C, M) and (C, H) policies first increases to and then remains unchanged with the increase of traffic saturation. In particular, the optimal CAVs penetration rate for (C, M) and (C, H) policies is always 0.53 when the traffic saturation is more than or equal to 1.79 and 2.24, respectively;(4) only when

关键词： Time delay

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A human-in-the-loop method for pulmonary nodule detection in CT scans

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an 710072 China Australian Institute for Machine Learning The University of Adelaide Adelaide 5000 SA Australia

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung cancer. Although extensive well-performed methods have been proposed for this task, they suffer from the domain shift issue between training and test images. Unsupervised domain adaptation (UDA) methods provide a promising means to mitigate the domain variance;however, their performance is still limited since no target domain supervision is introduced. To make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains, we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target domain. Specifically, we first train a detector model on source domain, and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme (USSS) to find a few target domain samples worth annotating most and obtain their human annotations. Finally, the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint (ZZC) strategy. We evaluate our method on the Nodule Analysis 2016 (LUNA16) and TianChi datasets. Experimental results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain. © The Author(s) 2024.

关键词： Domain adaptation Human-in-the-loop Pulmonary nodule detection Semi-supervised learning

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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