bluuebunny/crossref_metadata_2025 huggingface dataset:
Fatty Acids Composition of Blood Cell Membranes and Peripheral Inflammation in the PREDIMED Study: A Cross-Sectional Analysis
Authors: Muralidharan Jananee , Papandreou Christopher , Sala-Vila Aleix , Rosique-Esteban Nuria , Fitó Montserrat , Estruch Ramon , Angel Martínez-González Miguel , Corella Dolores , Ros Emilio , Razquín Cristina , Castañer Olga , Salas-Salvadó Jordi , Bulló Monica
Published: March 2019

There is limited evidence from epidemiological studies for the inflammatory or anti-inflammatory properties of fatty acids in blood cell membranes. Therefore, this study examined associations between baseline (n = 282) and 1-year (n = 143) changes in the levels of fatty acids in blood cell membranes with circulating inflammatory markers in older adults at high cardiovascular risk. The data for this cross-sectional analysis was obtained from a case-control study within the PREDIMED study. Linear regression with elastic net penalty was applied to test associations between measured fatty acids and inflammatory markers. Several fatty acids were associated with interferon-γ (IFNγ) and interleukins (ILs) IL-6, IL-8, and IL-10 at baseline and additionally also with IL-1b at 1 year. Omega-6 fatty acids were consistently positively associated with pro-inflammatory IL-6 and IL-8 at baseline. Omega-3 fatty acids including C20:5n3 and C18:3n3 were negatively associated with IFN-γ at 1 year. It is interesting to note that the cis and trans forms of C16:1n7 at 1 year were oppositely associated with the inflammatory markers. C16:1n7trans was negatively associated with IFN-γ, IL-6, IL-8, IL-10, and IL-1b, whereas C16:1n7cis was positively associated with IL-1b. This study adds to the growing body of evidence suggesting potential differences in inflammatory or anti-inflammatory properties of fatty acids in blood cell membranes.
Trans-Cinnamic Acid Stimulates White Fat Browning and Activates Brown Adipocytes
Authors: Kang Nam Hyeon , Mukherjee Sulagna , Yun Jong Won
Published: March 2019

Recently, pharmacological activation of brown fat and induction of white fat browning (beiging) have been considered promising strategies to treat obesity. To search for natural products that could stimulate the process of browning in adipocytes, we evaluated the activity of trans-cinnamic acid (tCA), a class of cinnamon from the bark of Cinnamomum cassia, by determining genetic expression using real time reverse transcription polymerase chain reaction (RT-PCR) and protein expression by immunoblot analysis for thermogenic and fat metabolizing markers. In our study tCA induced brown like-phenotype in 3T3-L1 white adipocytes and activated HIB1B brown adipocytes. tCA increased protein content of brown-fat-specific markers (UCP1, PRDM16, and PGC-1α) and expression levels of beige-fat-specific genes (Cd137, Cidea, Cited1, Tbx1, and Tmen26) in 3T3-L1 white adipocytes, as well as brown-fat-specific genes (Lhx8, Ppargc1, Prdm16, Ucp1, and Zic1) in HIB1B brown adipocytes. Furthermore, tCA reduced expression of key adipogenic transcription factors C/EBPα and PPARγ in white adipocytes, but enhanced their expressions in brown adipocytes. In addition, tCA upregulates lipid catabolism. Moreover, mechanistic study revealed that tCA induced browning in white adipocytes by activating the β3-AR and AMPK signaling pathways. tCA can induce browning, increase fat oxidation, reduce adipogenesis and lipogenesis in 3T3-L1 adipocytes, and activate HIB1B adipocytes, suggesting its potential to treat obesity.
An Oral Glucose Load Decreases Postprandial Extracellular Vesicles in Obese Adults with and without Prediabetes
Authors: Eichner Natalie Z. M. , Gilbertson Nicole M. , Musante Luca , La Salvia Sabrina , Weltman Arthur , Erdbrügger Uta , Malin Steven K.
Published: March 2019

Although extracellular vesicles (EVs) are a novel biomediator of type 2 diabetes (T2D) and cardiovascular disease (CVD), the effects of hyperglycemia on EVs in humans is unknown. We tested the hypothesis that a 75-g oral glucose tolerance test (OGTT) would promote changes in EVs in relation to CVD risk. Twenty-five obese adults (Age: 52.4 ± 3.2 year, BMI: 32.5 ± 1.2 kg/m2) were screened for normal glucose tolerance (NGT, n = 8) and prediabetes (PD, n = 17) using American Diabetes Association criteria (75 g OGTT and/or HbA1c). Body composition (bioelectrical impedance) and fitness (VO2peak) were measured. Arterial stiffness (augmentation index; AIx) was measured at 0, 60- and 120-min while insulin, glucose, and free fatty acids were evaluated every 30 min during the OGTT to assess CVD risk. Annexin V positive (AV+) and Annexin V negative (AV−) total EVs, platelet EVs (CD31+/CD41+; CD41+), leukocyte EVs (CD45+; CD45+/CD41−), platelet endothelial cell adhesion molecule (PECAM) (CD31+) and endothelial EVs (CD 31+/CD41−; CD105+) were collected at 0 and 120 min. There were no differences in age, BMI, or body fat between NGT and PD (all P > 0.63). Total EVs, AV+ CD31+ (PECAM), and AV+ CD31+/CD41− (endothelial) EVs decreased after the OGTT (P ≤ 0.04). Circulating insulin at 2-h correlated with elevated post-prandial AV− CD45+ (r = 0.48, P = 0.04) while arterial stiffness related to reduced total EVs (r = −0.49, P = 0.03) and AV+ CD41+ (platelet) (r = −0.52, P = 0.02). An oral glucose load lowers post-prandial total, platelet, and endothelial EVs in obese adults with NGT and prediabetes in relation to CVD risk.
An Artificial Neural Network Approach to Predict the Effects of Formulation and Process Variables on Prednisone Release from a Multipartite System
Authors: Manda Arthur , Walker Roderick B. , Khamanga Sandile M. M.
Published: March 2019

The impact of formulation and process variables on the in-vitro release of prednisone from a multiple-unit pellet system was investigated. Box-Behnken Response Surface Methodology (RSM) was used to generate multivariate experiments. The extrusion-spheronization method was used to produce pellets and dissolution studies were performed using United States Pharmacopoeia (USP) Apparatus 2 as described in USP XXIV. Analysis of dissolution test samples was performed using a reversed-phase high-performance liquid chromatography (RP-HPLC) method. Four formulation and process variables viz., microcrystalline cellulose concentration, sodium starch glycolate concentration, spheronization time and extrusion speed were investigated and drug release, aspect ratio and yield were monitored for the trained artificial neural networks (ANN). To achieve accurate prediction, data generated from experimentation were used to train a multi-layer perceptron (MLP) using back propagation (BP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) 57 training algorithm until a satisfactory value of root mean square error (RMSE) was observed. The study revealed that the in-vitro release profile of prednisone was significantly impacted by microcrystalline cellulose concentration and sodium starch glycolate concentration. Increasing microcrystalline cellulose concentration retarded dissolution rate whereas increasing sodium starch glycolate concentration improved dissolution rate. Spheronization time and extrusion speed had minimal impact on prednisone release but had a significant impact on extrudate and pellet quality. This work demonstrated that RSM can be successfully used concurrently with ANN for dosage form manufacture to permit the exploration of experimental regions that are omitted when using RSM alone.
Pharmacokinetics and Pharmacodynamics Modeling and Simulation Systems to Support the Development and Regulation of Liposomal Drugs
Authors: He Hua , Yuan Dongfen , Wu Yun , Cao Yanguang
Published: March 2019

Liposomal formulations have been developed to improve the therapeutic index of encapsulated drugs by altering the balance of on- and off-targeted distribution. The improved therapeutic efficacy of liposomal drugs is primarily attributed to enhanced distribution at the sites of action. The targeted distribution of liposomal drugs depends not only on the physicochemical properties of the liposomes, but also on multiple components of the biological system. Pharmacokinetic–pharmacodynamic (PK–PD) modeling has recently emerged as a useful tool with which to assess the impact of formulation- and system-specific factors on the targeted disposition and therapeutic efficacy of liposomal drugs. The use of PK–PD modeling to facilitate the development and regulatory reviews of generic versions of liposomal drugs recently drew the attention of the U.S. Food and Drug Administration. The present review summarizes the physiological factors that affect the targeted delivery of liposomal drugs, challenges that influence the development and regulation of liposomal drugs, and the application of PK–PD modeling and simulation systems to address these challenges.
Facile Fabrication of Magnetic, Durable and Superhydrophobic Cotton for Efficient Oil/Water Separation
Authors: Yu Mingguang , Wang Qing , Yang Wenxin , Xu Yonghang , Zhang Min , Deng Qianjun , Liu Guang
Published: March 2019

In this paper, we present a facile and efficient strategy for the fabrication of magnetic, durable, and superhydrophobic cotton for oil/water separation. The superhydrophobic cotton functionalized with Fe3O4 magnetic nanoparticles was prepared via the in situ coprecipitation of Fe2+/Fe3+ ions under ammonia solution on cotton fabrics using polyvinylpyrrolidone (PVP) as a coupling agent and hydrophobic treatment with tridecafluorooctyl triethoxysilane (FAS) in sequence. The as-prepared cotton demonstrated excellent superhydrophobicity with a water contact angle of 155.6° ± 1.2° and good magnetic responsiveness. Under the control of the external magnetic field, the cotton fabrics could be easily controlled to absorb the oil from water as oil absorbents, showing high oil/water separation efficiency, even in hot water. Moreover, the cotton demonstrated remarkable mechanical durable properties, being strongly friction-resistant against sandpaper and finger wipe, while maintaining its water repellency. This study developed a novel and efficient strategy for the construction of magnetic, durable, and superhydrophobic biomass-based adsorbent for oil/water separation, which can be easily scaled up for practical oil absorption.
Novel Chemical Cross-Linked Ionogel Based on Acrylate Terminated Hyperbranched Polymer with Superior Ionic Conductivity for High Performance Lithium-Ion Batteries
Authors: Zhao Kang , Song Hongzan , Duan Xiaoli , Wang Zihao , Liu Jiahang , Ba Xinwu
Published: March 2019

A new family of chemical cross-linked ionogel is successfully synthesized by photopolymerization of hyperbranched aliphatic polyester with acrylate terminal groups in an ionic liquid of 1-butyl-3-methylimidazolium tetrafluoroborate (BMIMBF4). The microstructure, viscoelastic behavior, mechanical property thermal stability, and ionic conductivities of the ionogels are investigated systematically. The ionogels exhibit high mechanical strength (up to 1.6 MPa) and high mechanical stability even at temperatures up to 200 °C. It is found to be thermally stable up to 371.3 °C and electrochemically stable above 4.3 V. The obtained ionogels show superior ionic conductivity over a wide temperature range (from 1.2 × 10−3 S cm−1 at 20 °C up to 5.0 × 10−2 S cm−1 at 120 °C). Moreover, the Li/LiFePO4 batteries based on ionogel electrolyte with LiBF4 show a higher specific capacity of 153.1 mAhg−1 and retain 98.1% after 100 cycles, exhibiting very stable charge/discharge behavior with good cycle performance. This work provides a new method for fabrication of novel advanced gel polymer electrolytes for applications in lithium-ion batteries.
Rheological Characterization of Next-Generation Ballistic Witness Materials for Body Armor Testing
Authors: Tao Ran , Rice Kirk D. , Djakeu Anicet S. , Mrozek Randy A. , Cole Shawn T. , Freeney Reygan M. , Forster Aaron M.
Published: March 2019

Roma Plastilina No. 1 (RP1), an artist modeling clay that has been used as a ballistic clay, is essential for evaluation and certification in standards-based ballistic resistance testing of body armor. It serves as a ballistic witness material (BWM) behind the armor, where the magnitude of the plastic deformation in the clay after a ballistic impact is the figure of merit (known as “backface signature”). RP1 is known to exhibit complex thermomechanical behavior that requires temperature conditioning and frequent performance-based evaluations to verify that its deformation response satisfies requirements. A less complex BWM formulation that allows for room-temperature storage and use as well as a more consistent thermomechanical behavior than RP1 is desired, but a validation based only on ballistic performance would be extensive and expensive to accommodate the different ballistic threats. A framework of lab-scale metrologies for measuring the effects of strain, strain rate, and temperature dependence on mechanical properties are needed to guide BWM development. The current work deals with rheological characterization of a candidate BWM, i.e., silicone composite backing material (SCBM), to understand the fundamental structure–property relationships in comparison to those of RP1. Small-amplitude oscillatory shear frequency sweep experiments were performed at temperatures that ranged from 20 °C to 50 °C to map elastic and damping contributions in the linear elastic regime. Large amplitude oscillatory shear (LAOS) experiments were conducted in the non-linear region and the material response was analyzed in the form of Lissajous curve representations with the values of perfect plastic dissipation ratio reported to identify the degree of plasticity. The results show that the SCBM exhibits dynamic properties that are similar in magnitude to those of temperature-conditioned RP1, but with minimal temperature sensitivity and weaker frequency dependence than RP1. Both SCBM and RP1 are identified as elastoviscoplastic materials, which is particularly important for accurate determination of backface signature in body armor evaluation. The mechanical properties of SCBM show some degree of aging and work history effects. The results from this work demonstrate that the rheological properties of SCBM, at small and large strains, are similar to RP1 with substantial improvements in BWM performance requirements in terms of temperature sensitivity and thixotropy.
Multifunctional Silicone Rubber Nanocomposites by Controlling the Structure and Morphology of Graphene Material
Authors: Sanchez-Hidalgo Ruben , Blanco Clara , Menendez Rosa , Verdejo Raquel , Lopez-Manchado Miguel A.
Published: March 2019

Multifunctional elastomer nanocomposites have been applied in several high-tech fields. The design of materials with tailored properties capable of tuning their performance is a topical challenge. Here, we demonstrate that it is possible to modulate the mechanical and transport properties of silicone rubber nanocomposites by controlling the structure, chemical composition and morphology of the graphene material. Intrinsic graphene properties, such as remaining oxygen groups, specific surface area, and aspect ratio, among others, have a profound effect on the final properties of the nanocomposite. Thus, the thermal conductivity benefits from larger filler size and high aromatic restoration. Whereas mechanical properties and electrical conductivity require a proper balance between filler/polymer matrix interaction and a partial aromatic restoration.
Comparison of Properties of PVA Nanocomposites Containing Reduced Graphene Oxide and Functionalized Graphene
Authors: Park Gi Tae , Chang Jin-Hae
Published: March 2019

The thermal properties, morphologies, oxygen barrier properties, and electrical conductivities of poly(vinyl alcohol) (PVA) hybrid films containing different nanofillers were compared. For the fabrication of the PVA hybrid films, we used reduced graphene oxide (RGO) synthesized from graphite or functionalized hexadecylamine-graphene sheets (HDA-GS) obtained from HDA and GS as a reinforcing filler. The properties of the PVA hybrid films fabricated by intercalating PVA and the fillers for different filler contents ranging from 3 to 10% w/w were then compared. The dispersions of the graphene fillers in the matrix polymers were examined using wide-angle X-ray diffraction and field emission scanning electron microscopy, and the changes in their thermal properties were observed using differential scanning calorimetry and thermogravimetric analysis. Moreover, we measured the oxygen permeability and electrical conductivity of the films to investigate their industrial applications. In addition, all the physical properties of the PVA composites obtained using the two nanofillers were compared.
“Using the Language of Christian Love and Charity”: What Liberal Religion Offers Higher Education in Prison
Authors: Atkins Charles , Dubler Joshua , Lloyd Vincent , Webb Mel
Published: March 2019

This article explores what religious frameworks and institutions have to contribute to college-in-prison. We first provide an historical overview of higher education programs in American prisons. Then, we limn the role religion can play in motivating people to commit themselves to educating incarcerated people. Because this work is so thorny, we document some of the generic challenges programs must face and show how religious languages can be an asset in navigating these challenges. Next, we present the pedagogical practices and educational philosophies expressed among the programs in our study. We conclude with some broader reflections about teaching incarcerated people, and, after wrestling with objections, we encourage our colleagues in religious studies—those with faith commitments as well as those without them—to get involved.
Editorial for the Special Issue “Microwave Indices from Active and Passive Sensors for Remote Sensing Applications”
Authors: Paloscia Simonetta , Santi Emanuele
Published: March 2019

Since the early 1980s, the capabilities of satellite sensors operating at microwaves for the remote sensing of Earth’s surface have been widely assessed in a number of studies (e [...]
A Comparison of Three Sediment Acoustic Models Using Bayesian Inversion and Model Selection Techniques
Authors: Zou Bo , Zhai Jingsheng , Qi Zhanfeng , Li Zhaoxing
Published: March 2019

Many geoacoustic models are used to establish the relationship between the physical and acoustic properties of sediments. In this work, Bayesian inversion and model selection techniques are applied to compare combinations of three geoacoustic models and corresponding scattering models—the fluid model with the effective density fluid model (EDFM), the grain-shearing elastic model with the viscosity grain-shearing (VGS(λ)) model, and the poroelastic model with the corrected and reparametrized extended Biot–Stoll (CREB) model. First, the resolution and correlation of parameters for the three models are compared based on estimates of the posterior probability distributions (PPDs), which are obtained by Bayesian inversion using the backscattering strength data. Then, model comparison and selection techniques are utilized to assess the matching degree of model predictions and measurements qualitatively and to ascertain the Bayes factors in favor of each quantitatively. These studies indicate that the fluid and poroelastic models outperform the grain-shearing elastic model, in terms of both parameter resolution and the ability to produce predictions in agreement with measurements for sandy sediments. The poroelastic model is considered to be the best, as the inversion based on it can provide more highly resolved information of sandy sediments. Finally, the attempt to implement geoacoustic inversion with different models provides a relatively feasible remote sensing scheme for various types of sediments under unknown conditions, which needs further validation.
Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping
Authors: Gomez Cécile , Dharumarajan Subramanian , Féret Jean-Baptiste , Lagacherie Philippe , Ruiz Laurent , Sekhar Muddu
Published: March 2019

The Sentinel-2 mission of the European Space Agency (ESA) Copernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. The objective of this work is to evaluate the ability of Sentinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. The tested inherent biophysical property was the soil texture. Soil texture classification was performed on each individual Sentinel-2 image with a linear support vector machine. Two sources of uncertainty were studied: uncertainties due to the Sentinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. The first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. The second uncertainty analysis was achieved from each individual Sentinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. The Simpson index was used to compute this diversity in the classification results. This work was carried out in an Indian cultivated region (84 km2, part of Berambadi catchment, in the Karnataka state). It used a time-series of six Sentinel-2 images acquired from February to April 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. The classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. The uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and iii) a higher level of allowed uncertainty leads to increased bare soil coverage. These results illustrate the potential of Sentinel-2 for providing input for modeling environmental processes and crop management.
Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera
Authors: Kelly Julia , Kljun Natascha , Olsson Per-Ola , Mihai Laura , Liljeblad Bengt , Weslien Per , Klemedtsson Leif , Eklundh Lars
Published: March 2019

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ±0.5 °C), the accuracy declined to ±5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera’s automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these systems.
Correction: Yahui, Che et al. Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms—Part 1: Global Evaluation. Remote Sensing 2018, 10, 1414
Authors: Che Yahui , Mei Linlu , Xue Yong , Guang Jie , She Lu , Li Ying , Heckel Andreas , North Peter
Published: March 2019

The authors wish to make the following corrections to this paper [...]
Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
Authors: Xu Xiaohua , Han Yi , Luo Jia , Wickert Jens , Asgarimehr Milad
Published: March 2019

Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are being followingly planned. Higher spatial and also temporal sampling rates of RO observations, achievable with higher number of GNSS/receiver satellites or optimization of the Low Earth Orbit (LEO) constellation, are being studied by high number of researches. The objective of this study is to design GNSS RO missions which provide multi-GNSS RO events (ROEs) with the optimal performance over the globe. The navigation signals from GPS, GLONASS, BDS, Galileo, and QZSS are exploited and two constellation patterns, the 2D-lattice flower constellation (2D-LFC) and the 3D-lattice flower constellation (3D-LFC), are used to develop the LEO constellations. To be more specific, two evolutionary algorithms, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used for searching the optimal constellation parameters. The fitness function of the evolutionary algorithms takes into account the spatio-temporal sampling rate. The optimal RO constellations are obtained for which consisting of 6–12 LEO satellites. The optimality of the LEO constellations is evaluated in terms of the number of global ROEs observed during 24 h and the coefficient value of variation (COV) representing the uniformity of the point-to-point distributions of ROEs. It is found that for a certain number of LEO satellites, the PSO algorithm generally performs better than the GA, and the optimal 2D-LFC generally outperforms the optimal 3D-LFC with respect to the uniformity of the spatial and temporal distributions of ROEs.
Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data
Authors: Su Wei , Huang Jianxi , Liu Desheng , Zhang Mingzheng
Published: March 2019

Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model with leaf angle distribution functions referred from terrestrial laser scanning points at four phenological stages during the growing season. Specifically, four inferred maximum-probability leaf angles were used in the Campbell ellipsoid leaf angle distribution function of PROSAIL. A Lookup table (LUT) is generated by running the PROSAIL model with inferred leaf angles, and the cost function is minimized to retrieve LAI. The results show that the leaf angle distribution functions are different for the corn plants at different phenological growing stages, and the incorporation of derived specific corn leaf angle distribution functions distribute the improvement of LAI retrieval using the PROSAIL model. This validation is done using in-situ LAI measurements and MODIS LAI in Baoding City, Hebei Province, China, and compared with the LAI retrieved using default leaf angle distribution function at the same time. The root-mean-square error (RMSE) between the retrieved LAI on 4 September 2014, using the modified PROSAIL model and the in-situ measured LAI was 0.31 m2/m2, with a strong and significant correlation (R2 = 0.82, residual range = 0 to 0.6 m2/m2, p < 0.001). Comparatively, the accuracy of LAI retrieved results using default leaf angle distribution is lower, the RMSE of which is 0.56 with R2 = 0.76 and residual range = 0 to 1.0 m2/m2, p < 0.001. This validation reveals that the introduction of inferred leaf angle distributions from TLS data points can improve the LAI retrieval accuracy using the PROSAIL model. Moreover, the comparations of LAI retrieval results on 10 July, 26 July, 19 August and 4 September with default and inferred corn leaf angle distribution functions are all compared with MODIS LAI products in the whole study area. This validation reveals that improvement exists in a wide spatial range and temporal range. All the comparisons demonstrate the potential of the modified PROSAIL model for retrieving corn canopy LAI from Landsat imagery by inferring leaf orientation from terrestrial laser scanning data.
Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data
Authors: Guillevic Pierre C. , Olioso Albert , Hook Simon J. , Fisher Joshua B. , Lagouarde Jean-Pierre , Vermote Eric F.
Published: March 2019

Thermal infrared remote sensing observations have been widely used to provide useful information on surface energy and water stress for estimating evapotranspiration (ET). However, the revisit time of current high spatial resolution (<100 m) thermal infrared remote sensing systems, sixteen days for Landsat for example, can be insufficient to reliably derive ET information for water resources management. We used in situ ET measurements from multiple Ameriflux sites to (1) evaluate different scaling methods that are commonly used to derive daytime ET estimates from time-of-day observations; and (2) quantify the impact of different revisit times on ET estimates at monthly and seasonal time scales. The scaling method based on a constant evaporative ratio between ET and the top-of-atmosphere solar radiation provided slightly better results than methods using the available energy, the surface solar radiation or the potential ET as scaling reference fluxes. On average, revisit time periods of 2, 4, 8 and 16 days resulted in ET uncertainties of 0.37, 0.55, 0.73 and 0.90 mm per day in summer, which represented 13%, 19%, 23% and 31% of the monthly average ET calculated using the one-day revisit dataset. The capability of a system to capture rapid changes in ET was significantly reduced for return periods higher than eight days. The impact of the revisit on ET depended mainly on the land cover type and seasonal climate, and was higher over areas with high ET. We did not observe significant and systematic differences between the impacts of the revisit on monthly ET estimates that are based on morning or afternoon observations. We found that four-day revisit scenarios provided a significant improvement in temporal sampling to monitor surface ET reducing by around 40% the uncertainty of ET products derived from a 16-day revisit system, such as Landsat for instance.
Experimental Configuration to Determine the Nonlinear Parameter β in PMMA and CFRP with the Finite Amplitude Method
Authors: Callejas Antonio , Rus Guillermo
Published: March 2019

Parameters to measure nonlinearity in polymethylmethacrylate (PMMA) and carbon fiber reinforced polymer (CFRP) materials have been determined with nonlinear ultrasound (NLUS). The nonlinear parameter β has been determined using the variation of the Finite Amplitude Method (FAM) with harmonic generation. Using this as a reference, the first contribution of this work consists of deducting the experimental configuration necessary to measure this nonlinear parameter in a correct and feasible way. Excitation level, frequency of the wave generated, number of cycles analysed and the distances transducer-specimen and specimen-hydrophone have been determined in both materials. The second contribution is a semi-analytical model that allows to obtain the nonlinear parameter in materials by removing water contribution and considering geometric and viscous attenuation, using the data obtained in an immersion tank. Finally, an application of this model has been carried out in PMMA in order to determinate the nonlinear parameter in this material. From the results, we confirm that the configuration determined in this paper to obtain the parameter β decreases the noise in the measurements.
Road Topology Refinement via a Multi-Conditional Generative Adversarial Network
Authors: Zhang Yang , Li Xiang , Zhang Qianyu
Published: March 2019

With the rapid development of intelligent transportation, there comes huge demands for high-precision road network maps. However, due to the complex road spectral performance, it is very challenging to extract road networks with complete topologies. Based on the topological networks produced by previous road extraction methods, in this paper, we propose a Multi-conditional Generative Adversarial Network (McGAN) to obtain complete road networks by refining the imperfect road topology. The proposed McGAN, which is composed of two discriminators and a generator, takes both original remote sensing image and the initial road network produced by existing road extraction methods as input. The first discriminator employs the original spectral information to instruct the reconstruction, and the other discriminator aims to refine the road network topology. Such a structure makes the generator capable of receiving both spectral and topological information of the road region, thus producing more complete road networks compared with the initial road network. Three different datasets were used to compare McGan with several recent approaches, which showed that the proposed method significantly improved the precision and recall of the road networks, and also worked well for those road regions where previous methods could hardly obtain complete structures.
Semantic Segmentation of Building Roof in Dense Urban Environment with Deep Convolutional Neural Network: A Case Study Using GF2 VHR Imagery in China
Authors: Qin Yuchu , Wu Yunchao , Li Bin , Gao Shuai , Liu Miao , Zhan Yulin
Published: March 2019

This paper presents a novel approach for semantic segmentation of building roofs in dense urban environments with a Deep Convolution Neural Network (DCNN) using Chinese Very High Resolution (VHR) satellite (i.e., GF2) imagery. To provide an operational end-to-end approach for accurately mapping build roofs with feature extraction and image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform building roof segmentation. We selected typical cities with dense and diverse urban environments in different metropolitan regions of China as study areas, and sample images were collected over cities. High performance GPU-mounted workstations are employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with convolution layers is developed for building roof segmentation. The validation shows that the overall accuracy (OA) and the mean Intersection Over Union (mIOU) of DCNN-based semantic segmentation results are 94.67% and 0.85, respectively, and the CRF-refined segmentation results achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas in dense urban environments with different building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline of operational built-up area monitoring, and the timely update of building roof map could be applied in urban management and assessment of human settlement-related sustainable development goals over large areas.
Novel SWIPT Schemes for 5G Wireless Networks
Authors: Rajaram Akashkumar , Khan Rabia , Tharranetharan Selvakumar , Jayakody Dushantha Nalin K. , Dinis Rui , Panic Stefan
Published: March 2019

In this paper, we present a few novel simultaneous wireless information and power transfer (SWIPT) schemes that can be effectively used in various 5G wireless network implementations. First, we study the possibility of integrating distributed energy beamforming with the data rate fairness beamforming in a cooperative communication system with multiple cooperative relays and multiple destination users communicating simultaneously. We show that the system exploits significant performance gain using such a joint energy and data rate fairness beamforming scheme. Further, we propose an enhanced version of the SWIPT scheme, the energy-efficient modulation-based non-orthogonal multiple access (M-NOMA) SWIPT scheme, and observe its system efficiency in terms of more harvested energy. Finally, we consider an energy-harvesting SWIPT scheme where the channel response is estimated using the energy-harvesting signal as pilots superimposed on the information signal. For such a scheme, we compute the optimum transmit power ratio between the pilot and information signals under varying SNR conditions and improve the accuracy of the decoding process at the reception.
Investigating Volcanic Plumes from Mt. Etna Eruptions of December 2015 by Means of AVHRR and SEVIRI Data
Authors: Marchese Francesco , Falconieri Alfredo , Filizzola Carolina , Pergola Nicola , Tramutoli Valerio
Published: March 2019

In early December 2015, a rapid sequence of strong paroxysmal events took place at the Mt. Etna crater area (Sicily, Italy). Intense paroxysms from the Voragine crater (VOR) generated an eruptive column extending up to an altitude of about 15 km above sea level. In the following days, other minor ash emissions occurred from summit craters. In this study, we present results achieved by monitoring Mt. Etna plumes by means of RSTASH (Robust Satellite Techniques-Ash) algorithm, running operationally at the Institute of Methodologies for Environmental Analysis (IMAA) on Advanced Very High Resolution Radiometer (AVHRR) data. Results showed that RSTASH detected an ash plume dispersing from Mt. Etna towards Ionian Sea starting from 3 December at 08:40 UTC, whereas it did not identify ash pixels on satellite data of same day at 04:20 UTC and 04:40 UTC (acquired soon after the end of first paroxysm from VOR), due to a mixed cloud containing SO2 and ice. During 8–10 December, the continuity of RSTASH detections allowed us to estimate the mass eruption rate (an average value of about 1.5 × 103 kg/s was retrieved here), quantitatively characterizing the eruptive activity from North East Crater (NEC). The work, exploiting information provided also by Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, confirms the important contribution offered by RSTASH in identifying and tracking ash plumes emitted from Mt. Etna, despite some operational limitations (e.g., cloud coverage). Moreover, it shows that an experimental RST product, tailored to SEVIRI data, for the first time used and preliminarily assessed here, may complement RSTASH detections providing information about areas mostly affected by volcanic SO2.
SynSys: A Synthetic Data Generation System for Healthcare Applications
Authors: Dahmen Jessamyn , Cook Diane
Published: March 2019

Creation of realistic synthetic behavior-based sensor data is an important aspect of testing machine learning techniques for healthcare applications. Many of the existing approaches for generating synthetic data are often limited in terms of complexity and realism. We introduce SynSys, a machine learning-based synthetic data generation method, to improve upon these limitations. We use this method to generate synthetic time series data that is composed of nested sequences using hidden Markov models and regression models which are initially trained on real datasets. We test our synthetic data generation technique on a real annotated smart home dataset. We use time series distance measures as a baseline to determine how realistic the generated data is compared to real data and demonstrate that SynSys produces more realistic data in terms of distance compared to random data generation, data from another home, and data from another time period. Finally, we apply our synthetic data generation technique to the problem of generating data when only a small amount of ground truth data is available. Using semi-supervised learning we demonstrate that SynSys is able to improve activity recognition accuracy compared to using the small amount of real data alone.
Screen-Printed Sensors for Colorimetric Detection of Hydrogen Sulfide in Ambient Air
Authors: Engel Laura , Tarantik Karina R. , Pannek Carolin , Wöllenstein Jürgen
Published: March 2019

A fast and sensitive method to monitor hydrogen sulfide (H2S) in ambient air based on a visible color change of a printed disposable sensor has been developed. As gas-sensitive material, an immobilized copper(II) complex of the azo dye 1-(2-pyridylazo)-2-naphtol (H-PAN) was synthesized and prepared in an ethyl cellulose matrix for screen printing. If H2S is present in ambient air, the gas sensitive layer changes its color from purple to yellow. A pre-primed polyethylene (PE) foil and a coated offset paper served as the printing substrate. The colorimetric response to the target gas was measured by UV/Vis spectroscopy in reflection at H2S concentrations between 1 to 20 ppm. Possible cross-sensitivities of the printed sensors towards methane (CH4), formaldehyde (CH2O), carbon monoxide (CO), ammonia (NH3), and nitrogen dioxide (NO2), as well as the long-term stability was investigated. Furthermore, reflection measurements of the Cu-PAN complex on an amorphous silica powder under gas admission served as preliminary test for the subsequent paste development.
Theoretical Investigation of a Highly Sensitive Refractive-Index Sensor Based on TM0 Waveguide Mode Resonance Excited in an Asymmetric Metal-Cladding Dielectric Waveguide Structure
Authors: Wang Xiangxian , Wu Xiaoxiong , Zhu Jiankai , Pang Zhiyuan , Yang Hua , Qi Yunping
Published: March 2019

This study proposes a highly sensitive refractive-index (RI) sensor based on a TM0 waveguide mode resonance excited in an asymmetric metal-cladding dielectric waveguide structure, where the analyte serves as the guiding layer. By scanning the wavelength at fixed angles of incidence, the reflection spectra of the sensor were obtained. The results showed that the resonance wavelength redshifted dramatically with increases in the analyte RI, which indicates that this approach can be used to sense both the resonance wavelength and the analyte RI. Based on this approach, we investigated the sensing properties, including the sensitivity and figure of merit, at fixed incident angles of 60° and 45°, at which the sensitivity of the sensor reached 7724.9 nm/RIU (refractive index units) and 1339 nm/RIU, respectively. Compared with surface plasmon resonance sensors, which are based on a similar structure, the proposed sensor can accept a more flexible range of incident angles and a wider sensing range of analyte RI. This approach thus has tremendous potential for use in numerous sensing domains, such as biochemical and medical analyses.
An LC Wireless Microfluidic Sensor Based on Low Temperature Co-Fired Ceramic (LTCC) Technology
Authors: Liang Yongyuan , Ma Mingsheng , Zhang Faqiang , Liu Feng , Liu Zhifu , Wang Dong , Li Yongxiang
Published: March 2019

This work reports a novel wireless microfluidic biosensor based on low temperature co-fired ceramic (LTCC) technology. The wireless biosensor consists of a planar spiral inductor and parallel plate capacitor (LC) resonant antenna, which integrates with microchannel bends in the LTCC substrate. The wireless response of the biosensor was associated to the changes of its resonant frequency due to the alteration in the permittivity of the liquid flow in the microchannel. The wireless sensing performance to different organic liquids with permittivity from 3 to 78.5 was presented. The measured results are in good agreement with the theoretical calculation. The wireless detection for the concentration of glucose in water solution was investigated, and an excellent linear response and repeatability were obtained. This kind of LC wireless microfluidic sensor is very promising in establishing wireless lab-on-a-chip for biomedical and chemical applications.
Towards Semantic Sensor Data: An Ontology Approach
Authors: Liu Jin , Li Yunhui , Tian Xiaohu , Sangaiah Arun Kumar , Wang Jin
Published: March 2019

In order to optimize intelligent applications driven by various sensors, it is vital to properly interpret and reuse sensor data from different domains. The construction of semantic maps which illustrate the relationship between heterogeneous domain ontologies plays an important role in knowledge reuse. However, most mapping methods in the literature use the literal meaning of each concept and instance in the ontology to obtain semantic similarity. This is especially the case for domain ontologies which are built for applications with sensor data. At the instance level, there is seldom work to utilize data of the sensor instances when constructing the ontologies’ mapping relationship. To alleviate this problem, in this paper, we propose a novel mechanism to achieve the association between sensor data and domain ontology. In our approach, we first classify the sensor data by making them as SSN (Semantic Sensor Network) ontology instances, and map the corresponding instances to the concepts in the domain ontology. Secondly, a multi-strategy similarity calculation method is used to evaluate the similarity of the concept pairs between the domain ontologies at multiple levels. Finally, the set of concept pairs with a high similarity is selected by the analytic hierarchy process to construct the mapping relationship between the domain ontologies, and then the correlation between sensor data and domain ontologies are constructed. Using the method presented in this paper, we perform sensor data correlation experiments with a simulator for a real world scenario. By comparison to other methods, the experimental results confirm the effectiveness of the proposed approach.
Sensitivity Enhancement of Two-Dimensional Materials Based on Genetic Optimization in Surface Plasmon Resonance
Authors: Xia Guo , Zhou Cuixia , Jin Shiqun , Huang Chan , Xing Jinyu , Liu Zhijian
Published: March 2019

Sensitivity is an important performance index for evaluating surface plasmon resonance (SPR) biosensors. Sensitivity enhancement has always been a hot topic. It is found that the different refractive indices of samples require different combinations of prism and metal film for better sensitivity. Furthermore, the sensitivity can be enhanced by coating two-dimensional (2D) materials with appropriate layers on the metal film. At this time, it is necessary to choose the best film configuration to enhance sensitivity. With the emergence of more and more 2D materials, selecting the best configuration manually is becoming more complicated. Compared with the traditional manual method of selecting materials and layers, this paper proposes an optimization method based on a genetic algorithm to quickly and effectively find the optimal film configuration that enhances sensitivity. By using this method, not only can the optimal number of layers of 2D materials be determined quickly, but also the optimal configuration can be conveniently found when many materials are available. The maximum sensitivity can reach 400°/RIU after optimization. The method provided application value for the relevant researchers seeking to enhance sensitivity.