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Carbon sink response of terrestrial vegetation ecosystems in the Yangtze River Delta and its driving mechanism

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Journal of Geographical Sciences 2024年第1期34卷 112-130页

作者： ZHAO Haixia FAN Jinding gu Binjie CHEN Yijiang Nanjing Institute of Geography and Limnology CASNanjing 210008China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou 215009JiangsuChina Nanjing College University of Chinese Academy of SciencesNanjing 211135China School of Agricultural and Food Science The University of QueenslandQueensland 4072Australia

The carbon cycle of terrestrial ecosystems is influenced by global climate change and human *** remote sensing data and land cover products,the spatio-temporal variation characteristics and trends of NEP in the Yangtze River Delta from 2000 to 2020 were analyzed based on the soil respiration *** driving influences of ecosystem structure evolution,temperature,rainfall,and human activities on NEP were *** results show that the NEP shows an overall distribution pattern of high in the southeast and low in the *** area of carbon sinks is larger than that of the carbon *** spatial heterogeneity is *** change trend is basically unchanged or significantly *** future change trend in most areas will be continuous *** with temperature,NEP are more sensitive to *** positive influence of human activities on NEP is mainly observed in north-central Anhui and northern Jiangsu coastal areas,while the negative influence is mainly found in highly urbanized *** the process of ecosystem structure,the contribution of unchanged areas to NEP change is greater than that of changed areas.

关键词： carbon sinks terrestrial vegetation ecosystems driving mechanisms climate change human activities ecosystem structure Yangtze River Delta

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Cell Consistency Evaluation Method Based on Multiple Unsupervised Learning Algorithms

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Big Data Mining and Analytics 2024年第1期7卷 42-54页

作者： Jiang Chang Xianglong gu Jieyun Wu Debu Zhang Stellantis China Technology Center Shanghai 200233China

Unsupervised learning algorithms can effectively solve sample *** address battery consistency anomalies in new energy vehicles,we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three vehicles using charging fragment data from actual operating *** extract battery-related features,such as the mean of maximum difference,standard deviation,and entropy of batteries and then apply principal component analysis to reduce the dimensionality and record the amount of preserved *** then build models through a collection of unsupervised learning algorithms for the anomaly detection of cell consistency *** also determine whether unsupervised and supervised learning algorithms can address the battery consistency problem and document the parameter tuning *** addition,we compare the prediction effectiveness of charging and discharging features modeled individually and in combination,determine the choice of charging and discharging features to be modeled in combination,and visualize the multidimensional data for fault *** results show that the unsupervised learning algorithm is effective in visualizing and predicting vehicle core conformance faults,and can accurately predict faults in real ***“distance+boxplot”algorithm shows the best performance with a prediction accuracy of 80%,a recall rate of 100%,and an F1 of *** proposed approach can be applied to monitor battery consistency faults in real time and reduce the possibility of disasters arising from consistency faults.

关键词： battery consistency charging segment data unsupervised learning

A hybrid attention model based on first-order statistical features for smoke recognition

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Science China(Technological Sciences) 2024年第3期67卷 809-822页

作者： guO Nan LIU JiaHui DI KeXin gu Ke QIAO JunFei Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Artificial Intelligence InstituteBeijing 100124China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to *** tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the propose

关键词： hybrid attention first-order pooling smoke and fire detection deep convolutional neural networks

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Simulation of Underground Reservoir Stability of Pumped Storage Power Station Based on Fluid-Structure Coupling

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1381-1399页

作者： Peng Qiao Shuangshuang Lan Hongbiao gu Zhengtan Mao Faculty of Architecture Civil and Transportation EngineeringBeijing University of TechnologyBeijing100020China College of Transportation Engineering Nanjing Tech UniversityNanjing210000China

Based on global initiatives such as the clean energy transition and the development of renewable energy,the pumped storage power station has become a new and significant way of energy storage and regulation,and its construction environment is more complex than that of a traditional *** particular,the stability of the rock strata in the underground reservoirs is affected by the seepage pressure and rock stress,which presents some challenges in achieving engineering safety and *** the advantages of the numerical simulation method in dealing deal with nonlinear problems in engineering stability,in this study,the stability of the underground reservoir of the Shidangshan(SDS)pumped storage power station was numerically calculated and quantitatively analyzed based on fluid-structure coupling theory,providing an important reference for the safe operation and management of the underground ***,using the COMSOL software,a suitablemechanicalmodel was created in accordance with the geological structure and project characteristics of the underground ***,the characteristics of the stress field,displacement field,and seepage field after excavation of the underground reservoir were simulated in light of the seepage effect of groundwater on the nearby rock of the underground ***,based on the construction specifications and Molar-Coulomb criterion,a thorough evaluation of the stability of the underground reservoir was performed through simulation of the filling and discharge conditions and anti-seepage strengthening *** findings demonstrate that the numerical simulation results have a certain level of reliability and are in accordance with the stress measured in the project *** underground reservoir excavation resulted in a maximum displacement value of the rock mass around the caverns of 3.56 mm in a typical section,and the safety coefficient of the parts,as determined using the Molar-Coulomb criterion,was highe

关键词： Underground reservoir fluid-structure coupling numerical simulation pumped storage power station filling and discharge

Structural energy storage composites based on modified carbon fiber electrode with metal-organic frame enhancing layered double hydroxide

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Nano Research 2024年第3期17卷 1552-1563页

作者： Jinrui Ye Zhongbao Wang Qin Lei Lei Sun Jinfeng gu School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Tangshan Research Institute Beijing Institute of TechnologyTangshan 063000China School of Materials Science and Engineering Shenyang Jianzhu UniversityShenyang 110168China Department of Stomatology PLA Strategic Support Force Characteristic Medical CenterBeijing 100101China Beijing Electromechanical Engineering Research Institute Beijing 100074China

Structural energy storage composites present advantages in simultaneously achieving structural strength and electrochemical *** of carbon fiber electrodes and resin structural electrolytes in energy storage composite poses challenges in maintaining good mechanical and electrochemical properties at reasonable cost and ***,we report a simple method to fabricate structural supercapacitor using carbon fiber electrodes(modified by Ni-layered double hydroxide(Ni-LDH)and in-situ growth of Co-metal-organic framework(Co-MOF)in a two-step process denoted as Co-MOF/Ni-LDH@CF)and bicontinuous-phase epoxy resin-based structural ***-MOF/Ni-LDH@CF as electrode material exhibits improved specific capacity(42.45 F·g^(-1))and cycle performance(93.3%capacity retention after 1000 cycles)in a three-electrode *** bicontinuous-phase epoxy resin-based structural electrolyte exhibits an ionic conductivity of 3.27×10^(-4) S·cm^(-1).The fabricated Co-MOF/Ni-LDH@CF/SPE-50 structural supercapacitor has an energy density of 3.21 Wh·kg^(-1) at a power density of 42.25 W·kg^(-1),whilst maintaining tensile strength and modulus of 334.6 MPa and 25.2 *** results show practical potential of employing modified commercial carbon fiber electrodes and epoxy resin-based structural electrolytes in structural energy storage applications.

关键词： carbon fiber electrode structural electrolyte structural energy storage composites supercapacitor

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

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Computers, Materials & Continua 2024年第6期79卷 3877-3896页

作者： Mingyuan Li Yuanli gu Qingqiao geng Hongru Yu School of Traffic and Transportation Beijing Jiaotong UniversityBeijing100044China Transport Planning and Research Institute Ministry of TransportBeijing100028China

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing *** model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel *** CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and *** on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction *** this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban *** combined prediction model is tested on a taxi travel demand dataset in a certain area of *** results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 ***,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed *** results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

关键词： Urban transport taxi travel demand prediction CEEMDAN-ConvLSTM modal components

Highly active and durable core-shell electrocatalysts for proton exchange membrane fuel cells

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Nano Research 2024年第10期17卷 8772-8784页

作者： Hsiwen Wu Fei Xiao Jing Wang Meng gu Minhua Shao Department of Chemical and Biological Engineering The Hong Kong University of Science and TechnologyClear Water BayHong KongChina Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Fok Ying Tung Research Institute The Hong Kong University of Science and TechnologyGuangzhou 511458China Energy Institute and Chinese National Engineering Research Center for Control&Treatment of Heavy Metal Pollution The Hong Kong University of Science and TechnologyClear Water BayHong KongChina

This work presents simple post-treatment methods to selectively and partially remove the Pd core of Pd-Pt core–shell(Pt@Pd/C)*** proton exchange membrane fuel cell with the post-treated Pt@Pd/C cathode(Pt loading:0.10 mg·cm^(-2))delivers an impressive peak power density of 1.2 W·cm^(-2).The partial removal of Pd core endows an ultrahigh oxygen reduction reaction(ORR)mass activity of 0.32 A·g_(PGM)-1 when normalized to the platinum group metal(PGM)mass,or equivalently 0.55 A·mg_(Pt)^(-1) at 0.9 V measured in a fuel *** post-treatment thickens the Pt shells and mitigates the Pd dissolution during potential *** a result,the post-treated core-shell catalyst demonstrates superior durability in ORR mass activity and polarization power density retention than untreated core-shell catalyst and benchmark Pt/***-situ inductively coupled plasmamass spectrometry(ICP-MS)results highlight that the amount of dissolved Pd in post-treated core–shell catalyst is 17-times lower than that of the untreated *** findings highlight the importance of structural tuning of catalysts in enhancing their mass activity and durability.

关键词： core-shell catalyst platinum monolayer proton exchange membrane fuel cell oxygen reduction reaction durability

Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model

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Chinese Journal of Integrative Medicine 2024年第2期30卷 163-170页

作者： gu Tian-yu YAN Zhuang-zhi JIANG Jie-hui School of Communication&Information Engineering Shanghai UniversityShanghai(200444)China School of Life Science Shanghai UniversityShanghai(200444)China

Objective:To develop a multimodal deep-learning model for classifying Chinese medicine constitution,i.e.,the balanced and unbalanced constitutions,based on inspection of tongue and face images,pulse waves from palpation,and health information from a total of 540 ***:This study data consisted of tongue and face images,pulse waves obtained by palpation,and health information,including personal information,life habits,medical history,and current symptoms,from 540 subjects(202 males and 338 females).Convolutional neural networks,recurrent neural networks,and fully connected neural networks were used to extract deep features from the *** fusion and decision fusion models were constructed for the multimodal ***:The optimal models for tongue and face images,pulse waves and health information were ResNet18,Gate Recurrent Unit,and entity embedding,*** fusion was superior to decision *** multimodal analysis revealed that multimodal data compensated for the loss of information from a single mode,resulting in improved classification ***:Multimodal data fusion can supplement single model information and improve classification *** research underscores the effectiveness of multimodal deep learning technology to identify body constitution for modernizing and improving the intelligent application of Chinese medicine.

关键词： Chinese medicine constitution classification multimodal deep learning tongue image face image pulsewave health information

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Dynamic Simulation of Distribution Networks Using Multi-stage Discontinuous Galerkin Method

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 185-196页

作者： Ruizhi Yu Wei gu Weiqi Liu Peixin Li Wei Liu School of Electrical Engineering Southeast UniversityNanjing 210096China. the School of Electrical Engineering Southeast UniversityNanjing 210096China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

Dynamic simulation plays a fundamental role in security evaluation of distribution networks(DNs).However,the strong stiffness and non-linearity of distributed generation(DG)models in DNs bring about burdensome computation and noteworthy instability on traditional methods which hampers the rapid response of simulation ***,a novel L-stable approximate analytical method with high accuracy is proposed to handle these *** method referred to as multistage discontinuous Galerkin method(MDGM),first derives approximate analytical solutions(AASs)of state variables which are explicit symbolic expressions concerning system ***,in each time window,it substitutes values for symbolic variables and trajectories of state variables are obtained *** paper applies MDGM to DG models to derive ***-truncation-error-based variable step size strategy is also developed to further improve simulation *** addition,this paper establishes detailed MDGM-based dynamic simulation *** case studies on a numerical problem,a modified 33-bus system and a practical large-scale DN,it can be seen that proposed method demonstrates fast and dependable performance compared with the traditional trapezoidal method.

关键词： Approximate analytical method distribution network distributed generation dynamic simulation multi-stage discontinuous Galerkin method variable step size