Evolution of Communities in the Medical Sciences: Evidence from the Medical Words Network

BACKGROUND: Classification of medical sciences into its sub-branches is crucial for optimum administration of healthcare and specialty training. Due to the rapid and continuous evolution of medical sciences, development of unbiased tools for monitoring the evolution of medical disciplines is required. METHODOLOGY/PRINCIPAL FINDINGS: Network analysis was used to explore how the medical sciences have evolved between 1980 and 2015 based on the shared words contained in more than 9 million PubMed abstracts. The k-clique percolation method was used to extract local research communities within the network. Analysis of the shared vocabulary in research papers reflects the trends of collaboration and splintering among different disciplines in medicine. Our model identifies distinct communities within each discipline that preferentially collaborate with other communities within other domains of specialty, and overturns some common perceptions. CONCLUSIONS/SIGNIFICANCE: Our analysis provides a tool to assess growth, merging, splitting and contraction of research communities and can thereby serve as a guide to inform policymakers about funding and training in healthcare

AUOTOMATIC CLASSIFICATION OF POINT CLOUDS EXTRACTED FROM ULTRACAM STEREO IMAGES

Automatic extraction of building roofs, street and vegetation are a prerequisite for many GIS (Geographic Information System) applications, such as urban planning and 3D building reconstruction. Nowadays with advances in image processing and image matching technique by using feature base and template base image matching technique together dense point clouds are available. Point clouds classification is an important step in automatic features extraction. Therefore, in this study, the classification of point clouds based on features color and shape are implemented. We use two images by proper overlap getting by Ultracam-x camera in this study. The images are from Yasouj in IRAN. It is semi-urban area by building with different height. Our goal is classification buildings and vegetation in these points. In this article, an algorithm is developed based on the color characteristics of the point’s cloud, using an appropriate DEM (Digital Elevation Model) and points clustering method. So that, firstly, trees and high vegetation are classified by using the point’s color characteristics and vegetation index. Then, bare earth DEM is used to separate ground and non-ground points. Non-ground points are then divided into clusters based on height and local neighborhood. One or more clusters are initialized based on the maximum height of the points and then each cluster is extended by applying height and neighborhood constraints. Finally, planar roof segments are extracted from each cluster of points following a region-growing technique

Planimetric Features Generalization for the Production of Small-Scale Map by Using Base Maps and the Existing Algorithms

Cartographic maps are representations of the Earth upon a flat surface in the smaller scale than it’s true. Large scale maps cover relatively small regions in great detail and small scale maps cover large regions such as nations, continents and the whole globe. Logical connection between the features and scale map must be maintained by changing the scale and it is important to recognize that even the most accurate maps sacrifice a certain amount of accuracy in scale to deliver a greater visual usefulness to its user. Cartographic generalization, or map generalization, is the method whereby information is selected and represented on a map in a way that adapts to the scale of the display medium of the map, not necessarily preserving all intricate geographical or other cartographic details. Due to the problems facing small-scale map production process and the need to spend time and money for surveying, today’s generalization is used as executive approach. The software is proposed in this paper that converted various data and information to certain Data Model. This software can produce generalization map according to base map using the existing algorithm. Planimetric generalization algorithms and roles are described in this article. Finally small-scale maps with 1:100,000, 1:250,000 and 1:500,000 scale are produced automatically and they are shown at the end

CLASSIFICATION OF URBAN FEATURE FROM UNMANNED AERIAL VEHICLE IMAGES USING GASVM INTEGRATION AND MULTI-SCALE SEGMENTATION

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was

EVALUATION OF SPATIAL AND TEMPORAL DISTRIBUTION CHANGES OF LST USING LANDSAT IMAGES (CASE STUDY:TEHRAN)

In traditional approach, the land surface temperature (LST) is estimated by the permanent or portable ground-based weather stations. Due to the lack of adequate distribution of weather stations, a uniform LST could not be achieved. Todays, With the development of remote sensing from space, satellite data offer the only possibility for measuring LST over the entire globe with sufficiently high temporal resolution and with complete spatially averaged rather than point values. the remote sensing imageries with relatively high spatial and temporal resolution are used as suitable tools to uniformly LST estimation. Time series, generated by remote sensed LST, provide a rich spatial-temporal infrastructure for heat island’s analysis. in this paper, a time series was generated by Landsat8 and Landsat7 satellite images to analysis the changes in the spatial and temporal distribution of the Tehran’s LST. In this process, The Normalized Difference Vegetation Index (NDVI) threshold method was applied to extract the LST; then the changes in spatial and temporal distribution of LST over the period 1999 to 2014 were evaluated by the statistical analysis. Finally, the achieved results show the very low temperature regions and the middle temperature regions were reduced by the rate of 0.54% and 5.67% respectively. On the other hand, the high temperature and the very high temperature regions were increased by 3.68% and 0.38% respectively. These results indicate an incremental procedure on the distribution of the hot regions in Tehran in this period. To quantitatively compare urban heat islands (UHI), an index called Urban Heat Island Ratio Index(URI) was calculated. It can reveal the intensity of the UHI within the urban area. The calculation of the index was based on the ratio of UHI area to urban area. The greater the index, the more intense the UHI was. Eventually, Considering URI between 1999 and 2014, an increasing about 0.03 was shown. The reasons responsible for the changes in spatio-temporal characteristics of the LST were the sharp increase in impervious surfaces, increased use of fossil fuels and greening policies