bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemical Constituents of Salix babylonica L. and Their Antibacterial Activity Against Gram-Positive and Gram-Negative Animal Bacteria
Authors: González-Alamilla Eddy Nathalye , Gonzalez-Cortazar Manases , Valladares-Carranza Benjamín , Rivas-Jacobo Marco Antonio , Herrera-Corredor Camelia Alejandra , Ojeda-Ramírez Deyanira , Zaragoza-Bastida Adrian , Rivero-Perez Nallely
Published: August 2019

The principle of animal wellbeing, which states that animals should be free from pain, injury, and disease, is difficult to maintain, because microorganisms are most frequently found to be resistant or multi-resistant to drugs. The secondary metabolites of plants are an alternative for the treatment of these microorganisms. The aim of this work was to determine the antibacterial effect of Salix babylonica L. hydroalcoholic extract (SBHE) against Escherichia coli, Staphylococcus aureus and Listeria monocytogenes, and identify the compounds associated with the activity. The SBHE showed activity against the three strains, and was subjected to a bipartition, obtaining aqueous fraction (ASB) with moderate activity and organic fraction (ACSB) with good activity against the three strains. The chromatographic separation of ACSB, allowed us to obtain ten fractions (F1AC to F10AC), and only three showed activity (F7AC, F8AC and F10AC). In F7AC, five compounds were identified preliminary by GC-MS, in F8AC and F10AC were identified luteolin (1) and luteolin 7-O-glucoside (2) by HPLC, respectively. The best antibacterial activity was obtained with F7AC (Listeria monocytogenes; MIC: 0.78 mg/mL, MBC: 0.78 mg/mL) and F8AC (Staphylococcus aureus; MIC: 0.39 mg/mL; MBC: 0.78 mg/mL). The results indicated that the compounds obtained from SBHE can be used as an alternative treatment against these microorganisms and, by this mechanism, contribute to animal and human health.
Development of Organelle Replacement Therapy Using a Stearyl-Polyhistidine Peptide against Lysosomal Storage Disease Cells
Authors: Hayashi Taiki , Okamoto Riku , Kawano Tsuyoshi , Iwasaki Takashi
Published: August 2019

We previously reported on a polyhistidine peptide, His16 peptide, as a new cell-penetrating peptide. This peptide is anticipated to be a new carrier for drug delivery systems (DDSs) for targeting intracellular lysosomes because it can transport macromolecules (e.g., liposomes) into these organelles. In the present study, we examined the application of His16 peptide as a DDS carrier against lysosomal storage disease (LSD) cells. LSDs are metabolic disorders caused by loss of specific lysosomal enzymes. For the treatment of LSD cells, we devised a system designated organelle replacement therapy (ORT). ORT is a strategy for transporting exogenous lysosomes containing all kinds of lysosomal enzymes from normal cells into endogenous lysosomes in LSD cells using His16 peptide. To develop the ORT system, we prepared His16 peptide-modified healthy lysosomes (His16-Lyso) by insertion of a stearyl-His16 peptide into a hydrophobic region in the lysosomal membrane. His16-Lyso showed cellular uptake and localization to endogenous lysosomes in LSD cells. His16-Lyso also restored the proliferation of LSD cells, which otherwise showed slower proliferation than normal cells. These results suggested that His16-Lyso replenished deficient lysosomal enzymes in LSD cells. The results further suggest that His16-Lyso are promising candidates as a treatment tool for LSD cells and to establish a foundation for ORT.
Targeting Glutathione Metabolism: Partner in Crime in Anticancer Therapy
Authors: Desideri Enrico , Ciccarone Fabio , Ciriolo Maria Rosa
Published: August 2019

Glutathione (GSH) is the predominant low-molecular-weight antioxidant with a ubiquitous distribution inside the cell. The steady-state level of cellular GSH is dependent on the balance between synthesis, hydrolysis, recycling of glutathione disulphide (GSSG) as well as cellular extrusion of reduced, oxidized, or conjugated-forms. The augmented oxidative stress typical of cancer cells is accompanied by an increase of glutathione levels that confers them growth advantage and resistance to a number of chemotherapeutic agents. Targeting glutathione metabolism has been widely investigated for cancer treatment although GSH depletion as single therapeutic strategy has resulted largely ineffective if compared with combinatorial approaches. In this review, we circumstantiate the role of glutathione in tumour development and progression focusing on how interfering with different steps of glutathione metabolism can be exploited for therapeutic purposes. A dedicated section on synthetic lethal interactions with GSH modulators will highlight the promising option of harnessing glutathione metabolism for patient-directed therapy in cancer.
Children in Need—Diagnostics, Epidemiology, Treatment and Outcome of Early Onset Anorexia Nervosa
Authors: Herpertz-Dahlmann Beate , Dahmen Brigitte
Published: August 2019

Knowledge of anorexia nervosa (AN) in childhood is scarce. This review gives a state-of-the-art overview on the definition, classification, epidemiology and etiology of this serious disorder. The typical features of childhood AN in comparison to adolescent AN and avoidant restrictive eating disorder (ARFID) are described. Other important issues discussed in this article are somatic and psychiatric comorbidity, differential diagnoses and medical and psychological assessment of young patients with AN. Special problems in the medical and psychological treatment of AN in children are listed, although very few studies have investigated age-specific treatment strategies. The physical and mental outcomes of childhood AN appear to be worse than those of adolescent AN, although the causes for these outcomes are unclear. There is an urgent need for ongoing intensive research to reduce the consequences of this debilitating disorder of childhood and to help patients recover.
Consumption of Milk and Alternatives and Their Contribution to Nutrient Intakes among Canadian Adults: Evidence from the 2015 Canadian Community Health Survey—Nutrition
Authors: Auclair Olivia , Han Yang , Burgos Sergio A.
Published: August 2019

As a staple food and dense source of nutrients, milk and alternatives play an important role in nutrient adequacy. The aims of this study were to quantify the consumption of milk and alternatives within Canadian self-selected diets and determine their contribution to intakes of nutrients and energy. First, 24-h dietary recalls from the 2015 Canadian Community Health Survey—Nutrition were used to assess 1-d food and nutrient intakes among Canadian adults ≥19 y (n = 13,616). Foods were classified as milk and alternatives according to the 2007 Canada’s Food Guide. Descriptive statistics were used to calculate daily servings of milk and alternatives by different age groups and demographic characteristics. Population ratios were used to discern their contribution to total intakes of nutrients and energy. Mean daily servings (±SE) were highest for milk (0.60 ± 0.02) and cheese (0.42 ± 0.01), intermediate for frozen dairy (0.16 ± 0.01) and yoghurt (0.14 ± 0.01), and lowest for soy and other dairy (<0.03). Intakes were lowest among Canadians 51 + y (1.3 ± 0.03), females (1.25 ± 0.03), non-Caucasians (1.06 ± 0.05), those with less than a secondary education (1.19 ± 0.05), and British Columbians (1.17 ± 0.05). Milk and alternatives contributed >20% to total intakes of calcium (52.62 ± 0.46%), vitamin D (38.53 ± 0.78%), saturated fat (28.84 ± 0.51%), vitamin B12 (27.73 ± 0.57%), vitamin A (26.16 ± 0.58%), phosphorus (24.76 ± 0.35%), and riboflavin (24.43 ± 0.37%), of which milk was the top source. Milk and alternatives contribute substantially to nutrient intakes and thus warrant further attention in terms of mitigating nutrient inadequacy among the Canadian population.
Dietary Inflammatory Index and Risk of Breast Cancer Based on Hormone Receptor Status: A Case-Control Study in Korea
Authors: Lee Seohyun , Quiambao Arlene Lansangan , Lee Jeonghee , Ro Jungsil , Lee Eun-Sook , Jung So-Youn , Sung Mi-Kyung , Kim Jeongseon
Published: August 2019

Breast cancer is the most common cancer in women globally, and the risk of developing breast cancer is associated with inflammation. The present study aimed to examine the association between the Dietary Inflammatory Index (DII®) and breast cancer in Korean women and investigate whether the tumor’s hormone receptor status affects this association. In this case-control study, we enrolled 364 breast cancer patients and 364 age-matched controls. DII scores were calculated from dietary intake evaluated by a 106-item food frequency questionnaire. The DII score was significantly higher in cases than in controls. After adjusting for potential confounders, the odds ratio (OR) of breast cancer was higher in the highest DII tertile (OR = 3.68, 95% confidence interval (CI): 2.34–5.80, p for trend < 0.0001) than in the lowest tertile. We found that higher DII scores were related to an increased risk of breast cancer for estrogen receptor (ER)+/progesterone receptor (PR)+ tumors regardless of menopausal status (OR = 2.59, 95% CI: 1.37–4.88 in the highest DII category, p for trend = 0.01 for premenopausal women; OR = 11.00, 95% CI: 2.93–41.30 in the highest DII category, p for trend = 0.0004 for postmenopausal women), but not for ER−/PR− status. Our results suggested that the DII scores are positively associated with breast cancer risk in Korean women and that this relationship is more robust in ER+/PR+ tumors.
Effect of Graphene Oxide Coating on Natural Fiber Composite for Multilayered Ballistic Armor
Authors: Costa Ulisses Oliveira , Nascimento Lucio Fabio Cassiano , Garcia Julianna Magalhães , Monteiro Sergio Neves , Luz Fernanda Santos da , Pinheiro Wagner Anacleto , Garcia Filho Fabio da Costa
Published: August 2019

Composites with sustainable natural fibers are currently experiencing remarkably diversified applications, including in engineering industries, owing to their lower cost and density as well as ease in processing. Among the natural fibers, the fiber extracted from the leaves of the Amazonian curaua plant (Ananas erectifolius) is a promising strong candidate to replace synthetic fibers, such as aramid (Kevlar™), in multilayered armor system (MAS) intended for ballistic protection against level III high velocity ammunition. Another remarkable material, the graphene oxide is attracting considerable attention for its properties, especially as coating to improve the interfacial adhesion in polymer composites. Thus, the present work investigates the performance of graphene oxide coated curaua fiber (GOCF) reinforced epoxy composite, as a front ceramic MAS second layer in ballistic test against level III 7.62 mm ammunition. Not only GOCF composite with 30 vol% fibers attended the standard ballistic requirement with 27.4 ± 0.3 mm of indentation comparable performance to Kevlar™ 24 ± 7 mm with same thickness, but also remained intact, which was not the case of non-coated curaua fiber similar composite. Mechanisms of ceramic fragments capture, curaua fibrils separation, curaua fiber pullout, composite delamination, curaua fiber braking, and epoxy matrix rupture were for the first time discussed as a favorable combination in a MAS second layer to effectively dissipate the projectile impact energy.
Hydrogen Bonding-Induced Assembled Structures and Photoresponsive Behavior of Azobenzene Molecule/Polyethylene Glycol Complexes
Authors: Tai Hsin-Tzu , Lin Yen-Chun , Ma Jing-Yao , Lo Chieh-Tsung
Published: August 2019

We investigated the self-assembled structures and photoresponsive and crystallization behaviors of supramolecules composed of 4-methoxy-4′-hydroxyazobenzene (Azo) molecules and polyethylene glycol (PEG) that were formed through hydrogen-bonding interactions. The Azo/PEG complexes exhibited the characteristics of photoresponse and crystallization, which originated from Azo and PEG, respectively. When Azo/PEG complexes were dissolved in solvents, hydrogen-bonding interaction hindered the rotation and inversion of mesogens, causing a reduction in the photoisomerization rate compared with the photoisomerization rate of the neat Azo. The confinement of Azo/PEG complexes in thin films further resulted in a substantial decrease in the photoisomerization rate but an increase in the amounts of H-aggregated and J-aggregated mesogens. Regarding PEG crystallization, ultraviolet irradiation of Azo/PEG complexes increased the quantity of high-polarity cis isomers, which improved the compatibility between mesogens and PEG, subsequently increasing the crystallization temperature of PEG. Moreover, the complexation of Azo and PEG induced microphase separation, forming a lamellar morphology. Within the Azo-rich microphases, mesogens aggregated to form tilted monosmectic layers. By contrast, PEG crystallization within the PEG-rich microphases was hard confined, indicating that the domain size of the lamellar morphology was unchanged during PEG crystallization.
Plasticization of Polylactide with Myrcene and Limonene as Bio-Based Plasticizers: Conventional vs. Reactive Extrusion
Authors: Brüster Berit , Adjoua Yann-Olivier , Dieden Reiner , Grysan Patrick , Federico Carlos Eloy , Berthé Vincent , Addiego Frédéric
Published: August 2019

Polylactide (PLA) was blended by conventional and reactive extrusion with limonene (LM) or myrcene (My) as bio-based plasticizers. As-processed blends were carefully analyzed by a multiscale and multidisciplinary approach to tentatively determine their chemical structure, microstructure, thermal properties, tensile and impact behaviors, and hydrothermal stability. The main results indicated that LM and My were efficient plasticizers for PLA, since compared to neat PLA, the glass transition temperature was reduced, the ultimate tensile strain was increased, and the impact strength was increased, independently of the type of extrusion. The addition of a free radical initiator during the extrusion of PLA/LM was beneficial for the mechanical properties. Indeed, the probable formation of local branched/crosslinked regions in the PLA matrix enhanced the matrix crystallinity, the tensile yield stress, and the tensile ultimate stress compared to the non-reactive blend PLA/LM, while the other properties were retained. For PLA/My blends, reactive extrusion was detrimental for the mechanical properties since My polymerization was accelerated resulting in a drop of the tensile ultimate strain and impact strength, and an increase of the glass transition temperature. Indeed, large inclusions of polymerized My were formed, decreasing the available content of My for the plasticization and enhancing cavitation from inclusion-matrix debonding.
Effect of Formation Route on the Mechanical Properties of the Polyethersulfone Composites Reinforced with Glass Fibers
Authors: Sherif Galal , Chukov Dilyus , Tcherdyntsev Victor , Torokhov Valerii
Published: August 2019

Interfacial interaction is one of the most important factors that affect the mechanical properties of the fiber reinforced composites. The effect of fabrics′ sizing removal from glass fibers’ surface by thermal treatment on the mechanical characteristics of polyethersulfone based composites at different fiber to polymer weight ratios was investigated. Three fiber to polymer weight ratios of 50/50, 60/40, and 70/30 were studied. Flexural and shear tests were carried out to illustrate the mechanical properties of the composites; the structure was studied using Fourier-transform infrared spectroscopy and scanning electron microscopy. It was shown that solution impregnation of glass fabrics with polyethersulfone before compression molding allows to achieve good mechanical properties of composites. The thermal treatment of glass fabrics before impregnation results in an increase in flexural and shear strength for all the composites due to the improvement of fiber–matrix interaction.
Synthesis, Structural Characterization and Ligand-Enhanced Photo-Induced Color-Changing Behavior of Two Hydrogen-Bonded Ho(III)-Squarate Supramolecular Compounds
Authors: Wang Chih-Chieh , Ke Szu-Yu , Feng Yun , Ho Mei-Lin , Chang Chung-Kai , Chuang Yu-Chun , Lee Gene-Hsiang
Published: August 2019

Two coordination polymers (CPs) with chemical formulas, [Ho2(C4O4)2(C2O4)(H2O)8]·4H2O (1) and [Ho(C4O4)1.5(H2O)3] (2), (C4O42− = dianion of squaric acid, C2O42− = oxalate), have been synthesized and their structures were determined by single-crystal X-ray diffractometer (XRD). In compound 1, the coordination environment of Ho(III) ion is eight-coordinate bonded to eight oxygen atoms from two squarate, one oxalate ligands and four water molecules. The squarates and oxalates both act as bridging ligands with μ1,2-bis-monodentate and bis-chelating coordination modes, respectively, connecting the Ho(III) ions to form a one-dimensional (1D) ladder-like framework. Adjacent ladders are interlinked via O–H⋅⋅⋅O hydrogen bonding interaction to form a hydrogen-bonded two-dimensional (2D) layered framework and then arranged orderly in an AAA manner to construct its three-dimensional (3D) supramolecular architecture. In compound 2, the coordination geometry of Ho(III) is square-antiprismatic eight coordinate bonded to eight oxygen atoms from five squarate ligands and three water molecules. The squarates act as bridging ligands with two coordination modes, μ1,2,3-trismonodentate and μ1,2-bis-monodentate, connecting the Ho(III) ions to form a 2D bi-layered framework. Adjacent 2D frameworks are then parallel stacked in an AAA manner to construct its 3D supramolecular architecture. Hydrogen bonding interactions between the squarate ligands and coordinated water molecules in 1 and 2 both play important roles on the construction of their 3D supramolecular assembly. Compounds 1 and 2 both show remarkable ligand-enhanced photo-induced color-changing behavior, with their pink crystals immediately turning to yellow crystals under UV light illumination.
Mapping Physiognomic Types of Indigenous Forest using Space-Borne SAR, Optical Imagery and Air-borne LiDAR
Authors: Dymond John R. , Zörner Jan , Shepherd James D. , Wiser Susan K. , Pairman David , Sabetizade Marmar
Published: August 2019

Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from the European Space Agency (ESA) Sentinel-1 and 2 missions, Advanced Land Orbiting Satellite (ALOS) PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest. A five-fold cross-validation (repeated 100 times) of ground data showed that the highest classification accuracy of 80.5% is achieved for bands 2, 3, 4, 8, 11, and 12 from Sentinel-2, the ratio of bands VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) from Sentinel-1, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on optical bands alone was 72.7% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.4%. The classification accuracy is sufficient for many management applications for indigenous forest, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management.
An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization
Authors: Liu Tao , Zhang Xing , Li Qingquan , Fang Zhixiang , Tahir Nadeem
Published: August 2019

One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.
CLARREO Pathfinder/VIIRS Intercalibration: Quantifying the Polarization Effects on Reflectance and the Intercalibration Uncertainty
Authors: Goldin Daniel , Xiong Xiaoxiong , Shea Yolanda , Lukashin Constantine
Published: August 2019

Atmospheric scattering and surface polarization affect radiance measurements of polarization-sensitive instruments on orbit. Neglecting the polarization effects may lead to an inaccurate radiance/reflectance determination and underestimated radiance/reflectance uncertainty. Of the two instruments, CERES and VIIRS, slated to be intercalibrated by the CLARREO Pathfinder (CPF), the latter is known to be sensitive to polarization. The Pathfinder mission is tasked with accurately determining the uncertainty contribution of polarization and will provide the benchmark for the determination of the polarization correction factor for polarization-sensitive instruments. In this article, we show the formalism necessary to correct the reflectance for sensitivity to polarization after the CLARREO Pathfinder/VIIRS intercalibration, as well as the associated polarization uncertainty contribution to the overall intercalibrated reflectance error. To illustrate its usage, the formalism is applied to three dominant scene types.
Multispectral UAS Data Accuracy for Different Radiometric Calibration Methods
Authors: Poncet Aurelie M. , Knappenberger Thorsten , Brodbeck Christian , Fogle Michael , Shaw Joey N. , Ortiz Brenda V.
Published: August 2019

Unmanned aircraft systems (UAS) allow us to collect aerial data at high spatial and temporal resolution. Raw images are taken along a predetermined flight path and processed into a single raster file covering the entire study area. Radiometric calibration using empirical or manufacturer methods is required to convert raw digital numbers into reflectance and to ensure data accuracy. The performance of five radiometric calibration methods commonly used was investigated in this study. Multispectral imagery was collected using a Parrot Sequoia camera. No method maximized data accuracy in all bands. Data accuracy was higher when the empirical calibration was applied to the processed raster rather than the raw images. Data accuracy achieved with the manufacturer-recommended method was comparable to the one achieved with the best empirical method. Radiometric error in each band varied linearly with pixel radiometric values. Smallest radiometric errors were obtained in the red-edge and near-infrared (NIR) bands. Accuracy of the composite indices was higher for the pixels representing a dense vegetative cover in comparison to a lighter cover or bare soil. Results provided a better understanding of the advantages and limitations of existing radiometric calibration methods as well as the impact of the radiometric error on data quality. The authors recommend that researchers evaluate the performance of their radiometric calibration before analyzing UAS imagery and interpreting the results.
Radon-Augmented Sentinel-2 Satellite Imagery to Derive Wave-Patterns and Regional Bathymetry
Authors: Bergsma Erwin W. J. , Almar Rafael , Maisongrande Philippe
Published: August 2019

Climatological changes occur globally but have local impacts. Increased storminess, sea level rise and more powerful waves are expected to batter the coastal zone more often and more intense. To understand climate change impacts, regional bathymetry information is paramount. A major issue is that the bathymetries are often non-existent or if they do exist, outdated. This sparsity can be overcome by space-borne satellite techniques to derive bathymetry. Sentinel-2 optical imagery is collected continuously and has a revisit-time around a few days depending on the orbital-position around the world. In this work, Sentinel-2 imagery derived wave patterns are extracted using a localized radon transform. A discrete fast-Fourier (DFT) procedure per direction in Radon space (sinogram) is then applied to derive wave spectra. Sentinel-2 time-lag between detector bands is employed to compute the spectral wave-phase shift and depth using the gravity wave linear dispersion. With this novel technique, regional bathymetries are derived at the test-site of Capbreton, France with an root mean squared (RMS)-error of 2.58 m and a correlation coefficient of 0.82 when compared to the survey for depths until 30 m. With the proposed method, the 10 m Sentinel-2 resolution is sufficient to adequately estimate bathymetries for a wave period of 6.5 s or greater. For shorter periods, the pixel resolution does not allow to detect a stable celerity. In addition to the wave-signature enhancement, the capability of the Radon Transform to augment Sentinel-2 20 m resolution imagery to 10 m is demonstrated, increasing the number of suitable bands for the depth inversion.
Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta
Authors: Dietmaier Annette , McDermid Gregory J. , Rahman Mir Mustafizur , Linke Julia , Ludwig Ralf
Published: August 2019

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.
Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images
Authors: Guo Shichen , Jin Qizhao , Wang Hongzhen , Wang Xuezhi , Wang Yangang , Xiang Shiming
Published: August 2019

Semantic segmentation in high-resolution remote-sensing (RS) images is a fundamental task for RS-based urban understanding and planning. However, various types of artificial objects in urban areas make this task quite challenging. Recently, the use of Deep Convolutional Neural Networks (DCNNs) with multiscale information fusion has demonstrated great potential in enhancing performance. Technically, however, existing fusions are usually implemented by summing or concatenating feature maps in a straightforward way. Seldom do works consider the spatial importance for global-to-local context-information aggregation. This paper proposes a Learnable-Gated CNN (L-GCNN) to address this issue. Methodologically, the Taylor expression of the information-entropy function is first parameterized to design the gate function, which is employed to generate pixelwise weights for coarse-to-fine refinement in the L-GCNN. Accordingly, a Parameterized Gate Module (PGM) was designed to achieve this goal. Then, the single PGM and its densely connected extension were embedded into different levels of the encoder in the L-GCNN to help identify the discriminative feature maps at different scales. With the above designs, the L-GCNN is finally organized as a self-cascaded end-to-end architecture that is able to sequentially aggregate context information for fine segmentation. The proposed model was evaluated on two public challenging benchmarks, the ISPRS 2Dsemantic segmentation challenge Potsdam dataset and the Massachusetts building dataset. The experiment results demonstrate that the proposed method exhibited significant improvement compared with several related segmentation networks, including the FCN, SegNet, RefineNet, PSPNet, DeepLab and GSN.For example, on the Potsdam dataset, our method achieved a 93.65% F 1 score and 88.06% I o U score for the segmentation of tiny cars in high-resolution RS images. As a conclusion, the proposed model showed potential for object segmentation from the RS images of buildings, impervious surfaces, low vegetation, trees and cars in urban settings, which largely varies in size and have confusing appearances.
LiDAR DEM Smoothing and the Preservation of Drainage Features
Authors: Lindsay John B. , Francioni Anthony , Cockburn Jaclyn M. H.
Published: August 2019

Fine-resolution Light Detection and Ranging (LiDAR) data often exhibit excessive surface roughness that can hinder the characterization of topographic shape and the modeling of near-surface flow processes. Digital elevation model (DEM) smoothing methods, commonly low-pass filters, are sometimes applied to LiDAR data to subdue the roughness. These techniques can negatively impact the representation of topographic features, most notably drainage features, such as headwater streams. This paper presents the feature-preserving DEM smoothing (FPDEMS) method, which modifies surface normals to smooth the topographic surface in a similar manner to approaches that were originally designed for de-noising three-dimensional (3D) meshes. The FPDEMS method has been optimized for application with raster DEM data. The method was compared with several low-pass filters while using a 0.5-m resolution LiDAR DEM of an agricultural area in southwestern Ontario, Canada. The findings demonstrated that the technique was better at removing roughness, when compared with mean, median, and Gaussian filters, while also preserving sharp breaks-in-slope and retaining the topographic complexity at broader scales. Optimal smoothing occurred with kernel sizes of 11–21 grid cells, threshold angles of 10°–20°, and 3–15 elevation-update iterations. These parameter settings allowed for the effective reduction in roughness and DEM noise and the retention of terrace scarps, channel banks, gullies, and headwater streams.
A Metric for Evaluating the Geometric Quality of Land Cover Maps Generated with Contextual Features from High-Dimensional Satellite Image Time Series without Dense Reference Data
Authors: Derksen Dawa , Inglada Jordi , Michel Julien
Published: August 2019

Land cover maps are a key resource for many studies in Earth Observation, and thanks to the high temporal, spatial, and spectral resolutions of systems like Sentinel-2, maps with a wide variety of land cover classes can now be automatically produced over vast areas. However, certain context-dependent classes, such as urban areas, remain challenging to classify correctly with pixel-based methods. Including contextual information into the classification can either be done at the feature level with texture descriptors or object-based approaches, or in the classification model itself, as is done in Convolutional Neural Networks. This improves recognition rates of these classes, but sometimes deteriorates the fine-resolution geometry of the output map, particularly in sharp corners and in fine elements such as rivers and roads. However, the quality of the geometry is difficult to assess in the absence of dense training data, which is usually the case in land cover mapping, especially over wide areas. This work presents a framework for measuring the geometric precision of a classification map, in order to provide deeper insight into the consequences of the use of various contextual features, when dense validation data is not available. This quantitative metric, named the Pixel Based Corner Match (PBCM), is based on corner detection and corner matching between a pixel-based classification result, and a contextual classification result. The selected case study is the classification of Sentinel-2 multi-spectral image time series, with a rich nomenclature containing context-dependent classes. To demonstrate the added value of the proposed metric, three spatial support shapes (window, object, superpixel) are compared according to their ability to improve the classification performance on this challenging problem, while paying attention to the geometric precision of the result. The results show that superpixels are the best candidate for the local statistics features, as they modestly improve the classification accuracy, while preserving the geometric elements in the image. Furthermore, the density of edges in a sliding window provides a significant boost in accuracy, and maintains a high geometric precision.
A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery
Authors: Zhang Hua , Zimba Paul V. , Nzewi Emmanuel U.
Published: August 2019

The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.
HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks
Authors: Dirvanauskas Darius , Maskeliūnas Rytis , Raudonis Vidas , Damaševičius Robertas , Scherer Rafal
Published: August 2019

We propose a method for generating the synthetic images of human embryo cells that could later be used for classification, analysis, and training, thus resulting in the creation of new synthetic image datasets for research areas lacking real-world data. Our focus was not only to generate the generic image of a cell such, but to make sure that it has all necessary attributes of a real cell image to provide a fully realistic synthetic version. We use human embryo images obtained during cell development processes for training a deep neural network (DNN). The proposed algorithm used generative adversarial network (GAN) to generate one-, two-, and four-cell stage images. We achieved a misclassification rate of 12.3% for the generated images, while the expert evaluation showed the true recognition rate (TRR) of 80.00% (for four-cell images), 86.8% (for two-cell images), and 96.2% (for one-cell images). Texture-based comparison using the Haralick features showed that there is no statistically (using the Student’s t-test) significant (p < 0.01) differences between the real and synthetic embryo images except for the sum of variance (for one-cell and four-cell images), and variance and sum of average (for two-cell images) features. The obtained synthetic images can be later adapted to facilitate the development, training, and evaluation of new algorithms for embryo image processing tasks.
Precise and Robust RTK-GNSS Positioning in Urban Environments with Dual-Antenna Configuration
Authors: Fan Peirong , Li Wenyi , Cui Xiaowei , Lu Mingquan
Published: August 2019

Robust and centimeter-level Real-time Kinematic (RTK)-based Global Navigation Satellite System (GNSS) positioning is of paramount importance for emerging GNSS applications, such as drones and automobile systems. However, the performance of conventional single-rover RTK degrades greatly in urban environments due to signal blockage and strong multipath. The increasing use of multiple-antenna/rover configurations for attitude determination in the above precise positioning applications, just as well, allows more information involved to improve RTK positioning performance in urban areas. This paper proposes a dual-antenna constraint RTK algorithm, which combines GNSS measurements of both antennas by making use of the geometric constraint between them. By doing this, the reception diversity between two antennas can be taken advantage of to improve the availability and geometric distribution of GNSS satellites, and what is more, the redundant measurements from a second antenna help to weaken the multipath effect on the first antenna. Particularly, an Ambiguity Dilution of Precision (ADOP)-based analysis is carried out to explore the intrinsic model strength for ambiguity resolution (AR) with different kinds of constraints. Based on the results, a Dual-Antenna with baseline VEctor Constraint algorithm (RTK) is developed. The primary advantages of the reported method include: (1) Improved availability and success rate of RTK, even if neither of the two single-antenna receivers can successfully solve the AR problem; and (2) reduced computational burden by adopting the concept of measurement projection. Simulated and real data experiments are performed to demonstrate robustness and precision of the algorithm in GNSS-challenged environments.
A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility
Authors: Bui Dieu Tien , Moayedi Hossein , Kalantar Bahareh , Osouli Abdolreza , Pradhan Biswajeet , Nguyen Hoang , Rashid Ahmad Safuan A
Published: August 2019

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.
Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture
Authors: Trakadas Panagiotis , Nomikos Nikolaos , Michailidis Emmanouel T. , Zahariadis Theodore , Facca Federico M. , Breitgand David , Rizou Stamatia , Masip Xavi , Gkonis Panagiotis
Published: August 2019

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.
Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks
Authors: Goyat Rekha , Rai Mritunjay Kumar , Kumar Gulshan , Saha Rahul , Kim Tai-Hoon
Published: August 2019

In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms.
Real-Time Photometric Calibrated Monocular Direct Visual SLAM
Authors: Liu Peixin , Yuan Xianfeng , Zhang Chengjin , Song Yong , Liu Chuanzheng , Li Ziyan
Published: August 2019

To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation of the camera. Secondly, the Shi–Tomasi corners of the input sequence were tracked, and optimization equations were established using the pixel tracking of sparse direct visual odometry (VO). Thirdly, the Levenberg–Marquardt (L–M) method was applied to solve the joint optimization equation, and the photometric calibration parameters in the VO were updated to realize the real-time dynamic compensation of the exposure of the input sequences, which reduced the effects of the light variations on SLAM’s (simultaneous localization and mapping) accuracy and robustness. Finally, a Shi–Tomasi corner filtered strategy was designed to reduce the computational complexity of the proposed algorithm, and the loop closure detection was realized based on the oriented FAST and rotated BRIEF (ORB) features. The proposed algorithm was tested using TUM, KITTI, EuRoC, and an actual environment, and the experimental results show that the positioning and mapping performance of the proposed algorithm is promising.
The Heading Weight Function: A Novel LiDAR-Based Local Planner for Nonholonomic Mobile Robots
Authors: Harik El Houssein Chouaib , Korsaeth Audun
Published: August 2019

In this paper, we present a novel method for obstacle avoidance designed for a nonholonomic mobile robot. The method relies on light detection and ranging (LiDAR) readings, which are mapped into a polar coordinate system. Obstacles are taken into consideration when they are within a predefined radius from the robot. A central part of the approach is a new Heading Weight Function (HWF), in which the beams within the aperture angle of the LiDAR are virtually weighted in order to generate the best trajectory candidate for the robot. The HWF is designed to find a solution also in the case of a local-minima situation. The function is coupled with the robot’s controller in order to provide both linear and angular velocities. We tested the method both by simulations in a digital environment with a range of different static obstacles, and in a real, experimental environment including static and dynamic obstacles. The results showed that when utilizing the novel HWF, the robot was able to navigate safely toward the target while avoiding all obstacles included in the tests. Our findings thus show that it is possible for a robot to navigate safely in a populated environment using this method, and that sufficient efficiency in navigation may be obtained without basing the method on a global planner. This is particularly promising for navigation challenges occurring in unknown environments where models of the world cannot be obtained.