bluuebunny/crossref_metadata_2025 huggingface dataset:
Metallogenic Setting and Evolution of the Pados-Tundra Cr-Bearing Ultramafic Complex, Kola Peninsula: Evidence from Sm–Nd and U–Pb Isotopes
Authors: Serov Pavel A. , Bayanova Tamara B. , Steshenko Ekaterina N. , Kunakkuzin Evgeniy L. , Borisenko Elena S.
Published: February 2020

The article presents new Sm–Nd and U–Pb geochronological data on rocks of the poorly studied Pados-Tundra Cr-bearing complex. It is part of the Notozero mafic–ultramafic complex (western Kola Peninsula) and occurs at the border of the Paleoproterozoic Lapland Granulite Belt and the Archean Belomorian composite terrain. The Pados-Tundra complex hosts two major zones, the Dunite and Orthopyroxenite Blocks. Dunites are associated with four levels of chromite mineralization. Isotope Sm–Nd studies of dunites, harzburgites, and orthopyroxenites from the central part of the complex have been carried out. The isochron Sm–Nd age on 11 whole-rock samples from a rhythmically layered series of the complex is 2485 ± 38 Ma; the mineral Sm–Nd isochron for harzburgites shows the age of 2475 ± 38 Ma. It corresponds with the time of large-scale rifting that originated in the Fennoscandian Shield. When the rhythmically layered series of the intrusion and its chromite mineralization were formed, hornblendite dykes intruded. The U–Pb and Sm–Nd research has estimated their age at ca. 2080 Ma, which is likely to correspond with the occurrence of the Lapland–Kola Ocean. According to isotope Sm–Nd dating on metamorphic minerals (rutile, amphibole), the age of postmetamorphic cooling of rocks in the complex to 650–600 °C is 1872 ± 76 Ma. The U–Pb age on rutile from a hornblendite dyke (1804 ± 10 Ma) indicates further cooling to 450–400 °C. The conducted research has determined the early Proterozoic age of rocks in the rhythmically layered series in the Pados-Tundra complex. It is close to the age of the Paleoproterozoic ore magmatic system in the Fennoscandian Shield that developed 2.53–2.40 Ga ago. Later episodes of alterations in rocks are directly related to main metamorphic episodes in the region at the turn of 1.9 Ga. Results of the current study expand the geography of the vast Paleoproterozoic East Scandinavian Large Igneous Province and can be applied for further studies of similar mafic–ultramafic complexes.
Power Generation: Feedstock for High-Value Sulfate Minerals
Authors: Staicu Lucian C. , Bajda Tomasz , Drewniak Lukasz , Charlet Laurent
Published: February 2020

Coal-fired power facilities generate a polymetallic effluent (Flue Gas Desulfurization—FGD) rich in sulfate. FGD effluents may be considered an important secondary resource. This paper investigates the recovery of sulfate as barite (BaSO4), a mineral with high commercial value and a critical raw material. Using equimolar BaCl2, >99% desulfurization of an FGD effluent produced by a coal-fired power plant operating in central Poland was achieved, yielding up to 16.5 kg high purity barite m−3. The recovered barite was characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric (TGA), scanning electron microscopy analysis (SEM), surface properties (PZC), density, and chemical stability (TCLP), and was compared with a commercial reference material. Barite recovery also led to the reduction in concentration of Al (86%), Cu (52%), K (69%), Mo (62%), Se (40%), Sr (91%), and U (75%) initially present in the FGD effluent. TCLP results indicate the entrapment and the stabilization of ~70% Se and ~90% Al in the barite structure. Based on this dataset, an in-depth characterization of the recovered barite is presented, and the removal mechanism of the elements is discussed. The study also provides a preliminary cost benefit analysis of the process. To our best knowledge, this is the first work showing barite recovery and metal removal from FGD effluents using a one-step process.
Extracellular Synthesis and Characterization of Silver Nanoparticles—Antibacterial Activity against Multidrug-Resistant Bacterial Strains
Authors: Ghodake Gajanan , Kim Min , Sung Jung-Suk , Shinde Surendra , Yang Jiwook , Hwang Kyojung , Kim Dae-Young
Published: February 2020

Herein, we report the use of a cell-free extract for the extracellular synthesis of silver nanoparticles (AgNPs) and their potential to address the growing threat of multidrug-resistant (MDR) pathogenic bacteria. The reproducibility of AgNP synthesis was good and AgNP formation kinetics were monitored as a function of various reaction factors via ultraviolet-visible absorption spectroscopy. This green method was dependent on the alkaline pH of the reaction mixture. With the addition of dilute sodium hydroxide, well-dispersed AgNPs could be produced in large quantities via the classical nucleation and growth route. The new biosynthetic route enabled the production of AgNPs within a narrow size range of 4 to 17 nm. The AgNPs were characterized using various techniques and their antibacterial activity against MDR pathogenic bacteria was evaluated. Field-emission scanning electron microscopic imaging revealed prominent morphological changes in Staphylococcus aureus cells due to mechanical damage, which led to cell death. Escherichia coli cells showed signs of contraction and intracellular fluid discharge as a consequence of disrupted cell membrane function. This new biologically-assisted extracellular strategy is potentially useful for the decontamination of surfaces and is expected to contribute to the development of new products containing AgNPs.
Bright Single-Photon Emitting Diodes Based on the Silicon-Vacancy Center in AlN/Diamond Heterostructures
Authors: Khramtsov Igor A. , Fedyanin Dmitry Yu.
Published: February 2020

Practical implementation of many quantum information and sensing technologies relies on the ability to efficiently generate and manipulate single-photon photons under ambient conditions. Color centers in diamond, such as the silicon-vacancy (SiV) center, have recently emerged as extremely attractive single-photon emitters for room temperature applications. However, diamond is a material at the interface between insulators and semiconductors. Therefore, it is extremely difficult to excite color centers electrically and consequently develop bright and efficient electrically driven single-photon sources. Here, using a comprehensive theoretical approach, we propose and numerically demonstrate a concept of a single-photon emitting diode (SPED) based on a SiV center in a nanoscale AlN/diamond heterojunction device. We find that in spite of the high potential barrier for electrons in AlN at the AlN/diamond heterojunction, under forward bias, electrons can be efficiently injected from AlN into the i-type diamond region of the n-AlN/i-diamond/p-diamond heterostructure, which ensures bright single-photon electroluminescence (SPEL) of the SiV center located in the i-type diamond region. The maximum SPEL rate is more than five times higher than what can be achieved in SPEDs based on diamond p-i-n diodes. Despite the high density of defects at the AlN/diamond interface, the SPEL rate can reach about 4 Mcps, which coincides with the limit imposed by the quantum efficiency and the lifetime of the shelving state of the SiV center. These findings provide new insights into the development of bright room-temperature electrically driven single-photon sources for quantum information technologies and, we believe, stimulate further research in this area.
Pyrene Coating Transition Metal Disulfides as Protection from Photooxidation and Environmental Aging
Authors: Canton-Vitoria Ruben , Sayed-Ahmad-Baraza Yuman , Humbert Bernard , Arenal Raul , Ewels Christopher , Tagmatarchis Nikos
Published: February 2020

Environmental degradation of transition metal disulfides (TMDs) is a key stumbling block in a range of applications. We show that a simple one-pot non-covalent pyrene coating process protects TMDs from both photoinduced oxidation and environmental aging. Pyrene is immobilized non-covalently on the basal plane of exfoliated MoS2 and WS2. The optical properties of TMD/pyrene are assessed via electronic absorption and fluorescence emission spectroscopy. High-resolution scanning transmission electron microscopy coupled with electron energy loss spectroscopy confirms extensive pyrene surface coverage, with density functional theory calculations suggesting a strongly bound stable parallel-stacked pyrene coverage of ~2–3 layers on the TMD surfaces. Raman spectroscopy of exfoliated TMDs while irradiating at 0.9 mW/4 μm2 under ambient conditions shows new and strong Raman bands due to oxidized states of Mo and W. Yet remarkably, under the same exposure conditions TMD/pyrene remain unperturbed. The current findings demonstrate that pyrene physisorbed on MoS2 and WS2 acts as an environmental barrier, preventing oxidative surface reactions in the TMDs catalyzed by moisture, air, and assisted by laser irradiation. Raman spectroscopy confirms that the hybrid materials stored under ambient conditions for two years remained structurally unaltered, corroborating the beneficial role of pyrene for not only hindering oxidation but also inhibiting aging.
Bisphenol S in Food Causes Hormonal and Obesogenic Effects Comparable to or Worse than Bisphenol A: A Literature Review
Authors: Thoene Michael , Dzika Ewa , Gonkowski Slawomir , Wojtkiewicz Joanna
Published: February 2020

In recent years, bisphenol analogues such as bisphenol S (BPS) have come to replace bisphenol A in food packaging and food containers, since bisphenol A (BPA) has been shown to leach into food and water, causing numerous negative health effects. Unfortunately, little or no research was done to determine the safety of these BPA-free products before they were marketed to the public as a healthier alternative. The latest studies have shown that some of these bisphenol analogues may be even more harmful than the original BPA in some situations. This article used a literature survey to investigate the bisphenol analogue BPS and compare it to BPA and other analogues with regards to increased obesity, metabolic disorders, cancer, and reproductive defects; among others. It was found that BPS works via different pathways than does BPA while causing equivalent obesogenic effects, such as activating preadipocytes, and that BPS was correlated with metabolic disorders, such as gestational diabetes, that BPA was not correlated with. BPS was also shown to be more toxic to the reproductive system than BPA and was shown to hormonally promote certain breast cancers at the same rate as BPA. Therefore, a strong argument may be made to regulate BPS in exactly the same manner as BPA.
Acai Extract Transiently Upregulates Erythropoietin by Inducing a Renal Hypoxic Condition in Mice
Authors: Shibuya Shuichi , Toda Toshihiko , Ozawa Yusuke , Yata Mario Jose Villegas , Shimizu Takahiko
Published: February 2020

Acai (Euterpe oleracea Mart. Palmae, Arecaceae) is a palm plant native to the Brazilian Amazon. It contains many nutrients, such as polyphenols, iron, vitamin E, and unsaturated fatty acids, so in recent years, many of the antioxidant and anti-inflammatory effects of acai have been reported. However, the effects of acai on hematopoiesis have not been investigated yet. In the present study, we administered acai extract to mice and evaluated its hematopoietic effects. Acai treatment significantly increased the erythrocytes, hemoglobin, and hematocrit contents compared to controls for four days. Then, we examined the hematopoietic-related markers following a single injection. Acai administration significantly increased the levels of the hematopoietic-related hormone erythropoietin in blood compared to controls and also transiently upregulated the gene expression of Epo in the kidney. Furthermore, in the mice treated with acai extract, the kidneys were positively stained with the hypoxic probe pimonidazole in comparison to the controls. These results demonstrated that acai increases the erythropoietin expression via hypoxic action in the kidney. Acai can be expected to improve motility through hematopoiesis.
Chrono-Nutrition and Diet Quality in Adolescents with Delayed Sleep-Wake Phase Disorder
Authors: Berendsen Maxime , Boss Myrthe , Smits Marcel , Pot Gerda K.
Published: February 2020

Background: Delayed sleep-wake phase disorder (DSPD), characterized by delayed sleep-onset and problems with awakening in the morning, is mostly prevalent in adolescents. Several studies have suggested chrono-nutrition could present a possible modifiable risk factor for DSPD. Objective: To describe differences in chrono-nutrition and diet quality in adolescents with DSPD compared to age-related controls. Methods: Chrono-nutrition and diet quality of 46 adolescents with DSPD, aged 13–20 years, and 43 controls were assessed via questionnaires. Diet quality included the Dutch Healthy Diet index (DHD-index) and Eating Choices Index (ECI). Results were analysed using logistic regression and Spearman’s partial correlation. Results: Compared with controls, DSPD patients consumed their first food of the day significantly later on weekdays (+32 ± 12 min, p = 0.010) and weekends (+25 ± 8 min, p = 0.005). They consumed their dinner more regularly (80.4% vs. 48.8%, p = 0.002) and consumed morning-snacks less frequently (3.0 ± 2.1 days vs. 4.2 ± 1.7 days, p = 0.006). No differences in clock times of breakfast, lunch, or dinner were found. Moreover, no significant differences in overall diet quality were observed. Conclusion: This descriptive study showed chrono-nutritional differences between adolescents with and without DPSD. Further studies are needed to explore features of chrono-nutrition as a possible treatment of DPSD.
Antimicrobial Resistance and Molecular Epidemiology of Corynebacterium striatum Isolated in a Tertiary Hospital in Turkey
Authors: Asgin Nergis , Otlu Baris
Published: February 2020

Although Corynebacterium striatum is part of the human flora, it has recently drawn attention both for its multidrug resistance and its role as an invasive infection/outbreak agent. This cross-sectional study aimed to determine the antimicrobial resistance and clonal relationships among C. striatum strains. In total, 81 C. striatum strains were identified using Phoenix-100TM (BD, Sparks, MD, USA). The antimicrobial resistance of the strains was determined using the Kirby–Bauer disk diffusion method. Clonal relatedness among the strains was performed via arbitrarily primed polymerase chain reaction (AP-PCR). All 81 C. striatum strains were resistant to penicillin, cefotaxime, ciprofloxacin, and tetracycline, but susceptible to vancomycin and linezolid. The resistance rates to gentamicin, erythromycin, and clindamycin were 34.6%, 79%, and 87.7% respectively. AP-PCR results showed no predominant clone among the C. striatum strains. Corynebacterium striatum is reportedly the cause of an increasing number of invasive infections/outbreaks. Moreover, treatment options are limited. The study showed that vancomycin, linezolid, and gentamicin can be selected for the empirical treatment of C. striatum infections. Although no single-clone outbreak was observed in our hospital, small clonal circulations were observed within some units, indicating cross-contamination. Therefore, a comprehensive infection control program is warranted in future.
Regional Geotourist Resources—Assessment and Management (A Case Study in SE Poland)
Authors: Zgłobicki Wojciech , Kukiełka Sylwia , Baran-Zgłobicka Bogusława
Published: February 2020

The appropriate identification of the geoheritage resources of a specific area is the sine qua non of the development of geotourism. The identification of tourists’ perception of abiotic nature sites of high scientific value is also particularly important because it determines the possibility of using the potential of geosites. In the study, a detailed analysis was carried out of the assets of geological, geomorphological and hydrologic sites in the Central Roztocze region (SE Poland) comprising the central part of the proposed Geopark “Stone Forest in Roztocze”. Data from the Polish Central Register of Geosites, the results of a geotourist assessment and questionnaire surveys were used in the analysis. These data indicate a high potential for geotourism development and consistency between scientific assessments and ratings from tourists. However, this potential is not used to a sufficient degree, while actions aimed at developing geotourism and establishing the Geopark are not appreciated by local authorities and institutions responsible for tourism development. The idea of geoparks and geotourism development is not supported by the State either, whether institutionally or financially. Based on the studies conducted, we propose practical measures that should be implemented to increase the use of the region’s geotourist assets.
Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data
Authors: Xu Min , Cao Chunxiang , Jia Peng
Published: February 2020

Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolution gridded population surface (HGPS). The approach proposed in this study holds potential to improve the precision and automation of high-resolution population estimation.
Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu
Authors: Liang Wanwan , Abidi Mongi , Carrasco Luis , McNelis Jack , Tran Liem , Li Yingkui , Grant Jerome
Published: February 2020

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.
Hailstorm Detection by Satellite Microwave Radiometers
Authors: Laviola Sante , Levizzani Vincenzo , Ferraro Ralph , Beauchamp James
Published: February 2020

Passive microwave measurements from satellites have been used to identify the signature of hail in intense thunderstorms. The scattering signal of hailstones is typically observed as a strong depression of upwelling brightness temperatures from the cloud to the satellite. Although the relation between scattering signal and hail diameter is often assumed linear, in this work a logistic model is used which seems to well approximate the complexity of the radiation extinction process by varying the hail cross-section. A novel probability-based method for hail detection originally conceived for AMSU-B/MHS and now extended to ATMS, GMI, and SSMIS, is presented. The measurements of AMSU-B/MHS were analyzed during selected hailstorms over Europe, South America and the US to quantify the extinction of radiation due to the hailstones and large ice aggregates. To this aim, a probabilistic growth model has been developed. The validation analysis based on 12-year surface hail observations over the US (NOAA official reports) collocated with AMSU-B overpasses have demonstrated the high performance of the hail detection method in distinguishing between moderate and severe hailstorms, fitting the seasonality of hail patterns. The flexibility of the method allowed its experimental application to other microwave radiometers equipped with MHS-like frequency channels revealing a high level of portability.
Extraction of Spatial and Temporal Patterns of Concentrations of Chlorophyll-a and Total Suspended Matter in Poyang Lake Using GF-1 Satellite Data
Authors: Xu Jian , Gao Chen , Wang Yeqiao
Published: February 2020

Poyang Lake is the largest freshwater lake in China. Its ecosystem services and functions, such as water conservation and the sustaining of biodiversity, have significant impacts on the security and sustainability of the regional ecology. The lake and wetlands of the Poyang Lake are among protected aquatic ecosystems with global significance. The Poyang Lake region has recently experienced increased urbanization and anthropogenic disturbances, which has greatly impacted the lake environment. The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are important indicators for assessing the water quality of lakes. In this study, we used data from the Gaofen-1 (GF-1) satellite, in situ measurements of the reflectance of the lake water, and the analysis of the Chl-a and TSM concentrations of lake water samples to investigate the spatial and temporal variation and distribution patterns of the concentrations of Chl-a and TSM. We analyzed the measured reflectance spectra and conducted correlation analysis to identify the spectral bands that are sensitive to the concentration of Chl-a and TSM, respectively. The study suggested that the wavelengths corresponding to bands 1, 3, and 4 of the GF-1 images were the most sensitive to changes in the concentration of Chl-a. The results showed that the correlation between the reflectance and TSM concentration was the highest for wavelengths that corresponded to band 3 of the GF-1 satellite images. Based on the analysis, bands 1, 3, and 4 of GF-1 were selected while using the APPEL (APProach by ELimination) model and were used to establish a model for the retrieval of Chl-a concentrations. A single-band model that was based on band 3 of GF-1 was established for the retrieval of TSM concentrations. The modeling results revealed the spatial and temporal variations of water quality in Poyang Lake between 2015 and 2016 and demonstrated the capacities of GF-1 in the monitoring of lake environment.
Remote Sensing Image Ship Detection under Complex Sea Conditions Based on Deep Semantic Segmentation
Authors: Chen Yantong , Li Yuyang , Wang Junsheng , Chen Weinan , Zhang Xianzhong
Published: February 2020

Under complex sea conditions, ship detection from remote sensing images is easily affected by sea clutter, thin clouds, and islands, resulting in unreliable detection results. In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network with a fully connected conditional random field. Based on the Resnet architecture, the remote sensing image is roughly segmented using a deep convolution neural network as the input. Using the Gaussian pairwise potential method and mean field approximation theorem, a conditional random field is established as the output of the recurrent neural network, thus achieving end-to-end connection. We compared the proposed method with other state-of-the-art methods on the dataset established by Google Earth and NWPU-RESISC45. Experiments show that the target detection accuracy of the proposed method and the ability of capturing fine details of images are improved. The mean intersection over union is 83.2% compared with other models, which indicates obvious advantages. The proposed method is fast enough to meet the needs for ship detection in remote sensing images.
Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats
Authors: Ciężkowski Wojciech , Szporak-Wasilewska Sylwia , Kleniewska Małgorzata , Jóźwiak Jacek , Gnatowski Tomasz , Dąbrowski Piotr , Góraj Maciej , Szatyłowicz Jan , Ignar Stefan , Chormański Jarosław
Published: February 2020

Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems.
Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin
Authors: Hufkens Koen , de Haulleville Thalès , Kearsley Elizabeth , Jacobsen Kim , Beeckman Hans , Stoffelen Piet , Vandelook Filip , Meeus Sofie , Amara Michael , Van Hirtum Leen , Van den Bulcke Jan , Verbeeck Hans , Wingate Lisa
Published: February 2020

Given the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin. Our study leveraged structure-from-motion and a convolutional neural network-based LULC classifier, using synthetic landscape-based image augmentation to map historical forest cover across a large orthomosaic (~93,431 ha) geo-referenced to ~4.7 ± 4.3 m at submeter resolution. A comparison with contemporary LULC data showed a shift from previously highly regular industrial deforestation of large areas to discrete smallholder farming clearing, increasing landscape fragmentation and providing opportunties for substantial forest regrowth. We estimated aboveground carbon gains through reforestation to range from 811 to 1592 Gg C, partially offsetting historical deforestation (2416 Gg C), in our study area. Efforts to quantify long-term canopy texture changes and their link to aboveground carbon had limited to no success. Our analysis provides methods and insights into key spatial and temporal patterns of deforestation and reforestation at a multi-decadal scale, providing a historical context for past and ongoing forest research in the area.
Impact of Surface Albedo Assimilation on Snow Estimation
Authors: Kumar Sujay , Mocko David , Vuyovich Carrie , Peters-Lidard Christa
Published: February 2020

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.
Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness
Authors: Sánchez-Gámez Pablo , Gabarro Carolina , Turiel Antonio , Portabella Marcos
Published: February 2020

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.
Construction of Nighttime Cloud Layer Height and Classification of Cloud Types
Authors: Chen Sijie , Cheng Chonghui , Zhang Xingying , Su Lin , Tong Bowen , Dong Changzhe , Wang Fu , Chen Binglong , Chen Weibiao , Liu Dong
Published: February 2020

A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general.
Evaluation of Satellite Precipitation Estimates over Australia
Authors: Chua Zhi-Weng , Kuleshov Yuriy , Watkins Andrew
Published: February 2020

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.
Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios
Authors: Shi Guoqiang , Ma Peifeng , Lin Hui , Huang Bo , Zhang Bowen , Liu Yuzhou
Published: February 2020

The improved spatial resolution of Synthetic Aperture Radar (SAR) images from newly launched sensors has promoted a more frequent use of distributed scatterer (DS) interferometry (DSI) in urban monitoring, pursuing sufficient and detailed measurements. However, the commonly used statistical methods for homogeneous pixel clustering by exploring amplitude information are firstly, computationally intensive; furthermore, their necessity when applied to high-coherent built scenarios is little discussed in the literature. This paper explores the potential of using phase information for the detection of homogeneous pixels on built surfaces. We propose a simple phase-correlated pixel (PCP) clustering and introduce a coherence-weighted phase link (WPL), i.e., PCPWPL, to pursue a faster processing of interferogram phase denoising. Rather than relying on the statistical tests of amplitude characteristics, we exploit vector correlation in the complex domain to identify PCPs with similar phase observations, thus, avoiding the intensive hypothesis test. A coherence-weighted phase linking is applied for DS phase reconstruction. The estimation of geophysical parameters, e.g., deformation, is completed using an integrated network of persistent scatterers (PS) and DS. Efficiency of the proposed method is fairly illustrated by both synthetic and real data experiments. Pros and cons of the proposed PCPWPL were analyzed with the comparison to a conventional amplitude-based strategy using an X-band CosmoSkyMed dataset. It is demonstrated that the use of phase correlation is sufficient for DS monitoring in built scenarios, with equivalent measurement quantity and cheaper computational cost.
Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection
Authors: Kundinger Thomas , Sofra Nikoletta , Riener Andreas
Published: February 2020

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.
Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images
Authors: Mittal Ansh , Kumar Deepika , Mittal Mamta , Saba Tanzila , Abunadi Ibrahim , Rehman Amjad , Roy Sudipta
Published: February 2020

An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.
A Stepwise and Hybrid Trust Evaluation Scheme for Tactical Wireless Sensor Networks
Authors: Lim Jihun , Keum Dooho , Ko Young-Bae
Published: February 2020

In tactical wireless sensor networks, tactical sensors are increasingly expected to be exploited for information collection in battlefields or dangerous areas on behalf of soldiers. The main function of these networks is to use sensors to measure radiation, nuclear, and biochemical values for the safety of allies and also to monitor and carry out reconnaissance of enemies. These tactical sensors require a network traffic flow that sends various types of measured information to the gateway, which needs high reliability. To ensure reliability, it must be able to detect malicious nodes that perform packet-dropping attacks to disrupt the network traffic flow, and energy-constrained sensors require energy-efficient methods to detect them. Therefore, in this paper, we propose a stepwise and hybrid trust evaluation scheme for locating malicious nodes that perform packet-dropping attacks in a tree-based network. Sensors send a query to the gateway by observing the traffic patterns of their child nodes. Moreover, depending on the situation, the gateway detects malicious nodes by choosing between gateway-assisted trust evaluation and gateway-independent trust evaluation. We implemented and evaluated the proposed scheme with the OPNET simulator, and the results showed that a higher packet delivery ratio can be achieved with significantly lower energy consumption.
Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck
Authors: Lu Xiaowei , Ai Yunfeng , Tian Bin
Published: February 2020

Road boundary detection is an important part of the perception of the autonomous driving. It is difficult to detect road boundaries of unstructured roads because there are no curbs. There are no clear boundaries on mine roads to distinguish areas within the road boundary line and areas outside the road boundary line. This paper proposes a real-time road boundary detection and tracking method by a 3D-LIDAR sensor. The road boundary points are extracted from the detected elevated point clouds above the ground point cloud according to the spatial distance characteristics and the angular features. Road tracking is to predict and update the boundary point information in real-time, in order to prevent false and missed detection. The experimental verification of mine road data shows the accuracy and robustness of the proposed algorithm.
Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries
Authors: Bijay-Singh  , Ali Ali M.
Published: February 2020

To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available.
A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration
Authors: Cai Huaiyu , Pang Weisong , Chen Xiaodong , Wang Yi , Liang Haolin
Published: February 2020

Aiming at the problems of feature point calibration method of 3D light detection and ranging (LiDAR) and camera calibration that are calibration boards in various forms, incomplete information extraction methods and large calibration errors, a novel calibration board with local gradient depth information and main plane square corner information (BWDC) was designed. In addition, the "three-step fitting interpolation method" was proposed to select feature points and obtain the corresponding coordinates of feature points in the LiDAR coordinate system and camera pixel coordinate system based on BWDC. Finally, calibration experiments were carried out, and the calibration results were verified by methods such as incremental verification and reprojection error comparison. The calibration results show that using BWDC and the "three-step fitting interpolation method" can solve quite accurate coordinate transformation matrix and intrinsic and external parameters of sensors, which dynamically change within 0.2% in the repeatable experiments. The difference between the experimental value and the actual value in the incremental verification experiment is about 0.5%. The average reprojection error is 1.8312 pixels, and the value changes at different distances do not exceed 0.1 pixels, which also show that the calibration method is accurate and stable.