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Removal of Cr(Ⅵ)by glutaraldehyde-crosslinked chitosan encapsulating microscale zero-valent iron:Synthesis,mechanism,and longevity

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Journal of Environmental Sciences 2024年第8期142卷 115-128页

作者： Yijun Duan Fang Liu Xiang Liu Miao Li School of Environment Tsinghua UniversityBeijing 100084China transportation institute of Inner Mongolia University Hohhot 010070China

Microscale zero-valent iron(mZVI)has shown great potential for groundwater Cr(Ⅵ)remediation.However,low Cr(Ⅵ)removal capacity caused by passivation restricted the wide use of mZVI.We prepared mZVI/GCS by encapsulating mZVI in a porous glutaraldehydecrosslinked chitosan matrix,and the formation of the passivation layer was alleviated by reducing the contact between zero-valent iron particles.The average pore diameter of mZVI/GCS was 8.775 nm,which confirmed the mesoporous characteristic of this material.Results of batch experiments demonstrated that mZVI/GCS exhibited high Cr(Ⅵ)removal efficiency in a wide range of pH(2-10)and temperature(5-35℃).Common groundwater coexisting ions slightly affected m ZVI/GCS.The material showed great reusability,and the average Cr(Ⅵ)removal efficiency was 90.41%during eight cycles.In this study,we also conducted kinetics and isotherms analysis.Pseudo-second-order model was the most matched kinetics model.The Cr(Ⅵ)adsorption process was fitted by both Langmuir and Freundlich isotherms models,and the maximum Langmuir adsorption capacity of m ZVI/GCS reached243.63 mg/g,which is higher than the adsorption capacities of materials reported in most of the previous studies.Notably,the column capacity for Cr(Ⅵ)removal of a mZVI/GCS-packed column was 6.4 times higher than that of a mZVI-packed column in a 50-day experiment.Therefore,mZVI/GCS with a porous structure effectively relieved passivation problems of mZVI and showed practical application prospects as groundwater Cr(Ⅵ)remediation material with practical application prospects.

关键词： Chitosan Glutaraldehyde mZVI Cr(Ⅵ) Groundwater

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Nickel-Catalyzed Stereoselective Migratory Carboboration of 1,4-Cyclohexadiene

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Chinese Journal of Chemistry 2024年第4期42卷 356-362页

作者： Yaoyu Ren Lujin Wang Chao Ding Yangyang Li Guoyin Yin The institute for Advanced studies Wuhan UniversityWuhanHubei430072 China

Multi-substituted cyclohexanes play a crucial role as scaffolds in bioactive compounds.While significant progress has been made in synthesizing substituted cyclohexanes,methods for the stereoselective assembly of 1,3-disubstituted cyclohexanes remain scarce.This study presents a novel approach involving nickel catalysis to achieve stereoselective carboboration of 1,4-cyclohexadiene.This innovative process allows for the simultaneous introduction of a boron group and an aryl or an alkyl fragment into the 1,4-cyclohexadiene framework under mild conditions,with exclusive regioselectivity and excellent cis configuration.The resulting products feature a double carbon bond and the incorporation of the boron group,offering significant potential for subsequent transformations and downstream applications.

关键词： 1,4-Cyclohexadiene Carboboration Nickel Chain-walking Stereoselectivity Difunctionalization

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Creep damage evolution process of asphalt binder based on viscoelastic characteristics

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Journal of Traffic and transportation Engineering(English Edition) 2024年第3期11卷 497-506页

作者： Xiaofei Qi Shuang Tian Liyan Shan Shuang Liu Yajie Wang School of transportation Science and Engineering Harbin Institute of Technology College of Water Conservancy & Architectural Engineering Shihezi University

The creep damage evolution of asphalt binder plays a significant role in investigating the formation mechanism of rutting. The rutting is the common distress at high temperatures for asphalt pavements. However, the reliability of existing creep damage parameters is under questioned, and these parameters cannot accurately illustrate the change of intrinsic microstructure for asphalt binder. In this paper, a new test protocol was given access to study the evolution of viscoelastic parameters during creep damage. It was completed by inserting the frequency sweep during creep test. The frequency sweep curve clusters were fitted by the generalized Kelvin-Voigt model for obtaining the change law of model parameters. Based on the change law and sensitivity analysis of model parameters,（E2+ E3）/2 was proposed as the creep damage variable. According to the curve of（E2+ E3）/2 versus loading time, two stages during the creep test could be identified: an approximate constant value in phase I and a linear decrease in phase II. Intrinsic differences about creep property of binders could be determined by this new proposed parameter. Above results not only ensure better understanding of the creep damage mechanism of binders, but also lay the theoretical foundation on predicting the anti-rutting performance of binders.

关键词： Asphalt binder Multi-stage creep test Damage variable Evolution law

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Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning

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Journal of Traffic and transportation Engineering(English Edition) 2024年第1期11卷 28-40页

作者： Mengqi Lyu Yanjie Ji Chenchen Kuai Shuichao Zhang School of transportation Southeast UniversityNanjing 211189China Shandong Provincial Communications Planning and Design institute Group Co. Ltd.Jinan 250101China School of Civil and transportation Engineering Ningbo University of TechnologyNingbo 315211China

Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy challenging.Therefore,this paper proposes a deep learning model for predicting block-level parking occupancy.First,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature selection.The results show that different types of POI data should consider different buffer radii.Then based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking occupancy.The results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM.When there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.

关键词： Smart parking Parking occupancy Short-term prediction Long short-term memory Boruta

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Snow and regolith albedo variations using CRISM data at McMurdo crater,Mars

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Earth and Planetary Physics 2024年第2期8卷 338-355页

作者： Sehajpal Singh Deepak Singh Centre of studies in Resources Engineering Indian Institute of Technology BombayMumbaiIndia

The cryosphere component provides the most reliable and insightful indications of any planet’s climate dynamics.Using data from the Compact Reconnaissance Imaging Spectrometer for Mars(CRISM),we develop a novel approach to determining the broadband Visible and Near Infrared(VNIR)albedo of the Martian surface.This study focuses on albedo changes in the McMurdo crater,part of Mars’s south polar layer deposits.We compare seasonal and interannual variations of the McMurdo surface albedo before,during,and after the Global Dust Storm(GDS)of Martian Year(MY)34.As the seasons progressed from spring to summer,the mean albedo in MY 32 and 34 plunged by over 40%,by about 35%in MY 33,and by slightly more than 30%in MY 35.Compared interannually,however,mean albedo values within both seasons(spring and summer)exhibited no significant differences in those same years.Notably,interannual albedo difference maps reveal albedo variation of more than±0.3 in certain regions of the crater.Considering only snow-covered pixels,interannual albedo differences suggest that Mars dust had a pervasive impact on Mars’s cryosphere.Variations in maximum and minimum albedo values as high as 0.5 were observed,depending upon differences in the dust levels in Martian snow/ice.The maximum and the minimum snow albedo values were lowest in MY 34,indicating the effect of the intense dust storm event that year.The average snow albedo decreased from 0.45 in MY 32 to 0.40 in MY 33 and to 0.33 in MY 34,and then rose back to 0.40 in MY 35.This trajectory suggests a temporary deposition of dust,partially reversed after the GDS by self-cleaning mechanisms(local aeolian process and CO_(2)sublimation/deposition cycle).

关键词： Mars Martian ice albedo dust storm Mars surface Martian climate

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Interface failure of segmental tunnel lining strengthened with steel plates based on fracture mechanics

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 137-149页

作者： Yazhen SUN Yang YU Jinchang WANG Longyan WANG School of transportation and Geomatics Engineering Shenyang Jianzhu UniversityShenyang 110168China School of Civil Engineering Shenyang Jianzhu UniversityShenyang 110168China institute of transportation Engineering Zhejiang UniversityHangzhou 310058China Center for Balance Architecture Zhejiang UniversityHangzhou310058China

Segmental tunnel lining strengthened with steel plates is widely used worldwide to provide a permanent strengthening method.Most existing studies assume an ideal steel-concrete interface,ignoring discontinuous deformation characteristics,making it difficult to accurately analyze the strengthened structure’s failure mechanism.In this study,interfacial fracture mechanics of composite material was applied to the segmental tunnel lining strengthened with steel plates,and a numerical three-dimensional solid nonlinear model of the lining structure was established,combining the extended finite element method with a cohesive-zone model to account for the discontinuous deformation characteristics of the interface.The results accurately describe the crack propagation process,and are verified by full-scale testing.Next,dynamic simulations based on the calibrated model were conducted to analyze the sliding failure and cracking of the steel-concrete interface.Lastly,detailed location of the interface bonding failure are further verified by model test.The results show that,the cracking failure and bond failure of the interface are the decisive factors determining the instability and failure of the strengthened structure.The proposed numerical analysis is a major step forward in revealing the interface failure mechanism of strengthened composite material structures.

关键词： segmental tunnel lining steel plate strengthening connecting interface cohesive-zone model extended finite element method

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Insights and reviews on battery lifetime prediction from research to practice

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Journal of Energy Chemistry 2024年第7期94卷 716-739页

作者： Xudong Qu Dapai Shi Jingyuan Zhao Manh-Kien Tran Zhenghong Wang Michael Fowler Yubo Lian Andrew FBurke Hubei Longzhong Laboratory Hubei University of Arts and ScienceXiangyang 441000HubeiChina institute of transportation studies University of California DavisDavisCA 95616USA Department of Chemical Engineering University of WaterlooWaterlooOntario N2L 3G1Canada BYD Automotive Engineering Research institute Shenzhen 518118GuangdongChina

The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric Al and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.

关键词： Batteries Lifetime Health Machine learning Deep learning Field Real world

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Intelligent fault diagnosis methods toward gas turbine: A review

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Chinese Journal of Aeronautics 2024年第4期37卷 93-120页

作者： Xiaofeng LIU Yingjie CHEN Liuqi XIONG Jianhua WANG Chenshuang LUO Liming ZHANG Kehuan WANG School of transportation Science and Engineering Beihang UniversityBeijing 100191China Systems Engineering Research institute China State Shipbuilding CorporationBeijing 100094China

Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

关键词： Fault diagnosis Health management Gas turbine Artificial intelligence Intelligent diagnosis method

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Research on virtual coupling technology in rail transit train collision protection

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transportation Safety and Environment 2024年第1期6卷 40-46页

作者： Gaosong Li Jinbai Zou Weijie Ma Meng Lan School of Railway transportation Shanghai Institute of TechnologyShanghai 201418China Shanghai Metro First Operation Co. LtdShanghai 201418China

In the research on the new generation of intelligent rail transit system,the prevention of train collision accidents is still a focus.Virtual coupling technology controls the running speed and interval distance of two or more trains cooperatively through Trainto-Train(T2T)direct communication technology.The aim is to form a coordinated logical system and jointly complete the train control and dispatching.In this paper,a virtual coupling train formation control method based on the model predictive control(MPC)framework is proposed.From the establishment of the virtual coupling system,the construction of an MPC model and decoupling method,how to integrate virtual coupling technology into the rail transit train collision protection is studied.The safety and reliability of the system are calculated through fault tree and the method of constructing reliability function.The research shows that if virtual coupling technology is used for train collision protection,the overall safety and reliability of the train control system is improved.

关键词： virtual coupling model predictive control collision protection

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BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

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Computers, Materials & Continua 2024年第3期78卷 4489-4507页

作者： Yunhe Wang Yuxin Xia Shuai Liu College of Information Science and Engineering Institute of Interdisciplinary StudiesHunan Normal UniversityChangsha410081China School of Educational Sciences Institute of Interdisciplinary StudiesHunan Normal UniversityChangsha410081China

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

关键词： Action recognition deep learning GCN behavior dependence context clue self-attention

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