Reform Paths of Rural Land Property Rights System in the Context of Agricultural Modernization

The reform of rural land property rights system can promote large-scale and intensive agricultural production,improve the quality of laborers,improve agricultural production efficiency,increase farmers'income,and effectively promote the development of agricultural modernization.In the context of the reform of the"separation of three powers",the rural land property rights system still has problems in terms of ownership,use rights,disposal rights,and income rights,which affect the healthy development of agricultural modernization.In this situation,it is necessary to further clarify the subject of rural land ownership,thoroughly improve the right to use rural land,vigorously improve the right to dispose of rural land,effectively protect the right to benefit from rural land,and deeply promote the reform of the rural land property rights system in order to effectively promote the development of agricultural modernization.

Analysis of Geographical Distribution of Missing Children Based on the Missing Children Mobile GIS Mutual Assistance System of China

The Missing Children Mobile GIS Mutual Assistance System of China (MCMAS) is a mobile service software based on mobile GIS platform software, and it is committed to providing the most convenient and efficient system of personally mutual tracing services for missing children family and society. Relying on collaborative utilization of location-based service technology, face image intelligent recognition technology, cloud computing technology, public big data sharing technology, and mobile GIS technology, the MCMAS has achieved prominent application effects since it was deployed. At present, the MCMAS is running soundly, and it has received and released the information about 1011 missing children from May 25, 2016 to May 25, 2017. In order to explore the geographical distribution features and the influencing factors of missing children, the data of missing children are used for spatial and visual analysis by the data mining and GIS technologies. At the same time, we have built the spatial thermodynamic diagram of the big data of China missing children. By comparing provinces and cities with a higher proportion of missing children, the results showed that: 1) The high proportion of missing children spatially concentrated in the eastern part of the China. 2) The number of missing children was significantly correlated with the population density and economic status of the city. Furthermore, the paper macro-levelly presents a basic basis for rescuing the missing children from two aspects: regionally spatial characteristics and influencing factors.

Plasmonic polydopamine-modified TiO_(2)nanotube substrates for surface-assisted laser desorption/ionization mass spectrometry imaging

Mass spectrometry imaging(MSI)has made the spatio-chemical characterization of a broad range of small-molecule metabolites within biological tissues possible.However,available matrix-assisted laser desorption/ionization mass spectrometry(MALDI-MS)suffers from severe background interferences in low-mass ranges and inhomogeneous matrix deposition.Thus,surface-assisted LDI-MS(SALDI-MS)has been an attractive alternative for high-sensitivity detection and imaging of small biomolecules.In this study,we construct a new composite substrate,hydrophobic polydopamine(hPDA)-modified TiO_(2)nanotube(TDNT)coated with plasmonic gold nanoparticle(AuNP-hPDA-TDNT),as a dual-polarity SALDI substrate using an easy and cost-effective fabrication approach.Benefitting from the synergistic effects of TDNT semiconductor and plasmonic PDA modification,this SALDI substrate exhibits superior performance for dual-polarity detection of a vast diversity of small molecules.Highly reduced background interferences,lower detection limits,and spot-to-spot repeatability can be achieved using AuNP-hPDA-TDNT substrates.Due to its unique imprinting performance,various metabolites and lipids can be visualized within jatropha integerrima petals,ginkgo leaves,strawberry fruits,and latent fingerprints.More valuably,the universality of this matrix-free substrate is demonstrated for mapping spatial distribution of lipids within mouse brain tissue sections.Considered together,this AuNP-hPDA-TDNT material is expected to be a promising SALDI substrate in various fields,especially in nanomaterial development and life sciences.

Local climate zone mapping using remote sensing:a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime lightdata

The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments.In this study,we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations,i.e.daytime Ziyuan-3 stereo imageries(2.1 m)and Luojia-1 nighttime light(NTL)data(130 m).Firstly,we performed land cover classification using multiple machine learning methods(i.e.random forest(RF)and XGBoost algorithms)and various features(i.e.spectral,textural,multi-view features,3D urban structure parameters(USPs),and NTL).In addition,we developed a set of new cumulative elevation indexes to improve building roughness assessments.The indexes can estimate building roughness directly from fused point clouds generated by both along-and across-track modes.Finally,based on the land cover and building roughness results,we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping.The results for Beijing,China,show that our method yielded satisfactory accuracy for LCZ mapping,with an overall accuracy(OA)of 90.46%.The overall accuracy of land cover classification using 3D USPs generated from both along-and across-track modes increased by 4.66%,compared to that of using the single along-track mode.Additionally,the OA value of LCZ mapping using 2D and 3D USPs(88.18%)achieved a better result than using only 2D USPs(83.83%).The use of NTL data increased the classification accuracy of LCZs E(bare rock or paved)and F(bare soil or sand)by 6.54%and 3.94%,respectively.The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livabillity in urban areas.Ultimately,this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update

An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.

Identifying urban functional zones by capturing multi-spatial distribution patterns of points of interest

Urban Functional Zone(UFZ)identification is vital for urban planning,renewal,and development.Point of Interest(POI),as one of the most popular data in UFZ studies,is transformed into a geo-corpus under specific sampling strategies,which can be used with Natural Language Processing(NLP)technology to extract geo-semantic features and identify UFZs.However,existing studies only capture a single spatial distribution pattern of POIs,while ignoring the other spatial distribution information.In this paper,we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings.Subsequently,random forest model was leveraged to classify UFZs based on those embeddings.A set of combination experiments were designed for performance validation.The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%,with an improvement of 8.5%compared to the baseline methods.The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.