bluuebunny/crossref_metadata_2025 huggingface dataset:
Post-Prandial Changes in Salivary Glucocorticoids: Effects of Dietary Cholesterol and Associations with Bile Acid Excretion
Authors: Anderson Graham W. , Kenyon Christopher J. , Al-Dujaili Emad A.S.
Published: February 2019

Mechanisms to explain post-prandial increases in circulating glucocorticoids are not well understood and may involve increased adrenal secretion and/or altered steroid metabolism. We have compared salivary levels of cortisol and cortisone levels in healthy male and female volunteers fed either a low or cholesterol-rich midday meal. Urinary levels of steroids, bile acids and markers of lipid peroxidation were also measured. Males and females showed expected circadian changes in salivary steroids and postprandial peaks within 1h of feeding. After a high-cholesterol meal, postprandial cortisol increases were higher in males whereas post-prandial cortisone levels were higher in females. Urinary cortisol but not cortisone levels were higher on the day when males and females ate a high-cholesterol meal. Urinary bile acid excretion and anti-oxidant markers of lipid peroxidation, thiobarbituric acid reactive substances (TBARS), and total phenol content were not affected by dietary cholesterol but tended to be higher in males. Cross-tabulation of correlation coefficients indicated positive associations between urinary markers of peroxidation, bile acids, and cortisol:cortisone ratios. We conclude that dietary cholesterol (a substrate for steroidogenesis) does not have an acute effect on adrenal glucocorticoid synthesis and that gender but not a high-cholesterol meal may influence the interconversion of cortisol and cortisone. Longer term studies of the effects of dietary cholesterol are needed to analyze the associations between bile acids, steroid metabolism, and secretion and lipid peroxidation.
Activation of Lipid Mediator Formation Due to Lipoprotein Apheresis
Authors: Weylandt Karsten-H. , Schmöcker Christoph , Ostermann Annika I. , Kutzner Laura , Willenberg Ina , Kiesler Stefanie , Steinhagen-Thiessen Elisabeth , Schebb Nils Helge , Kassner Ursula
Published: February 2019

Lipoprotein apheresis reliably reduces low-density lipoprotein (LDL) cholesterol in patients with atherosclerotic disease and therapy-refractory hypercholesterolemia or elevated lipoprotein (a) (Lp(a)). Besides lowering lipoproteins and triglycerides, apheresis also decreases levels of essential omega-6 and omega-3 polyunsaturated fatty acids (n-6 and n-3 PUFAs) in blood plasma. In contrast, heparin-induced extracorporeal low-density lipoprotein precipitation (HELP) lipid apheresis might increase the formation of potentially pro-inflammatory and pro-thrombotic lipid mediators derived from n-6 and n-3 PUFAs. The study presented here analyzed lipid mediator profiles in the plasma of patients with hyperlipidemia treated by one of three different apheresis methods, either HELP, direct absorption (DA), or membrane filtration (MDF), in a direct pre- and post-apheresis comparison. Using gas chromatography and liquid chromatography tandem mass spectrometry (LC-MS/MS) we were able to analyze fatty acid composition and the formation of lipid mediators called oxylipins. Our data illustrate—particularly in HELP-treated patients—significant decreases of essential omega-6 and omega-3 polyunsaturated fatty acids in blood plasma but significant increases of PUFA-derived lipoxygenase-, as well as cyclooxygenase- and cytochrome P450-derived lipid mediators. Given that n-3 PUFAs in particular are presumed to be cardioprotective and n-3 PUFA-derived lipid mediators might limit inflammatory reactions, these data indicate that n-3 PUFA supplementation in the context of lipid apheresis treatment might have additional benefits through apheresis-triggered protective n-3 PUFA-derived lipid mediators.
Infant Complementary Feeding of Prebiotics for the Microbiome and Immunity
Authors: McKeen Starin , Young Wayne , Mullaney Jane , Fraser Karl , McNabb Warren C. , Roy Nicole C.
Published: February 2019

Complementary feeding transitions infants from a milk-based diet to solid foods, providing essential nutrients to the infant and the developing gut microbiome while influencing immune development. Some of the earliest microbial colonisers readily ferment select oligosaccharides, influencing the ongoing establishment of the microbiome. Non-digestible oligosaccharides in prebiotic-supplemented formula and human milk oligosaccharides promote commensal immune-modulating bacteria such as Bifidobacterium, which decrease in abundance during weaning. Incorporating complex, bifidogenic, non-digestible carbohydrates during the transition to solid foods may present an opportunity to feed commensal bacteria and promote balanced concentrations of beneficial short chain fatty acid concentrations and vitamins that support gut barrier maturation and immunity throughout the complementary feeding window.
Physicochemical Properties and Digestion of Lotus Seed Starch under High-Pressure Homogenization
Authors: Guo Zebin , Zhao Beibei , Chen Liding , Zheng Baodong
Published: February 2019

Lotus seed starch (LS), dispersed (3%, w/v) in deionized water was homogenized (0–180 MPa) with high-pressure homogenization (HPH) for 15 min. The effects of HPH treatment on the physicochemical properties of the starch system were investigated. The properties were affected by HPH to various extents, depending on the pressure. These influences can be explained by the destruction of the crystalline and amorphous regions of pressurized LS. The short-range order of LS was reduced by HPH and starch structure C-type was transformed into B-type, exhibiting lower transition temperatures and enthalpy. The LS absorbed a great deal of water under HPH and rapidly swelled, resulting in increased swelling power, solubility and size distribution. It then showed “broken porcelain-like” morphology with reduced pasting properties. Digestion of pressurized LS complex investigated by a dynamic in vitro rat stomach–duodenum model showed higher digestion efficiency and the residues exhibited gradual damage in morphology.
Impact of Nitric Oxide (NO) on the ROS Metabolism of Peroxisomes
Authors: Corpas Francisco J. , del Río Luis A. , Palma José M.
Published: February 2019

Nitric oxide (NO) is a gaseous free radical endogenously generated in plant cells. Peroxisomes are cell organelles characterized by an active metabolism of reactive oxygen species (ROS) and are also one of the main cellular sites of NO production in higher plants. In this mini-review, an updated and comprehensive overview is presented of the evidence available demonstrating that plant peroxisomes have the capacity to generate NO, and how this molecule and its derived products, peroxynitrite (ONOO−) and S-nitrosoglutathione (GSNO), can modulate the ROS metabolism of peroxisomes, mainly throughout protein posttranslational modifications (PTMs), including S-nitrosation and tyrosine nitration. Several peroxisomal antioxidant enzymes, such as catalase (CAT), copper-zinc superoxide dismutase (CuZnSOD), and monodehydroascorbate reductase (MDAR), have been demonstrated to be targets of NO-mediated PTMs. Accordingly, plant peroxisomes can be considered as a good example of the interconnection existing between ROS and reactive nitrogen species (RNS), where NO exerts a regulatory function of ROS metabolism acting upstream of H2O2.
Solid-State Foaming of Acrylonitrile-Butadiene-Styrene/Recycled Polyethylene Terephthalate Using Carbon Dioxide as a Blowing Agent
Authors: Kwon Dong Eui , Park Byung Kyu , Lee Youn-Woo
Published: February 2019

In this study, a single paragraph of acrylonitrile-butadiene-styrene (ABS)/recycled polyethylene terephthalate (R-PET) polymeric foams is prepared using CO2 as a blowing agent. First, the sorption kinetics of subcritical and supercritical CO2 are first studied at saturation temperatures from −20 to 40 °C and a pressure of 10 MPa, in order to estimate the diffusion coefficient and the sorption amount. As the sorption temperature increases, the diffusion coefficient of CO2 increases while the sorption amount decreases. Then, a series of two-step solid-state foaming experiments are conducted. In this process, a specimen is saturated with liquid CO2 and foamed by dipping the sample in a high-temperature medium at 60 to 120 °C. The effects of foaming temperature and depressurization rate on the morphology and structure of ABS/R-PET microcellular foams are examined. The mean cell size and the variation of the cell size distribution increases as the foaming temperature and the depressurization rate increases.
The Persistence Length of Semiflexible Polymers in Lattice Monte Carlo Simulations
Authors: Zhang Jing-Zi , Peng Xiang-Yao , Liu Shan , Jiang Bang-Ping , Ji Shi-Chen , Shen Xing-Can
Published: February 2019

While applying computer simulations to study semiflexible polymers, it is a primary task to determine the persistence length that characterizes the chain stiffness. One frequently asked question concerns the relationship between persistence length and the bending constant of applied bending potential. In this paper, theoretical persistence lengths of polymers with two different bending potentials were analyzed and examined by using lattice Monte Carlo simulations. We found that the persistence length was consistent with theoretical predictions only in bond fluctuation model with cosine squared angle potential. The reason for this is that the theoretical persistence length is calculated according to a continuous bond angle, which is discrete in lattice simulations. In lattice simulations, the theoretical persistence length is larger than that in continuous simulations.
Adsorption of Ethylenediaminetetraacetic Acid on a Gel-Type Ion-Exchange Resin for Purification of Liquid Waste Containing Cs Ions
Authors: Kim Jongho , Park Chan Woo , Lee Kune-Woo , Lee Taek Seung
Published: February 2019

Because of its excellent chelating property, ethylenediaminetetraacetic acid (EDTA) is used as a complex agent, not only for heavy metals, but also for radioactive isotopes during the decontamination of nuclear facilities. The removal of EDTA was investigated by adsorption with commercially available, gel-type, anion-exchange resins (AERs), which are based on cross-linked polystyrene with positive tertiary amine groups. Because of the positive charge on AERs, they could adsorb EDTA effectively even in a solution mixed with ions of cesium (Cs) via electrostatic attraction. Because EDTA adsorption by cation-exchange resins (CERs) was not possible, it was concluded that the negative charges on CERs do not contribute to the interaction with EDTA. The maximum adsorption capacity (qmax) of AER (2 g/L) for EDTA removal, calculated by the Langmuir isotherm model was 0.47 mmol/g for initial EDTA concentrations in the range of 0.01–1 mM in the EDTA/Cs mixed solution. The Langmuir isotherm model was found to be suitable for EDTA adsorption on AERs, indicative of monolayer adsorption. The results clearly suggested that the AERs could efficiently remove EDTA, regardless of the presence of nuclides, such as Cs ions in the aqueous solution.
Maleinized Linseed Oil as Epoxy Resin Hardener for Composites with High Bio Content Obtained from Linen Byproducts
Authors: Fombuena Vicent , Petrucci Roberto , Dominici Franco , Jordá-Vilaplana Amparo , Montanes Néstor , Torre Luigi
Published: February 2019

Green composites, with more than 78 wt.% of products obtained from linen linum usitatissimum, were developed in this research work. Epoxidized linseed oil (ELO) was used as bio-based resin, a mix of nadic methyl anhydride (MNA) and maleinized linseed oil (MLO) were used as cross-linkers and finally, flax fabrics were used to obtain composite laminates by resin transfer molding (RTM). The flax fibers were modified using amino-silane, glycidyl-silane and maleic anhydride treatment in order to increase the compatibility between lignocellulosic fibers and the polymeric matrix. Mechanical and thermal properties were studied by flexural, tensile and impact test, as well as dynamic mechanical analyses (DMA) to study the viscoelastic behavior. Contrary to what could be expected, when fibers are previously treated in presence of MLO, a reduction of anchorage points is obtained causing a substantial increase in the ductile properties compared with composites without previous fiber treatment or without MLO.
Regenerated Antheraea pernyi Silk Fibroin/Poly(N-isopropylacrylamide) Thermosensitive Composite Hydrogel with Improved Mechanical Strength
Authors: Wang Boxiang , Zhang Song , Wang Yifan , Si Bo , Cheng Dehong , Liu Li , Lu Yanhua
Published: February 2019

At present, Antheraea pernyi silk fibroin (ASF) has attracted research efforts to investigate it as a raw material for fabrication of biomedical devices because of its superior cytocompatibility. Nevertheless, native ASF is not easily processed into a hydrogel without any crosslinking agent, and a single hydrogel shows poor mechanical properties. In this paper, a series of ASF/poly (N-isopropylacrylamide) (PNIPAAm) composite hydrogels with different ASF contents were manufactured by a simple in situ polymerization method without any crosslinking agent. Meanwhile, the structures, morphologies and thermal properties of composite hydrogels were investigated by XRD, FTIR, SEM, DSC and TGA, respectively. The results indicate that the secondary structure of silk in the composite hydrogel can be controlled by changing the ASF content and the thermal stability of composite hydrogels is enhanced with an increase in crystalline structure. The composite hydrogels showed similar lower critical solution temperatures (LCST) at about 32 °C, which matched well with the LCST of PNIPAAm. Finally, the obtained thermosensitive composite hydrogels exhibited enhanced mechanical properties, which can be tuned by varying the content of ASF. This strategy to prepare an ASF-based responsive composite hydrogel with enhanced mechanical properties represents a valuable route for developing the fields of ASF, and, furthermore, their attractive applications can meet the needs of different biomaterial fields.
Pricing Options and Computing Implied Volatilities using Neural Networks
Authors: Liu Shuaiqiang , Oosterlee Cornelis W. , Bohte Sander M.
Published: February 2019

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent’s iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.
Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification
Authors: Xu Zongxia , Zhang Zhenxin , Zhong Ruofei , Chen Dong , Sun Taochun , Deng Xin , Li Zhen , Qin Cheng-Zhi
Published: February 2019

Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point clusters) in consideration of the density distribution of ground objects. The space projection method is first used to convert the three-dimensional point cloud into a two-dimensional (2D) image. The image is then mapped to the 2D manifold space, and restricted centroidal Voronoi tessellation is built for initial segmentation of content-sensitive point clusters. Thus, the segmentation results take the entity content (density distribution) into account, and the initial classification unit is adapted to the density of ground objects. The normalized cut is then used to segment the initial point clusters to construct content-sensitive multilevel point clusters. Following this, the point-based hierarchical features of each point cluster are extracted, and the multilevel point-cluster feature is constructed by sparse coding and latent Dirichlet allocation models. Finally, the hierarchical classification framework is created based on multilevel point-cluster features, and the AdaBoost classifiers in each level are trained. The recognition results of different levels are combined to effectively improve the classification accuracy of the ALS point cloud in the test process. Two scenes are used to experimentally test the method, and it is compared with three other state-of-the-art techniques.
Monitoring 40-Year Lake Area Changes of the Qaidam Basin, Tibetan Plateau, Using Landsat Time Series
Authors: Li Huiying , Mao Dehua , Li Xiaoyan , Wang Zongming , Wang Cuizhen
Published: February 2019

Areal changes of high-altitude inland lakes on the Qaidam Basin (QB) of the Tibetan Plateau are reliable indicators of climate change and anthropogenic disturbance. Due to the physical difficulties to access, our knowledge of the spatial patterns and processes of climatic and human impacts on the Basin has been limited. Focusing on lake area changes, this study used long-term Landsat images to map the patterns of lakes and glaciers in 1977, 1990, 2000, and 2015, and to monitor the spatially explicit changes of lakes between 1977 and 2015. Results revealed that the total number of lakes (area > 0.5 km2) increased by 18, while their total area expanded by 29.8%, from 1761.5 ± 88.1 km2 to 2285.9 ± 91.4 km2. Meanwhile, glaciers have decreased in area by 259.16 km2 in the past four decades. The structural equation model (SEM) was applied to examine the integrative effects of natural and anthropogenic factors on lake area. Precipitation change exhibited the most significant influence on lake area in the QB from 1977 to 2000, while human activities also played an important role in the expansion of lakes in the QB in the period 2000–2015. In particular, extensive exploitation of salt lakes as mining resources resulted in severe changes in lake area and landscape. The continuously expanding salt lakes inundated the road infrastructure nearby, posing great threats to road safety. This study shed new light on the impacts of recent environmental changes and human interventions on lakes in the Qaidam Basin, which could assist policy-making for protecting the lakes and for strengthening the ecological improvement of this vast, arid basin.
Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
Authors: Ettehadi Osgouei Paria , Kaya Sinasi , Sertel Elif , Alganci Ugur
Published: February 2019

In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification.
Modeling and Precise Processing for Spaceborne Transmitter/Missile-Borne Receiver SAR Signals
Authors: Tang Shiyang , Guo Ping , Zhang Linrang , Lin Chunhui
Published: February 2019

The spaceborne transmitter/missile-borne receiver (ST/MR) synthetic aperture radar (SAR) could provide several unique advantages, such as wide coverage, unrestricted geography, a small detection probability of the missile, and forward-looking imaging. However, it is also accompanied by problems in imaging, including geometric model establishment and focusing algorithm design. In this paper, an ST/MR SAR model is first presented and then the flight-path constraint, characterized by geometric configurations, is derived. Considering the impacts brought about by the maneuvers of the missile, a non-‘Stop-Go’ mathematical model is devised and it can avoid the large errors introduced by the acceleration, which is neglected in the traditional model. Finally, a two-dimensional (2-D) scaling algorithm is developed to focus the ST/MR data. Without introducing any extra operations, it can greatly remove the spatial variations of the range, azimuth, and cross-coupling phases simultaneously and entirely in the 2-D hybrid domain. Simulation results verify the effectiveness of the proposed model and focusing approach.
An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor
Authors: Li Qiang , Wang Qi , Li Xuelong
Published: February 2019

A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small.
Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea
Authors: Snapir Boris , Waine Toby , Biermann Lauren
Published: February 2019

Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel’s length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem.
Improving Ecotope Segmentation by Combining Topographic and Spectral Data
Authors: Radoux Julien , Bourdouxhe Axel , Coos William , Dufrêne Marc , Defourny Pierre
Published: February 2019

Ecotopes are the smallest ecologically distinct landscape features in a landscape mapping and classification system. Mapping ecotopes therefore enables the measurement of ecological patterns, process and change. In this study, a multi-source GEOBIA workflow is used to improve the automated delineation and descriptions of ecotopes. Aerial photographs and LIDAR data provide input for landscape segmentation based on spectral signature, height structure and topography. Each segment is then characterized based on the proportion of land cover features identified at 2 m pixel-based classification. The results show that the use of hillshade bands simultaneously with spectral bands increases the consistency of the ecotope delineation. These results are promising to further describe biotopes of high ecological conservation value, as suggested by a successful test on ravine forest biotope.
Atmospheric Correction for Tower-Based Solar-Induced Chlorophyll Fluorescence Observations at O2-A Band
Authors: Liu Xinjie , Guo Jian , Hu Jiaochan , Liu Liangyun
Published: February 2019

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O2-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O2-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E790/E660). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O2-A band.
New Approaches for Robust and Efficient Detection of Persistent Scatterers in SAR Tomography
Authors: Zhu Xiaoxiang , Dong Zhen , Yu Anxi , Wu Manqing , Li Dexin , Zhang Yongsheng
Published: February 2019

Persistent scatterer interferometry (PSI) has the ability to acquire submeter-scale digital elevation model (DEM) and millimeter-scale deformation. A limitation to the application of PSI is that only single persistent scatterers (SPSs) are detected, and pixels with multiple dominant scatterers from different sources are discarded in PSI processing. Synthetic aperture radar (SAR) tomography is a promising technique capable of resolving layovers. In this paper, new approaches based on a novel two-tier network aimed at robust and efficient detection of persistent scatterers (PSs) are presented. The calibration of atmospheric phase screen (APS) and the detection of PSs can be jointly implemented in the novel two-tier network. A residue-to-signal ratio (RSR) estimator is proposed to evaluate whether the APS is effectively calibrated and to select reliable PSs with accurate estimation. In the first-tier network, a Delaunay triangulation network is constructed for APS calibration and SPS detection. RSR thresholding is used to adjust the first-tier network by discarding arcs and SPS candidates (SPSCs) with inaccurate estimation, yielding more than one main network in the first-tier network. After network adjustment, we attempt to establish reliable SPS arcs to connect the main isolated networks, and the expanded largest connected network is then formed with more manual structure information subtracted. Furthermore, rather than the weighted least square (WLS) estimator, a network decomposition WLS (ND-WLS) estimator is proposed to accelerate the retrieval of absolute parameters from the expanded largest connected network, which is quite useful for large network inversion. In the second-tier network, the remaining SPSs and all the double PSs (DPSs) are detected and estimated with reference to the expanded largest connected network. Compared with traditional two-tier network-based methods, more PSs can be robustly and efficiently detected by the proposed new approaches. Experiments on interferometric high resolution TerraSAR-X SAR images are given to demonstrate the merits of the new approaches.
Did Ecological Restoration Hit Its Mark? Monitoring and Assessing Ecological Changes in the Grain for Green Program Region Using Multi-source Satellite Images
Authors: Tang Yuzhi , Shao Quanqin , Liu Jiyuan , Zhang Haiyang , Yang Fan , Cao Wei , Wu Dan , Gong Guoli
Published: February 2019

Ecological restoration programs are expected to control environmental deterioration and enhance ecosystem functions under a scenario of increasing human disturbance. The largest ecological restoration program ever implemented in China, the first round of the countrywide Grain for Green Program (GGP), finished in 2010. However, it is not known whether the ecological changes that resulted from the GGP met the restoration goal across the whole implementation region. In this study, we monitored and assessed the ecological changes in the whole GGP region in China over the lifetime of the first round of implementation (2000–2010), by establishing a comprehensive assessment indicator system composed of ecosystem pattern, ecosystem quality (EQ), and key ecosystem services (ESs). Remote sensing interpretation, ecological model simulations based on multi-source images, and trend analysis were used to generate land use and land cover (LULC) datasets and estimate ES and ESs indicators. Results showed that while forest increased by 0.77%, artificial land increased more intensely by 22.38%, and cropland and grassland decreased by 1.81% and 0.68%, respectively. The interconversion of cropland and forest played a primary role in ecosystem pattern change. The increase in ecosystem quality measures, including fractional vegetation cover (0.1459% yr−1), leaf area index (0.0121 yr−1), and net primary productivity (2.6958 gC m−2 yr−1), and the mitigation of ecosystem services deterioration in soil water loss (−0.0841 t ha yr−1) and soil wind loss (−1.0071 t ha yr−1) in the GGP region, indicated the positive ecological change in the GGP region to some extent, while southern GGP subregions improved more than the those in the north on the whole. The GGP implementation other than climate change impacted ecological change, with contributions of 14.23%, 9.94%, 8.23%, 30.45%, and 18.05% in the ecological outputs mentioned above, respectively. However, the water regulation did not improve (−2283 t km−2 yr−1), revealing trade-offs between ecosystem services and inappropriate afforestation in ecological restoration programs. Future GGP implementation should change the practice of large-scale afforestation, and focus more on the restoration of existing forest and cultivation of young plantings, formulating rational and specific plans and designs for afforestation areas through the establishment of near-natural vegetation communities, instead of single-species plantations, guided by regional climate and geographical characteristics.
Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models
Authors: Wang Li , Chang Qingrui , Li Fenling , Yan Lin , Huang Yong , Wang Qi , Luo Lili
Published: February 2019

A in situ hyperspectral dataset containing multiple growth stages over multiple growing seasons was used to build paddy rice leaf area index (LAI) estimation models with a special focus on the effects of paddy rice growth stage development. The univariate regression method applied to the vegetation index (VI), the traditional multivariate calibration method of partial least squares regression (PLSR), and modern machine learning methods such as support vector regression (SVR), random forests (RF), and artificial neural networks (ANN) based on the original and first-derivative hyperspectral data were evaluated in this study for paddy rice LAI estimation. All the models were built on the whole growing season and on each separate vegetative, reproductive and ripening growth stage of paddy rice separately. To ensure a fair comparison, the models of the whole growing season were also validated on data for each separate growth stage of the standalone validation dataset. Moreover, the optimal band pairs for calculating narrowband difference vegetative index (DVI), normalized difference vegetation index (NDVI) and simple ratio vegetation index (SR) were determined for the whole growing season and for each separate growth stage separately. The results showed that for both the whole growing season and for each single growth stage, the red-edge and near-infrared band pairs are optimal for formulating the narrowband DVI, NDVI and SR. Among the four multivariate calibration methods, SVR and RF yielded more accurate results than the other two methods. The SVR and RF models built on first-derivative spectra provided more accurate results than the corresponding models on the original spectra for both whole growing season models and separate growth stage models. Comparing the prediction accuracy based on the whole growing season revealed that the RF and SVR models showed an advantage over the VI models. However, comparing the prediction accuracy based on each growth stage separately showed that the VI models provided more accurate results for the vegetative growth stages. The SVR and RF models provided more accurate results for the ripening growth stage. However, the whole growing season RF model on first-derivative spectra could provide reasonable accuracy for each single growth stage.
Responses of a Resistive Soot Sensor to Different Mono-Disperse Soot Aerosols
Authors: Reynaud Adrien , Leblanc Mickaël , Zinola Stéphane , Breuil Philippe , Viricelle Jean-Paul
Published: February 2019

Since 2011, the Euro 5b European standard limits the particle number (PN) emissions in addition to the particulate mass (PM) emissions. New thermal engine equipped vehicles also have to auto-diagnose their own particulate filter (Diesel particulate filter or gasoil particulate filter) using on-board diagnostic (OBD) sensors. Accumulative resistive soot sensors seem to be good candidates for PM measurements. The aim of this study is to bring more comprehension about soot microstructures construction in order to link the response of such a sensor to particle size and concentration. The sensor sensitivity to the particle size has been studied using successively an electrostatic and an aerodynamic classification, showing the same trend.
Geographic and Opportunistic Recovery with Depth and Power Transmission Adjustment for Energy-Efficiency and Void Hole Alleviation in UWSNs
Authors: Mateen Abdul , Awais Muhammad , Javaid Nadeem , Ishmanov Farruh , Afzal Muhammad Khalil , Kazmi Saqib
Published: February 2019

Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end-to-end delay and maximum energy dissipation. The provisioning of the aforementioned factors has become a challenging task for the research community. In UWSNs, battery consumption is inevitable and has a direct impact on the performance of the network. Most of the time energy dissipates due to the creation of void holes and imbalanced network deployment. In this work, two routing protocols are proposed to avoid the void hole and extra energy dissipation problems which, due to which lifespan of the network increases. To show the efficacy of the proposed routing schemes, they are compared with the state of the art protocols. Simulation results show that the proposed schemes outperform the counterparts.
Gesture Prediction Using Wearable Sensing Systems with Neural Networks for Temporal Data Analysis
Authors: Kanokoda Takahiro , Kushitani Yuki , Shimada Moe , Shirakashi Jun-ichi
Published: February 2019

A human gesture prediction system can be used to estimate human gestures in advance of the actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for human–computer interaction. Therefore, the gesture prediction system must be able to capture hand movements that are both complex and quick. We have already reported a method that allows strain sensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human gestures with high sensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction by artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs. Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs, which enabled the proposed system to successfully predict hand gestures in real time. A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs.
Enhanced Hydrogen Detection in ppb-Level by Electrospun SnO2-Loaded ZnO Nanofibers
Authors: Lee Jae-Hyoung , Kim Jin-Young , Kim Jae-Hun , Kim Sang
Published: February 2019

High-performance hydrogen sensors are important in many industries to effectively address safety concerns related to the production, delivering, storage and use of H2 gas. Herein, we present a highly sensitive hydrogen gas sensor based on SnO2-loaded ZnO nanofibers (NFs). The xSnO2-loaded (x = 0.05, 0.1 and 0.15) ZnO NFs were fabricated using an electrospinning technique followed by calcination at high temperature. Microscopic analyses demonstrated the formation of NFs with expected morphology and chemical composition. Hydrogen sensing studies were performed at various temperatures and the optimal working temperature was selected as 300 °C. The optimal gas sensor (0.1 SnO2 loaded ZnO NFs) not only showed a high response to 50 ppb hydrogen gas, but also showed an excellent selectivity to hydrogen gas. The excellent performance of the gas sensor to hydrogen gas was mainly related to the formation of SnO2-ZnO heterojunctions and the metallization effect of ZnO.
A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements
Authors: Wang Weihang , Liu Peilin , Ying Rendong , Wang Jun , Qian Jiuchao , Jia Jialu , Gao Jiefeng
Published: February 2019

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.
Design of a 1-bit MEMS Gyroscope using the Model Predictive Control Approach
Authors: Wu Xiaofeng , Xie Zhicheng , Bai Xueliang , Kwan Trevor
Published: February 2019

In this paper, a bi-level Delta-Sigma modulator-based MEMS gyroscope design is presented based on a Model Predictive Control (MPC) approach. The MPC is popular because of its capability of handling hard constraints. In this work, we propose to combine the 1-bit nature of the bi-level Delta-Sigma modulator output with the MPC to develop a 1-bit processing-based MPC (OBMPC). This paper will focus on the affine relationship between the 1-bit feedback and the in-loop MPC controller, as this can potentially remove the multipliers from the controller. In doing so, the computational requirement of the MPC control is significantly alleviated, which makes the 1-bit MEMS Gyroscope feasible for implementation. In addition, a stable constrained MPC is designed, so that the input will not overload the quantizer while maintaining a higher Signal-to-Noise Ratio (SNR).
High-Sensitivity Real-Time Tracking System for High-Speed Pipeline Inspection Gauge
Authors: Piao Guanyu , Guo Jingbo , Hu Tiehua , Deng Yiming
Published: February 2019

Real-time tracking of pipeline inspection gauges (PIGs) is an important aspect of ensuring the safety of oil and gas pipeline inline inspections (ILIs). Transmitting and receiving extremely low frequency (ELF) magnetic signals is one of the preferred methods of tracking. Due to the increase in physical parameters of the pipeline including transportation speed, wall thickness and burial depth, the ELF magnetic signals received are short transient (1-second duration) and very weak (10 pT), making the existing above-ground-marker (AGM) systems difficult to operate correctly. Based on the short transient very weak characteristics of ELF signals studied with a 2-D finite-element method (FEM) simulation, a data fusion model was derived to fuse the envelope decay rates of ELF signals by a least square (LS) criterion. Then, a fast-decision-tree (FDT) method is proposed to estimate the fused envelope decay rate to output the maximized orthogonal signal power for the signal detection through a determined topology and a fast calculation process, which was demonstrated to have excellent real-time detection performance. We show that simulation and experimental results validated the effectiveness of the proposed FDT method, and describe the high-sensitivity detection and real-time implementation of a high-speed PIG tracking system, including a transmitter, a receiver, and a pair of orthogonal search coil sensors.
The Intertwined Relationship between Power and Patriarchy: Examples from Resource Extractive Industries
Authors: Suliman Nadine Naguib
Published: February 2019

This study examines the relationships between extractive industries, power and patriarchy, raising attention to the negative social and environmental impacts these relationships have had on communities globally. Wealth accumulation, gender and environment inequality have occurred for decades or more as a result of patriarchal structures, controlled by the few in power. The multiple indirect ways these concepts have evolved to function in modern day societies further complicates attempts to resolve them and transform the social and natural world towards a more sustainable model. Partly relying on queer ecology, this paper opens space for uncovering some hidden mechanisms of asserting power and patriarchal methods of domination in resource-extractive industries and impacted populations. I hypothesize that patriarchy and gender inequality have a substantial impact on power relations and control of resources, in particular within the energy industry. Based on examples from the literature used to illustrate these processes, patriarchy-imposed gender relations are embedded in communities with large resource extraction industries and have a substantial impact on power relations, especially relative to wealth accumulation. The paper ends with a call for researchers to consider these issues more deeply and conceptually in the development of case studies and empirical analysis.
Monitoring Training Load, Well-Being, Heart Rate Variability, and Competitive Performance of a Functional-Fitness Female Athlete: A Case Study
Authors: Tibana Ramires Alsamir , Sousa Nuno Manuel Frade de , Prestes Jonato , Feito Yuri , Ernesto Carlos , Voltarelli Fabrício Azevedo
Published: February 2019

The aim of this case study was to quantify the magnitude of internal load, acute/chronic workload ratio (ACWR), well-being perception, and heart rate variability (HRV) following 38 weeks of functional-fitness training in a female elite athlete. The internal load was obtained with session rating perceived exertion (session-RPE) while the ACWR was calculated by dividing the acute workload by the chronic workload (four-week average). Furthermore, HRV measurements were analyzed via a commercially available smartphone (HRV4training) each morning upon waking whilst in a supine position. The magnitude of internal load was: the weekly mean total during the 38 weeks was 2092 ± 861 arbitrary units (AU); during the preparation for the Open 2018 was 1973 ± 711 AU; during the Open 2018 it was 1686 ± 412 AU; and during the preparation for the Latin America Regional was 3174 ± 595 AU. The mean ACWR was 1.1 ± 0.5 and 50% of the weeks were outside of the ‘safe zone’. The well-being during the 38 weeks of training was 19.4 ± 2.3 points. There were no correlations between training load variables (weekly training load, monotony, ACWR, and HRV), and recuperation subjective variables (well-being, fatigue, sleep, pain, stress, and mood). This case study showed that the training load can be varied in accordance with preparation for a specific competition and ACWR revealed that 50% of the training weeks were outside of the ‘safe zone’, however, no injuries were reported by the athlete. The effectiveness and cost of these methods are very practical during real world functional-fitness.