bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimized Extraction of Polysaccharides from Bergenia emeiensis Rhizome, Their Antioxidant Ability and Protection of Cells from Acrylamide-induced Cell Death
Authors: Zeng Chen , Feng Shiling
Published: August 2020

Bergeniaemeiensis is a traditional herb in Chinese folk medicine. Most related studies are focused on the bioactivity of bergenin, neglecting other bioactive compounds. In our previous work, polysaccharides were identified in B. emeiensis rhizome. To evaluate the extraction process and the antioxidant ability of these polysaccharides, a response surface method and antioxidant assays were applied. The results showed that the yield of polysaccharides was highly affected by extraction time, followed by temperature and solvent-to-sample ratio. Under the optimal conditions (43 °C, 30 min and 21 mL/g), the yield was 158.34 ± 0.98 mg/g. After removing other impurities, the purity of the polysaccharides from B. emeiensis (PBE) was 95.97 ± 0.92%. The infrared spectrum showed that PBE had a typical polysaccharide structure. Further investigations exhibited the PBE could scavenge well DPPH and ABTS free radicals and chelate Fe2+, showing an excellent antioxidant capacity. In addition, PBE also enhanced the cell viability of HEK 239T and Hep G2 cells under acrylamide-exposure conditions, exhibiting great protection against the damage induced by acrylamide. Therefore, PBE can be considered a potential natural antioxidant candidate for use in the pharmaceutical industry as a health product.
Hypoxic Treatment Decreases the Physiological Action of the Herbicide Imazamox on Pisum sativum Roots
Authors: Gil-Monreal Miriam , Royuela Mercedes , Zabalza Ana
Published: August 2020

The inhibition of acetolactate synthase (ALS; EC 2.2.1.6), an enzyme located in the biosynthetic pathway of branched-chain amino acids, is the target site of the herbicide imazamox. One of the physiological effects triggered after ALS inhibition is the induction of aerobic ethanol fermentation. The objective of this study was to unravel if fermentation induction is related to the toxicity of the herbicide or if it is a plant defense mechanism. Pea plants were exposed to two different times of hypoxia before herbicide application in order to induce the ethanol fermentation pathway, and the physiological response after herbicide application was evaluated at the level of carbohydrates and amino acid profile. The effects of the herbicide on total soluble sugars and starch accumulation, and changes in specific amino acids (branched-chain, amide, and acidic) were attenuated if plants were subjected to hypoxia before herbicide application. These results suggest that fermentation is a plant defense mechanism that decreases the herbicidal effect.
Influence of Graphene Platelet Aspect Ratio on the Mechanical Properties of HDPE Nanocomposites: Microscopic Observation and Micromechanical Modeling
Authors: Tarani Evangelia , Chrysafi Iouliana , Kállay-Menyhárd Alfréd , Pavlidou Eleni , Kehagias Thomas , Bikiaris Dimitrios N. , Vourlias George , Chrissafis Konstantinos
Published: August 2020

A series of high-density polyethylene nanocomposites filled with different diameter sizes (5, 15, and 25 μm) of graphene nanoplatelets at various amounts (0.5–5 wt.%) are prepared by the melt-mixing method. The effect of diameter size and filler content on the mechanical properties is reported, and the results are discussed in terms of morphology and the state of dispersion within the polymer matrix. The measured stiffness and strength of the nanocomposites were found to be mainly influenced by the filler aspect ratio and the filler-matrix adhesion. Fractography was utilized to study the embrittleness of the nanocomposites, and the observations revealed that a ductile to brittle transition is caused by a micro-deformation mechanism change in the nanocomposites. Several micromechanical models for the prediction of mechanical properties of nanocomposites, taking into consideration filler aspect ratio, percolation effect, and interphase regions, are considered. The three-phase model proposed by Ji accurately predicts the stiffness of graphene nanoplatelets with a higher diameter size, while Takayanagi modified model II was found to show good agreement with the experimental results of smaller ones at low filler content. This study demonstrates that the diameter size of the filler plays a central role in determining the mechanical properties.
Polymers in the Medical Antiviral Front-Line
Authors: Jarach Natanel , Dodiuk Hanna , Kenig Samuel
Published: August 2020

Antiviral polymers are part of a major campaign led by the scientific community in recent years. Facing this most demanding of campaigns, two main approaches have been undertaken by scientists. First, the classic approach involves the development of relatively small molecules having antiviral properties to serve as drugs. The other approach involves searching for polymers with antiviral properties to be used as prescription medications or viral spread prevention measures. This second approach took two distinct directions. The first, using polymers as antiviral drug-delivery systems, taking advantage of their biodegradable properties. The second, using polymers with antiviral properties for on-contact virus elimination, which will be the focus of this review. Anti-viral polymers are obtained by either the addition of small antiviral molecules (such as metal ions) to obtain ion-containing polymers with antiviral properties or the use of polymers composed of an organic backbone and electrically charged moieties like polyanions, such as carboxylate containing polymers, or polycations such as quaternary ammonium containing polymers. Other approaches include moieties hybridized by sulphates, carboxylic acids, or amines and/or combining repeating units with a similar chemical structure to common antiviral drugs. Furthermore, elevated temperatures appear to increase the anti-viral effect of ions and other functional moieties.
Physically Crosslinked Hydrogels Based on Poly (Vinyl Alcohol) and Fish Gelatin for Wound Dressing Application: Fabrication and Characterization
Authors: Ren Teng , Gan Jing , Zhou Liping , Chen Hao
Published: August 2020

We developed the interpenetrating double network composite hydrogel based on poly (vinyl alcohol) (PVA) and fish gelatin (FG) via thermal treatment and repeated freeze-thawing. A function of salicylic acid was incorporated into the hydrogel to improve its antibacterial properties. The color values, water contents, water evaporation rate, and swelling behavior were investigated. The drug-loading performance of the composite hydrogel was demonstrated by loading salicylic acid in various hydrogel systems. Moreover, the cumulative dissolution percentage of salicylic acid and the antibacterial activity of composite hydrogel were carried out. The results revealed that as FG concentration increased from 0% to 3.75% (w/v), gels changed from white to slight yellow and the swelling ratio increased from 54% to 83% (within 8 h). The presence of FG decreased the water content of gels which ranged from 86% to 89% and also decreased water evaporation rate. All gels presented the swelling index within 0.5–1.0, indicating a non-Fickian diffusion mechanism. The drug sustained dissolution behavior of pure PVA and composite hydrogel showed the same trend. Besides, the presence of the obvious bacteriostatic zones means that drug-loaded composite hydrogels have an effective antibacterial property. These results demonstrated that PVA/FG-based interpenetrating hydrogel is an appropriate biomaterial for drug-carrying wound dressing application.
Glycerin/NaOH Aqueous Solution as a Green Solvent System for Dissolution of Cellulose
Authors: Li Ke , Yang Huiyu , Jiang Lang , Liu Xin , Lang Peng , Deng Bo , Li Na , Xu Weilin
Published: August 2020

Dissolving cellulose in water-based green solvent systems is highly desired for further industrial applications. The green solvent glycerin—which contains hydrogen-bonding acceptors—was used together with NaOH and water to dissolve cellulose. This mixed aqueous solution of NaOH and glycerin was employed as the new green solvent system for three celluloses with different degree of polymerization. FTIR (Fourier-transform infrared), XRD (X-ray diffractometer) and TGA (thermogravimetric analysis) were used to characterize the difference between cellulose before and after regenerated by HCl. A UbbeloHde viscometer was used to measure the molecule weight of three different kinds of cellulose with the polymerization degree of 550, 600 and 1120. This solvent system is useful to dissolve cellulose with averaged molecule weight up to 2.08 × 105 g/mol.
Surface Quality Evaluation of Removable Thermoplastic Dental Appliances Related to Staining Beverages and Cleaning Agents
Authors: Porojan Liliana , Vasiliu Roxana-Diana , Porojan Sorin-Daniel , Bîrdeanu Mihaela-Ionela
Published: August 2020

(1) Background: Thermoplastic materials are not inert and subject to changes in the oral environment, which affect their surface quality. Color stability and topographic characteristics of clear thermoplastic appliances are critical considerations. The study aimed to evaluate the optical changes and surface topography of different thermoplastic materials related to staining beverages and cleaning agents. (2) Methods: Thermoplastic polyethylene terephthalate glycol (PET-G) material specimens were selected for the study: S (Duran, Scheu-Dental GmbH, Iserlohn, Germany), D (Biolon, Dreve Dentamid GmbH, Unna, Germany), and B (Crystal, Bio Art Dental Equipment, Sao Carlos, Brazil). Four different media were involved for immersion: coffee (C) and black tea (T) at 55 °C, Coca-Cola (K) at 5 °C, and distilled water (W) at 22 °C. As for cleaning, chemical options and mechanical brushing were selected (P-powder, T-tablets, and X-brushing). Color changes, and mean surface roughness were measured at 24 h, 48 h, and after 7 days. Statistical analysis was performed. After the testing period, atomic force microscopy (AFM) analyses and SEM images were registered in order to characterize the surface topography. (3) Results: Quantitative color change evaluations revealed a slight change in color after 24 h and an extremely marked change after 48 h, respective 7 days. Mean roughness values are kept below the clinically acceptable limit of 0.20 µm for all samples. Related to mean nanoroughness values Sa, and 3D evaluations of the surface quality, Biolon samples have demonstrated the most constant behavior, while Crystal samples are visibly influenced by water immersion. Related to the cleaning method, the topography of Duran samples was influenced by mechanical brushing. (4) Conclusions: Nanoscale investigations provided high accuracy and more realistic surface quality examinations of the examined samples compared to profilometry. Both SEM and AFM should be used for a more detailed description of the surface topography.
Comprehensive Improvement of Mixed-Flow Pump Impeller Based on Multi-Objective Optimization
Authors: Wang Mengcheng , Li Yanjun , Yuan Jianpin , Meng Fan , Appiah Desmond , Chen Jiaqi
Published: August 2020

The spanwise distribution of impeller exit circulation (SDIEC) has a significant effect on the impeller performance, therefore, there is a need for its consideration in the optimization design of mixed-flow pumps. In this study, a combination optimization system, including a 3D inverse design method (IDM), computational fluid dynamics (CFD), Latin hypercube sampling (LHS) method, response surface model (RSM), and non-dominated sorting genetic algorithm (NSGA-Ⅱ) was used to improve the performance of the mixed-flow pump after considering the effect of SDIEC on the performance of the impeller. The CFD results confirm the accuracy and credibility of the optimization results because of the good agreement the CFD results established with the experimental measurements. Compared with the original impeller, the pump efficiency of the preferred impeller at 0.8Qdes, 1.0Qdes, and 1.2Qdes improved by 0.63%, 3.39%, and 3.77% respectively. The low-pressure region on the blade surface reduced by 96.92% while the pump head difference was less than 1.84% at the design point. In addition, a comparison of the flow field of the preferred impeller and the original impeller revealed the effect of SDIEC on mixed-flow pump performance improvement and flow mechanism.
Scale-Up of Mixing Equipment for Suspensions
Authors: Jirout Tomáš , Rieger František , Ceres Dorin
Published: August 2020

This paper deals with the scale-up of equipment for the mixing of suspensions. The measurement of just-suspended agitator speeds was carried out with standard, pitched, four-blade turbines and folded, four-blade turbines in three vessels (290 mm, 600 mm, and 800 mm in diameter) for several particle sizes and concentrations. The results of measurements confirmed that scale-up based on dimensionless Froude number dependence, on the relative particle size and concentration, can be used. On the basis of the results, a scale-up rule for agitator speeds in a given suspension and equipment geometry was recommended, and various conclusions reported by different investigators were discussed.
Estimating Limits of Detection and Quantification of Ibuprofen by TLC-Densitometry at Different Chromatographic Conditions
Authors: Jampilek Josef , Dolowy Malgorzata , Pyka-Pajak Alina
Published: August 2020

Ibuprofen is one of the best-known nonsteroidal anti-inflammatory and analgesic drugs. Following the previous work, the current study is focused on estimating the effect of different chromatographic conditions on the sensitivity of thin-layer chromatography in combination with UV densitometry, i.e., the detection and quantification of ibuprofen in a wide range of its concentrations including the lowest limits of detection (LOD) and quantification (LOQ). For this purpose, a reliable and easy-to-use calculation procedure for LOD and LOQ determination is presented in this work. In addition, the impact of type plates and mobile phase composition on the LOD and LOQ, respectively, of this active substance is accurately described. The results of detection and the quantification level of ibuprofen obtained under applied chromatographic conditions confirmed the utility of silica gel plates as well as silica gel bonded phases (i.e., reversed-phase (RP) plates) in the thin-layer chromatography (TLC)-densitometric analysis of ibuprofen at a low level, i.e., from several nanograms (below one microgram) to a few micrograms/spot. Among all chromatographic systems tested, the best are those consisting of silica gel 60F254 plates with concentrating zone (1.05583) and the mixture of n-hexane:ethyl acetate:glacial acetic acid in ratio 36:12:2 (v/v/v) as well as RP plates, i.e., RP-18F254 aluminum plates developed with methanol:water in volume composition 40:10 (v/v). These chromatographic systems allowed quantifying ibuprofen in the amount of 0.229 µg/spot and 0.228 µg/spot, which is less than 1 µg/spot. It can be stated that different chromatographic systems in combination with UV densitometric scanning at 224 nm proposed in this work can be successfully applied for the cost-effective and sensitive determination of ibuprofen as a widely used drug component as well as a residual in domestic wastewater. It was found that the modification of silica gel as well as the layer thickness of unmodified or modified silica gel 60 can influence the quality of chromatograms and the detection/quantification of ibuprofen in both normal phase (NP) and RP systems. Therefore, to obtain the best possible LOD and LOQ values of ibuprofen with precoated layers, suitable mobile phase and chromatographic plates are required.
Temperature Variation at Solid-Fluid Interface of Thin Film Lubricated Contact Problems
Authors: Szávai Szabolcs , Kovács Sándor
Published: August 2020

Many calculating methods have been already developed for solving contact problems of parts such as gears, cams, and followers under fluid film lubrication conditions considering the temperature and pressure dependence. Similarly, the determination of the elasto-hydrodynamic pressure distribution the processes taking place in the lubricant and the contacting bodies, as well as in their environment, have to be dealt with simultaneously for the determination of the temperature field. A system of equation for the modelling of thermo-elastohydrodynamic lubrication between two contacting bodies containing hydrodynamic, thermodynamic, and strength problems is a highly non-linear system which becomes even more so if the temperature and pressure dependence of the material properties are considered. To solve this system, scientists started to use the finite element formulation in the 1960s and it was found to be a promising and reliable method. Earlier, the lubrication analysts used only the h-version finite element method (h-FEM) till 1991, when the first usage of the p-version finite element method (p-FEM) was published in the literature. In order to reduce the problem, in case of point or line contact, the contact bodies can be handled as semi-infinite ones. Following this simplification that had been successfully applied for the gap size determination, a substructure model was defined using analytical solution of the moving heat source. Instead of an iterative way between the solid and fluid problem, in this paper we present an efficient solution when thermal model for lubricant and surfaces were coupled and solved by a direct numerical method in one step.
Using CFD Simulations to Guide the Development of a New Spray Dryer Design
Authors: Langrish Timothy A. G. , Harrington James , Huang Xing , Zhong Chao
Published: August 2020

A new spray-drying system has been designed to overcome the limitations caused by existing designs. A key feature of the approach has been the systematic use of Computational Fluid Dynamics (CFD) to guide innovation in the design process. An example of an innovation is the development of a box-shaped transitional feature between the bottom of the main drying chamber and the entrance to the secondary chamber. In physical experiments, the box design performed better in all three representative operating conditions, including the current conditions, a higher feed solids concentration (30% solids rather than 8.8%), and a higher inlet drying temperature (230 °C rather than 170 °C). The current conditions showed a 3% increase in yield (solids recovery) while the 30% feed condition improved the yield by 7.5%, and the higher temperature test increased the yield by 13.5%. Statistical analysis showed that there were significant reductions in the wall flux at the high solids feed concentration. The observed deposition in the box was primarily from the predicted particle impacts by an inertial deposition process on the base of the box, which underwent little degradation due to lower temperatures. There is therefore evidence that the box design is a better design alternative under all operating conditions compared with other traditional designs.
The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk
Authors: Balasooriya Uditha , Li Johnny Siu-Hang , Li Jackie
Published: August 2020

We investigate the impact of model uncertainty on hedging longevity risk with index-based derivatives and assessing longevity basis risk, which arises from the mismatch between the hedging instruments and the portfolio being hedged. We apply the bivariate Lee–Carter model, the common factor model, and the M7-M5 model, with separate cohort effects between the two populations, and various time series processes and simulation methods, to build index-based longevity hedges and measure the hedge effectiveness. Based on our modeling and simulations on hypothetical scenarios, the estimated levels of hedge effectiveness are around 50% to 80% for a large pension plan, and the model selection, particularly in dealing with the computed time series, plays a very important role in the estimation. We also experiment with a modified bootstrapping approach to incorporate the uncertainty of model selection into the modeling of longevity basis risk. The hedging results under this approach may approximately be seen as a “weighted” average of those calculated from the different model candidates.
Joshi’s Split Tree for Option Pricing
Authors: Leduc Guillaume , Nurkanovic Hot Merima
Published: August 2020

In a thorough study of binomial trees, Joshi introduced the split tree as a two-phase binomial tree designed to minimize oscillations, and demonstrated empirically its outstanding performance when applied to pricing American put options. Here we introduce a “flexible” version of Joshi’s tree, and develop the corresponding convergence theory in the European case: we find a closed form formula for the coefficients of 1/n and 1/n3/2 in the expansion of the error. Then we define several optimized versions of the tree, and find closed form formulae for the parameters of these optimal variants. In a numerical study, we found that in the American case, an optimized variant of the tree significantly improved the performance of Joshi’s original split tree.
Mapping of Peat Thickness Using a Multi-Receiver Electromagnetic Induction Instrument
Authors: Beucher Amélie , Koganti Triven , Iversen Bo V. , Greve Mogens H.
Published: August 2020

Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw ECa measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity (σ) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of ECa data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw ECa data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R2) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of ECa data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping.
Observations and Recommendations for Coordinated Calibration Activities of Government and Commercial Optical Satellite Systems
Authors: Helder Dennis , Anderson Cody , Beckett Keith , Houborg Rasmus , Zuleta Ignacio , Boccia Valentina , Clerc Sebastien , Kuester Michele , Markham Brian , Pagnutti Mary
Published: August 2020

One of the biggest changes in the world of optical remote sensing over the last several years is the sheer increase in the number of sensors that are imaging the Earth in moderate to high spatial resolution. With respect to the calibration of these sensors, they are broadly classified into two types, namely government systems and commercial systems. Because of the differences in the design and mission of these sensor types, calibration approaches are often substantially different. Thus, an opportunity exists to foster discussion between calibration teams for these sensors with the goal of improving overall sensor calibration and data interoperability. The approach used to accomplish this task was a one-day workshop where team members from both government and commercial sensors could share best practices, discuss methods for collaboration and improvement, and make recommendations for continuing activities. Five major recommendations were developed from the event that focused on coordinated activities using pseudo invariant calibration sites (PICS), broader and more consistent communication, collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products. Workshop participants concluded that regular interactions between these teams could foster a better calibration of all sensor systems and accelerate the improved interoperability of surface products.
Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions
Authors: Runge Alexandra , Grosse Guido
Published: August 2020

Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances.
Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset
Authors: Katz Daniel S. W. , Batterman Stuart A. , Brines Shannon J.
Published: August 2020

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.
Internal Waves at the UK Continental Shelf: Automatic Mapping Using the ENVISAT ASAR Sensor
Authors: Kurekin Andrey A. , Land Peter E. , Miller Peter I.
Published: August 2020

Oceanic internal waves occur within stratified water along the boundary between water layers of different density and are generated when strong tidal currents flow over seabed topography. Their amplitude can exceed 50 m and they transport energy over long distances and cause vertical mixing when the waves break. This study presents the first fully automated methodology for the mapping of internal waves using satellite synthetic aperture radar (SAR) data and applies this to explore their spatial and temporal distribution within UK shelf seas. The new algorithm includes enhanced edge detection and spatial processing to target the appearance of these features on satellite images. We acquired and processed over 7000 ENVISAT ASAR scenes covering the UK continental shelf between 2006 and 2012, to automatically generate detailed maps of internal waves. Monthly and annual internal wave climatology maps of the continental shelf were produced showing spatial and temporal variability, which can be used to predict where internal waves have the most impact on the seabed environment and ecology in UK shelf seas. These observations revealed correlations between the temporal patterns of internal waves and the seasons when the continental shelf waters were more stratified. The maps were validated using well-known seabed topographic features. Concentrations of internal waves were automatically identified at Wyville-Thomson Ridge in June 2008, at the continental shelf break to the east of Rosemary Bank in January 2010 and in the Faroe-Shetland Channel in June 2011. This new automated methodology has been shown to be robust for mapping internal waves using a large SAR dataset and is recommended for studies in other regions worldwide and for SAR data acquired by other sensors.
Non-Tidal Mass Variations in the IGS Second Reprocessing Campaign: Interpretations and Noise Analysis from GRACE and Geophysical Models
Authors: Deng Liansheng , Chen Hua , Ma Ailong , Chen Qusen
Published: August 2020

Vertical deformations caused by non-tidal mass variations still remain in global navigation satellite system (GNSS) height time series, and can be computed from both Gravity Recovery and Climate Experiment (GRACE) and geophysical models. In this study, we provide a thorough evaluation of the relationships between these different techniques in the global scale by comparing non-tidal vertical deformations from IGS second reprocessing campaign (IG2), GRACE and Global Geophysical Fluid Center (GGFC) solutions, and investigate the noise properties of the GNSS corrected by GRACE solutions and GNSS corrected by GGFC solutions for global stations using optimal noise models. Our results demonstrate that the consistency between seasonal vertical deformations derived from GNSS, GRACE and GGFC is high. When correcting GNSS deformations with GRACE and GGFC solutions, 81% and 73% of the 186 stations have the weight root mean square (WRMS) reduction, respectively. The WRMS variations averaged over all stations are −12.3% and −5.6%, respectively for GNSS corrected by GRACE and GNSS corrected by GGFC solutions. The obvious difference occurs in the GNSS corrected by GGFC solutions WRMS increase, with the mean increase value up to 29%, mainly happening to stations located on islands or small peninsulas. In addition, noise properties of the GNSS corrected by GRACE solutions and GNSS corrected by GGFC solutions for global stations are investigated using optimal noise models. After correcting non-tidal loading effects, the solutions of GNSS corrected by GRACE solutions have the lowest noise level, and can occupy 5% of the noise behavior presenting in global stations, while the solutions of GNSS corrected by GGFC solutions can bring more than 5% of the noise into global stations, implying that GRACE correction solutions can present more favorable results when interpreting GNSS non-tidal loading deformations.
Coastline Fractal Dimension of Mainland, Island, and Estuaries Using Multi-temporal Landsat Remote Sensing Data from 1978 to 2018: A Case Study of the Pearl River Estuary Area
Authors: Hu Xinyi , Wang Yunpeng
Published: August 2020

The Pearl River Estuary Area was selected for this study. For the past 40 years, it has been one of the most complex coasts in China, yet few studies have analyzed the complexity and variations of the area’s different coastlines. In this investigation, the coastlines of the Pearl River Estuary Area were extracted from multi-temporal Landsat remote sensing data from 1978, 1988, 1997, 2008, and 2018. The coastline of this area was classified into mainland, island, and estuarine. To obtain more detailed results of the mainland and island, we regarded this area as the main body, rezoned into different parts. The box-counting dimension was applied to compute the bidimensional (2D) fractal dimension. Coastline length and the fractal dimension of different types of coastline and different parts of the main body were calculated and compared. The fractal dimension of the Pearl River Estuary Area was found to have increased significantly, from 1.228 to 1.263, and coastline length also increased during the study period. The island and mainland showed the most complex coastlines, while estuaries showed the least complexity during the past forty years. A positive correlation was found between length and 2D-fractal dimension in some parts of the study area. Land reclamation had the strongest influence on fractal dimension variations.
Comparison of CORINE Land Cover Data with National Statistics and the Possibility to Record This Data on a Local Scale—Case Studies from Slovakia
Authors: Falťan Vladimír , Petrovič František , Oťaheľ Ján , Feranec Ján , Druga Michal , Hruška Matej , Nováček Jozef , Solár Vladimír , Mechurová Veronika
Published: August 2020

Monitoring of land cover (LC) provides important information of actual land use (LU) and landscape dynamics. LC research results depend on the size of the area, purpose and applied methodology. CORINE Land Cover (CLC) data is one of the most important sources of LU data from a European perspective. Our research compares official CLC data (third hierarchical level of nomenclature at a scale of 1:100,000) and national statistics (NS) of LU in Slovakia between 2000 and 2018 at national, county, and local levels. The most significant differences occurred in arable land and permanent grassland, which is also related to the recording method and the development of agricultural land management. Due to the abandonment of agricultural areas, a real recorded increase in forest cover due to forest succession was not introduced in the official records of Land register. New modification of CLC methodology for identifying LC classes at a scale of 1:10,000 and fifth hierarchical level of CLC is firstly applied for local case studies representing lowland, basin, and mountain landscape. The size of the least identified and simultaneously recorded area was established at 0.1 ha the minimum width of a polygon was established at 10 m, the minimum recorded width of linear elements such as communications was established at 2 m. The use of the fifth CLC level in the case studies areas generated average boundary density 17.2 km/km2, comparing to the 2.6 km/km2 of the third level. Therefore, when measuring the density of spatial information by the polygon boundary lengths, the fifth level carries 6.6 times more information than the third level. Detailed investigation of LU affords better verification of national statistics data at a local level. This study also contributes to a more detailed recording of the current state of the Central European landscape and its changes.
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning
Authors: Kruk Ryan , Fuller M. Christopher , Komarov Alexander S. , Isleifson Dustin , Jeffrey Ian
Published: August 2020

Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data.
Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data
Authors: Chang Shouzhi , Wang Zongming , Mao Dehua , Guan Kehan , Jia Mingming , Chen Chaoqun
Published: August 2020

Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.
Crop Mapping from Sentinel-1 Polarimetric Time-Series with a Deep Neural Network
Authors: Qu Yang , Zhao Wenzhi , Yuan Zhanliang , Chen Jiage
Published: August 2020

Timely and accurate agricultural information is essential for food security assessment and agricultural management. Synthetic aperture radar (SAR) systems are increasingly available in crop mapping, as they provide all-weather imagery. In particular, the Sentinel-1 sensor provides dense time-series data, thus offering a unique opportunity for crop mapping. However, in most studies, the Sentinel-1 complex backscatter coefficient was used directly which limits the potential of the Sentinel-1 in crop mapping. Meanwhile, most of the existing methods may not be tailored for the task of crop classification in time-series polarimetric SAR data. To solve the above problem, we present a novel deep learning strategy in this research. To be specific, we collected Sentinel-1 time-series data in two study areas. The Sentinel-1 image covariance matrix is used as an input to maintain the integrity of polarimetric information. Then, a depthwise separable convolution recurrent neural network (DSCRNN) architecture is proposed to characterize crop types from multiple perspectives and achieve better classification results. The experimental results indicate that the proposed method achieves better accuracy in complex agricultural areas than other classical methods. Additionally, the variable importance provided by the random forest (RF) illustrated that the covariance vector has a far greater influence than the backscatter coefficient. Consequently, the strategy proposed in this research is effective and promising for crop mapping.
Informatics and the Challenge of Determinism
Authors: Moore Philip , Pham Hai Van
Published: August 2020

Motivation: There is a paradox at the heart of informatics where practical implementation generally fails to understand the socio-technical impact of novel technologies and disruptive innovation when adopted in ‘real-world’ systems. This phenomenon, termed technological determinism, is manifested in a time-lag between the adoption of novel technologies and an understanding of the underlying theory which develops following research into their adoption. Methods: We consider informatics theory as it relates to: social informatics and how humans’ function in society, the relationship between society and technology, information systems, information systems design, and human–computer interactions. The challenges posed by novel technologies and disruptive innovation are considered as they relate to information systems and information systems design. Open research questions with directions for future research are discussed with an introduction to and our proposed approach to socio-technical information system design. Significance: We conclude that the adoption of disruptive innovation presents both opportunities and threats for all stakeholders in computerised systems. However, determinism is a topic requiring research to generate a suitable level of understanding and technological determinism remains a significant challenge.
Clipping Noise Compensation with Neural Networks in OFDM Systems
Authors: Sang Tzu-Hsien , Xu You-Cheng
Published: August 2020

The application of deep learning (DL) to solve physical layer issues has emerged as a prominent topic. In this paper, the mitigation of clipping effects for orthogonal frequency division multiplexing (OFDM) systems with the help of a Neural Network (NN) is investigated. Unlike conventional clipping recovery algorithms, which involve costly iterative procedures, the DL-based method learns to directly reconstruct the clipped part of the signal while the unclipped part is protected. Furthermore, an interpretation of the learned weight matrices of the neural network is presented. It is observed that parts of the network, in effect, implement transformations very similar to the (Inverse) Discrete Fourier Transform (DFT/IDFT) to provide information in both the time and frequency domains. The simulation results show that the proposed method outperforms existing algorithms for recovering clipped OFDM signals in terms of both mean square error (MSE) and Bit Error Rate (BER).
CITIES: Ibero-American Research Network for Sustainable, Efficient, and Integrated Smart Cities
Authors: Nesmachnow Sergio , Hernández-Callejo Luis
Published: August 2020

This article describes CITIES, the Ibero-American research network for integrated, sustainable, and efficient smart cities. General/specific goals of the network are commented, and participant members are introduced. The main activities developed within the network are described, including research, education, outreach, and dissemination. Finally, some key aspects of the current and future work are presented.
Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review
Authors: Su Wen-Hao
Published: August 2020

Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.