bluuebunny/crossref_metadata_2025 huggingface dataset:
Nanocomposite Biopolymer Arboblend V2 Nature AgNPs
Authors: Mazurchevici Simona-Nicoleta , Motaș Justina Georgiana , Diaconu Mariana , Lisa Gabriela , Lohan Nicoleta Monica , Glod Mihai , Nedelcu Dumitru
Published: September 2021

Due to the pressing problems of today’s world, regarding both the finding of new, environmentally friendly materials which have the potential to replace classic ones, and the need to limit the accelerated spread of bacteria in hospitals, offices and other types of spaces, many researchers have chosen to develop their work in this field. Thus, biopolymeric materials have evolved so much that they are gradually becoming able to remove fossil-based plastics from major industries, which are harmful to the environment and implicitly to human health. The biopolymer employed in the present study, Arboblend V2 Nature with silver nanoparticle content (AgNP) meets both aspects mentioned above. The main purpose of the paper is to replace several parts and products in operation which exhibit antibacterial action, preventing the colonization and proliferation of bacteria (Streptococcus pyogenes and Staphylococcus aureus, by using the submerged cultivation method), but also the possibility of degradation in different environments. The biopolymer characterization followed the thermal behavior of the samples, their structure and morphology through specific analyses, such as TGA (thermogravimetric analysis), DSC (differential scanning calorimetry), SEM (scanning electron microscopy) and XRD (X-ray diffraction). The obtained results offer the possibility of use of said biocomposite material in the medical field because of its antibacterial characteristics that have proved to be positive, and, therefore, suitable for such applications. The thermal degradation and the structure of the material highlighted the possibility of employing it in good conditions at temperatures up to 200 °C. Two types of samples were used for thermal analysis: first, in the form of granules coated with silver nanoparticles, and second, test specimen cut from the sample obtained by injection molding from the coated granules with silver nanoparticles.
Mechanical, Thermal, Electrical Characteristics and EMI Absorption Shielding Effectiveness of Rubber Composites Based on Ferrite and Carbon Fillers
Authors: Kruželák Ján , Kvasničáková Andrea , Hložeková Klaudia , Plavec Roderik , Dosoudil Rastislav , Gořalík Marek , Vilčáková Jarmila , Hudec Ivan
Published: September 2021

In this work, rubber composites were fabricated by incorporation of manganese-zinc ferrite alone and in combination with carbon-based fillers into acrylonitrile-butadiene rubber. Electromagnetic parameters and electromagnetic interference (EMI) absorption shielding effectiveness of composite materials were examined in the frequency range 1 MHz–3 GHz. The influence of ferrite and fillers combination on thermal characteristics and mechanical properties of composites was investigated as well. The results revealed that ferrite imparts absorption shielding efficiency to the composites in tested frequency range. The absorption shielding effectiveness and absorption maxima of ferrite filled composites shifted to lower frequencies with increasing content of magnetic filler. The combination of carbon black and ferrite also resulted in the fabrication of efficient EMI shields. However, the EMI absorption shielding effectiveness was lower, which can be ascribed to higher electrical conductivity and higher permittivity of those materials. The highest conductivity and permittivity of composites filled with combination of carbon nanotubes and ferrite was responsible for the lowest absorption shielding effectiveness within the examined frequency range. The results also demonstrated that combination of ferrite with carbon-based fillers resulted in the enhancement of thermal conductivity and improvement of mechanical properties.
Novel Cellulose Triacetate (CTA)/Cellulose Diacetate (CDA) Blend Membranes Enhanced by Amine Functionalized ZIF-8 for CO2 Separation
Authors: Raza Ayesha , Japip Susilo , Liang Can Zeng , Farrukh Sarah , Hussain Arshad , Chung Tai-Shung
Published: September 2021

Currently, cellulose acetate (CA) membranes dominate membrane-based CO2 separation for natural gas purification due to their economical and green nature. However, their lower CO2 permeability and ease of plasticization are the drawbacks. To overcome these weaknesses, we have developed high-performance mixed matrix membranes (MMMs) consisting of cellulose triacetate (CTA), cellulose diacetate (CDA), and amine functionalized zeolitic imidazolate frameworks (NH2-ZIF-8) for CO2 separation. The NH2-ZIF-8 was chosen as a filler because (1) its pore size is between the kinetic diameters of CO2 and CH4 and (2) the NH2 groups attached on the surface of NH2-ZIF-8 have good affinity with CO2 molecules. The incorporation of NH2-ZIF-8 in the CTA/CDA blend matrix improved both the gas separation performance and plasticization resistance. The optimized membrane containing 15 wt.% of NH2-ZIF-8 had a CO2 permeability of 11.33 Barrer at 35 °C under the trans-membrane pressure of 5 bar. This is 2-fold higher than the pristine membrane, while showing a superior CO2/CH4 selectivity of 33. In addition, the former had 106% higher CO2 plasticization resistance of up to about 21 bar and an impressive mixed gas CO2/CH4 selectivity of about 40. Therefore, the newly fabricated MMMs based on the CTA/CDA blend may have great potential for CO2 separation in the natural gas industry.
Gelatin-Based Hybrid Scaffolds: Promising Wound Dressings
Authors: Ndlovu Sindi P. , Ngece Kwanele , Alven Sibusiso , Aderibigbe Blessing A.
Published: September 2021

Wound care is a major biomedical field that is challenging due to the delayed wound healing process. Some factors are responsible for delayed wound healing such as malnutrition, poor oxygen flow, smoking, diseases (such as diabetes and cancer), microbial infections, etc. The currently used wound dressings suffer from various limitations, including poor antimicrobial activity, etc. Wound dressings that are formulated from biopolymers (e.g., cellulose, chitin, gelatin, chitosan, etc.) demonstrate interesting properties, such as good biocompatibility, non-toxicity, biodegradability, and attractive antimicrobial activity. Although biopolymer-based wound dressings display the aforementioned excellent features, they possess poor mechanical properties. Gelatin, a biopolymer has excellent biocompatibility, hemostatic property, reduced cytotoxicity, low antigenicity, and promotes cellular attachment and growth. However, it suffers from poor mechanical properties and antimicrobial activity. It is crosslinked with other polymers to enhance its mechanical properties. Furthermore, the incorporation of antimicrobial agents into gelatin-based wound dressings enhance their antimicrobial activity in vitro and in vivo. This review is focused on the development of hybrid wound dressings from a combination of gelatin and other polymers with good biological, mechanical, and physicochemical features which are appropriate for ideal wound dressings. Gelatin-based wound dressings are promising scaffolds for the treatment of infected, exuding, and bleeding wounds. This review article reports gelatin-based wound dressings which were developed between 2016 and 2021.
Thermal Stability and Dynamic Mechanical Analysis of Benzoylation Treated Sugar Palm/Kenaf Fiber Reinforced Polypropylene Hybrid Composites
Authors: Mohd Izwan S. , Sapuan S.M. , Zuhri M.Y.M. , Mohamed A.R.
Published: September 2021

This research was performed to evaluate the mechanical and thermal properties of sugar palm fiber (SPF)- and kenaf fiber (KF)-reinforced polypropylene (PP) composites. Sugar palm/kenaf was successfully treated by benzoylation treatment. The hybridized bio-composites (PP/SPF/KF) were fabricated with overall 10 weight percentage (wt%) relatively with three different fibers ratios between sugar palm-treated and kenaf-treated (7:3, 5:5, 3:7) and vice versa. The investigations of thermal stability were then carried out by using diffraction scanning calorimetry (DSC) and thermogravimetry analysis (TGA). The result of a flammability test showed that the treated hybrid composite (PP/SPF/KF) was the specimen that exhibited the best flammability properties, having the lowest average burning rate of 28 mm/min. The stiffness storage modulus (E’), loss modulus (E”), and damping factor (Tan δ) were examined by using dynamic mechanical analysis (DMA). The hybrid composite with the best ratio (PP/SPF/KF), T-SP5K5, showed a loss modulus (E”) of 86.2 MPa and a damping factor of 0.058. In addition, thermomechanical analysis (TMA) of the studies of the dimension coefficient (µm) against temperature were successfully recorded, with T-SP5K5 achieving the highest dimensional coefficient of 30.11 µm at 105 °C.
The Holonic Christ: Catholicity as Individuation and Integration
Authors: Nicastro Robert
Published: September 2021

The paradigm shift ushered in by the new science of the early twentieth century discloses the universe as a dynamic, energetic, and complex web of relationality. In view of this renewed sense of undivided wholeness, this article seeks to advance the growing synthesis of theology and depth psychology by way of a revised meaning of catholicity. Specifically, the article utilizes Carl Jung’s theory of individuation to suggest that catholicity is the conscious movement of the psyche toward wholeness, an outcome that Jung associated with Christ. The article introduces the term “Holonic Christ” to describe Christ as both the regulating principle of wholeness in nature and the co-recipient of the activity of deepening wholeness through the psychological development of the self-reflective individual. In this way, the article contends that catholicity is the process of individuation in its fullest form: a dynamic energy of the psyche that urges us in the direction of a more integrated personality, an ever-expanding community, and an eschatological remediation of divine self-contradiction made possible by human self-actualization. The article concludes with a discussion of the far-reaching religious implications of this study and explains why this new understanding of Christ is valuable to theological discourse.
The Evolution of Suffering, Epiphenomenalism, and the Phenomena of Life: Evidential Problems for Naturalists
Authors: Madden James D.
Published: September 2021

Paul Draper argues that the central issue in the debate over the problem of suffering is not whether the theist can offer a probable explanation of suffering, but whether theism or naturalism can give a better explanation for the facts regarding the distribution of pain as we find them. He likewise maintains a comparison of relative probabilities considering the facts of suffering; atheological naturalism is to be preferred. This essay proceeds in two phases: (a) It will be argued that mainstream positions in naturalistic philosophy of mind make it difficult to take pain as anything but epiphenomenal and therefore not subject to evolutionary explanation. While the distribution of suffering is a difficulty for the theist, the naturalist has equal difficulty explaining the fact that there is any suffering at all in the first place. Thus, the facts of suffering offer no advantage to the atheist. (b) Phenomenologists suggest that there is an intrinsic connection between animal life, pain, and normativity (including a summum bonum). The mere occurrence of life and normativity are, at least prima facie, more likely on the assumption of theism than atheism, so the theist may have a probabilistic advantage relative to the atheist. Phases (a) and (b) together support the overall conclusion that the facts of pain as we find them in the world (including that there is any pain at all) are at least as great, if not greater, a challenge for the atheist as they are the theist.
Religious Minorities’ Rights in International Law: Acknowledging Intersectionality, Enhancing Synergy
Authors: Angeletti Silvia
Published: September 2021

International human rights law on minorities sets forth a complex system of provisions affecting religious groups; still, the question of defining religious minorities remains largely unsettled. While assessing the legal framework of protection established under the UN system, this article explores the current debate drawing on the two key concepts of intersectionality and synergy. The intersectionality approach suggests that belonging to a religious minority cannot be dissociated from other features defining personal and groups’ identities (e.g., culture, ethnic origin, gender, language) and that this distinctive quality marks the multiple forms of violations and cross-cutting discriminations of which religious minorities are often victims. On the other hand, the synergy approach implies a comprehensive understanding of the legal sources protecting individual freedom of conscience and religion as well as religious minorities. Moreover, it entails fruitful cooperation among the relevant institutional and civil actors and the need for inclusive participation of persons belonging to religious minorities in democratic decision-making processes to the aim of the accomplishment of equal and full enjoyment of their human rights.
Protecting Linguistic and Religious Minorities: Looking for Synergies among Legal Instruments
Authors: Ruiz Vieytez Eduardo J.
Published: September 2021

Language and religion are two main cultural markers of collective identities and articulating factors at play in the majority-minority game. However, from a legal and political point of view, language and religion work very differently as factors for determining minorities. This is due, on the one hand, to their different connection with public bodies and, on the other hand, to the different role played by the two identity markers, more substantive in the case of religion and more instrumental in the case of language. Different forms of protection of linguistic and religious diversity and minorities have been developed so far. The two fields of protection have evolved separately and there has hardly been any dialogue between them. This article aims to analyze whether and how the usual forms of protection of linguistic diversity and linguistic minorities can be useful for the management of religious-based diversity or minorities. In this respect, linguistic diversity management draws more inspiration from religious diversity management techniques than the reverse. Nevertheless, a number of techniques that have been applied to the linguistic diversity protection may also play a potential role for the protection of religious diversity, opening the door to further synergies among legal instruments.
Protestant Congregational Song in the Philippines: Localization through Translation and Hybridization
Authors: Stallsmith Glenn
Published: September 2021

Historically, the language of Protestant congregational song in the Philippines was English, which was tied to that nation’s twentieth-century colonial history with the United States. The development of Filipino songs since the 1970s is linked to this legacy, but church musicians have found ways to localize their congregational singing through processes of translation and hybridization. Because translation of hymn texts from English has proven difficult for linguistic reasons, Papuri, a music group that produces original Tagalog-language worship music, bypasses these difficulties while relying heavily on American pop music styles. Word for the World is a Pentecostal congregation that embraces English-language songs as a part of their theology of presence, obviating the need for translation by singing in the original language. Day by Day Ministries, the third case study, is a congregation that translates beyond language texts, preparing indigenous Filipino cultural expressions for urban audiences by composing hybridized songs that merge pre-Hispanic and contemporary forms.
An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening
Authors: Addesso Paolo , Restaino Rocco , Vivone Gemine
Published: September 2021

The spatial resolution of multispectral data can be synthetically improved by exploiting the spatial content of a companion panchromatic image. This process, named pansharpening, is widely employed by data providers to augment the quality of images made available for many applications. The huge demand requires the utilization of efficient fusion algorithms that do not require specific training phases, but rather exploit physical considerations to combine the available data. For this reason, classical model-based approaches are still widely used in practice. We created and assessed a method for improving a widespread approach, based on the generalized Laplacian pyramid decomposition, by combining two different cost-effective upgrades: the estimation of the detail-extraction filter from data and the utilization of an improved injection scheme based on multilinear regression. The proposed method was compared with several existing efficient pansharpening algorithms, employing the most credited performance evaluation protocols. The capability of achieving optimal results in very different scenarios was demonstrated by employing data acquired by the IKONOS and WorldView-3 satellites.
FTIR Measurements of Greenhouse Gases over Thessaloniki, Greece in the Framework of COCCON and Comparison with S5P/TROPOMI Observations
Authors: Mermigkas Marios , Topaloglou Chrysanthi , Balis Dimitrios , Koukouli Maria Elissavet , Hase Frank , Dubravica Darko , Borsdorff Tobias , Lorente Alba
Published: September 2021

In this work, column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) are presented for the first time at a mid-latitude urban station, Thessaloniki, Greece, using the Bruker EM27/SUN ground-based low-resolution Fourier Transform spectrometer operated according to the requirements of the Collaborative Carbon Column Observing Network (COCCON). Two years of measurements are presented and examined for seasonal variability. The observed XCO2 levels show the expected seasonal cycle (spring maximum, late summer minimum) with a peak-to-peak amplitude of 12 ppm, with maximum values reported for winter 2021 exceeding 416 ppm. The XCH4 values are shown to increase in the second half of the year, with autumn showing the highest mean value of 1.878 ± 0.01 ppm. The XCO levels, following anthropogenic sources, show high winter and low summer values, exhibiting a rise again in August and September with a maximum value of 114 ± 3 ppb and a minimum in summer 2020 of 76 ± 3 ppb. Additionally, methane and carbon monoxide products obtained from the TROPOspheric Monitoring Instrument (TROPOMI), Sentinel-5P space borne sensor, are compared with the ground-based measurements. We report a good agreement between products. The relative mean bias for methane and carbon monoxide are −0.073 ± 0.647% and 3.064 ± 5.566%, respectively. Furthermore, a 15-day running average is subtracted from the original daily mean values to provide ΔXCO2, ΔXCO and ΔXCH4 residuals, so as to identify local sources at a synoptic scale. ΔXCO and ΔXCO2 show the best correlation in the winter (R2 = 0.898, slope = 0.007) season due to anthropogenic emissions in this period of the year (combustion of fossil fuels or industrial activities), while in summer no correlation is found. ΔXCO and ΔXCH4 variations are similar through both years of measurements and have a very good correlation in all seasons including winter (R2 = 0.804, slope = 1.209). The investigation of the X-gases comparison is of primary importance in order to identify local sources and quantify the impact of these trace gases to the deregulation of earth-climate system balance.
Evaluating the Impact of Grazing Cessation and Reintroduction in Mixed Prairie Using Raster Time Series Analysis of Landsat Data
Authors: Xu Dandan , Harder Jeff K. , Xu Weixin , Guo Xulin
Published: September 2021

Great efforts have been made to manage and restore native prairies to protect native species, enrich biodiversity, protect ecological resilience, and maintain ecosystem services. Much of this has been focused on preventing degradation from overgrazing and crop conversion. Understanding the consequences of management polices is important to identify best practices. Previous research has compared restoration outcomes from variable intensity grazing, prescribed fire, and grazing removal. However, few studies have explored the optimal durations of management practices and variation in restoration outcomes among vegetation communities. This study evaluates whether the impact of grazing cessation and reintroduction varies among native vegetation communities and measures the effective time periods of grazing cessation and reintroduction. Restoration outcomes were evaluated using four biophysical indicators (fresh biomass, soil organic matter, green cover, and litter cover) and two vegetation indices (normalized difference vegetation index (NDVI) and normalized difference water index (NDWI)) measured from Landsat images using seasonal Kalman filter and raster time series analysis. The results show that: (i) Grazing cessation increased soil organic matter and green cover while decreasing fresh biomass compared to moderate grazing management, while grazing reintroduction influences those indicators in an opposite direction; (ii) The effective time period for prairie conservation is about 11–14 years and varies among vegetation communities and biophysical indicators; (iii) The effective intensity of grazing cessation is highest in valley grassland, moderate in upland grassland, and mildest in sloped grassland; (iv) Grazing reintroduction returned the three native vegetation communities to the initial condition (i.e., the stage in 1985 before large grazers were removed), with less time than the time consumed for grazing cessation to restore the prairie ecosystem to the maximum changes; (v) Grazing reintroduction effectively influences upland and valley grasslands for 7 to 9 years, varying from different indicators, while it continuously affected sloped grassland with no clear time lag; (vi) The intensity of grazing reintroduction was strongest in sloped grassland, moderate in upland grassland, and mildest in valley grassland. The results of this study suggest expected time periods for prairie management methods to achieve results.
Combining Optical and Radar Satellite Imagery to Investigate the Surface Properties and Evolution of the Lordsburg Playa, New Mexico, USA
Authors: Eibedingil Iyasu G. , Gill Thomas E. , Van Pelt R. Scott , Tong Daniel Q.
Published: September 2021

Driven by erodible soil, hydrological stresses, land use/land cover (LULC) changes, and meteorological parameters, windblown dust events initiated from Lordsburg Playa, New Mexico, United States, threaten public safety and health through low visibility and exposure to dust emissions. Combining optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas—a potent landform for wind erosion. The optical images provide a long-term data record, while radar images can observe land surface irrespective of clouds, darkness, and precipitation. As a home for optical and radar imagery, powerful algorithms, cloud computing infrastructure, and application programming interface applications, Google Earth Engine (GEE) is an invaluable resource facilitating acquisition, processing, and analysis. In this study, the fractional abundance of soil, vegetation, and water endmembers were determined from pixel mixtures using the linear spectral unmixing model in GEE for Lordsburg Playa. For this approach, Landsat 5 and 8 images at 30 m spatial resolution and Sentinel-2 images at 10–20 m spatial resolution were used. Employing the Interferometric Synthetic Aperture Radar (InSAR) techniques, the playa’s land surface changes and possible sinks for sediment loading from the surrounding catchment area were identified. In this data recipe, a pair of Sentinel-1 images bracketing a monsoon day with high rainfall and a pair of images representing spring (dry, windy) and monsoon seasons were used. The combination of optical and radar images significantly improved the effort to identify long-term changes in the playa and locations within the playa susceptible to hydrological stresses and LULC changes. The linear spectral unmixing algorithm addressed the limitation of Landsat and Sentinel-2 images related to their moderate spatial resolutions. The application of GEE facilitated the study by minimizing the time required for acquisition, processing, and analysis of images, and storage required for the big satellite data.
A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data
Authors: He Yibo , Hu Zhenqi , Wu Kan , Wang Rui
Published: September 2021

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.
Experimental OMPS Radiance Assimilation through One-Dimensional Variational Analysis for Total Column Ozone in the Atmosphere
Authors: Liu Quanhua , Cao Changyong , Grassotti Christopher , Liang Xingming , Chen Yong
Published: September 2021

This experiment is the first ultraviolet radiance assimilation for atmospheric ozone in the troposphere and stratosphere. The experiment has provided better understanding of which observations need to be assimilated, what bias correction scheme may be optimal, and how to obtain surface reflectance. A key element is the extension of the Community Radiative Transfer Model (CRTM) to handle fully polarized radiances, which presents challenges in terms of computational resource requirements. In this study, a scalar (unpolarized) treatment of radiances was used. The surface reflectance plays an important role in assimilating the nadir mapper (NM) radiance of the Ozone Mapping and Profiler Suite (OMPS). Most OMPS NM measurements are affected by the surface reflection of solar radiation. We propose a linear spectral reflectance model that can be determined inline by fitting two OMPS NM channel radiances at 347.6 and 371.8 nm because the two channels have near zero sensitivity on atmospheric ozone. Assimilating a transformed reflectance measurement variable, the N value can overcome the difficulty in handling the large dynamic range of radiance and normalized radiance across the spectrum of the OMPS NM. It was found that the error in bias correction, surface reflectance, and neglecting polarization in radiative transfer calculations can be largely mitigated by using the two estimated surface reflectance. This study serves as a preliminary demonstration of direct ultraviolet radiance assimilation for total column ozone in the atmosphere.
Monitoring Drought through the Lens of Landsat: Drying of Rivers during the California Droughts
Authors: Gao Shang , Li Zhi , Chen Mengye , Allen Daniel , Neeson Thomas , Hong Yang
Published: September 2021

Water scarcity during severe droughts has profound hydrological and ecological impacts on rivers. However, the drying dynamics of river surface extent during droughts remains largely understudied. Satellite remote sensing enables surveys and analyses of rivers at fine spatial resolution by providing an alternative to in-situ observations. This study investigates the seasonal drying dynamics of river extent in California where severe droughts have been occurring more frequently in recent decades. Our methods combine the use of Landsat-based Global Surface Water (GSW) and global river bankful width databases. As an indirect comparison, we examine the monthly fractional river extent (FrcSA) in 2071 river reaches and its correlation with streamflow at co-located USGS gauges. We place the extreme 2012–2015 drought into a broader context of multi-decadal river extent history and illustrate the extraordinary change between during- and post-drought periods. In addition to river extent dynamics, we perform statistical analyses to relate FrcSA with the hydroclimatic variables obtained from the National Land Data Assimilation System (NLDAS) model simulation. Results show that Landsat provides consistent observation over 90% of area in rivers from March to October and is suitable for monitoring seasonal river drying in California. FrcSA reaches fair (>0.5) correlation with streamflow except for dry and mountainous areas. During the 2012–2015 drought, 332 river reaches experienced their lowest annual mean FrcSA in the 34 years of Landsat history. At a monthly scale, FrcSA is better correlated with soil water in more humid areas. At a yearly scale, summer mean FrcSA is increasingly sensitive to winter precipitation in a drier climate; and the elasticity is also reduced with deeper ground water table. Overall, our study demonstrates the detectability of Landsat on the river surface extent in an arid region with complex terrain. River extent in catchments of deficient water storage is likely subject to higher percent drop in a future climate with longer, more frequent droughts.
Integrating Normal Vector Features into an Atrous Convolution Residual Network for LiDAR Point Cloud Classification
Authors: Zhang Chunjiao , Xu Shenghua , Jiang Tao , Liu Jiping , Liu Zhengjun , Luo An , Ma Yu
Published: September 2021

LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the external surface of the object itself and will not store useless redundant information to describe the occupation. Therefore, point clouds have become the research focus of 3D data models and are widely used in large-scale scene reconstruction, virtual reality, digital elevation model production, and other fields. Since point clouds have various characteristics, such as disorder, density inconsistency, unstructuredness, and incomplete information, point cloud classification is still complex and challenging. To realize the semantic classification of LiDAR point clouds in complex scenarios, this paper proposes the integration of normal vector features into an atrous convolution residual network. Based on the RandLA-Net network structure, the proposed network integrates the atrous convolution into the residual module to extract global and local features of the point clouds. The atrous convolution can learn more valuable point cloud feature information by expanding the receptive field. Then, the point cloud normal vector is embedded in the local feature aggregation module of the RandLA-Net network to extract local semantic aggregation features. The improved local feature aggregation module can merge the deep features of the point cloud and mine the fine-grained information of the point cloud to improve the model’s segmentation ability in complex scenes. Finally, to resolve the imbalance of the distribution of the various categories of point clouds, the original loss function is optimized by adopting a reweighted method to prevent overfitting so that the network can focus on small target categories in the training process to effectively improve the classification performance. Through the experimental analysis of a Vaihingen (Germany) urban 3D semantic dataset from the ISPRS website, it is verified that the proposed algorithm has a strong generalization ability. The overall accuracy (OA) of the proposed algorithm on the Vaihingen urban 3D semantic dataset reached 97.9%, and the average reached 96.1%. Experiments show that the proposed algorithm fully exploits the semantic features of point clouds and effectively improves the accuracy of point cloud classification.
Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
Authors: Miao Yingjie , Li Jingchun , Bao Yao , Liu Feifeng , Hu Cheng
Published: September 2021

The increasing accessibility of unmanned aerial vehicles (UAVs) drives the demand for reliable, easy-to-deploy surveillance systems to consolidate public security. This paper employs passive bistatic radar (PBR) based on a digital audio broadcast (DAB) satellite for UAV monitoring in applications with power density limitations on electromagnetic radiation. An advanced version of the extensive cancellation algorithm (ECA) based on data segmentation and coefficients filtering is designed to improve the efficiency of multipath clutter suppression while retaining robustness, for which the effectiveness is verified by theoretical derivation and simulation. The detectability of small UAVs with DAB satellite-based PBR is validated with experimental results, with which the influence of target altitude and bistatic geometry are also analyzed.
Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning
Authors: Qi Yuan , Dong Xuhua , Chen Pengchao , Lee Kyeong-Hwan , Lan Yubin , Lu Xiaoyang , Jia Ruichang , Deng Jizhong , Zhang Yali
Published: September 2021

Automatic acquisition of the canopy volume parameters of the Citrus reticulate Blanco cv. Shatangju tree is of great significance to precision management of the orchard. This research combined the point cloud deep learning algorithm with the volume calculation algorithm to segment the canopy of the Citrus reticulate Blanco cv. Shatangju trees. The 3D (Three-Dimensional) point cloud model of a Citrus reticulate Blanco cv. Shatangju orchard was generated using UAV tilt photogrammetry images. The segmentation effects of three deep learning models, PointNet++, MinkowskiNet and FPConv, on Shatangju trees and the ground were compared. The following three volume algorithms: convex hull by slices, voxel-based method and 3D convex hull were applied to calculate the volume of Shatangju trees. Model accuracy was evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results show that the overall accuracy of the MinkowskiNet model (94.57%) is higher than the other two models, which indicates the best segmentation effect. The 3D convex hull algorithm received the highest R2 (0.8215) and the lowest RMSE (0.3186 m3) for the canopy volume calculation, which best reflects the real volume of Citrus reticulate Blanco cv. Shatangju trees. The proposed method is capable of rapid and automatic acquisition for the canopy volume of Citrus reticulate Blanco cv. Shatangju trees.
Visual Localization of the Tianwen-1 Lander Using Orbital, Descent and Rover Images
Authors: Wan Wenhui , Yu Tianyi , Di Kaichang , Wang Jia , Liu Zhaoqin , Li Lichun , Liu Bin , Wang Yexin , Peng Man , Bo Zheng , Ye Lejia , Wang Runzhi , Yin Li , Yang Meiping , Shi Ke , He Ximing , Zhang Zuoyu , Zhang Hui , Lu Hao , Bao Shuo
Published: September 2021

Tianwen-1, China’s first Mars exploration mission, was successfully landed in the southern part of Utopia Planitia on 15 May 2021 (UTC+8). Timely and accurately determining the landing location is critical for the subsequent mission operations. For timely localization, the remote landmarks, selected from the panorama generated by the earliest received Navigation and Terrain Cameras (NaTeCam) images, were matched with the Digital Orthophoto Map (DOM) generated by high resolution imaging camera (HiRIC) images to obtain the initial result based on the triangulation method. Then, the initial localization result was refined by the descent images received later and the NaTeCam DOM. Finally, the lander location was determined to be (25.066°N, 109.925°E). Verified by the new orbital image with the lander and Zhurong rover visible, the localization accuracy was within a pixel of the HiRIC DOM.
Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression
Authors: Zhang Dou , Geng Xiaolei , Chen Wanxu , Fang Lei , Yao Rui , Wang Xiangrong , Zhou Xiao
Published: September 2021

Global greening over the past 30 years since 1980s has been confirmed by numerous studies. However, a single-dimensional indicator and non-spatial modelling approaches might exacerbate uncertainties in our understanding of global change. Thus, comprehensive monitoring for vegetation’s various properties and spatially explicit models are required. In this study, we used the newest enhanced vegetation index (EVI) products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 to detect the inconsistency trend of annual peak and average global vegetation growth using the Mann–Kendall test method. We explored the climatic factors that affect vegetation growth change from 2001 to 2018 using the spatial lag model (SLM), spatial error model (SEM) and geographically weighted regression model (GWR). The results showed that EVImax and EVImean in global vegetated areas consistently showed linear increasing trends during 2001–2018, with the global averaged trend of 0.0022 yr−1 (p < 0.05) and 0.0030 yr−1 (p < 0.05). Greening mainly occurred in the croplands and forests of China, India, North America and Europe, while browning was almost in the grasslands of Brazil and Africa (18.16% vs. 3.08% and 40.73% vs. 2.45%). In addition, 32.47% of the global vegetated area experienced inconsistent trends in EVImax and EVImean. Overall, precipitation and mean temperature had positive impacts on vegetation variation, while potential evapotranspiration and vapour pressure had negative impacts. The GWR revealed that the responses of EVI to climate change were inconsistent in an arid or humid area, in cropland or grassland. Climate change could affect vegetation characteristics by changing plant phenology, consequently rendering the inconsistency between peak and mean greening. In addition, anthropogenic activities, including land cover change and land use management, also could lead to the differences between annual peak and mean vegetation variations.
High-Resolution Terrain Modeling Using Airborne LiDAR Data with Transfer Learning
Authors: Li Huxiong , Ye Weiya , Liu Jun , Tan Weikai , Pirasteh Saied , Fatholahi Sarah Narges , Li Jonathan
Published: September 2021

This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.
Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors
Authors: Tomaszewska Monika A. , Henebry Geoffrey M.
Published: September 2021

Degradation in the highland pastures of the Kyrgyz Republic, a small country in Central Asia, has been reported in several studies relying on coarse spatial resolution imagery, primarily MODIS. We used the results of land surface phenology modeling at higher spatial resolution to characterize spatial and temporal patterns of phenometrics indicative of the seasonal peak in herbaceous vegetation. In particular, we explored whether proximity to villages was associated with substantial decreases in the seasonal peak values. We found that terrain features—elevation and aspect—modulated the strength of the influence of village proximity on the phenometrics. Moreover, using contrasting hotter/drier and cooler/wetter years, we discovered that the growing season weather can interact with aspect to attenuate the negative influences of dry conditions on seasonal peak values. As these multiple contingent and interactive factors that shape the land surface phenology of the highland pastures may be blurred and obscured in coarser spatial resolution imagery, we discuss some limitations with prior and recent studies of pasture degradation.
Underwater Hyperspectral Imaging (UHI): A Review of Systems and Applications for Proximal Seafloor Ecosystem Studies
Authors: Montes-Herrera Juan C. , Cimoli Emiliano , Cummings Vonda , Hill Nicole , Lucieer Arko , Lucieer Vanessa
Published: September 2021

Marine ecosystem monitoring requires observations of its attributes at different spatial and temporal scales that traditional sampling methods (e.g., RGB imaging, sediment cores) struggle to efficiently provide. Proximal optical sensing methods can fill this observational gap by providing observations of, and tracking changes in, the functional features of marine ecosystems non-invasively. Underwater hyperspectral imaging (UHI) employed in proximity to the seafloor has shown a further potential to monitor pigmentation in benthic and sympagic phototrophic organisms at small spatial scales (mm–cm) and for the identification of minerals and taxa through their finely resolved spectral signatures. Despite the increasing number of studies applying UHI, a review of its applications, capabilities, and challenges for seafloor ecosystem research is overdue. In this review, we first detail how the limited band availability inherent to standard underwater cameras has led to a data analysis “bottleneck” in seafloor ecosystem research, in part due to the widespread implementation of underwater imaging platforms (e.g., remotely operated vehicles, time-lapse stations, towed cameras) that can acquire large image datasets. We discuss how hyperspectral technology brings unique opportunities to address the known limitations of RGB cameras for surveying marine environments. The review concludes by comparing how different studies harness the capacities of hyperspectral imaging, the types of methods required to validate observations, and the current challenges for accurate and replicable UHI research.
Cyclonic Process of the “Voice of the Sea” Microseism Generation and Its Remote Monitoring
Authors: Dolgikh Grigory I. , Chupin Vladimir A. , Gusev Egor S. , Timoshina Galina A.
Published: September 2021

The article presents the results of microseismic signals of the “voice of the sea” registration by a two-coordinate laser strainmeter during the passage of typhoons through and near the water area of the Sea of Japan. It was established that the “voice of the sea” microseisms appear and disappear almost simultaneously with primary microseisms, i.e., the powerful “voice of the sea” microseisms exist only in the presence of powerful wind waves, generated by a passing typhoon. According to the processing results of the obtained experimental data, the “voice of the sea” microseisms generation area is located in the “sea-land” transition zone, i.e., near and/or in the surf zone. Based on the data of the two-coordinate laser strainmeter, we determined the bearing of the “voice of the sea” microseisms generation area. The movement of this area coincides with the movement of the rear part of tropical cyclones.
Acute Abdominal Disorders in Dairy Cattle: What Can Clinicians Do under Field Conditions?
Authors: Muiño Rodrigo , Hernández Joaquín , Castillo Cristina
Published: September 2021

Acute abdomen (AA) is the term used to define a pathological condition that affects the abdominal cavity and frequently manifests itself with acute clinical symptoms capable of compromising a cow’s life. Therefore, it should be considered as an emergency that should be evaluated as quickly as possible to adopt the appropriate therapeutic measure (medical or surgical). In an AA situation, the clinician must consider the appropriate diagnostic approach as well as the therapeutic choice. Given the emergency, the veterinarian’s experience will be decisive for the appropriate resolution. In this article, we briefly review the causes that may lead to this condition, as well as the different diagnostic tests available in field conditions to establish the correct diagnosis and treatment.
A Survey on Data-Driven Predictive Maintenance for the Railway Industry
Authors: Davari Narjes , Veloso Bruno , Costa Gustavo de Assis , Pereira Pedro Mota , Ribeiro Rita P. , Gama João
Published: September 2021

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.
Structure, Surface Morphology, Chemical Composition, and Sensing Properties of SnO2 Thin Films in an Oxidizing Atmosphere
Authors: Izydorczyk Weronika , Izydorczyk Jacek
Published: September 2021

We conducted experiments on SnO2 thin layers to determine the dependencies between the stoichiometry, electrochemical properties, and structure. This study focused on features such as the film structure, working temperature, layer chemistry, and atmosphere composition, which play a crucial role in the oxygen sensor operation. We tested two kinds of resistive SnO2 layers, which had different grain dimensions, thicknesses, and morphologies. Gas-sensing layers fabricated by two methods, a rheotaxial growth and thermal oxidation (RGTO) process and DC reactive magnetron sputtering, were examined in this work. The crystalline structure of SnO2 films synthesized by both methods was characterized using XRD, and the crystallite size was determined from XRD and AFM measurements. Chemical characterization was carried out using X-ray photoelectron (XPS) and Auger electron (AES) spectroscopy for the surface and the near-surface film region (in-depth profiles). We investigated the layer resistance for different oxygen concentrations within a range of 1–4%, in a nitrogen atmosphere. Additionally, resistance measurements within a temperature range of 423–623 K were analyzed. We assumed a flat grain geometry in theoretical modeling for comparing the results of measurements with the calculated results.