ALBERT

Description: In this study, a multiple linear regression model to evaluate the risk of morbidity and mortality of ischemic cardiovascular disease is demonstrated. In this model, predictor variables are selected from physiological chemicals in a blood test of the subjects. Meanwhile, the calculated risk score is selected as a response variable. Four major latent variables including hepatic, nephric, metabolic, and BMI (Body Mass Index) are revealed by performing statistical and principal component analysis for the collected survey data. The analyzed result also shows that the cardiac disorder is correlated with symptoms of abnormal BMI, hepatic disorder, nephric disorder, and metabolic disorder. Thus, the risk of morbidity and mortality of ischemic cardiovascular disease can be assessed from the proposed multiple regression model.

Description: Water-related parasitic diseases are directly dependent on water bodies for their spread or as a habitat for indispensable intermediate or final hosts. Along with socioeconomic development and improvement of sanitation, overall prevalence is declining in the China. However, the heterogeneity in economic development and the inequity of access to public services result in considerable burden due to parasitic diseases in certain areas and populations across the country. In this review, we demonstrated three aspects of ten major water-related parasitic diseases, i.e., the biology and pathogenicity, epidemiology and recent advances in research in China. General measures for diseases control and special control strategies are summarized.

Description: Remote Sensing, Vol. 9, Pages 1001: One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm Remote Sensing doi: 10.3390/rs9101001 Authors: Zurui Ao Yanjun Su Wenkai Li Qinghua Guo Jing Zhang Automatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classification methods require training samples of all classes to construct a reliable classifier. However, complete training samples are normally hard and costly to collect, and a common circumstance is that only training samples for a class of interest are available, in which traditional supervised classification methods may be inappropriate. In this study, we investigated the possibility of using a novel one-class classification algorithm, i.e., the presence and background learning (PBL) algorithm, to classify LiDAR data in an urban scenario. The results demonstrated that the PBL algorithm implemented by back propagation (BP) neural network (PBL-BP) could effectively classify a single class (e.g., building, tree, terrain, power line, and others) from airborne LiDAR point cloud with very high accuracy. The mean F-score for all of the classes from the PBL-BP classification results was 0.94, which was higher than those from one-class support vector machine (SVM), biased SVM, and maximum entropy methods (0.68, 0.82 and 0.93, respectively). Moreover, the PBL-BP algorithm yielded a comparable overall accuracy to the multi-class SVM method. Therefore, this method is very promising in the classification of the LiDAR point cloud.

Description: Sensors, Vol. 18, Pages 1238: Frequency Based Design Partitioning to Achieve Higher Throughput in Digital Cross Correlator for Aperture Synthesis Passive MMW Imager Sensors doi: 10.3390/s18041238 Authors: Muhammad Asif Xiangzhou Guo Jing Zhang Jungang Miao Digital cross-correlation is central to many applications including but not limited to Digital Image Processing, Satellite Navigation and Remote Sensing. With recent advancements in digital technology, the computational demands of such applications have increased enormously. In this paper we are presenting a high throughput digital cross correlator, capable of processing 1-bit digitized stream, at the rate of up to 2 GHz, simultaneously on 64 channels i.e., approximately 4 Trillion correlation and accumulation operations per second. In order to achieve higher throughput, we have focused on frequency based partitioning of our design and tried to minimize and localize high frequency operations. This correlator is designed for a Passive Millimeter Wave Imager intended for the detection of contraband items concealed on human body. The goals are to increase the system bandwidth, achieve video rate imaging, improve sensitivity and reduce the size. Design methodology is detailed in subsequent sections, elaborating the techniques enabling high throughput. The design is verified for Xilinx Kintex UltraScale device in simulation and the implementation results are given in terms of device utilization and power consumption estimates. Our results show considerable improvements in throughput as compared to our baseline design, while the correlator successfully meets the functional requirements.

Description: Sustainability, Vol. 10, Pages 1459: Domestic Wheat Trade and Its Associated Virtual Cropland Flow in China, 2010–2015 Sustainability doi: 10.3390/su10051459 Authors: Xiaolin Chen Xiaojie Liu Litao Liu Yali Zhang Jinhua Guo Jing Huang Meijun Zhou Yang Zhao Liang Wu Lun Yang Fei Lun How to meet food demands with limited cropland has become a serious problem worldwide, especially in China. Global and national food trade can alleviate regional food unbalance among different countries or regions to some extent. The embedded virtual land also flows with food trade, and reasonable food trade can save global croplands and protect local ecosystems. Therefore, it is of great importance to study how trade influences land use as well as its associated environmental consequences. Recent studies have mainly focused on global food trade and its associated virtual land flow; however, only a few have focused on national food trade and its associated virtual land flow. Thus, this study aims to explore the domestic wheat trade and its associated virtual land flows in China during the period 2010–2015, based on the CHINAGRO model and previous studies. The Huang-Huai-Hai Plain and its surrounding regions were the main producers and consumers of wheat and virtual wheat. Without wheat stocks and international wheat trade, the annual domestic wheat trade accounted for 16.43% of the total national wheat production in China. Anhui was the largest net exporter of wheat, followed by Henan, while the developed areas such as Beijing, Guangdong, and Shanghai were the main net importers. Additionally, as an important transfer region, Jiangsu was the largest exporter and importer of wheat virtual cropland, but it finally presented as a net exporter of wheat cropland. During this period, domestic wheat trade led to a total land savings of 2.62 Mha/a, accounting for 10.80% of Chinese wheat cropland. However, compared with the year 2010, Shandong, Hebei, and Sichuan turned into wheat net importers in 2015, which was due to different reasons such as national agriculture polices and economic development. In addition, the net virtual cropland per capita has been greatly influenced by local economic development.

Description: Sensors, Vol. 17, Pages 2396: Silicon Nanocrystals with pH-Sensitive Tunable Light Emission from Violet to Blue-Green Sensors doi: 10.3390/s17102396 Authors: Jing Wang Junhong Guo Jing Chen We fabricated a silicon nanocrystal (NC) suspension with visible, continuous, tunable light emission with pH sensitivity from violet to blue-green. Transmission electron microscopy (TEM) images and X-ray diffraction (XRD) pattern analysis exhibit the highly crystalline nanoparticles of silicon. Photoluminescence (PL) spectra and photoluminescence excitation (PLE) spectra at different pH values, such as 1, 3, 5, 7, 9, and 11, reveal the origins of light emission from the silicon NC suspension, which includes both the quantum confinement effect and surface bonding. The quantum confinement effect dominates the PL origins of silicon NCs, especially determining the tunability and the emission range of PL, while the surface bonding regulates the maximum peak center, full width at half maximum (FWHM), and offsets of PL peaks in response to the changing pH value. The peak fitting of PLE curves reveals one of the divided PLE peaks shifts towards a shorter wavelength when the pH value increases, which implies correspondence with the surface bonding between silicon NCs and hydrogen atoms or hydroxyl groups. The consequent detailed analysis of the PL spectra indicates that the surface bonding results in the transforming of the PL curves towards longer wavelengths with the increasing pH values, which is defined as the pH sensitivity of PL. These results suggest that the present silicon NCs with pH-sensitive tunable light emission could find promising potential applications as optical sources, bio-sensors, etc.

Description: Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.