bluuebunny/crossref_metadata_2025 huggingface dataset:
Expression of Selenoprotein Genes and Association with Selenium Status in Colorectal Adenoma and Colorectal Cancer
Authors: Hughes David J. , Kunická Tereza , Schomburg Lutz , Liška Václav , Swan Niall , Souček Pavel
Published: November 2018

Dietary selenium (Se) intake is essential for synthesizing selenoproteins that are important in countering oxidative and inflammatory processes linked to colorectal carcinogenesis. However, there is limited knowledge on the selenoprotein expression in colorectal adenoma (CRA) and colorectal cancer (CRC) patients, or the interaction with Se status levels. We studied the expression of seventeen Se pathway genes (including fifteen of the twenty-five human selenoproteins) in RNA extracted from disease-normal colorectal tissue pairs, in the discovery phase of sixty-two CRA/CRC patients from Ireland and a validation cohort of a hundred and five CRC patients from the Czech Republic. Differences in transcript levels between the disease and paired control mucosa were assessed by the Mann-Whitney U-test. GPX2 and TXNRD3 showed a higher expression and GPX3, SELENOP, SELENOS, and SEPHS2 exhibited a lower expression in the disease tissue from adenomas and both cancer groups (p-values from 0.023 to <0.001). In the Czech cohort, up-regulation of GPX1, SELENOH, and SOD2 and down-regulation of SELENBP1, SELENON, and SELENOK (p-values 0.036 to <0.001) was also observed. We further examined the correlation of gene expression with serum Se status (assessed by Se and selenoprotein P, SELENOP) in the Irish patients. While there were no significant correlations with both Se status markers, SELENOF, SELENOK, and TXNRD1 tumor tissue expression positively correlated with Se, while TXNRD2 and TXNRD3 negatively correlated with SELENOP. In an analysis restricted to the larger Czech CRC patient cohort, Cox regression showed no major association of transcript levels with patient survival, except for an association of higher SELENOF gene expression with both a lower disease-free and overall survival. Several selenoproteins were differentially expressed in the disease tissue compared to the normal tissue of both CRA and CRC patients. Altered selenoprotein expression may serve as a marker of functional Se status and colorectal adenoma to cancer progression.
Beneficial Effects of Resveratrol Administration—Focus on Potential Biochemical Mechanisms in Cardiovascular Conditions
Authors: Wiciński Michał , Socha Maciej , Walczak Maciej , Wódkiewicz Eryk , Malinowski Bartosz , Rewerski Sebastian , Górski Karol , Pawlak-Osińska Katarzyna
Published: November 2018

Resveratrol (RV) is a natural non-flavonoid polyphenol and phytoalexin produced by a number of plants such as peanuts, grapes, red wine and berries. Numerous in vitro studies have shown promising results of resveratrol usage as antioxidant, antiplatelet or anti-inflammatory agent. Beneficial effects of resveratrol activity probably result from its ability to purify the body from ROS (reactive oxygen species), inhibition of COX (cyclooxygenase) and activation of many anti-inflammatory pathways. Administration of the polyphenol has a potential to slow down the development of CVD (cardiovascular disease) by influencing on certain risk factors such as development of diabetes or atherosclerosis. Resveratrol induced an increase in Sirtuin-1 level, which by disrupting the TLR4/NF-κB/STAT signal cascade (toll-like receptor 4/nuclear factor κ-light-chain enhancer of activated B cells/signal transducer and activator of transcription) reduces production of cytokines in activated microglia. Resveratrol caused an attenuation of macrophage/mast cell-derived pro-inflammatory factors such as PAF (platelet-activating factor), TNF-α (tumour necrosis factor-α and histamine. Endothelial and anti-oxidative effect of resveratrol may contribute to better outcomes in stroke management. By increasing BDNF (brain-derived neurotrophic factor) serum concentration and inducing NOS-3 (nitric oxide synthase-3) activity resveratrol may have possible therapeutical effects on cognitive impairments and dementias especially in those characterized by defective cerebrovascular blood flow.
Cod Residual Protein Prevented Blood Pressure Increase in Zucker fa/fa Rats, Possibly by Inhibiting Activities of Angiotensin-Converting Enzyme and Renin
Authors: Vildmyren Iselin , Drotningsvik Aslaug , Oterhals Åge , Ween Ola , Halstensen Alfred , Gudbrandsen Oddrun Anita
Published: November 2018

Hypertension is the leading risk factor for cardiovascular disease, and prevention of high blood pressure through diet and lifestyle should be a preferred approach. High intake of fish is associated with lower blood pressure, possibly mediated through the proteins since peptides with angiotensin-converting enzyme (ACE) inhibiting capacities have been identified in fish skin, backbone, and fillet. The effects of cod meals made from residual materials and fillet on blood pressure were investigated in obese Zucker fa/fa rats which spontaneously develop high blood pressure. Rats were fed diets containing water-soluble (stickwater) or water-insoluble (presscake) fractions of protein-rich meals from cod residual materials (head, gut, backbone with muscle residuals, skin, trimmings) or fillet. Rats were fed diets containing 25% of total protein from cod meal and 75% of protein from casein, or casein as the sole protein source (control group) for four weeks. Results show that a diet containing residual presscake meal with high gut content prevented blood pressure increase, and this cod residual meal also showed the strongest in vitro inhibitions of ACE and renin activities. In conclusion, a diet containing water-insoluble proteins (presscake meal) with high gut content prevented increase in blood pressure in obese Zucker fa/fa rats.
The Problem of Context for Similarity: An Insight from Analogical Cognition
Authors: Armary Pauline , Dokic Jérôme , Sander Emmanuel
Published: November 2018

Similarity is central for the definition of concepts in several theories in cognitive psychology. However, similarity encounters several problems which were emphasized by Goodman in 1972. At the end of his article, Goodman banishes similarity from any serious philosophical or scientific investigations. If Goodman is right, theories of concepts based on similarity encounter a huge problem and should be revised entirely. In this paper, we would like to analyze the notion of similarity with some insight from psychological works on analogical cognition. Analogical cognition compares two situations or objects in order to find similarities between them. In doing so, the analogical process sorts the different features of the two situations or objects and determines the most important ones. The analogical process is also highly sensitive to context. Context-sensitivity is desirable at some level, but it is also problematic as it leads to a computational explosion. To answer this problem, we would like to consider salience as a possible heuristic in the analogical process. We will distinguish three forms of salience: Sensory, categorical, and operational. By taking salience into account, we can introduce a shortcut into the computation of similarity and circumvent computational explosion.
Synthesis and Properties of Gelatin Methacryloyl (GelMA) Hydrogels and Their Recent Applications in Load-Bearing Tissue
Authors: Sun Mingyue , Sun Xiaoting , Wang Ziyuan , Guo Shuyu , Yu Guangjiao , Yang Huazhe
Published: November 2018

Photocrosslinked gelatin methacryloyl (GelMA) hydrogels have attracted great concern in the biomedical field because of their good biocompatibility and tunable physicochemical properties. Herein, different approaches to synthesize GelMA were introduced, especially, the typical method using UV light to crosslink the gelatin-methacrylic anhydride (MA) precursor was introduced in detail. In addition, the traditional and cutting-edge technologies to characterize the properties of GelMA hydrogels and GelMA prepolymer were also overviewed and compared. Furthermore, the applications of GelMA hydrogels in cell culture and tissue engineering especially in the load-bearing tissue (bone and cartilage) were summarized, followed by concluding remarks.
Additive Manufacturing of Polypropylene: A Screening Design of Experiment Using Laser-Based Powder Bed Fusion
Authors: Flores Ituarte Iñigo , Wiikinkoski Olli , Jansson Anton
Published: November 2018

The use of commodity polymers such as polypropylene (PP) is key to open new market segments and applications for the additive manufacturing industry. Technologies such as powder-bed fusion (PBF) can process PP powder; however, much is still to learn concerning process parameters for reliable manufacturing. This study focusses in the process–property relationships of PP using laser-based PBF. The research presents an overview of the intrinsic and the extrinsic characteristic of a commercial PP powder as well as fabrication of tensile specimens with varying process parameters to characterize tensile, elongation at break, and porosity properties. The impact of key process parameters, such as power and scanning speed, are systematically modified in a controlled design of experiment. The results were compared to the existing body of knowledge; the outcome is to present a process window and optimal process parameters for industrial use of PP. The computer tomography data revealed a highly porous structure inside specimens ranging between 8.46% and 10.08%, with porosity concentrated in the interlayer planes in the build direction. The results of the design of experiment for this commercial material show a narrow window of 0.122 ≥ Ev ≥ 0.138 J/mm3 led to increased mechanical properties while maintaining geometrical stability.
Functional Supramolecular of Inclusion Complex of Herbicide Fluroxypyr with HPβCD
Authors: Gao Shuang , Bie Chao , Liu Yanyan , Zhang Tianyu , Fu Ying , Ye Fei
Published: November 2018

In this study, hydroxypropyl-β-cyclodextrin (HPβCD) was used to form an inclusion complex with fluroxypyr to enhance water solubility and thermal stability. The inclusion complex was prepared by a saturated solution method and characterized by FT-IR, SEM, TGA, and XRD. All results indicated that fluroxypyr successfully entered the HPβCD cavity. In addition, the study of phase solubility identifies that the water solubility of fluroxypyr was greatly improved after the formation of the inclusion complex, and TGA analysis suggested that the formation of the inclusion complex improved the thermal stability. Bioactivity assay tests showed that the inclusion complex still had strong herbicidal activity. Our research showed that HPβCD was a promising carrier for improving the properties of fluroxypyr and, thus, expanding its use in agrochemical formulations.
Radical Scavenging Activities of Novel Cationic Inulin Derivatives
Authors: Chen Yuan , Mi Yingqi , Zhang Jingjing , Dong Fang , Li Qing , Ji Naiyun , Guo Zhanyong
Published: November 2018

Many saccharides are attractive targets for biomaterial applications, due to their abundance, biocompatibility, and biodegradability. In this article, a synthesis process of 6-N-substituted cationic inulin derivatives, including 6-pyridyl-6-deoxyinulin bromide (PIL), 6-(2-amino-pyridyl)-6-deoxyinulin bromide (2APIL), 6-(3-amino-pyridyl)-6-deoxyinulin bromide (3APIL), 6-(4-amino-pyridyl)-6-deoxyinulin bromide (4APIL), 6-(2,3-diamino-pyridyl)-6-deoxyinulin bromide (2,3DAPIL), 6-(3,4-diamino-pyridyl)-6-deoxyinulin bromide (3,4DAPIL), and 6-(2,6-diamino-pyridyl)-6-deoxyinulin bromide (2,6DAPIL) was described. The C6-OH of inulin was first activated by PPh3/N-bromosuccinimide (NBS) bromination. Then, pyridine and different kinds of amino-pyridine groups (different position and different numbers of amino) were grafted onto inulin, respectively, via nucleophilic substitution. Then, we confirmed their structure by Fourier transform infrared spectroscopy (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy. After this, their radical scavenging activities against hydroxyl radical and diphenylpicryl phenylhydrazine (DPPH) radical were tested in vitro. Each derivative showed a distinct improvement in radical scavenging activity when compared to inulin. The hydroxyl-radical scavenging effect decreased in the following order: 3APIL > PIL > 3,4DAPIL > 4APIL > 2,3DAPIL > 2,6DAPIL > 2APIL. Amongst them, 3APIL revealed the most powerful scavenging effect on hydroxyl radicals, as well as DPPH radicals. At 1.6 mg/mL, it could completely eliminate hydroxyl radicals and could clear 65% of DPPH radicals. The results also showed that the steric hindrance effect and the substitute position of the amino group had an effect on the radical scavenging activity. Moreover, the application prospects of inulin derivatives as natural antioxidant biomaterials are scientifically proven in this paper.
Feasibility of Using Gangue and Fly Ash as Filling Slurry Materials
Authors: Wang Hao , Jiao Jian , Wang Yumin , Du Weisheng
Published: November 2018

When used as filling aggregates, the physical and mechanical properties of gangue and fly ash are crucial for the design of filling and transportation systems. The mineral composition of gangue and fly ash affects the filling body’s strength, and the fluidity of the slurry affects the mining engineering process. In this study, gangue and fly ash samples were characterized by X-ray diffraction to ensure their suitability as filling materials. Tests were carried out with an Intelligent Torque Rheometer, and the optimal ratio of the slurry’s components was determined. After fitting the data using ORIGIN software, the following curves were obtained: (1) rheological parameters versus slurry mass fraction; (2) rheological parameters versus component ratio. On the basis of the curves, the recommended ratio of the components in the paste-like slurry was determined. We found that the mass concentration of the slurry must be kept strictly below 78%, and the optimal slurry composition includes coal gangue, fly ash, and a gelling agent at a weight ratio of 8:3:1. In order to illustrate the effectiveness of the filling technology, surface sink curves from different filling techniques in two closely situated positions of the Xinyang Coal Mine were compared. The results showed that the use of a paste-like slurry can effectively reduce surface subsidence. Furthermore, it will lead to cost-effective engineering designs for the sustainable development of coal mines.
Sparse Cost Volume for Efficient Stereo Matching
Authors: Lu Chuanhua , Uchiyama Hideaki , Thomas Diego , Shimada Atsushi , Taniguchi Rin-ichiro
Published: November 2018

Stereo matching has been solved as a supervised learning task with convolutional neural network (CNN). However, CNN based approaches basically require huge memory use. In addition, it is still challenging to find correct correspondences between images at ill-posed dim and sensor noise regions. To solve these problems, we propose Sparse Cost Volume Net (SCV-Net) achieving high accuracy, low memory cost and fast computation. The idea of the cost volume for stereo matching was initially proposed in GC-Net. In our work, by making the cost volume compact and proposing an efficient similarity evaluation for the volume, we achieved faster stereo matching while improving the accuracy. Moreover, we propose to use weight normalization instead of commonly-used batch normalization for stereo matching tasks. This improves the robustness to not only sensor noises in images but also batch size in the training process. We evaluated our proposed network on the Scene Flow and KITTI 2015 datasets, its performance overall surpasses the GC-Net. Comparing with the GC-Net, our SCV-Net achieved to: (1) reduce 73.08 % GPU memory cost; (2) reduce 61.11 % processing time; (3) improve the 3PE from 2.87 % to 2.61 % on the KITTI 2015 dataset.
Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO
Authors: Hua Ting , Zhao Wenwu , Liu Yanxu , Wang Shuai , Yang Siqi
Published: November 2018

Numerous global-scale land-cover datasets have greatly contributed to the study of global environmental change and the sustainable management of natural resources. However, land-cover datasets inevitably experience information loss because of the nature of the uncertainty in the interpretation of remote-sensing images. Therefore, analyzing the spatial consistency of multi-source land-cover datasets on the global scale is important to maintain the consistency of time and consider the effects of land-cover changes on spatial consistency. In this study, we assess the spatial consistency of five land-cover datasets, namely, GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO, at the global and continental scales through climate and elevation partitions. The influencing factors of surface conditions and data producers on the spatial inconsistency are discussed. The results show that the global overall consistency of the five datasets ranges from 49.2% to 67.63%. The spatial consistency of Europe is high, and the multi-year value is 66.57%. In addition, the overall consistency in the EF climatic zone is very high, around 95%. The surface conditions and data producers affect the spatial consistency of land-cover datasets to different degrees. CCI LC and GLCNMO (2013) have the highest overall consistencies on the global scale, reaching 67.63%. Generally, the consistency of these five global land-cover datasets is relatively low, increasing the difficulty of satisfying the needs of high-precision land-surface-process simulations.
Characteristics of BeiDou-3 Experimental Satellite Clocks
Authors: Lv Yifei , Geng Tao , Zhao Qile , Liu Jingnan
Published: November 2018

The characteristics of the improved Atomic Frequency Standard (AFS) operated on the latest BeiDou-3 experimental satellites are analyzed from day-of-year (DOY) 254 to 281, of the year 2017, considering the following three aspects: stability, periodicity, and prediction precision. The two-step method of Precise Orbit Determination (POD) is used to obtain the precise clock offsets. We presented the stability of such new clocks and studied the influence of the uneven distribution of the ground stations on the stability performance of the clock. The results show that the orbit influence on the Medium Earth Orbit (MEO) clock offsets is the largest of three satellite types, especially from 3 × 10 3 s to 8.64 × 10 4 s. Considering this orbit influence, the analysis shows that the Passive Hydrogen Maser (PHM) clock carried on C32 is approximately 2.6 × 10 − 14 at an interval of 10 4 , and has the best stability for any averaging intervals among the BeiDou satellite clocks, which currently achieves a level comparable to that of the PHM clock of Galileo, and the rubidium (Rb) clocks of Global Positioning System (GPS) Block IIF. The stability of the improved Rb AFS on BeiDou-3 is also superior to that of BeiDou-2 from 3 × 10 2 s to 3 × 10 3 s, and comparable to that of Rb AFS on the Galileo. Moreover, the periodicity of the PHM clock and the improved Rb clock are presented. For the PHM clock, the amplitudes are obviously reduced, while the new Rb clocks did not show a visible improvement, which will need further analysis in the future. As expected, the precision of the short-term clock prediction is improved because of the better characteristics of AFS. The Root Mean Square (RMS) of 1-h clock prediction is less than 0.16 ns.
Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor
Authors: Schultz Michael , Shapiro Aurélie , Clevers Jan G. P. W. , Beech Craig , Herold Martin
Published: November 2018

Forest cover and vegetation degradation was monitored across the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa and the performance of three different methods in detecting degradation was assessed using reference data. Breaks for Additive Season and Trend (BFAST) Monitor was used to identify potential forest cover and vegetation degradation using Landsat Normalized Difference Moisture Index (NDMI) time series data. Parametric probability-based magnitude thresholds, non-parametric random forest in conjunction with Soil-Adjusted Vegetation Index (SAVI) time series, and the combination of both methods were evaluated for their suitability to detect degradation for six land cover classes ranging from closed canopy forest to open grassland. The performance of degradation detection was largely dependent on tree cover and vegetation density. Satisfactory accuracies were obtained for closed woodland (user’s accuracy 87%, producer’s accuracy 71%) and closed forest (user’s accuracy 92%, producer’s accuracy 90%), with lower accuracies for open canopies. The performance of the three methods was more similar for closed canopies and differed for land cover classes with open canopies. Highest user’s accuracy was achieved when methods were combined, and the best performance for producer’s accuracy was obtained when random forest was used.
Using TanDEM-X Pursuit Monostatic Observations with a Large Perpendicular Baseline to Extract Glacial Topography
Authors: Hong Sang-Hoon , Wdowinski Shimon , Amelung Falk , Kim Hyun-Cheol , Won Joong-Sun , Kim Sang-Wan
Published: November 2018

Space-based Interferometric Synthetic Aperture Radar (InSAR) applications have been widely used to monitor the cryosphere over past decades. Owing to temporal decorrelation, interferometric coherence often severely degrades on fast moving glaciers. TanDEM-X observations can overcome the temporal decorrelation because of their simultaneous measurements by two satellite constellations. In this study, we used the TanDEM-X pursuit monostatic mode with large baseline formation following a scientific phase timeline to develop highly precise topographic elevation models of the Petermann Glacier of Northwest Greenland. The large baseline provided the advantage of extracting topographic information over low relief areas, such as the surface of a glacier. As expected, coherent interferometric phases (>0.8) were well maintained over the glaciers, despite their fast movement, due to the nearly simultaneous TanDEM-X measurements. The height ambiguity, which was defined as the altitude difference corresponding to a 2π phase change of the flattened interferogram, of the dataset was 10.63 m, which was favorable for extracting topography in a low relief region. We validated the TanDEM-X derived glacial topography by comparing it to the SAR/Interferometric radar altimeter observations acquired by CryoSat-2 and the IceBridge Airborne Topographic Mapper laser altimeter measurements. Both observations showed very good correlation within a few meters of the offsets (−12.5~−3.1 m), with respect to the derived glacial topography. Routine TanDEM-X observations will be very useful to better understand the dynamics of glacial movements and topographic change.
Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China
Authors: Lu Lei , Zhang Tingjun , Wang Tiejun , Zhou Xiaoming
Published: November 2018

Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are widely used in ecology, hydrology, vegetation monitoring, and global circulation models. Compared to the collection-5 (C5) LST products, the newly released collection-6 (C6) LST products have been refined over bare soil pixels. This study aims to evaluate the C6 MODIS 1-km LST product using multi-year in situ data covering barren surfaces. Evaluation using all in situ data shows that the MODIS C6 LSTs are underestimated with a root-mean-square error (RMSE) of 2.59 K for the site in the Gobi area, 3.05 K for the site in the sand desert area, and 2.86 K for the site in the desert steppe area at daytime. For nighttime LSTs, the RMSEs are 2.01 K, 2.88 K, and 1.80 K for the three sites, respectively. Both biases and RMSEs also show strong seasonal signals. Compared to the error of C5 1-km LSTs, the RMSE of C6 1-km LST product is smaller, especially for daytime LSTs, with a value of 2.24 K compared to 3.51 K. The large errors in the sand desert region are presumably due to the lack of global representativeness of the magnitude of emissivity adjustment and misclassification for the barren surface causing error in emissivities. It indicates that the accuracy of the MODIS C6 LST product might be further improved through emissivity adjustment with globally representative magnitude and accurate land cover classification. From this study, the MODIS C6 1-km LST product is recommended for applications.
Utilizing Multilevel Features for Cloud Detection on Satellite Imagery
Authors: Wu Xi , Shi Zhenwei
Published: November 2018

Cloud detection, which is defined as the pixel-wise binary classification, is significant in satellite imagery processing. In current remote sensing literature, cloud detection methods are linked to the relationships of imagery bands or based on simple image feature analysis. These methods, which only focus on low-level features, are not robust enough on the images with difficult land covers, for clouds share similar image features such as color and texture with the land covers. To solve the problem, in this paper, we propose a novel deep learning method for cloud detection on satellite imagery by utilizing multilevel image features with two major processes. The first process is to obtain the cloud probability map from the designed deep convolutional neural network, which concatenates deep neural network features from low-level to high-level. The second part of the method is to get refined cloud masks through a composite image filter technique, where the specific filter captures multilevel features of cloud structures and the surroundings of the input imagery. In the experiments, the proposed method achieves 85.38% intersection over union of cloud in the testing set which contains 100 Gaofen-1 wide field of view images and obtains satisfactory visual cloud masks, especially for those hard images. The experimental results show that utilizing multilevel features by the combination of the network with feature concatenation and the particular filter tackles the cloud detection problem with improved cloud masks.
Comparison of Artificial Intelligence and Physical Models for Forecasting Photosynthetically-Active Radiation
Authors: Feng Lan , Qin Wenmin , Wang Lunche , Lin Aiwen , Zhang Ming
Published: November 2018

Different kinds of radiative transfer models, including a relative sunshine-based model (BBM), a physical-based model for tropical environment (PBM), an efficient physical-based model (EPP), a look-up-table-based model (LUT), and six artificial intelligence models (AI) were introduced for modeling the daily photosynthetically-active radiation (PAR, solar radiation at 400–700 nm), using ground observations at twenty-nine stations, in different climatic zones and terrain features, over mainland China. The climate and terrain effects on the PAR estimates from the different PAR models have been quantitatively analyzed. The results showed that the Genetic model had overwhelmingly higher accuracy than the other models, with the lowest root mean square error (RMSE = 0.5 MJ m−2day−1), lowest mean absolute bias error (MAE = 0.326 MJ m−2day−1), and highest correlation coefficient (R = 0.972), respectively. The spatial–temporal variations of the annual mean PAR (APAR), in the different climate zones and terrains over mainland China, were further investigated, using the Genetic model; the PAR values in China were generally higher in summer than those in the other seasons. The Qinghai Tibetan Plateau had always been the area with the highest APAR (8.668 MJ m−2day−1), and the Sichuan Basin had always been the area with lowest APAR (4.733 MJ m−2day−1). The PAR datasets generated by the Genetic model, in this study, could be used in numerous PAR applications, with high accuracy.
Three-Dimensional Cloud Volume Reconstruction from the Multi-angle Imaging SpectroRadiometer
Authors: Lee Byungsuk , Di Girolamo Larry , Zhao Guangyu , Zhan Yizhe
Published: November 2018

Characterizing 3-D structure of clouds is needed for a more complete understanding of the Earth’s radiative and latent heat fluxes. Here we develop and explore a ray casting algorithm applied to data from the Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite, in order to reconstruct 3-D cloud volumes of observed clouds. The ray casting algorithm is first applied to geometrically simple synthetic clouds to show that, under the assumption of perfect, clear-conservative cloud masks, the reconstruction method yields overestimation in the volume whose magnitude depends on the cloud geometry and the resolution of the reconstruction grid relative to the image pixel resolution. The method is then applied to two hand-picked MISR scenes, fully accounting for MISR’s viewing geometry for reconstructions over the Earth’s ellipsoidal surface. The MISR Radiometric Camera-by-camera Cloud Mask (RCCM) at 1.1-km resolution and the custom cloud mask at 275-m resolution independently derived from MISR’s red, green, and blue channels are used as input cloud masks. A wind correction method, termed cloud spreading, is applied to the cloud masks to offset potential cloud movements over short time intervals between the camera views of a scene. The MISR cloud-top height product is used as a constraint to reduce the overestimation at the cloud top. The results for the two selected scenes show that the wind correction using the cloud spreading method increases the reconstructed volume up to 4.7 times greater than without the wind correction, and that the reconstructed volume generated from the RCCM is up to 3.5 times greater than that from the higher-resolution custom cloud mask. Recommendations for improving the presented cloud volume reconstructions, as well as possible future passive remote sensing satellite missions, are discussed.
Improving the Accuracy of Open Source Digital Elevation Models with Multi-Scale Fusion and a Slope Position-Based Linear Regression Method
Authors: Tian Yu , Lei Shaogang , Bian Zhengfu , Lu Jie , Zhang Shubi , Fang Jie
Published: November 2018

The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall accuracy of public domain DEMs by data fusion. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods—the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD), and nonlinear adaptive multi-scale decomposition (N-AMD)—are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D—30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the Ice Cloud and Land Elevation Satellite (ICESat) global land surface altimetry data as a reference. The fused DEMs offer significant improvements of 29.6% and 19.3% in RMSE at a mountainous site, and 27.4% and 15.5% over a low-relief region, compared to the SRTM-1 and AW3D30, respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region, respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.
Quantitative Assessment of Desertification in an Arid Oasis Using Remote Sensing Data and Spectral Index Techniques
Authors: Lamqadem Atman , Saber Hafid , Pradhan Biswajeet
Published: November 2018

Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the country. This study aims to map the degree of desertification in middle Draa Valley in 2017 using a Sentinel-2 MSI (multispectral instrument) image. Firstly, three indices, namely, tasselled cap brightness (TCB), greenness (TCG) and wetness (TCW) were extracted using the tasselled cap transformation method. Secondly, other indices, such as normalized difference vegetation index (NDVI) and albedo, were retrieved. Thirdly, a linear regression analysis was performed on NDVI–albedo, TCG–TCB and TCW–TCB combinations. Results showed a higher correlation between TCW and TCB (r = −0.812) than with that of the NDVI–albedo (r = −0.50). On the basis of this analysis, a desertification degree index was developed using the TCW–TCB feature space classification. A map of desertification grades was elaborated and divided into five classes, namely, nondesertification, low, moderate, severe and extreme levels. Results indicated that only 6.20% of the study area falls under the nondesertification grade, whereas 26.92% and 32.85% fall under the severe and extreme grades, respectively. The employed method was useful for the quantitative assessment of desertification with an overall accuracy of 93.07%. This method is simple, robust, powerful, and easy to use for the management and protection of the fragile arid and semiarid lands.
Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data
Authors: Kim Yunbin , Sa Jaewon , Chung Yongwha , Park Daihee , Lee Sungju
Published: November 2018

The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic feeding, operation of play equipment, and location detection. Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification).
An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal
Authors: Lee Jae-Neung , Byeon Yeong-Hyeon , Pan Sung-Bum , Kwak Keun-Chang
Published: November 2018

We herein propose an EigenECG Network (EECGNet) based on the principal component analysis network (PCANet) for the personal identification of electrocardiogram (ECG) from human biosignal data. The EECGNet consists of three stages. In the first stage, ECG signals are preprocessed by normalization and spike removal. The R peak points in the preprocessed ECG signals are detected. Subsequently, ECG signals are transformed into two-dimensional images to use as the input to the EECGNet. Further, we perform patch-mean removal and PCA algorithm similar to the PCANet from the transformed two-dimensional images. The second stage is almost the same as the first stage, where the mean removal and PCA process are repeatedly performed in the cascaded network. In the final stage, the binary quantization, block sliding, and histogram computation are performed. Thus, this EECGNet performs well without the use of back-propagation to obtain features from the visual content. We constructed a Chosun University (CU)-ECG database from an ECG sensor implemented by ourselves. Further, we used the well-known MIT Beth Israel Hospital (BIH) ECG database. The experimental results clearly reveal the good performance and effectiveness of the proposed method compared with conventional algorithms such as PCA, auto-encoder (AE), extreme learning machine (ELM), and ensemble extreme learning machine (EELM).
Template-Based Step Detection with Inertial Measurement Units
Authors: Oudre Laurent , Barrois-Müller Rémi , Moreau Thomas , Truong Charles , Vienne-Jumeau Aliénor , Ricard Damien , Vayatis Nicolas , Vidal Pierre-Paul
Published: November 2018

This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.
Pencil Lead as a Material for Microfluidic 3D-Electrode Assemblies
Authors: Witkowska Nery Emilia , Kundys-Siedlecka Magdalena , Furuya Yoshitaka , Jönsson-Niedziółka Martin
Published: November 2018

We present an electrochemical, microfluidic system with a working electrode based on an ordered 3D array of pencil leads. The electrode array was integrated into a plexiglass/PDMS channel. We tested the setup using a simple redox probe and compared the results with computer simulations. As a proof of concept application of the device we showed that the setup can be used for determination of dopamine concentration in physiological pH and ultrasensitive, although only qualitative, detection of p-nitrophenol with a limit of detection below 1 nmol L−1. The observed limit of detection for p-nitrophenol is not only much lower than achieved with similar methods but also sufficient for evaluation of exposure to pesticides such as methyl parathion through urinalysis. This low cost setup can be fabricated without the need for clean room facilities and in the future, due to the ordered structure of the electrode could be used to better understand the process of electroanalysis and electrode functionalization. To the best of our knowledge it is the first application of pencil leads as 3D electrochemical sensor in a microfluidic channel.
Towards the Internet of Flying Robots: A Survey
Authors: Huang Hailong , Savkin Andrey V.
Published: November 2018

The Internet of Flying Robots (IoFR) has received much attention in recent years thanks to the mobility and flexibility of flying robots. Although a lot of research has been done, there is a lack of a comprehensive survey on this topic. This paper analyzes several typical problems in designing IoFR for real applications, including wireless communication support, monitoring targets of interest, serving a wireless sensor network, and collaborating with ground robots. In particular, an overview of the existing publications on the coverage problem, connectivity of flying robots, energy capacity limitation, target searching, path planning, flying robot navigation with collision avoidance, etc., is presented. Beyond the discussion of these available approaches, some shortcomings of them are indicated and some promising future research directions are pointed out.
Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments
Authors: Ullah Ihsan , Qureshi Muhammad Bilal , Khan Uzair , Memon Sufyan Ali , Shi Yifang , Peng Dongliang
Published: November 2018

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch–Tung–Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.
Web Page Design Recommendations for People with Down Syndrome Based on Users’ Experiences
Authors: Alonso-Virgós Lucía , Rodríguez Baena Luís , Pascual Espada Jordán , González Crespo Rubén
Published: November 2018

At present, there is a high number of people with Down syndrome interested and trained to be an active part of society. According to the data extracted by our surveys we know that only 6% of the population with Down syndrome feels isolated in daily activities. However, when the activity requires the use of a computer, the percentage of people who feel isolated increases to 18%. This means that there are obvious website accessibility barriers that make it difficult for users with Down syndrome. To solve this problem, it is considered necessary to make an exhaustive study about Down syndrome. We know that the trisomy of chromosome 21 causes a series of symptoms that directly affect ones Internet browsing capabilities. For example, speech disturbances make communication and speed difficult. This guide is based on a neurological study of Down syndrome. Alterations in listening make understanding audio, retention of audio concepts and speed difficult. The alterations in the physiognomy of movement make it difficult for them to act quickly. Many of these alterations are caused by cognitive disability. After assessing the needs, the benefits of Web Content Accessibility Guidelines 2.0 (WCAG 2.0), and the existing usability guidelines are analyzed and those that may be useful for this profile are extracted. User tests are carried out through two websites developed specifically for this study with the aim of demonstrating the level of effectiveness of each of the planned guidelines. Considering the neurological characteristics of this intellectual disability, research is developed that seeks to extract a list of useful accessibility and usability guidelines for web developers.
IoT-Based Strawberry Disease Prediction System for Smart Farming
Authors: Kim Sehan , Lee Meonghun , Shin Changsun
Published: November 2018

Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models.
A Joint Multi-Path and Multi-Channel Protocol for Traffic Routing in Smart Grid Neighborhood Area Networks
Authors: Astudillo León Juan Pablo , De la Cruz Llopis Luis J.
Published: November 2018

In order to improve the management mechanisms of the electric energy transport infrastructures, the smart grid networks have associated data networks that are responsible for transporting the necessary information between the different elements of the electricity network and the control center. Besides, they make possible a more efficient use of this type of energy. Part of these data networks is comprised of the Neighborhood Area Networks (NANs), which are responsible for interconnecting the different smart meters and other possible devices present at the consumers’ premises with the control center. Among the proposed network technologies for NANs, wireless technologies are becoming more relevant due to their flexibility and increasing available bandwidth. In this paper, some general modifications are proposed for the routing protocol of the wireless multi-hop mesh networks standardized by the IEEE. In particular, the possibility of using multiple paths and transmission channels at the same time, depending on the quality of service needs of the different network traffic, is added. The proposed modifications have been implemented in the ns-3 simulator and evaluated in situations of high traffic load. Simulation results show improvements in the network performance in terms of packet delivery ratio, throughput and network transit time.