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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第1期34卷 55-61页

作者： Xing Xiaohong Guo Mingsen Chen Jialin wang jiechen NanHang Jincheng College Nanjing 211156P.R.China The State Key Laboratory of Mechanics and Control of the Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China

Piezoelectric friction-inertial motor is known for its promise for a long-range and high-resolution motion.The movement of the slider/rotor of the motor is achieved by stick-slip effect.We report a relaxor-based-ferroelectric-single-crystal cymbal actuator and a miniature piezoelectric friction-inertial linear motor(abbreviated as PFILM)fabricated with the cymbal actuator.The cymbal actuator is fabricated with a 10 mm diameter disk of 0.70Pb(Mg_(1/3)Nb_(2/3))O_3-0.30PbTiO_3 single crystal.The displacement of the cymbal actuator increases almost proportionally from 0to 23μm with driving voltage up to 500 V,and the minimal hysteresis is observed.The cymbalPFILM with 20 mm motion range works under driving voltage frequency of ca.100 Hz to ca.5kHz,the fastest speed is obtained with 3.5kHz and the no-load speed is 14mm/s and the maximum thrust force is 98 mN.Compared with a PFILM based on multilayer piezoelectric ceramic,the proposed motor has a larger stroke under DC/quasistatic input voltage in fine motion mode,but a smaller driving force in long-travel mode due to lower resonance frequency.

关键词： piezoelectric single crystal piezoelectric ceramic cymbal displacement amplification mechanism friction-inertial motor

Spatial Accessibility to Shopping Malls in Nanjing,China:Comparative Analysis with Multiple Transportation Modes

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Chinese Geographical Science 2020年第4期30卷 710-724页

作者： LI Ang CHEN Jie QIAN Tianlu ZHANG Wenhan wang jiechen Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China School of Architecture and Surveying Engineering Datong UniversityDatong 037009China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China

This study attempts to evaluate the rationality of the spatial layout of shopping malls and identify the urban area with poor shopping mall accessibility,as well as to provide a reference and assist decision-making for planning.Using the case of Nanjing,China,this study developed a method to evaluate the accessibility of shopping malls by three transport modes(car traffic,public transit,and bicycle).Specially,we divide the urban space into a regular hexagonal grid and harvest the total travel time from each of 7204 hexagon centroids to each of 129 shopping malls using the Baidu Internet map.The door-to-door travel time approach is used to evaluate all travel stages(walking,waiting,transfer,and transportation)based on travel time calculations.We further divide the shopping malls into two levels(super-regional and regional)based on the Dianping App’s information and develop the indicator of accessibility to shopping malls:the number of shopping malls within tolerance time thresholds and apply the closest facility and cumulative opportunities methods to measure accessibility scores.The results show that the accessibility estimations vary greatly with transport modes.The accessibility of shopping malls presents a concentric ring trend centered on the city center under the car traffic and bicycle modes.And public transit accessibility tends to axially extend due to the topology of bus routes and metro lines.In particular,we observe that the accessibility of shopping malls in Nanjing has an uneven spatial distribution pattern,with high accessibility values in the central urban areas and lots of underserved areas in urban fringe regions.Based on the accessibility measurements,we finally map the poor accessibility area and propose corresponding implications for urban planning.

关键词： spatial accessibility shopping mall access transport mode door-to-door travel time internet maps

Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model： A Case Study in Nanjing, China

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Chinese Geographical Science 2018年第6期28卷 1048-1060页

作者： wang jiechen WU Jiayi NI Jianhua CHEN Jie XI Changbai Department of Geographic Information Science Nanjing University Nanjing 210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing 210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023 China

With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.

关键词：道路等级道路交通聚类模型特征时间特征和城市南京案例

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基于人类活动视角的中国陆域荒野地评估方法

生态学报 2024年第9期44卷 3844-3854页

作者：李云昊余博洋吴朝宁李思青王结臣南京大学地理与海洋科学学院自然资源部国土卫星遥感应用重点实验室江苏省地理信息技术重点实验室南京210023 江苏省地理信息资源开发与利用协同创新中心南京210023

随着人类活动逐渐增加,自然生境向人工基质的转换已成为全球野生动物生存的严重威胁,准确识别荒野地的空间范围对生态治理和生物多样性保护具有重要意义。当前,利用较高精度的新型数据,考虑区域尺度上的地理特征构建指标模型,并识别国... 详细信息

随着人类活动逐渐增加,自然生境向人工基质的转换已成为全球野生动物生存的严重威胁,准确识别荒野地的空间范围对生态治理和生物多样性保护具有重要意义。当前,利用较高精度的新型数据,考虑区域尺度上的地理特征构建指标模型,并识别国家尺度荒野地的实践还较少。从人类活动对自然影响的视角出发构建荒野地识别模型,首先考虑微观视角下人类活动对复杂地理环境的响应特征,构建多要素综合的人类非持续性活动强度计算模型;引入新型数据源,构建空间特征提取算法并建立人工环境和光污染影响指标,明确人类长期活动区;刻画可能的荒野地空间范围,并定量评价其荒野程度。对中国陆域荒野地的识别结果表明:(1)基于人类活动视角构建的荒野地识别模型可有效模拟中国陆域范围内的荒野度分布,其大致呈现“西高东低”的空间格局;(2)中国荒野地占中国陆域面积的22.47%,主要分布在大小兴安岭、青藏高原、内蒙古高原、塔里木盆地等地区;(3)结果与基于人类足迹法的荒野地数据对比,重合率超过80%,表现出良好的识别能力,且为部分区域提供了具有参考性的识别结果。

关键词：荒野地人类活动可达性光污染

人类活动胁迫下野生动物生境变迁研究——以中国仰鼻猴为例

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生态学报 2024年第13期44卷 5735-5745页

作者：秦淑洁钱天陆李思青吴朝宁王结臣南京大学地理与海洋科学学院自然资源部国土卫星遥感应用重点实验室江苏省地理信息技术重点实验室南京210023 南京邮电大学物联网学院南京210023 江苏省地理信息资源开发与利用协同创新中心南京210023

人类活动是威胁当今世界生物多样性的重要因素之一,越来越多的“难民物种”受人类活动干扰被限制在了次优的栖息地。以我国受威胁情况较为严重的珍稀物种仰鼻猴为例,基于地形变量、人类活动变量、气候变量及古今分布点(历史时期:20世纪... 详细信息

人类活动是威胁当今世界生物多样性的重要因素之一,越来越多的“难民物种”受人类活动干扰被限制在了次优的栖息地。以我国受威胁情况较为严重的珍稀物种仰鼻猴为例,基于地形变量、人类活动变量、气候变量及古今分布点(历史时期:20世纪前;现今时期:20世纪至今)数据,利用MaxEnt模型对其古今生境开展预测,结果表明:古今以来,人类活动导致仰鼻猴丧失了我国东部921756km^(2)的适宜生境,保留适生区内的生境质量下降,新增适生区分散且破碎,仰鼻猴重新适应了历史时期的次适生区甚至不适生区。古今以来,仰鼻猴对气候变量适应能力的改变远小于对地形变量,与可以自由选择适宜生境时相比,现今时期的仰鼻猴生境内的海拔更高、地形起伏度更大,最冷月最低温以及年度气温变化的适宜范围相比更窄,仰鼻猴提高了对多种环境因素的适应能力,但同时对环境变化的敏感度升高,并面临着种群隔离度增加等风险,已无法占据最佳生态位。建议关注现有适宜生境附近的人类活动强度,尽可能减少与仰鼻猴产生空间或资源的竞争,加强对该物种的保护。

关键词：仰鼻猴 MaxEnt模型人类活动胁迫生境变迁环境适应

The O2-ZmGRAS11 transcriptional regulatory network orchestrates the coordination of endosperm cell expansion and grain filling in maize

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Molecular Plant 2022年第3期15卷 468-487页

作者： Chen Ji Lina Xu Yujie Li Yuxin Fu Shuai Li Qiong wang Xing Zeng Zhongqin Zhang Zhiyong Zhang Wenqin wang jiechen wang Yongrui Wu National Key Laboratory of Plant Molecular Genetics CAS Center for Excellence in Molecular Plant SciencesShanghai Institute of Plant Physiology&EcologyShanghai 200032China University of the Chinese Academy of Sciences Beijing 100049China Triticeae Research Institute Sichuan Agricultural UniversityChengdu 611130China School of Agriculture and Biology Shanghai Jiao Tong UniversityShanghai 200240China College of Agronomy Northeast Agricultural UniversityHarbin 150030China Hebei Sub-center of the Chinese National Maize Improvement Center College of AgronomyAgricultural University of HebeiBaodingChina School of Life Sciences Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230027China College of Life Science Shanghai Normal University100 Guilin RoadShanghai 200233China

Maize(Zea mays)endosperm filling is coordinated with cell expansion to enlarge the grain size,but the mechanism coupling the two processes is poorly understood.Starchy endosperm cells basically contain no visible vacuoles for cell expansion.During grain filling,efficient synthesis of storage compounds leads to reduced cytoplasm and thus lowered cell turgor pressure.Although bioactive gibberellins(GAs)are essential for cell expansion,they accumulate at a low level at this stage.In this study,we identified an endosperm-specific GRAS domain-containing protein(ZmGRAS11)that lacks the DELLA domain and promotes cell expansion in the filling endosperm.The zmgras11 loss-of-function mutants showed normal grain filling but delayed cell expansion,thereby resulting in reduced kernel size and weight.Overexpression of ZmGRAS11 led to larger endosperm cells and therefore increased kernel size and weight.Consistent with this,ZmGRAS11 positively regulates the expression of ZmEXPB12,which is essential for cell expansion,at the endosperm filling stage.Moreover,we found that Opaque2(O2),a central transcription factor that regulates endosperm filling,could directly bind to the promoter of ZmGRAS11 and activate its expression.Taken together,these results suggest that endosperm cell expansion is coupled with endosperm filling,which is orchestrated by the O2-ZmGRAS11-centered transcriptional regulatory network.Our findings also provide potential targets for maize yield improvement by increasing the storage capacity of endosperm cells.

关键词： maize endosperm cell expansion ZmGRAS11 O2 ZmEXPB12

LjCYC Genes Constitute Floral Dorsoventral Asymmetry in Lotus japonicus

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Journal of Integrative Plant Biology 2010年第11期52卷 959-970页

作者： jiechen wang Yumei wang Da Luo National Key Laboratory of Plant Molecular Genetics Institute of Plant Physiology and EcologyShanghai Institutes for Biological Sciencesthe Chinese Academy of SciencesShanghai 200032China Graduate University of Chinese Academy of Sciences Beijing 100049China Department of Cell Biology Shanghai Jiaotong University Medical of SchoolShanghai 200025China

Previous study shows that LjCYC2,a CYC-like TCP （TB1,CYC and PCFs） gene in the model legume,Lotus japonicus,is involved in dorsal petal development,which together with the other two homologous genes,LjCYC1 and LjCYC3,belongs to an LjCYC gene cluster.In this report,we modified the transformation system in L.japonicus,and constructed different RNAi transgenes to target different LjCYC genes.The expression of three endogenous LjCYC genes was specifically suppressed by different specific RNAi transgenes,and a chimerical RNAi transgene that contains the specific sequences from LjCYC1 and LjCYC2 was found to downregulate the expression of both endogenous genes simultaneously.Effects of silencing three LjCYC genes were mainly restricted on either dorsal or lateral petals,demonstrating their dorsalizing and lateralizing activities during the development of zygomorphic flower.Furthermore,abolishing the expression of three LjCYC genes could give rise to complete loss of dorsoventral （DV） differentiation in the flower whose petals all resembled the ventral one in the wild type and displayed intact organ internal （IN） asymmetry.Our data demonstrate that during zygomorphic flower development,the DV asymmetry is constituted by the LjCYC genes,while the floral organ IN asymmetry is independently determined by other genetic factors.

关键词： Genes Corolla Organizations Transgenics Flowering Flowers

Sea-water-level prediction via combined wavelet decomposition,neuro-fuzzy and neural networks using SLA and wind information

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Acta Oceanologica Sinica 2020年第5期39卷 157-167页

作者： Bao wang Bin wang Wenzhou Wu Changbai Xi jiechen wang School of Geography and Ocean Science Nanjing UniversityNanjing 210023China National Marine Environmental Forecasting Center Beijing 100081China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing UniversityNanjing 210023China

Sea-water-level(SWL)prediction significantly impacts human lives and maritime activities in coastal regions,particularly at offshore locations with shallow water levels.Long-term SWL forecasts,which are conventionally obtained via harmonic analysis,become ineffective when nonperiodic meteorological events predominate.Artificial intelligence combined with other data-processing methods can effectively forecast highly nonlinear and nonstationary inflow patterns by recognizing historical relationships between input and output.These techniques are considerably useful in time-series data predictions.This paper reports the development of a hybrid model to realize accurate multihour SWL forecasting by combining an adaptive neuro-fuzzy inference system(ANFIS)with wavelet decomposition while using sea-level anomaly(SLA)and wind-shear-velocity components as inputs.Numerous wavelet-ANFIS(WANFIS)models have been tested using different inputs to assess their applicability as alternatives to the artificial neural network(ANN),wavelet ANN(WANN),and ANFIS models.Different error definitions have been used to evaluate results,which indicate that integrated wavelet-decomposition and ANFIS models improve the accuracy of SWL prediction and that the inputs of SLA and wind-shear velocity exhibit superior prediction capability compared to conventional SWL-only models.

关键词： sea-water level prediction ANFIS wavelet decomposition wind

A new trapezoidal-mesh based data model for spatial operations

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International Journal of Digital Earth 2012年第2期5卷 165-183页

作者： jiechen wang Can Cui Gang Chen Yingxia Pu Jinsong Ma Department of Geographic Information Science Nanjing UniversityNanjing 210093China Urban Geography at the Urban and Regional Research Centre Utrecht(URU) Faculty of GeosciencesUtrecht UniversityUtrecht 3584CSNetherlands

This paper presents a new spatial data model based on trapezoidal-mesh for implementing spatial operations within geographical information systems(GIS).Based only on the solid foundation of spatial operations,diversified application models can be established to bridge the gap between Digital Earth models and the real world with its real-world problems(‘connecting through location’).In this paper,the involved polygon features are decomposed into a series of trapezoidalmeshes.Then,geo-processing operations are employed on these meshes rather than the original polygon features,resulting in a relatively simple spatial computation.As a kind of model designed by integrating raster with vector,the model presented here has advantages over other models when carrying out spatial operations insofar as providing a solid foundation for achieving the grand goal of Digital Earth.The concept of this data model and the two extensive examples of its application in spatial operations are elaborated upon in this article.As a result,this article and the research that supports it,proves that the adoption of the trapezoidal-mesh model greatly improves the efficiency of spatial operations in GIS.

关键词： trapezoidal-mesh spatial data model spatial operation geographical information systems(GIS)

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

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Acta Oceanologica Sinica 2021年第11期40卷 104-118页

作者： Bao wang Shichao Liu Bin wang Wenzhou Wu jiechen wang Dingtao Shen Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology/Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources/School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Key Laboratory of Marine Hazards Forecasting National Marine Environmental Forecasting CenterMinistry of Natural ResourcesBeijing 100081China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing UniversityNanjing 210023China Changjiang River Scientific Research Institute Changjiang Water Resources CommissionWuhan 430010China

Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.

关键词： storm surge prediction CNN LSTM combination