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An envelope-based machine learning workflow for locating earthquakes in the southern Sichuan Basin

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Earthquake Research Advances 2024年第2期4卷 45-54页

作者： Kang Wang jie zhang Ji Zhang zhangyu Wang Ziyu Li School of Earth and Space Sciences University of Science and Technology of ChinaHefei230026China

The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations.However,the application of machine learning to earthquake location problems faces challenges in regions with limited available training data.To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets,we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc.We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time.The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results.In practical application,ENVloc is applied to several data intercepted at different starting points.We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location.We apply ENVloc to observed data acquired in the southern Sichuan Basin,China,between September 2018 and March 2019.The results show that the average difference with the catalog in latitude,longitude,depth,and origin time is 0.02,0.02,2 km,and 1.25 s,respectively.These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking,and can be used in earthquake monitoring in near-real time.

关键词： Waveform envelope Earthquake location Local seismicity Synthetic data Sparse stations

Mannitol inhibits the proliferation of neural stem cell by a p38 mitogen-activated protein kinase-dependent signaling pathway

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Chinese Journal of Traumatology 2024年第1期27卷 42-52页

作者： Hai-Zhen Duan Xin Zhou Quan Hu Meng-Long Liu Shu-Hong Wang Ji Zhang Xu-Heng jiang Tian-Xi zhang An-Yong Yu Department of Emergency Affiliated Hospital of Zunyi Medical UniversityZunyi563000Guizhou provinceChina Dazhou Vocational College of Chinese Medicine Dazhou635000Sichuan provinceChina Dachuan District Traditional Chinese Medicine Hospital Dazhou635000Sichuan provinceChina

Purpose:Mannitol is one of the first-line drugs for reducing cerebral edema through increasing the extracellular osmotic pressure.However,long-term administration of mannitol in the treatment of cerebral edema triggers damage to neurons and astrocytes.Given that neural stem cell(NSC)is a subpopulation of main regenerative cells in the central nervous system after injury,the effect of mannitol on NSC is still elusive.The present study aims to elucidate the role of mannitol in NSC proliferation.Methods:C57 mice were derived from the animal house of Zunyi Medical University.A total of 15 pregnant mice were employed for the purpose of isolating NSCs in this investigation.Initially,mouse primary NSCs were isolated from the embryonic cortex of mice and subsequently identified through immunofluorescence staining.In order to investigate the impact of mannitol on NSC proliferation,both cell counting kit-8 assays and neurospheres formation assays were conducted.Thein vitro effects of mannitol were examined at various doses and time points.In order to elucidate the role of Aquaporin 4(AQP4)in the suppressive effect of mannitol on NSC proliferation,various assays including reverse transcription polymerase chain reaction,western blotting,and immunocytochemistry were conducted on control and mannitol-treated groups.Additionally,the phosphorylated p38(p-p38)was examined to explore the potential mechanism underlying the inhibitory effect of mannitol on NSC proliferation.Finally,to further confirm the involvement of the p38 mitogen-activated protein kinase-dependent(MAPK)signaling pathway in the observed inhibition of NSC proliferation by mannitol,SB203580 was employed.All data were analyzed using SPSS 20.0 software(SPSS,Inc.,Chicago,IL).The statistical analysis among multiple comparisons was performed using one-way analysis of variance(ANOVA),followed by Turkey’’s post hoc test in case of the data following a normal distribution using a Shapiro-Wilk normality test.Comparisons between 2 groups were determined using Student’s t-te

关键词： Mannitol Cerebral edema Neural stem cell Proliferation AQP4 p38 MAPK signaling pathway

Five quinolizidine alkaloids with anti-tobacco mosaic virus activities from two species of Sophora

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Chinese Chemical Letters 2024年第6期35卷 380-384页

作者： Ji Zhang Tong zhang Qiao An Peng zhang Cai-Yan Tian Chun-Mao Yuan Ping Yi Zhan-Xing Hu Xiao-jiang Hao State Key Laboratory of Functions and Applications of Medicinal Plants Guizhou Medical UniversityGuiyang 550014China Natural Products Research Center of Guizhou Province Guiyang 550014China School of Pharmaceutical Sciences Guizhou Medical UniversityGuiyang 550025China School of Pharmacy Guizhou University of Traditional Chinese MedicineGuiyang 550025China State Key Laboratory of Phytochemistry and Plant Resources in West China Kunming Institute of BotanyChinese Academy of ScienceKunming 650201China Research Unit of Chemical Biology of Natural Anti-Virus Products Chinese Academy of Medical SciencesKunming 650201China

Three novel matrine-type alkaloids(1-3)and two unprecedented aloperine-type alkaloids(4 and 5)were isolated from the root of Sophora tonkinensis and the seeds of Sophora alopecuroides respectively.Notably,compound 1 possessed an unprecedented 6/5/6 tricyclic skeleton,while compounds 2 and 3 characterized by rare 6/6/5/6 tetracyclic system and 6/6/6/6/6 pentacyclic system respectively.Moreover,compound 4 possessed an unprecedented 6/7/6/6 tetracyclic core,and compound 5 characterized by rare 6/6/6/6tetracyclic skeleton.Their structures were elucidated by comprehensive spectroscopic data analysis and electronic circular dichroism(ECD)calculations.Biological tests indicated that compound 5 displayed significant anti-tobacco mosaic virus(TMV)activity compared with the positive control ningnanmycin.

关键词： Matrine-type alkaloids Aloperine-type alkaloids Sophora tonkinensis Sophora alopecuroides Anti-tobacco mosaic virus

Performance evaluation for multi-target tracking with temporal dimension specifics

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Chinese Journal of Aeronautics 2024年第2期37卷 446-458页

作者： Zhenzhen SU Hongbing ji Cong TIAN Yongquan zhang School of Computer Science and Technology Xidian UniversityXi’an 710071China School of Electronic Engineering Xidian UniversityXi’an 710071China

With the great development of Multi-Target Tracking(MTT)technologies,many MTT algorithms have been proposed with their own advantages and disadvantages.Due to the fact that requirements to MTT algorithms vary from the application scenarios,performance evaluation is significant to select an appropriate MTT algorithm for the specific application scenario.In this paper,we propose a performance evaluation method on the sets of trajectories with temporal dimension specifics to compare the estimated trajectories with the true trajectories.The proposed method evaluates the estimate results of an MTT algorithm in terms of tracking accuracy,continuity and clarity.Furthermore,its computation is based on a multi-dimensional assignment problem,which is formulated as a computable form using linear programming.To enhance the influence of recent estimated states of the trajectories in the evaluation,an attention function is used to reweight the trajectory errors at different time steps.Finally,simulation results show that the proposed performance evaluation method is able to evaluate many aspects of the MTT algorithms.These evaluations are worthy for selecting suitable MTT algorithms in different application scenarios.

关键词： Multi-target tracking Temporal dimension specifics Performance evaluation Random finite sets Linear programming

Compound Product of Hypermatrices

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Journal of Systems Science & Complexity 2024年第1期37卷 169-183页

作者： CHENG Daizhan MENG Min zhang Xiao ji Zhengping Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Department of Control Science and Engineering School of Electronics and Information EngineeringTongji UniversityShanghai 201210China National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100149China.

The semi-tensor product(STP)of matrices is generalized to multidimensional arrays,called the compound product of hypermatrices.The product is first defined for three-dimensional hypermatrices with compatible orders and then extended to general cases.Three different types of hyperdeterminants are introduced and certain properties are revealed.The Lie groups and Lie algebras corresponding to the hypermatrix products are constructed.Finally,these results are applied to dynamical systems.

关键词： Compound semi-tensor product hyperdeterminants hypereigenvalues and hypereigenvectors hypergeneral linear groups hypermatrices.

Gas–solid reactor optimization based on EMMS-DPM simulation and machine learning

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Particuology 2024年第6期89卷 131-143页

作者： Haolei zhang Aiqi Zhu ji Xu Wei Ge State Key Laboratory of Mesoscience and Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190ChinaFormerly State Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China Innovation Academy for Green Manufacture Chinese Academy of SciencesBeijing 100190China

Design,scaling-up,and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience,which is usually time-consuming,high cost,and high risk.Although numerical simulation can reproduce high resolution details of hydrodynamics,thermal transfer,and reaction process in reactors,it is still challenging for industrial reactors due to huge computational cost.In this study,by combining the numerical simulation and artificial intelligence(AI)technology of machine learning(ML),a method is proposed to efficiently predict and optimize the performance of industrial reactors.A gas–solid fluidization reactor for the methanol to olefins process is taken as an example.1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model,and thus,the reactor performance data set is constructed.To develop an efficient reactor performance prediction model influenced by multiple factors,the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique,which has better performance than the methods based on the artificial neural network.Furthermore,the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization problems.Results show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield,while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure drop.The two objectives are thus conflicting,and the practical operations need to be compromised under different circumstance.

关键词： Discrete particle method Artificial intelligence Machine learning Particle swarm optimization Industrial reactor optimization

3D coarse-grained DPM simulation of the MIP reaction-regeneration loop

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Particuology 2024年第6期89卷 57-66页

作者： Yanhao Ye Shuai zhang ji Xu Wei Ge State Key Laboratory of Mesoscience and Engineering(formerly State Key Laboratory of Multiphase Complex Systems) Institute of Process EngineeringChinese Academy of SciencesBeijing100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing100049China

The reactor-regenerator loop is the core facility of the maximizing iso-paraffin(MIP)process.Although the discrete particle method(DPM)simulation can provide detailed information at the particle scale,it has been unable to simulate such a complex loop system due to limitations of coarse-grained(CG)models,computing software,and hardware.In this study,a newly proposed soft-shell CG-DPM model with a CG ratio of up to 800 is used to simulate a 3.5 Mt/a industrial-scale MIP reactor-regenerator loop.The solid fraction distribution obtained is found to agree well with in-situ measurements.Hydrodynamic properties including the distribution of solid fraction,gas and solid velocity,standard derivation of solid fraction with time,temporal distribution of the flow field,and particle residence time distribution are measured in the simulation,which are meaningful to better design and operate such systems in the future.

关键词： Discrete particle method Coarse-grain Maximizing iso-paraffins Full-loop Reaction-regeneration system

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

Polyoxometalates for continuous power generation by atmospheric humidity

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Nano Research 2024年第3期17卷 1875-1885页

作者： Tuo ji Weilin Chen Zhenhui Kang Liming zhang Key Laboratory of Polyoxometalate and Reticular Material Chemistry of Ministry of Education Northeast Normal UniversityChangchun 130024China Institute of Functional Nano&Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&Devices DepartmentSoochow UniversitySuzhou 215123China Macao Institute of Materials Science and Engineering Macao University of Science and TechnologyTaipaMacao 999078China Institute of Functional State Key Laboratory of Luminescence and Applications Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China

Atmospheric humidity is a sustainable low-value energy widely existing in natural environment,which is a promising candidate to solve the noncontinuous and low efficiency of low-value energy power generation.Here the mono-substituted Dawson-type polyoxometalates are constructed to be highly dispersed organic ammonium-polyoxoanion clusters and are assembled into thin films power generators with micropores,working in atmospheric humidity.The optimal polyoxometalates generator with the thickness of 7.2μm and the area of 0.36 cm^(2) produces a voltage of 0.68 V and a current density of 19.5μA·cm^(-2) under simulated natural environment,and works continuously and stably under almost all-natural environments(humidity 10%–90%).The highly dispersed polyoxometalate nanoclusters can form microporous in polyoxometalate films to effectively absorb atmospheric humidity and spontaneously form distribution gradient of water,which is the structural basis of power generation.The continuous power generation may be maintained by the effective adsorption and utilization of H_(2)O,the huge electrostatic field of organic ammonium-polyoxoanion clusters,and the reasonably designed polyoxometalates containing inorganic small ions with high mobility.It is the first humidity generator designed with polyoxometalates,which may provide a new research direction for polyoxometalates in sustainable utilization of low-value energy.

关键词： atmospheric humidity continuous power generations nanomaterials polyoxoanions polyoxometalates