A piezoelectric-driven microneedle platform for skin disease therapy

With over a million cases detected each year,skin disease is a global public health problem that diminishes the quality of life due to its difficulty to eradicate,propensity for recurrence,and potential for post-treatment scarring.Photodynamic therapy(PDT)is a treatment with minimal invasiveness or scarring and few side effects,making it well tolerated by patients.However,this treatment requires further research and development to improve its effective clinical use.Here,a piezoelectric-driven microneedle(PDMN)platform that achieves high efficiency,safety,and non-invasiveness for enhanced PDT is proposed.This platform induces deep tissue cavitation,increasing the level of protoporphyrin IX and significantly enhancing drug penetration.A clinical trial involving 25 patients with skin disease was conducted to investigate the timeliness and efficacy of PDMN-assisted PDT(PDMN-PDT).Our findings suggested that PDMN-PDT boosted treatment effectiveness and reduced the required incubation time and drug concentration by 25%and 50%,respectively,without any anesthesia compared to traditional PDT.These findings suggest that PDMN-PDT is a safe and minimally invasive approach for skin disease treatment,which may improve the therapeutic efficacy of topical medications and enable translation for future clinical applications.

Geospatial big data for urban planning and urban management

The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines.For example,a huge number of Landsat images are utilized in mapping high-resolution global forest cover and the global forest changes in the twenty-first century are explored(Hansen et al.2013),which is impossible without the support of geospatial big data and the related automatic processing techniques.Based on the huge enterprise registration data in China,the economic and social development situations and trends are revealed by the non-statistic data and novel approaches(Li et al.2018).City-wide fine-grained urban population distribution at building level is achieved by integrating and fusing multisource geospatial big data(Yao et al.2017),which is usually not desired in traditional research.Geospatial Big Data provides a new transforming paradigm of scientific research especially at the crossroads of broad disciplines,including but not limited to the humanities,the physical sciences,engineering,and so on.

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions.In this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)data.We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution.To increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images.These phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey data.We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment.The results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.

Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling

Population spatialization is widely used for spatially downscaling census population data to finer-scale.The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level(AUlevel)and transfer it to generate the gridded population.However,the statistical characteristic of attributes at the pixel-level differs from that at the AU-level,thus leading to prediction bias via the cross-scale modeling(i.e.scale mismatch problem).In addition,integrating multi-source data simply as covariates may underutilize spatial semantics,and lead to incorrect population disaggregation;while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation.To address the scale mismatch in downscaling,this paper proposes a Cross-Scale Feature Construction(CSFC)method.More specifically,by grading pixel-level attributes,we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level.Meanwhile,fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling.Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization.Through the comparison with traditional feature construction method and the ablation experiments,the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps.Furthermore,accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization.

Distributed cache replacement method for geospatial data using spatiotemporal localitybased sequence

Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.

Text GCN-SW-KNN:a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.

Construction of a refined population analysis unit based on urban forms and population aggregation patterns

The population analysis unit(PAU)is the basic unit employed in studies of urban populations.The commonly used PAUs are mostly administrative divisions,regular geographic grids.However,these units are different from urban forms,and cannot be used to consider the characteristics of population distributions and flow changes.In this study,we proposed a method for constructing a fine population analysis zone(FPAZ)based on the population aggregation pattern and urban form elements.First,considering the spatial structure of a city and the fine-grained demands of population analysis,the basic analysis unit was divided according to the functional heterogeneity of the population activity region at the micro-scale by combining urban form elements.Next,a population aggregation preference model was established by considering the spatial distribution characteristics of the local aggregation of the urban population flow and long-term stability characteristics depending on the dynamic changes in entrances and exits.Finally,we divided the FPAZ combined with the microstructural elements.Experimental results showed that compared with other types of PAUs,the FPAZ was more consistent with the urban morphology and was an appropriate and general spatial unit for expressing the accurate characteristics of population distributions and changes at the micro-scale.

Geoinformatics education and outreach:looking forward

Geoinformatics education is a key factor for sustainable development of geo-spatial sciences and industries.There have been a variety of educational activities focusing on education and training,technology transfer,and capability building in photogrammetry,remote sensing,and spatial information science,together known as Geoinformatics.Geoinformatics education is an essential mission and even determinant in the ISPRS society.The paper discusses key issues in Geoinformatics education.It reviews educational activities from the ISPRS perspective and summarizes lessons learned from these actions.A vision towards future trends of Geoinformatics education in the ISPRS is provided.

Prediction of network public opinion features in urban planning based on geographical case-based reasoning

As a significant part of sustainable urban development proposed by the United Nations,urban planning is related to the ecological environment and transportation,especially affecting quality of life and social well-being. In the process of urban planning,the public express their opinions on open network platforms,resulting in large quantities of network public opinion data,which has important implications for evaluating urban planning. Based on the idea of geographical case-based reasoning (CBR),this paper constructs an expression framework for urban planning cases in the form of a ‘case problem–case attribute–case result’ triad. On this basis,this paper proposes a similarity calculation method of urban planning cases that integrates case attribute. Finally,based on an improvement to the traditional k-nearest neighbors method,the proposed public opinion feature calculation model considers similarity weights,which allow us to predict network public opinion features,including viewpoint-level emotional tendency and concerned groups of urban planning cases. The experimental result shows similarity weights (SWs) model could effectively improve the prediction accuracy,where the average MIC-F1 score reached more than 74%. Based on CBR,the proposed method can predict the development trends of public opinion in future planning cases,and provide scientific and reasonable decision support for urban planning.

Planning-oriented optimization strategy for visualizing power distribution networks:from global to local

The spatial distribution of power facilities is uneven,thereby making the topology of geographical wiring diagrams(GWDs)based on the actual coordinates unclear.A single-line diagram has the advantage of a clear topology but it lacks spatial locations.A GWD has the advantage of accurate spatial locations but it lacks a clear topology.Visualizing distribution networks for planning requires both features.We proposed a new planning-oriented method for optimizing the visualization of distribution networks.From the global perspective,we proposed an improved force-directed(FD)algorithm by introducing a space restriction strategy and node–edge repulsion strategy to promote the expansion of the distance between distribution facilities within a limited buffer.We then constructed the constrained Delaunay triangulation to identify the compact districts(CDs)and used a genetic algorithm to optimize the parameters for the improved FD algorithm.A novel visualization evaluation indicator was also proposed for quantitatively assessing the visualizations.From a local perspective,the fisheye algorithm was used to optimize the CDs to further improve the visualization of the distribution network.We verified the proposed methods with real-world data.We used limited spatial displacement in exchange for maximum topology clarity to balance the accurate spatial location and topology clarity.